Qualification: 
Ph.D
kp_soman@amrita.edu

Dr. Soman K. P. currently serves as Head and Professor at Amrita Center for Computational Engineering and Networking (CEN), Coimbatore Campus.

Projects Undertaken at CEN:

Sponsored :

  • Software Radio applications in implementing Virtual labs for MHRD, New Delhi, India
  • Particle Image Velocimetry and Planar Laser Induced Fluorescence studies for flow visualization and characterization (funded by ISRO)
  • Massively Parallel Support Vector Machines for target identification (funded by DRDO)
  • Fault simulation and analysis of spacecraft structures using wavelets (funded by ISRO)
  • Study of methodologies for detection of digital contents plagiarism and other piracies (funded by DIT), New Delhi, India
  • Implementation of Image Fusion Algorithms for ADE, Bangalore
  • Video summarization for ISRO, Hyderabad
  • English to Tamil Translation system for IT ministry, New Delhi, India
  • English to Dravidian Language Translation system for MHRD
  • Source Code Plagiarism Detection Engine for IT ministry, New Delhi, India

Publications

Publication Type: Conference Paper

Year of Publication Title

2020

K. Radhika, Devika, K., Aswathi, T., Sreevidya, P., Sowmya, V., and Dr. Soman K. P., “Performance Analysis of NASNet on Unconstrained Ear Recognition”, in Nature Inspired Computing for Data Science, 2020.[Abstract]


Recent times are witnessing greater influence of Artificial Intelligence (AI) on identification of subjects based on biometrics. Traditional biometric recognition algorithms, which were constrained by their data acquisition methods, are now giving way to data collected in the unconstrained manner. Practically, the data can be exposed to factors like varying environmental conditions, image quality, pose, image clutter and background changes. Our research is focused on the biometric recognition, through identification of the subject from the ear. The images for the same are collected in an unconstrained manner. The advancements in deep neural network can be sighted as the main reason for such a quantum leap. The primary challenge of the present work is the selection of appropriate deep learning architecture for unconstrained ear recognition. Therefore the performance analysis of various pretrained networks such as VGGNet, Inception Net, ResNet, Mobile Net and NASNet is attempted here. The third challenge we addressed is to optimize the computational resources by reducing the number of learnable parameters while reducing the number of operations. Optimization of selected cells as in NASNet architecture is a paradigm shift in this regard.

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2020

V. R. Kurup, Anupama, M. A., Vinayakumar, R., Sowmya, V., and Dr. Soman K. P., “Capsule Network for Plant Disease and Plant Species Classification”, in Computational Vision and Bio-Inspired Computing, Cham, 2020.[Abstract]


In deep learning perspective, convolutional neural network (CNN) forms the backbone of image processing. For reducing the drawbacks and also to get better performance than conventional neural network, the new architecture of CNN known as, capsulenet is implemented. In this paper, we analyze capsulenet for two datasets, which are based on plants. In the modern world, most of the diseases are contaminating due to the lack of hygienic food. One of the main reasons for this is, diseases affecting crop species. So, the first model is built for plant disease diagnosis using the images of plant leaves. The corresponding dataset consists of 54,306 images of 14 plant species. The proposed architecture with capsulenet gives an accuracy around 94%. The second task is plant leaves classification. This dataset consists of 2,997 images of 11 plants. The prediction model with capsulenet gives an accuracy around 85%. In the recent years, the use of mobile phones is rapidly increasing. Here for both the models, the images of plant leaves are taken using mobile phone cameras. So, this method can be extended to various plants and can be adopted in large scale manner.

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2020

S. Nirmal, Sowmya, V., and Dr. Soman K. P., “Open Set Domain Adaptation for Hyperspectral Image Classification Using Generative Adversarial Network”, in Lecture Notes in Networks and Systems, 2020.[Abstract]


Hyperspectral image (HSI) classificationNirmal, S. attracted lotsSowmya, V. of attentionSoman, K. P. due to its complexity in dealing with large dimensions. In recent years, the techniques for dealing with the HSI have been evolved, ensuring the increase in efficiency to some extent in classification and other perspectives. Domain adaptation is a well-established technique for using any trained classification model, when the feature space from target domain is a subset of feature space from source domain. The objective of this paper is to create an efficient and effective model for HSI classification by implementing open set (OS) domain adaptation and generative adversarial network (GAN). This has advantages in quite few ways, such as creating a single training model that deals with various HSI data set with common classes, classifying the features in any data to specific trained classes and unknown (to be labelled) making it easy to annotate. The proposed open set domain adaptation for HSI classification is evaluated using Salinas and Pavia. The proposed method resulted in the classification accuracy for unknown classes as 99.07% for Salinas and 81.65% for Pavia

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2020

K. Nagarajan, Ananthu, J., Menon, V. Krishna, Dr. Soman K. P., Gopalakrishnan, E. A., and Ramesh, A., “An Approach to Detect and Classify Defects in Cantilever Beams Using Dynamic Mode Decomposition and Machine Learning”, in Smart Innovation, Systems and Technologies, Singapore, 2020.[Abstract]


Defects in structures will affect its natural vibrations. With the advent of pure data-driven modeling techniques such as Dynamic Mode Decomposition (DMD), the defected modes can be separated from the normal modes by using vibration data from various points on the structural element. In this work we simulate the vibrations of a cantilever beam in Abaqus® without defect and with different defects. We apply DMD to compute the spatial modes of vibration in each of these cases. Furthermore we train a Support Vector Machine (SVM) classifier with the Eigen-modes we have computed, to identify defects. We also analyze this data visually using t-SNE plots.

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2020

A. Jayanarayan, Sowmya, V., and Dr. Soman K. P., “Remote Sensing Image Super-Resolution Using Residual Dense Network”, in Advances in Intelligent Systems and Computing, Singapore, 2020.[Abstract]


Jayanarayan, AbhijithSowmya, V.Soman, K. P.Image super-resolution (SR) is a wide research topic, as it has found multiple applications in different fields. We implement image super-resolution for satellite images using a residual dense network (RDN). RDN is a CNN-based model, but unlike most CNN-based super-resolution models, it utilizes the hierarchic features from the input low resolution (LR) images and combines both the specific and general features present in the image, therefore resulting in a better performance. The novelty of our work lies in two aspects. First, we apply the residual dense network to remote sensing data to obtain higher structure similarity index metric (SSIM) and peak signal-to-noise ratio (PSNR) values than the existing models. Second, we use transfer learning due to the lack of training samples in remote sensing domain. Our RDN is first trained using an external dataset DIVerse2K (DIV2K). This model is then used to obtain high-resolution(HR) images of the remote sensing U.C Merced dataset, and the corresponding PSNR and SSIM values are computed for different scaling factors such as \$\$\backslashtimes \$\$2, \$\$\backslashtimes \$\$4 and \$\$\backslashtimes \$\$8. The experimental results obtained using the proposed work demonstrates the better performance of RDN for the super-resolution of remote sensing images, when compared to the existing methods like super-resolution generative adversarial network (SRGAN) and transferred generative adversarial network (TGAN).

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2020

S. Srinivasan, Ravi, V., V, S., Krichen, M., Ben Noureddine, D., Anivilla, S., and Dr. Soman K. P., “Deep Convolutional Neural Network Based Image Spam Classification”, in 2020 6th Conference on Data Science and Machine Learning Applications (CDMA), 2020.[Abstract]


With the tremendous growth of the internet, cyberspace is facing several threats from the attackers. Threats like spam emails account for 55% of total emails according to the Symantec monthly threat report. Over time, the attackers moved on to image spam to evade the text-based spam filters. To deal with this, the researchers have several machine learning and deep learning approaches that use various features like metadata, color, shape, texture features. But the Deep Convolutional Neural Network (DCNN) and transfer learning-based pre-trained CNN models are not explored much for Image spam classification. Therefore, in this work, 2 DCNN models along with few pre-trained ImageNet architectures like VGG19, Xception are trained on 3 different datasets. The effect of employing a Cost-sensitive learning approach to handle data imbalance is also studied. Some of the proposed models in this work achieves an accuracy up to 99% with zero false positive rate in best case.

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2019

B. Premjith, Chandni Chandran V., Shriganesh Bhat, Dr. Soman K. P., and Prabaharan P., “A Machine Learning Approach for Identifying Compound Words from a Sanskrit Text”, in Proceedings of the 6th International Sanskrit Computational Linguistics Symposium, IIT Kharagpur, India, 2019.[Abstract]


In this paper, we propose a classification framework for finding the compound words
from a given Sanskrit text. The compound word identification plays a significant role in
learning the elucidations of verses in Ayurveda text books which are written in Sanskrit.
This process was modelled using several classification algorithms and we examined
their efficacy with varying word embedding dimensions. Sanskrit words were vectorized using fastText word embedding method. The results show that the performance of
K-Nearest Neighbor is better than other classifiers and the prediction accuracy is 90.38%.

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2019

V. Prasad K., B. Premjith, Chandni Chandran V., Dr. Soman K. P., and Prabaharan Poornachandran, “Deep learning based Character-level approach for Morphological Inflection Generation”, in 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India, 2019.[Abstract]


In this paper, we present our work on morphological inflection generation of Sanskrit using a deep learning approach. Sanskrit is a morphologically rich language which came into use from the Vedic period. A basic understanding of the language formation is needed to study the abundant literature in it. Here a computational model for word formation in Sanskrit is proposed using deep learning based models. They are applied here to attain the morphological changes that a root word undergoes to result in the surface form. The approach is in character level so as to capture the character level transformations. The best performance was obtained from the Bidirectional Gated Recurrent Unit architecture with an accuracy of 98.42% and an F1-Score of 0.9838. This model is purely dependent on the dataset and does not require any external linguistic resources.

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2019

A. Gopalakrishnan, Dr. Soman K. P., and B. Premjith, “Part-of-Speech Tagger for Biomedical Domain Using Deep Neural Network Architecture”, in 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India, 2019.[Abstract]


POS tagging is the process of classifying words into their parts of speech like noun, verb, preposition etc. to a word. It is the most important and basic process in NLP. It is acts as an essential preprocess for other applications in natural language processing (NLP) like sentiment analysis, NER, speech recognition and so on. POS tagging is treated as a sequence labeling problem in which it labels words with their appropriate Part-Of-Speech. This work implementing a POS tagger for biomedical domain using deep neural network architecture. The experiment is RNN, LSTM, and GRU will give better performance since they are able to access more context information and which we evaluated using publicly accessible dataset from GENIA. Most of the applications in NLP became solved due to the advancement of neural network or deep learning.

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2019

B. Premjith, Dr. Soman K. P., and Sreelakshmi K., “Amrita CEN at HASOC 2019: Hate Speech Detection in Roman and Devanagiri Scripted Text”, in FIRE 2019 - Forum for Information Retrieval Evaluation, Kolkata, India, 2019.[Abstract]


Nowadays the usage of social media sites like Facebook and Twitter has increased rapidly which has lead to huge flooding of data in the social media sites. Though these social media sites give free opportunities to people to express and share their thoughts they also end up in spread of huge amount of hate content. In this paper we present a domain specific word embedding model for classification of English tweets to Non Hate-Offensive and Hate-Offensive and a fastText model for Hindi text classification. The classification is done using the dataset got from HASOC 2019 shared task. Deep learning algorithm is used as the classifier.

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2019

K. K. Shahina, P. V. Jyothsna, G. Prabha, B. Premjith, and Dr. Soman K. P., “A Sequential Labelling Approach for the Named Entity Recognition in Arabic Language Using Deep Learning Algorithms”, in 2019 International Conference on Data Science and Communication (IconDSC), Bangalore, India, 2019.[Abstract]


Named Entity Recognition (NER) involves finding and categorizing minute text components into pre- defined categories such as name of person, location etc. NER is a type of information extraction task which has a crucial role in improving the performance of various NLP applications. For a morphologically abundant Semitic language like Arabic, the NER task is highly challenging due to its unique morphological characteristics and peculiarities. This paper introduces a deep learning based approach for Arabic NER which make use of well-known deep neural network (DNN) architectures like Recurrent neural network (RNN), Long short term memory (LSTM), Gated recurrent unit (GRU), stacked and bidirectional versions of these three architectures. ANERcorp dataset is used for the evaluation of the Arabic NER model and Accuracy is chosen as the performance metric. On model evaluation, it is observed that bidirectional variants of DNNs provide better accuracy measures compared to their unidirectional variants.

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2019

B. Premjith, Dr. Soman K. P., and Prabaharan Poornachandran, “Amrita_CEN@FACT: Factuality Identification in Spanish Text”, in IberLEF@SEPLN, 2019.[Abstract]


This paper presents the description of the system used by the team Amrita CEN for the shared task on FACT (Factuality Analysis and Classification Task) at IberLEF2019 (Iberian Languages Evaluation Forum) workshop. The goal of the task was to automatically annotate an event with its factuality status. Factuality status is categorized into three as Fact, Counter Fact and Undefined. Our proposed system predicts the factuality of an event with a prediction accuracy of 72.1%. The classification model for this task was trained using Random Forest classifier which uses word embedding of the events as input features. The word embedding of an event was generated by using Word2vec algorithm. Random Forest was implemented by giving higher weights to minority classes and lesser weights to majority classes so that more number of elements in the minority class will be predicted precisely

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2019

Neethu Mohan, Dr. Soman K. P., and S. Kumar, S., “A Data-driven Approach for Estimating Power System Frequency and Amplitude Using Dynamic Mode Decomposition”, in Proceedings of the Conference on the Industrial and Commercial Use of Energy, ICUE, 2019, vol. 2018-October.[Abstract]


To ensure power system stability, control and quality supply of power, it is essential to monitor power system parameters such as frequency and amplitude. This paper proposes a data-driven approach based on dynamic mode decomposition (DMD) algorithm for the accurate estimation of frequency and amplitude in smart grid. In the proposed approach, to extract the multiple frequency components, including harmonics, inter-harmonics and subharmonics, a stacked measurement matrix is created by appending multiple time-shifted versions of power signals. An optimal hard-thresholding is performed on the singular values of the measurement matrix to deal with the uncertainties and high-level noises. Further, the frequency and amplitude are computed based on the extracted dynamic modes. The performance of the proposed approach is confirmed through various experiments conducted on different power system scenarios under noisy and noiseless conditions. The effectiveness of the DMD based method is verified by comparing the results with several state-of-the-art methods. The promising results suggest that the proposed approach can be used as an efficient candidate for estimating the power system frequency and amplitude. © 2018 Asian Institute of Technology.

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2019

Sajith Variyar V. V., Sowmya, V., Gopalakrishnan, E. A., Dr. Soman K. P., Sivanpillai, R., and Brown, G. K., “Opportunities and challenges of launching UAVs within wooded areas”, in ASPRS Annual Conference and International Lidar Mapping Forum 2019, 2019.

2019

R. K. Renu, Sowmya V., and Dr. Soman K. P., “Spatio-Spectral Compression and Analysis of Hyperspectral Images using Tensor Decomposition”, in 2018 24th National Conference on Communications, NCC 2018, Hyderabad; India, 2019.[Abstract]


Hyperspectral images are large cubes of data which are commonly processed band-wise as two-dimensional image patches. This 2D processing might lead to loose the spectral efficiency contained in the image. Introducing Hyperspectral image as third-order tensors helps to preserve the spectral and spatial efficiency of the image. Multilinear Singular Value Decomposition (MLSVD) is an extension of Singular Value Decomposition (SVD) to 3D which can be used for compressing the image spatially and spectrally. The efficiency of compression is verified by reconstructing the image using Low Multilinear Rank Approximation (LMLRA). The proposed method has been validated with Signal to Noise Ratio (SNR), pixel reflectance spectrum and pixel-wise classification of the reconstructed image. © 2018 IEEE.

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2019

Sajith Variyar V. V., Dr. E. A. Gopalakrishnan, Sowmya V., and Dr. Soman K. P., “A complex network approach for plant growth analysis using images”, in Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019, Melmaruvathur; India, 2019.[Abstract]


The process of plant growth monitoring and analysis changed its perspective from the way it was. The recent farming practices demand vision based sensors for monitoring and analyses of the plant growth characteristics from images acquired by satellites and Unmanned Aerial Vehicles (UAV's). The advanced plant phenotyping systems are equipped with digital cameras to report the plant growth on a daily basis. The time determined images from plant monitoring system require a better computational representation to understand and study the plant life cycle, plant to plant interaction and correlations between plants with-in the community. This paper presents a new and yet simple approach towards plant growth analysis and its correlations in community by applying the theory of complex network on visible images from a plant phenotyping system. The method is highly promising in the area of precision agriculture when we have large area to monitor. © 2019 IEEE.

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2019

M. A. Anupama, Sowmya V., and Dr. Soman K. P., “Breast cancer classification using capsule network with preprocessed histology images”, in Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019, Melmaruvathur; India, 2019.[Abstract]


Breast cancer is one of the most dangerous forms of cancer exists among women. The breast cancer is diagnosed using histology images. The purpose of this paper is to classify different types of breast cancer using histology images. The classification of histology images can be effectively done by image processing techniques. Among different image processing algorithms, deep learning gives the best performance for image classification applications. There are different convolutional neural network(CNN) architectures used for classification purpose such as AlexNet, Inception-Net, ResNet etc. Since conventional convolutional neural networks have lot of drawbacks, the architecture used for the current study is capsule networks, which captures the spatial and orientational information. The proposed work shows that the accuracy of Capsule Network model is improved due to the pre-processing of the histology images. © 2019 IEEE.

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2018

V. S. Mohan, Vinayakumar, R., Dr. Soman K. P., and Poornachandran, P., “SPOOF Net: Syntactic Patterns for Identification of Ominous Online Factors”, in 2018 IEEE Security and Privacy Workshops (SPW), 2018.[Abstract]


With more emphasis on internet as a primary mechanism for information access and communication, it is highly important that the platform stays safe and secure for anyone who uses it. Online scams and cybercrimes are becoming a common threat to the technology and systems that help mitigate these issues are in high demand. Businesses all over the world invest heavily to stay secure in the cyberspace and rely on security experts in defending their business from online threats. The immense scale of the internet and the dynamicity of the threat it holds forces the adoption of automated threat detection systems. Several cybersecurity use cases exist, but the two use cases discussed here are DGA detection and Malicious URL detection. This paper addresses the drawbacks of previous rule-based and machine learning based detection methods. Here, embedding concepts from NLP is incorporated into cybersecurity use cases to propose a new in house model christened S.P.O.O.F Net, which is a combination of a Convolutional Neural Network and Long Short Term Memory Network. The proposed model is benchmarked with machine learning algorithm incorporating bi-gram feature engineering techniques and also a conventional CNN with character level embedding (same as the one used for S.P.O.O.F Net). It was observed that S.P.O.O.F Net gave better performance over the aforementioned methods with accuracy scores of 98.3% for DGA detections and 99% for malicious URL detection. This work also aims to demonstrate the possibilities of incorporating NLP concepts to cybersecurity use cases and provide future researches a new thinking curve to develop systems in this domain.

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2018

B. Premjith, Dr. Soman K. P., and Prabaharan Poornachandran, “A deep learning based Part-of-Speech (POS) tagger for Sanskrit language by embedding character level features”, in FIRE'18, 2018.[Abstract]


Part-of-Speech (POS) tagging is an important task in Natural Language Processing and numerous taggers have been developed for POS tagging in several languages. In Sanskrit also, one of the oldest languages in the world, many POS taggers were developed. However, less attention was given to the machine learning based POS tagging. In this paper, various deep learning algorithms are used for implementing a POS tagger for Sanskrit. This problem is framed as a sequence labeling problem at the character level. Therefore, a word to be POS tagged is considered as a sequence of characters and the sequential relationship among the characters in a word is captured with the deep learning algorithms such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) networks, Gate Recurrent Unit (GRU) and their bidirectional versions. The character level formulation of the problem reduces the memory requirement compared to the word level implementations and also increases the accuracy of labeling. The performance of the labeling task was analyzed with the different combinations of hyper-parameters. We obtained the accuracy score of 97.86% with Bidirectional GRU. The character level implementations of both uni and bidirectional forms of RNN, LSTM and GRU outperformed all world level implementations in terms of accuracy, number of trainable parameters and the storage requirement.

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2018

G. Prabha, P. V. Jyothsna, Shahina, K. K., B. Premjith, and Dr. Soman K. P., “A Deep Learning Approach for Part-of-Speech Tagging in Nepali Language”, in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India, 2018.[Abstract]


Part of Speech (POS) tagging is the most fundamental task in various natural language processing(NLP) applications such as speech recognition, information extraction and retrieval and so on. POS tagging involves annotation of appropriate tag for each token in the corpus based on its context and the syntax of the language. In computational linguistics, optimal POS tagger is of paramount importance since tagging errors can critically affect the performance of the complex NLP systems. Developing an efficient POS tagger for morphologically rich languages like Nepali is a challenging task. In this paper, a deep learning based POS tagger for Nepali text is proposed which is built using Recurrent Neural Network (RNN), Long Short-Term Memory Networks (LSTM), Gated Recurrent Unit (GRU) and their bidirectional variants. Performance metrics such as accuracy, precision, recall and F1-score were chosen for the model evaluation. It is observed from the results that our model shows significant improvement and outperforms the state-of-art POS taggers with more than 99% accuracy.

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2018

Neethu Mohan, S. Kumar, S., and Dr. Soman K. P., “An ℓ1 -Norm Based Optimization Approach for Power Line Interference Removal in ECG Signals”, in Progress in Intelligent Computing Techniques: Theory, Practice, and Applications, Singapore, 2018.[Abstract]


Accurate analysis and proper interpretation of electrophysical recordings like ECG is a real necessity in medical diagnosis. Presence of artifacts and other noises can corrupt the ECG signals and can lead to an improper disease diagnosis. Power line interferences (PLI) occurring at 50/60 Hz is a major source of noises which could corrupt the ECG signals. This motivates the removal of PLI from ECG signals and is a foremost preprocessing task in ECG signal analysis. In this paper, we deal an \$\${\backslashell _1}\$\$ℓ1norm based optimization approach for PLI removal in ECG signals. The sparsity inducing property of \$\${\backslashell _1}\$\$ℓ1norm is used for efficient removal of power noises. The effectiveness of this approach is evaluated on ECG signals corrupted with power line interferences and random noises.

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2018

A. Chandran, Anjali, T., Neethu Mohan, and Dr. Soman K. P., “Overlapping Group Sparsity Induced Condition Monitoring in Rotating Machineries”, in Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016), Cham, 2018.[Abstract]


Maintenance of rotating parts in machines is not easy. Prediction of faults in advance reduces the frequency of breakdown and improves the life time of machines. This paper proposes a machine condition monitoring system, which formulates the fault diagnosis problem as a machine learning based pattern classification problem. The vibration signals acquired from rotating machines are initially processed by a group-sparse denoising algorithm namely Overlapping Group Shrinkage (OGS). In OGS, the group sparse signal denoising problem is casted as a convex optimization problem with a group sparsity promoting penalty function. The denoised signals are then processed by Variational Mode Decomposition (VMD), which decomposes the signal into specific frequency modes. For representing the signal in the feature space, energy of each mode is extracted and is classified by LS-SVM classifier. The performance of the proposed condition monitoring system is evaluated in terms of classification accuracies and is compared with statistical features.

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2018

V. G. Sujadevi, Dr. Soman K. P., S. Kumar, S., and Neethu Mohan, “A Novel Cyclic Convolution Based Regularization Method for Power-Line Interference Removal in ECG Signal”, in Advances in Signal Processing and Intelligent Recognition Systems, Cham, 2018.[Abstract]


Applying signal processing to bio-signal record such as electrocardiogram or ECG signals provide vital insights to the details in diagnosis. The diagnosis will be exact when the extracted information about the ECG is accurate. However, these records usually gets corrupted/contaminated with several artifacts and power-line interferences (PLI) thereby affects the quality of diagnosis. Power-line interferences occurs in the range close to 50 Hz/60 Hz. The challenge is to remove the interferences without altering the original characteristics of ECG signal. Since the ECG signals frequency range is close to PLI, several articles discuss PLI removal methods which are mathematically complex and computationally intense. The present paper proposes a novel PLI removal method that uses a simple optimization method involving a circular convolution based \$\${\backslashell _2}\$\$-norm regularization. The solution is obtained in closed form and hence computationally simple and fast. The effectiveness of the proposed method is evaluated using output signal-to-noise-ratio (SNR) measure, and is found to be state-of-the-art.

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2018

Neethu Mohan and Dr. Soman K. P., “Power System Frequency and Amplitude Estimation Using Variational Mode Decomposition and Chebfun Approximation System”, in 2018 Twenty Fourth National Conference on Communications (NCC), Hyderabad, India, 2018.[Abstract]


The accurate estimation of power system frequency and amplitude is essential for power system monitoring, stability, control, and protection. This work proposes a novel approach for power system frequency and amplitude estimation based on variational mode decomposition (VMD) algorithm and Cheb-function (Chebfun) approximation system. In this work, the spectral information of power signals is extracted using VMD as sub-signals or modes. Each mode is further interpolated by Chebyshev polynomials in continuous domain using Chebfun system. The instantaneous frequency and amplitude are estimated based on zero crossings and local extrema locations of the continuous function. The robustness of the approach is evaluated on various power system scenarios and the results are compared with other existing methods. The promising results suggest that the proposed approach can be used as an efficient candidate for power system frequency and amplitude estimation.

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2018

A. Unnikrishnan, Sowmya V., and Dr. Soman K. P., “Deep alexnet with reduced number of trainable parameters for satellite image classification”, in Procedia Computer Science, 2018.[Abstract]


The emergence of machine learning algorithms enhanced the effectiveness of satellite image applications. On account of the high variability in-born in satellite data, a large portion of the present classification proposals are not sensible for handling satellite datasets. In this paper, we assessed profound learning systems in view of Convolutional Neural Networks for the precise order of multispectral remote sensing information. The Normalized Difference Vegetation Index (NDVI) is a solitary parameter for detecting landcover by utilizing the red and near-infrared band (NIR) information of the electromagnetic spectrum. NDVI is used to break down remote detecting images and survey the presence or absence of live green vegetation. The experiment is conducted on new publicaly available SAT-4 dataset, where the classes involved are types of vegetation. As NDVI computation require just two band data, it takes the benefit of both RED and NIR band information to classify diverse land covers. In the present work, the AlexNet architecture with two band information along with the reduced number of filters were trained and high-level features obtained from tested model managed to classify different land cover classes in the dataset. The proposed architecture is compared against the benchmark and results are estimated in terms of accuracy, precision and total number of trainable parameters. The proposed design brings about the aggregate diminishment of trainable parameters, while retaining high accuracy and precision, when compared against the existing architecture, which utilizes four bands. © 2018 The Authors. Published by Elsevier B.V.

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2018

C. Jayaprakash, Damodaran, B. B., Sowmya V., and Dr. Soman K. P., “Dimensionality Reduction of Hyperspectral Images for Classification using Randomized Independent Component Analysis”, in 2018 5th International Conference on Signal Processing and Integrated Networks, SPIN 2018, Amity School of Engineering and Technology, Noida, India, 2018.[Abstract]


Independent Component Analysis (ICA) is a commonly used technique for the dimensionality reduction of Hyperspectral images (HSI) to capture the linear relationship in the original input features of the image. Even though kernel methods were introduced to capture the nonlinear features, they possess high computational complexity, while dealing with a large number of pixels of HSI. Recent research has introduced Random Fourier feature maps (RFF) to project high dimensional data to low dimension. In this paper, we propose a nonlinear component analysis for the dimensionality reduction of HSI based on RFF maps. The proposed method has experimented on two dataset namely Pavia University and Salinas scene. It is verified that the feature extracted using RFF maps outperforms the conventional and kernel methods, in terms of classification accuracy. © 2018 IEEE.

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2018

V. S. Mohan, Sowmya V., and Dr. Soman K. P., “Deep Neural Networks as Feature Extractors for Classification of Vehicles in Aerial Imagery”, in 2018 5th International Conference on Signal Processing and Integrated Networks, SPIN 2018, 2018.[Abstract]


Detection of vehicles from aerial images have several real world implications in surveillance, military applications, traffic lot management, border patrol and traffic monitoring. The system proposed in this paper intends to automate the process of detecting vehicles from aerial images, rather than relying on a human operator. Here, we identify an optimum classification strategy for the proposed detection system, which is the initial stage of designing a vehicle detection pipeline. This research focuses on the feature extraction capabilities of standard neural network models like, Alexnet [6] and VGG-16 [7], which are compared against classic feature extraction techniques, like Histogram of Oriented Gradients and Singular Value Decomposition. The extracted features are benchmarked across standard machine learning algorithms such as Support Vector Machine and random forest. It is observed that the neural net extracted features gives an overall classification accuracy of 99% on the VEDAI dataset. The classification was treated as a binary class problem with vehicles as one class and rest everything as non-vehicles. © 2018 IEEE.

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2018

Vijay Krishna Menon, Sajith Variyar V. V., Dr. Soman K. P., Gopalakrishnan, E. A., Kottayil, S. K., Almas, M. Shoaib, and Nordström, L., “A Spark™ Based Client for Synchrophasor Data Stream Processing”, in 2018 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE), Phuket, Thailand, 2018.[Abstract]


The SCADA based monitoring systems, having a very low sampling of one reading per 2-4 seconds is known to produce roughly 4.3 Tera Bytes (TiBs) of data annually. With synchrophasor technology, this will go up at least 100 times more as the rate of streaming is as high as 50/100 (60/120) Hz. Phasor data concentrators (PDCs) transmit byte streams encapsulating a comprehensive list of power system parameter including multiple phasor measurements, instantaneous frequency estimates, rate of change of frequency and several analog and digital quantities; this high volume and velocity of data makes it truly ‘Big Data’. This helps in making the power grid a lot more observable, enabling real-time monitoring of crucial grid events such as voltage stability, grid stress and transient oscillations. Synchrophasor technology uses the IEEE C37.118.2-2011™ Phasor Measurement Unit (PMU) / PDC communication protocol for data exchange which has no direct interface with any contemporary big data stream APIs or protocols. In this paper we propose a streaming interface in Apache Spark™, a popular big data platform, using Scala programming language, implementing a complete IEEE C37.118.2-2011™ client inside a stream receiver so that we can effortlessly receive synchrophasor data directly to Spark™ applications for real-time processing and archiving.

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2018

N. Damodaran, Sowmya V., Govind, D., and Dr. Soman K. P., “Effect of decolorized images in scene classification using deep convolution features”, in Procedia Computer Science, 2018, vol. 143, pp. 954-961.[Abstract]


Scene classification is considered as an imperative issue for computer vision and has got extensive consideration in the recent past. Due to recent developments in high performance computing units such as GPUs, popularly known deep learning algorithm namely, convolutional neural networks (CNNs), exploits huge datasets to give powerful models. The paper proposes the use of transfer learning technique, by which a pre-trained model known as Places-CNN is used to generate feature vectors for each scene image of the dataset. The scene-classification experiments are conducted on the Oliva Torralba (OT) scene dataset, which consists of eight outdoor scene categories. The features were extracted from the fully connected layer of the pre-trained Places CNN architecture. The deep features were extracted from the input color images and the grayscale images converted using two different techniques based on singular value decomposition (SVD). The results obtained from classification experiments show that, models trained on SVD-Decolorized and Modified-SVD decolorized images give comparable performance to the input color images. Unlike the color images, which use three planes (RGB) of information, the grayscale images use only one plane of information. The grayscale images were able to retain the required shape and texture information from the original RGB images and, thus sufficient to categorize the classes of scene images. © 2018 The Authors. Published by Elsevier B.V.

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2018

N. V. Jacob, Sowmya V., and Dr. Soman K. P., “A Comparative Analysis of Total Variation and Least Square Based Hyperspectral Image Denoising Methods”, in Proceedings of the 2018 IEEE International Conference on Communication and Signal Processing, ICCSP 2018, Adhiparasakthi Engineering CollegeMelmaruvathur, Chennai; India, 2018, pp. 58-63.[Abstract]


Hyperspectral image HSI with high spectral resolution will be always degraded by the noise accumulation. Therefore, image denoising is a fundamental preprocessing technique which improves the precision of successive processes like image classification, unmixing etc. In this paper, we compare least square LS weighted regularization in spectral domain with spatial least square and total variation TV denoising techniques. These methods are experimented on real, and noise simulated hyperspectral image datasets. The contrast and edges of the image are well preserved in the spectral LS. The image contrast varies in spatial LS, and edge informations are lost in TV. The experimental results show that, the spectral LS is superior to other two techniques in terms of visual interpretation, Signal-to-Noise Ratio SNR and Structural Similarity SSIM Index. © 2018 IEEE.

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2018

A. Vazhayil, Vinayakumar, R., and Dr. Soman K. P., “Comparative Study of the Detection of Malicious URLs Using Shallow and Deep Networks”, in 2018 9th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2018, 2018.[Abstract]


In the modern epoch, all information is easily accessible through websites and due to this reason people rely completely on online resources. On the contrary to its advantages, privacy and security in online media are the main concern worldwide because of the rise in phishing attacks launched online. The number of phishing websites increases every month targeting more than 450 brands, as per the reports published by anti-phishing working groups(APWG). Traditionally blacklists are used to detect the URL attacks. But with the exponential increase in the number of phishing websites, this method has its own limitations and it also fails to detect newly generated phishing URLs which can be solved using machine learning or deep learning techniques. Here we present a comparative study between classical machine learning technique - logistic regression using bigram, deep learning techniques like convolution neural network(CNN) and CNN long short-term memory(CNN-LSTM) as architectures used to detect malicious URLs. On comparison CNN-LSTM gave the best accuracy of about 98% for the classification of phishing URLs.

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2018

V. K. Rahul, Vinayakumar, R., Dr. Soman K. P., and Poornachandran, P., “Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security”, in 2018 9th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2018, 2018.[Abstract]


Intrusion detection system (IDS) has become an essential layer in all the latest ICT system due to an urge towards cyber safety in the day-to-day world. Reasons including uncertainty in finding the types of attacks and increased the complexity of advanced cyber attacks, IDS calls for the need of integration of Deep Neural Networks (DNNs). In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS). A DNN with 0.1 rate of learning is applied and is run for 1000 number of epochs and KDDCup-'99' dataset has been used for training and benchmarking the network. For comparison purposes, the training is done on the same dataset with several other classical machine learning algorithms and DNN of layers ranging from 1 to 5. The results were compared and concluded that a DNN of 3 layers has superior performance over all the other classical machine learning algorithms.

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2018

B. Premjith, Dr. Soman K. P., and Anand Kumar M., “A deep learning approach for Malayalam morphological analysis at character level”, in Procedia Computer Science, 2018, vol. 132, pp. 47-54.[Abstract]


Morphological analysis is one of the fundamental tasks in computational processing of natural languages. It is the study of the rules of word construction by analysing the syntactic properties and morphological information. In order to perform this task, morphemes have to be separated from the original word. This process is termed as sandhi splitting. Sandhi splitting is important in the morphological analysis of agglutinative languages like Malayalam, because of the richness in morphology, inflections and sandhi. Due to sandhi, many morphological changes occur at the conjoining position of morphemes. Therefore, determining the morpheme boundaries becomes a tough task, especially in languages like Malayalam. In this paper, we propose a deep learning approach for learning the rules for identifying the morphemes automatically and segmenting them from the original word. Then, individual morphemes can be further analysed to identify the grammatical structure of the word. Three different systems were developed for this analysis using Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) and obtained accuracies 98.08%, 97.88% and 98.16% respectively. © 2018 The Authors. Published by Elsevier Ltd.

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2017

R. Vinayakumar, Dr. Soman K. P., and Poornachandran, P., “Evaluating Shallow and Deep Networks for Secure Shell (ssh)Taffic Analysis”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.[Abstract]


The family of recurrent neural network (RNN) mechanisms are largely used for the various tasks in natural language processing, speech recognition, image processing and many others due to they established as a powerful mechanism to capture dynamic temporal behaviors in arbitrary length of large-scale sequence data. This paper attempts to know the effectiveness of various RNN mechanisms on the traffic classification specifically for Secure Shell (SSH) protocol by modeling the feature sets of statistical flows as time-series obtained from various public and private traces. These traces are from NIMS (Network Information Management and Security Group), DARPA (Defense Advanced Research Projects Agency) 1999 Week1, DARPA 1999 Week3, MAWI (Measurement and Analysis on the WIDE Internet), and NLANR (National Laboratory for Applied Network Research) Active Measurement Project (AMP). A various configurations of network topologies, network parameters and network structures are used for family of RNN architectures to identify an optimal architecture. The experiments are run up to 1000 epochs with learning rate in the range [0.01-05] on both the binary and multiclass classification settings. RNN mechanisms have performed well in comparison to the other classical machine learning algorithms. Moreover, long short-term memory (LSTM) mechanism is a modified RNN, as achieved highest accuracy in cross-validation and testing of binary and multi-class classification cases. The background reason to that is, RNN mechanisms have capability to capture the dynamic temporal dependencies by storing information and updating them, when it is necessary across time-steps.

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2017

R. Vinayakumar, Dr. Soman K. P., and Poornachandran, P., “Deep Encrypted Text Categorization”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.[Abstract]


Long short-term memory (LSTM) is a significant approach to capture the long-range temporal context in sequences of arbitrary length. This had shown astonishing performance in sentence and document modeling. To leverage this, we use LSTM network to the encrypted text categorization at character and word level of texts. These texts are transformed in to dense word-vectors by using bag-of-words embedding. Dense word vectors are fed in to recurrent layers to capture the contextual information and followed by dense and activation layer with nonlinear activation function such as softmax for classification. The optimal network architecture has found by conducting various experiments with varying network parameters and network structures. All the experiments are run up to 1000 epochs with learning rate in the range [0.01-0.5]. Most of the LSTM network structures substantially performed well in 5-fold cross-validation. Based on the 5-fold cross-validation results, we claim that the character level inputs are more efficient in dealing with the encrypted texts in comparison to word level, due to the fact that character level input keeps more information from low-level textual representations. Character level based LSTM models achieved highest accuracy as 0.99 and the word level achieved highest accuracy as 0.94 in the classification settings of 5-fold cross validation using LSTM networks. On the real-world test data of CDMC 2016 e-News categorization task, word level LSTM models attained its highest accuracy as 0.43.

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2017

R. Vinayakumar, Dr. Soman K. P., and Poornachandran, P., “Secure Shell (ssh) Traffic Analysis with Flow based Features using Shallow and Deep Networks”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.[Abstract]


The primary objective of this work is to evaluate the effectiveness of various shallow and deep networks for characterizing and classifying the encrypted traffic such as secure shell (SSH). The SSH traffic statistical feature sets are estimated from various private and public traces. Private trace is NIMS (Network Information Management and Security Group) and public traces are MAWI (Measurement and Analysis on the WIDE Internet), NLANR's (National Laboratory for Applied Network Research) Active Measurement Project (AMP). To select optimal deep networks, experiments are done for various network parameters, network structures and network topologies. All the experiments are run up to 1000 epochs with learning rate in the range [0.01-0.5]. The various shallow and deep networks are trained using public traces and evaluated on the private trace and vice-versa. Results indicate that there is a possibility to detect SSH traffic with acceptable detection rate. The deep network has performed well in comparison to the shallow networks. Moreover, the performance of various shallow networks is comparable.

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2017

R. Vinayakumar, Dr. Soman K. P., and Poornachandran, P., “Long Short-term Memory based Operation log Anomaly Detection”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.[Abstract]


Long short-term memory (LSTM) architecture is an important approach for capturing long-range temporal dependencies in sequences of arbitrary length. Moreover, stacked-LSTM (S-LSTM: formed by adding recurrent LSTM layer to the existing LSTM network in hidden layer) has capability to learn temporal behaviors quickly with sparse representations. To apply this to anomaly detection, we model the operation log samples of normal and anomalous events occurred in 1 minute time interval as time-series with the aim to detect and classify the events as either normal or anomalous. To select an appropriate LSTM network, experiments are conducted for various network parameters and network structures with the dataset provided by Cyber Security Data Mining Competition (CDMC2016). The experiments are run up to 1000 epochs with learning rate in the range [0.01-05]. S-LSTM network architecture has showed its strength by achieving the highest accuracy 0.996 with false positive rate 0.02 on the provided real-world test data by CDMC2016.

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2017

R. Vinayakumar, Dr. Soman K. P., and Poornachandran, P., “Deep Android Malware Detection and Classification”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.

2017

R. Vinayakumar, Dr. Soman K. P., and Poornachandran, P., “Evaluating Effectiveness of Shallow and Deep Networks to Intrusion Detection System”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.[Abstract]


Network intrusion detection system (NIDS) is a tool used to detect and classify the network breaches dynamically in information and communication technologies (ICT) systems in both academia and industries. Adopting a new and existing machine learning classifiers to NIDS has been a significant area in security research due to the fact that the enhancement in detection rate and accuracy is of important in large volume of security audit data including diverse and dynamic characteristics of attacks. This paper evaluates the effectiveness of various shallow and deep networks to NIDS. The shallow and deep networks are trained and evaluated on the KDDCup `99' and NSL-KDD data sets in both binary and multi-class classification settings. The deep networks are performed well in comparison to the shallow networks in most of the experiment configurations. The main reason to this might be a deep network passes information through several layers to learn the underlying hidden patterns of normal and attack network connection records and finally aggregates these learned features of each layer together to effectively distinguish the normal and various attacks of network connection records. Additionally, deep networks have not only performed well in detecting and classifying the known attacks additionally in unknown attacks too. To achieve an acceptable detection rate, we used various configurations of network settings and its parameters in deep networks. All the various configurations of deep network are run up to 1000 epochs in training with a learning rate in the range [0.01-0.5] to effectively capture the time varying patterns of normal and various attacks.

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2017

R. Vinayakumar, Dr. Soman K. P., and Poornachandran, P., “Applying Deep Learning Approaches for Network Traffic Prediction”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.[Abstract]


Network traffic prediction aims at predicting the subsequent network traffic by using the previous network traffic data. This can serve as a proactive approach for network management and planning tasks. The family of recurrent neural network (RNN) approaches is known for time series data modeling which aims to predict the future time series based on the past information with long time lags of unrevealed size. RNN contains different network architectures like simple RNN, long short term memory (LSTM), gated recurrent unit (GRU), identity recurrent unit (IRNN) which is capable to learn the temporal patterns and long range dependencies in large sequences of arbitrary length. To leverage the efficacy of RNN approaches towards traffic matrix estimation in large networks, we use various RNN networks. The performance of various RNN networks is evaluated on the real data from GÉANT backbone networks. To identify the optimal network parameters and network structure of RNN, various experiments are done. All experiments are run up to 200 epochs with learning rate in the range [0.01-0.5]. LSTM has performed well in comparison to the other RNN and classical methods. Moreover, the performance of various RNN methods is comparable to LSTM.

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2017

R. Vinayakumar, Dr. Soman K. P., and Poornachandran, P., “Applying Convolutional Neural Network for Network Intrusion Detection”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.[Abstract]


Recently, Convolutional neural network (CNN) architectures in deep learning have achieved significant results in the field of computer vision. To transform this performance toward the task of intrusion detection (ID) in cyber security, this paper models network traffic as time-series, particularly transmission control protocol / internet protocol (TCP/IP) packets in a predefined time range with supervised learning methods such as multi-layer perceptron (MLP), CNN, CNN-recurrent neural network (CNN-RNN), CNN-long short-term memory (CNN-LSTM) and CNN-gated recurrent unit (GRU), using millions of known good and bad network connections. To measure the efficacy of these approaches we evaluate on the most important synthetic ID data set such as KDDCup 99. To select the optimal network architecture, comprehensive analysis of various MLP, CNN, CNN-RNN, CNN-LSTM and CNN-GRU with its topologies, network parameters and network structures is used. The models in each experiment are run up to 1000 epochs with learning rate in the range [0.01-05]. CNN and its variant architectures have significantly performed well in comparison to the classical machine learning classifiers. This is mainly due to the reason that CNN have capability to extract high level feature representations that represents the abstract form of low level feature sets of network traffic connections.

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2017

S. Selvin, Vinayakumar, R., Dr. E. A. Gopalakrishnan, Menon, V. K., and Dr. Soman K. P., “Stock Price Prediction using LSTM, RNN and CNN-sliding Window Model”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.[Abstract]


Stock market or equity market have a profound impact in today's economy. A rise or fall in the share price has an important role in determining the investor's gain. The existing forecasting methods make use of both linear (AR, MA, ARIMA) and non-linear algorithms (ARCH, GARCH, Neural Networks), but they focus on predicting the stock index movement or price forecasting for a single company using the daily closing price. The proposed method is a model independent approach. Here we are not fitting the data to a specific model, rather we are identifying the latent dynamics existing in the data using deep learning architectures. In this work we use three different deep learning architectures for the price prediction of NSE listed companies and compares their performance. We are applying a sliding window approach for predicting future values on a short term basis. The performance of the models were quantified using percentage error.

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2017

Vinayakumar R, B. Premjith, Sachin Kumar S., Dr. Soman K. P., and Prabaharan Poornachandran, “deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets”, in Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Copenhagen, Denmark, 2017.[Abstract]


This working note presents the methodology used in deepCybErNet submission to the shared task on Emotion Intensities in Tweets (EmoInt) WASSA-2017. The goal of the task is to predict a real valued score in the range [0-1] for a particular tweet with an emotion type. To do this, we used Bag-of-Words and embedding based on recurrent network architecture. We have developed two systems and experiments are conducted on the Emotion Intensity shared Task 1 data base at WASSA-2017. A system which uses word embedding based on recurrent network architecture has achieved highest 5 fold cross-validation accuracy. This has used embedding with recurrent network to extract optimal features at tweet level and logistic regression for prediction. These methods are highly language independent and experimental results shows that the proposed methods are apt for predicting a real valued score in than range [0-1] for a given tweet with its emotion type.

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2017

Vinayakumar R, Sachin Kumar S, B. Premjith, Prabaharan Poornachandran, and Dr. Soman K. P., “Deep Stance and Gender Detection in Tweets on Catalan Independence@Ibereval 2017”, in IberEval 2017 Evaluation of Human Language Technologies for Iberian Languages Workshop 2017,, Murcia, Spain, 2017.[Abstract]


This paper discusses deepyCybErNet submission methodology to the task on Stance and Gender Detection in Tweets on Catalan Independence@Ibereval 2017. The goal of the task is to detect the stance and gender of the user in tweets on the subject ”independence of Catalonia”. Tweets are available in two languages: Spanish and Catalan. In task 1 and 2, the system has to determine whether the tweet is in favor of, against or neutral to the tweets on the subject pertaining to the task in Spanish and Catalan languages respectively. In task 3 and 4, the system has to decide whether the person who tweets is a male or female. We submitted three systems for this task a Bag-of-Words (BOW) representation for tweets with logistic regression classifier, Recurrent Neural Network (RNN) based approach, Long Short Term Memory (LSTM) based approach and gated recurrent based approach. These methods are highly language independent and can be used for the declarations of stance of tweets and identifying the gender of twitter user in any language. These methods have performed better in detecting stance and gender in tweets of Catalan language than in those of Spanish.

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2017

V. G. Sujadevi, Dr. Soman K. P., Kumar, S. S., Neethu Mohan, and Arunjith, A. S., “Denoising of phonocardiogram signals using variational mode decomposition”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.[Abstract]


Recent advances in signal processing and the revolution by the mobile technologies have spurred several innovations in all the areas and albeit more so in home based tele-medicine. We used variational mode decomposition (VMD) based denoising on large-scale phonocardiogram (PCG) data sets and achieved better accuracy. We have also implemented a reliable, external hardware and mobile based phonocardiography system that uses VMD signal processing technique to denoise the PCG signal that visually displays the waveform and inform the end-user and send the data to cloud based analytics system.

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2017

Neethu Mohan, Dr. Soman K. P., and Vinayakumar, R., “Deep power: Deep learning architectures for power quality disturbances classification”, in 2017 International Conference on Technological Advancements in Power and Energy ( TAP Energy), Kollam, India, 2017.[Abstract]


The transformation of the conventional electric power grid to modern smart grid are subjected to power system quality and reliability problems. In order to ensure reliable, secure and quality supply of power, it is important to characterize and classify the power quality disturbances. Power quality (PQ) disturbance classification schemes implicitly relies o n feature engineering to extract unique and accurate features such as statistical information, spatio-temporal characteristics, stationary and non-stationary behavior of PQ signals. This paper explores the potentiality of deep learning algorithms to characterize and classify various PQ disturbances in smart grid. Deep learning algorithms have the inherent capability to automatically learn optimal features from raw input data and thus to avoid time-consuming feature engineering. To understand the effectiveness of various deep learning mechanisms, different architectures namely convolution neural network (CNN), recurrent neural network (RNN), identity-recurrent neural network (I-RNN), long short-term memory (LSTM), gated recurrent units (GRU) and convolutional neural network-long short-term memory (CNN-LSTM) are studied in this paper. Several experiments are conducted to propose an optimal deep learning architecture with specific network parameters and topologies. The performance of the proposed deep learning architecture is evaluated on a set of synthetic single and combined PQ disturbances and real-time PQ events. The proposed architecture is found to be accurate for real-time characterization and classification of power quality disturbances in smart grid.

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2017

S. Jose, Neethu Mohan, Sowmya V., and Dr. Soman K. P., “Least square based image deblurring”, in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, Manipal, Mangalore, India, 2017.[Abstract]


An image can be basically defined as an object that represents visual observation, which can be created and stored in the electronic form, produced from an optical device. When we take a photograph, there can be many problems associated with that particular image. Among them, one of the main issue is the blur of the image. Blur can be defined as something which will become vague or less distinct. A blurred image looks sharper or more detailed, if we are able to perceive all the objects and their shapes correctly in it. The main cause for blur is the out of focus issue of the camera/sensor. An image which is in out of focus will appear in a blurred state. Even if, at the present time, with an auto focus facility, sometimes we will not get the image in the correct focus. Most probably, a part of the image will be crisp and clear, however rest will be ill-defined. Image deblurring is a common and important process in fields like digital photography, medical imaging and astronomy. Hence, removing or dropping the total amount of blur is the most important task before being applying to the image analysis techniques. In this paper, a colour image deblurring algorithm based on the concept of least squares is proposed. The 1D least square based deconvolution technique is extended to colour image deblurring. The proposed approach is experimented on standard test images and the results are compared with classical total variation image deblurring algorithm. The effectiveness of the proposed approach is evaluated in terms of standard quality metrics such as PSNR and SSIM. © 2017 IEEE.

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2017

V. S. Dev, Rajan, S., Sowmya V., and Dr. Soman K. P., “Hyperspectral image denoising: A least square approach using wavelet filters”, in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, Manipal, Mangalore, India, 2017.[Abstract]


An image is an artifact that depicts visual perception, having a similar appearance to an object or person, thus providing a depiction of it. Images accomodate various types of noises which are due to sensor defects, lens distortion, software artifacts, blur etc. Denoising an image not only aims at removing the undesired noise but also, at retaining the features of the original image. The same goes for hyperspectral images which have numerous bands (each band contains information of the same object or location taken under different wavelengths of light) as compared to the red, green and blue bands of a color image. The need for better denoising techniques have brought about the birth of different image denoising algorithms, each with its own unique characteristics. Total Variation Denoising (TVD) is an advent for noise removal developed so as to retain sharp edges in the underlying signal. It is characterised as an optimization problem. Denoising using Legendre-Fenchel transform also widely used. Least Square based denoising technique is computationally demanding and gives better results. This paper compares the efficiency of various image denoising techniques like, total variation denoising, legendre-fenchel transform and wavelet transform denoising with the proposed method of least square denoising. This paper focuses on the hyperspectral image denoising technique based on least square approach using different wavelet filters. The proposed technique gives satisfactory denoising output with less computational time when compared with existing methods. © 2017 IEEE.

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2017

Rahul K Pathinarupothi, Dhara Prathap J, Ekanath Srihari Rangan, Gopalakrishnan E A, Vinaykumar R, and Dr. Soman K. P., “Single Sensor Techniques for Sleep Apnea Diagnosis Using Deep Learning”, in IEEE International Conference on Healthcare Informatics (ICHI 2017), Park City, Utah, USA, 2017.[Abstract]


A large number of obstructive sleep apnea (OSA) cases are under-diagnosed due unavailability, inconvenience or expense of sleep labs. Hence, an automated detection by applying computational techniques to multivariate signals has already become a well-researched subject. However, the best-known techniques that use various features have not achieved the gold standard of polysomnography (PSG) tests. In this paper, we substantiate the medical conjecture that OSA directly impacts body parameters such as Instantaneous Heart Rate (IHR) and blood oxygen saturation (SpO2). We then use a deep learning technique called LSTM-RNN (long short-term memory recurrent neural networks) to experimentally prove that OSA severity detection can be solely based on either IHR or SpO2 signals, which can be easily, obtained using off-the-shelf non-intrusive wearable single sensors. The results obtained from LSTM-RNN model shows an area under curve (AUC) of 0.98 associated with very high accuracy on a dataset of more than 16,000 apnea non-apnea minutes. These results have encouraged our collaborating doctors to further come up with a diagnostic protocol that is based on LSTM-RNN, SpO2, and IHR, thereby increasing the chances of larger adoption among medical community.

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2017

V. S. Dev, Sajith Variyar V. V., and Dr. Soman K. P., “Steering Angle Estimation for Autonomous Vehicle”, in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017.[Abstract]


The modern era has witnessed huge spike in the number of road accidents and fatalities. The common origin of traffic accidents is driver error. This is not going to change anytime soon thanks to the immense number of cell-phone users, in-car entertainment audio and video systems, and finally abundant traffic. There's one death every four minutes due to road accident in India. Such severe situation asks for a better scope that can propitiate the burden on the human drivers in certain critical instants. This demands a computationally efficient and fast responsive decision-making methods. But the course of autonomous vehicles is still in its early stages with companies still booming to build a fully autonomous vehicle. Volvo, Google, Tesla, BMW and more are still in the autonomous car research area. The mechanization and industrial science related to this field will mature over time, it is only a matter of time where the capability of driverless cars will outpace the outlook of any vehicle in the city. This paper exemplifies a novel approach to figure out the steering angle required to control the vehicle in the center of the road.

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2017

Sowmya V., Ajay, A., Dr. Govind D., and Dr. Soman K. P., “Improved color scene classification system using deep belief networks and support vector machines”, in 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuching, Sarawak; Malaysia, 2017, pp. 12-14 .[Abstract]


In general, the three main modules of the color scene classification systems are image decolorization, feature extraction and classification. The work presented in this paper focuses on image decolorization and classification as two stages. The first stage or objective of this paper is to improve the performance of the color scene classification system using deep belief networks (DBN) and support vector machines (SVM). Therefore, color scene classification system termed as AGMM-DBN-SVM is proposed using the existing feature extraction technique called bags of visual words (BoW) derived from the dense scale-invariant feature transform (SIFT) and adapted gaussian mixture models (AGMM). The second stage of the presented work is to combine the proposed AGMM-DBN-SVM classification models obtained for the two different image decolorization methods called rgb2gray and singular value decomposition (SVD) based color-to-grayscale image mapping techniques to significantly increase the performance of the proposed color scene classification system. The effectiveness of the proposed framework is experimented on Oliva Torralba (OT) scene dataset containing 8 different classes. The classification rate of the proposed color scene classification system applied on OT 8 scene dataset is significantly greater than the one of the existing benchmarks color scene classification system developed using AGMM and SVM.

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2017

Rahul K Pathinarupothi, Vinaykumar R, Ekanath Srihari Rangan, Dr. E. A. Gopalakrishnan, and Dr. Soman K. P., “Instantaneous heart rate as a robust feature for sleep apnea severity detection using deep learning”, in IEEE International Conference on Biomedical and Health Informatics, Orlando, Florida, 2017, pp. 293-296.[Abstract]


Automated sleep apnea detection and severity identification has largely focused on multivariate sensor data in the past two decades. Clinically too, sleep apnea is identified using a combination of markers including blood oxygen saturation, respiration rate etc. More recently, scientists have begun to investigate the use of instantaneous heart rates for detection and severity measurement of sleep apnea. However, the best-known techniques that use heart rate and its derivatives have been able to achieve less than 85% accuracy in classifying minute-to-minute apnea data. In our research reported in this paper, we apply a deep learning technique called LSTM-RNN (long short-term memory recurrent neural network) for identification of sleep apnea and its severity based only on instantaneous heart rates. We have tested this model on multiple sleep apnea datasets and obtained perfect accuracy. Furthermore, we have also tested its robustness on an arrhythmia dataset (that is highly probable in mimicking sleep apnea heart rate variability) and found that the model is highly accurate in distinguishing between the two.

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2017

N. S. Deve, Jasmineniketha, M., Dr. Geetha Srikanth, and Dr. Soman K. P., “Agricultural drought analysis for Thuraiyur taluk of Tiruchirappali District using NDVI and land surface temperature data”, in Proceedings of 2017 11th International Conference on Intelligent Systems and Control (ISCO), 2017, pp. 155-159.[Abstract]


Drastic changes in temperature and rainwater leads to the significant impact on drought which affects agricultural growth. Agricultural drought is a term which explains about reduction in the yield of crops due to abnormalities in rainfall as well as decline in soil moisture that affects agriculture, economy, social aspect, and environment. A trivial variation in the monsoon mainly affects the yield as well as the crops significantly. With the help of remote sensing data agricultural monitoring, management and assessment is done to calculate vegetation and temperature variations. Thuraiyur taluk in Tiruchirappalli District, of Tamilnadu (India) lies in a plain region between 11° 10′ N latitude and 78° 37′ E longitude. It depends mainly on the agriculture therefore the influence of drought affects the yield and the living of humans. The current study deals with the vegetation stress in the Thuraiyur taluk of Tiruchirappalli district with the usage of the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI). The Landsat data is utilized for the computation of LST and NDVI. The mixture of LST and NDVI, helps to monitor agricultural drought and also as a counsel for farmers. By computing the relationship between LST and NDVI, it is noted that they have a high negative correlation. The correlation between LST and NDVI is -0.763 for the year 2013 and -0.685 for the year 2016. The LST when interrelated with the vegetation index helps to identify the agricultural drought, as demonstrated in the current study. © 2017 IEEE.

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2016

Dr. M. Anand Kumar, Dr. Soman K. P., and Dr. Soman K. P., “Amrita-CEN@MSIR-FIRE2016: Code-mixed question classification using BoWs and RNN Embeddings”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 122-125.[Abstract]


Question classification is a key task in many question answering applications. Nearly all previous work on question classification has used machine learning and knowledge-based methods. This working note presents an embedding based Bag-of-Words method and Recurrent Neural Network to achieve an automatic question classification in the code-mixed Bengali-English text. We build two systems that classify questions mostly at the sentence level. We used a recurrent neural network for extracting features from the questions and Logistic regression for classification. We conduct experiments on Mixed Script Information Retrieval (MSIR) Task 1 dataset at FIRE20161. The experimental result shows that the proposed method is appropriate for the question classification task.

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2016

S. Singh, Dr. M. Anand Kumar, and Dr. Soman K. P., “CEN@Amrita: Information retrieval on CodeMixed Hindi English tweets using vector space models”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 131-134.[Abstract]


One of the major challenges nowadays is Information retrieval from social media platforms. Most of the information on these platforms is informal and noisy in nature. It makes the Information retrieval task more challenging. The task is even more difficult for twitter because of its character limitation per tweet. This limitation bounds the user to express himself in condensed set of words. In the context of India, scenario is little more complicated as users prefer to type in their mother tongue but lack of input tools force them to use Roman script with English embeddings. This combination of multiple languages written in the Roman script makes the Information retrieval task even harder. Query processing for such CodeMixed content is a difficult task because query can be in either of the language and it need to be matched with the documents written in any of the language. In this work, we dealt with this problem using Vector Space Models which gave significantly better results than the other participants. The Mean Average Precision (MAP) for our system was 0.0315 which was second best performance for the subtask. More »»

2016

Dr. M. Anand Kumar, Singh, S., Kavirajan, B., and Dr. Soman K. P., “DPIL@FIRE 2016: Overview of shared task on detecting paraphrases in Indian Languages (DPIL)”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 233-238.[Abstract]


This paper explains the overview of the shared task "Detecting Paraphrases in Indian Languages" (DPIL) conducted at FIRE 2016. Given a pair of sentences in the same language, participants are asked to detect the semantic equivalence between the sentences. The shared task is proposed for four Indian languages namely Tamil, Malayalam, Hindi and Punjabi. The dataset created for the shared task has been made available online and it is the first open-source paraphrase detection corpora for Indian languages.

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2016

H. B. Barathi Ganesh, Dr. M. Anand Kumar, and Dr. Soman K. P., “Distributional semantic representation for text classification and information retrieval”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 126-130.[Abstract]


The objective of this experiment is to validate the performance of the distributional semantic representation of text in the classification (Question Classification) task and the Information Retrieval task. Followed by the distributional representation, first level classification of the questions is performed and relevant tweets with respect to the given queries are retrieved. The distributional representation of text is obtained by performing Non - Negative Matrix Factorization on top of the Document - Term Matrix in the training and test corpus. To improve the semantic representation of the text, phrases are also considered along with the words. This proposed approach achieved 80% as a F-1 measure and 0.0377 as a mean average precision against the its respective Mixed Script Information Retrieval task1 and task 2 test sets.

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2016

P. V. Veena, G. Devi, R., Dr. M. Anand Kumar, and Dr. Soman K. P., “AMRITA-CEN@FIRE 2016: Consumer Health Information Search using keyword and word embedding features”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 197-200.[Abstract]


This work is submitted to Consumer Health Information Search (CHIS) Shared Task in Forum for Information Retrieval Evaluation (FIRE) 2016. Information retrieval from any part of web should include informative content relevant to the search of web user. Hence the major task is to retrieve only relevant documents according to the users query. The given task includes further refinement of the classification process into three categories of relevance such as support, oppose and neutral. Any user reading an article from web must know whether the content of that article supports or opposes title of the article. This seems to be a big challenge to the system. Our proposed system is developed based on the combination of Keyword based features and Word embedding based features. Classification of sentences is done by machine learning based classifier, Support Vector Machine (SVM).

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2016

H. B. Barathi Ganesh, Dr. M. Anand Kumar, and Dr. Soman K. P., “Distributional semantic representation in health care text classification”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 201-204.[Abstract]


This paper describes about the our proposed system in the Consumer Health Information Search (CHIS) task. The objective of the task 1 is to classify the sentences in the document into relevant or irrelevant with respect to the query and task 2 is analysing the sentiment of the sentences in the documents with respect to the given query. In this proposed approach distributional representation of text along with its statistical and distance measures are carried over to perform the given tasks as a text classification problem. In our experiment, Non - Negative Matrix Factorization utilized to get the distributed representation of the document as well as queries, distance and correlation measures taken as the features and Random Forest Tree utilized to perform the classification. The proposed approach yields 70.19% in task 1 and 34.64% in task 2 as an average accuracy.

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2016

H. B. Barathi Ganesh, Dr. M. Anand Kumar, and Dr. Soman K. P., “Conditional random fields for code mixed Entity Recognition”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 309-312.[Abstract]


Entity Recognition is an essential part of Information Extraction, where explicitly available information and relations are extracted from the entities within the text. Plethora of information is available in social media in the form of text and due to its nature of free style representation, it introduces much complexity while mining information out of it. This complexity is enhanced more by representing the text in more than one language and the usage of transliterated words. In this work we utilized sequential modeling algorithm with hybrid features to perform the Entity Recognition on the corpus given by CMEE-IL (Code Mixed Entity Extraction - Indian Language) organizers. The experimented approach performed great on both the Tamil-English and Hindi-English tweet corpus by attaining nearly 95% against the training corpus and 45.17%, 31.44% against the testing corpus.

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2016

R. G. Devi, Veena, P. V., Dr. M. Anand Kumar, and Dr. Soman K. P., “AMRITA-CEN@FIRE 2016: Code-mix entity extraction for Hindi-English and Tamil-English tweets”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 304-308.[Abstract]


Social media text holds information regarding various important aspects. Extraction of such information serves as the basis for the most preliminary task in Natural Language Processing called Entity extraction. The work is submitted as a part of Shared task on Code Mix Entity Extraction for Indian Languages(CMEE-IL) at Forum for Information Retrieval Evaluation (FIRE) 2016. Three different methodology is proposed in this paper for the task of entity extraction for code-mix data. Proposed systems include approaches based on the Embedding models and feature based model. Creation of trigram embedding and BIO tag formatting were done during feature extraction. Evaluation of the system is carried out using machine learning based classifier, SVM-Light. Overall accuracy through cross validation has proven that the proposed system is efficient in classifying unknown tokens too

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2016

S. V. Skanda, Singh, S., G. Devi, R., Veena, P. V., Dr. M. Anand Kumar, and Dr. Soman K. P., “CEN@Amrita FIRE 2016: Context based character embeddings for entity extraction in code-mixed text”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 321-324.[Abstract]


This paper presents the working methodology and results on Code Mix Entity Extraction in Indian Languages (CMEE-IL) shared the task of FIRE-2016. The aim of the task is to identify various entities such as a person, organization, movie and location names in a given code-mixed tweets. The tweets in code mix are written in English mixed with Hindi or Tamil. In this work, Entity Extraction system is implemented for both Hindi-English and Tamil-English code-mix tweets. The system employs context based character embedding features to train Support Vector Machine (SVM) classifier. The training data was tokenized such that each line containing a single word. These words were further split into characters. Embedding vectors of these characters are appended with the I-O-B tags and used for training the system. During the testing phase, we use context embedding features to predict the entity tags for characters in test data. We observed that the cross-validation accuracy using character embedding gave better results for Hindi-English twitter dataset compare to Tamil-English twitter dataset.

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2016

S. Sa Kumar, Neethu Mohan, Prabaharan, Pb, Dr. Soman K. P., J., M., and J., J., “Total Variation Denoising Based Approach for R-peak Detection in ECG Signals”, in Procedia Computer Science, 2016, vol. 93, pp. 697-705.[Abstract]


Detecting R-peak signal from electrocardiogram or ECG signal plays a vital role in cardiac monitoring system and ECG applications. In this paper, Total Variation Denoising (TVD) based approach is proposed to find the locations of R-peaks in the ECG signal. One advantage of using TVD method is that it preserves the sharp slopes or the peaks in the signal. This motivated to use TVD method for R-peak detection problem. The proposed approach is evaluated using the first channel, 48 ECG records from MIT-BIH Arrhythmia database. The accuracy of TVD based approach is calculated on all the 48 records. The proposed method gives 9 false-negative or FN beats, 126 false-positive or FP beats, positive-predictivity of 99.885%, sensitivity of 99.914%, with an overall accuracy of 99.79%. © 2016 The Authors. Published by Elsevier B.V.

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2016

G. R. Devi, Veena, P. V., Kumar, M. A., and Dr. Soman K. P., “Entity Extraction for Malayalam Social Media Text Using Structured Skip-gram Based Embedding Features from Unlabeled Data”, in Procedia Computer Science, 2016, vol. 93, pp. 547-553.[Abstract]


Social media text is generally informal and noisy but sometimes tends to have informative content. Extracting these informative content such as entities is a challenging task. The main aim of this paper is to extract entities from Malayalam social media text efficiently. The social media corpus used in our system is from FIRE2015 entity extraction task. This data is initially subjected to pre-processing and feature extraction and then proceeds with entity extraction. Apart from the conventional stylometric features like prefixes, suffixes, hash tags etc., and POS tags, unsupervised word embedding features obtained from Structured Skip-gram model are utilized to train the system. The extracted features is given to the Support vector machine classifier to build and train model. Testing of the system resulted in better accuracy than the existing systems evaluated in FIRE2015 tasks. Unsupervised features retrieved using Structured Skip-gram model contributes to the reason for achieving better performance.

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2016

, M, Akumar, and Dr. Soman K. P., “Amrita_CEN at SemEval-2016 : Complex Word identification using word embeddings”, in International Workshop on Semantic Evaluation (SemEval 2016), 2016, 2016.

2016

Vijay Krishna Menon, Vasireddy, N. Chakravart, Jam, S. Aswin, Pedamallu, V. Teja Navee, Sureshkumar, V., and Dr. Soman K. P., “Bulk Price Forecasting using Spark over NSE Data Set”, in International Conference on Data Mining and Big Data, Bali, Indonesia, 2016.[Abstract]


Financial forecasting is a widely applied area, making use of statistical prediction using ARMA, ARIMA, ARCH and GARCH models on stock prices. Such data have unpredictable trends and non-stationary property which makes even the best long term predictions grossly inaccurate. The problem is countered by keeping the prediction shorter. These methods are based on time series models like auto regressions and moving averages, which require computationally costly recurring parameter estimations. When the data size becomes considerable, we need Big Data tools and techniques, which do not work well with time series computations. In this paper we discuss such a finance domain problem on the Indian National Stock Exchange (NSE) data for a period of one year. Our main objective is to device a light weight prediction for the bulk of companies with fair accuracy, useful enough for algorithmic trading. We present a minimal discussion on these classical models followed by our Spark RDD based implementation of the proposed fast forecast model and some results we have obtained.

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2016

Vijay Krishna Menon and Dr. Soman K. P., “A New Evolutionary Parsing Algorithm for LTAG”, in International Conference on Advanced Computing, Networking, and Informatics (ICACNI'16), , Rourkela, Odisha, 2016.

2016

M. P, M, S., Sowmya V., and Dr. Soman K. P., “Low Contrast Satellite Image Restoration based on adaptive Histogram Equalization and Discrete Wavelet Transform”, in - 5th IEEE International Conference on Communication and Signal Processing-ICCSP'15, Adhiparasakthi Engineering College, Melmaruvathur , 2016.[Abstract]


Normally images obtained from satellites are of low-contrast type which hides major information carried by the image. Hence, image restoration is necessary in the image processing domain to extract all the information present in the images. The low contrast satellite image restoration based on adaptive histogram equalization combined with Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) is proposed in this paper. The proposed technique is experimented on three different satellite images. The effectiveness of the method introduced in this paper is shown by comparing it against the existing techniques based on gamma correction and histogram equalization combined with DCT and DWT. The comparison is done based on the standard parameters called Peak Signal to Noise Ratio (PSNR) and Standard Deviation. The result and analysis on the basis of PSNR values shows that adaptive histogram equalization combined with DWT is more effective approach compared to adaptive histogram equalization combined with DCT.

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2016

S. M, K.S, G. Krishnan, and Dr. Soman K. P., “Image denoising based on weighted Regularized Least Squares”, in International conference on Soft computing systems (ICSCS’16), 2016.

2016

P. A, R, M., Sowmya V., and Dr. Soman K. P., “X-ray Image Classification Based On Tumor using GURLS and LIBSVM”, in International Conference on Communications and Signal Processing (ICCSP’16), Adhiparasakthi Engineering College, Melmaruvathur , 2016.[Abstract]


In today's world, X-ray imaging is the low cost diagnostic technique when compared with all other medical imaging techniques. In this paper, the proposed method is to classify X-ray images based on tumor. The features are extracted using Singular Value Decomposition (SVD) and classified using different kernels in Library for Support Vector Machine (Lib-SVM) and Grand Unified Regularized Least Squares (GURLS). The proposed method is experimented on X-ray image dataset which is approved by an Oncologist. The effectiveness of proposed method is validated based on classification parameters. The experiment result analysis shows that Gaussian-ho in GURLS provides 95% classification accuracy which is 5% higher than RBF kernel in LibSVM. The performance of the proposed system is validated by an Oncologist.

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2016

N. PV, Nair, Y. C., variyar, S., and Dr. Soman K. P., “Environment Monitoring Multimode OPerable Autonomous Rover”, in International conference on Soft computing systems (ICSCS’16), , 2016.

2016

D. Pankaj, S, S. Kumar, Neethu Mohan, and Dr. Soman K. P., “Image Fusion Using Variational Mode Decomposition”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), 2016.

2016

S. Rajan and Dr. Soman K. P., “Low Contrast Image Enchancement using Adaptive Histogram, Discrete Wavelet and Cosine Transform”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), 2016.

2016

P. R, V, S., and Dr. Soman K. P., “Least Square based signal denoising using wavelet filters”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), , 2016.

2016

V. M, P, G., and Dr. Soman K. P., “Study of Diurnal Temperature Changes Caused by Anthropogenic Activity Using Meteorological Data in Coimbatore District ”, in International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES-2016), 2016.

2016

A. Balaji S, P, G., and Dr. Soman K. P., “Change Detection of Forest Vegetation using Remote Sensing and GIS Techniques in Kalakkad Mundanthurai Tiger Reserve (A Case Study)”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), 2016.

2016

A. S and Dr. Soman K. P., “Automatic Modulation Classification using Convolutional Neural Network”, in International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES-2016), 2016.

2016

Y. C. Nair, PV, N., Dr. Soman K. P., and Vijay Krishna Menon, “Real Time Vehicular Data Analysis utilising Big Data Platforms on Cost Effective ECU Networks”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), 2016.

2016

M. Kaviarasan, Dr. Geetha Srikanth, and Dr. Soman K. P., “GIS-based ground water quality monitoring in Thiruvannamalai district, Tamil Nadu, India”, in Proceedings of the International Conference on Soft Computing Systems, ICSCS 2015; Kumaracoil; India, 2016, vol. 397, pp. 685-700.[Abstract]


Ground water is a vital resource for drinking water around the world. The economic and ecological stability of many countries heavily relay upon groundwater availability. With rapid developments in industrial and agricultural sectors, the need for ground water is greater than ever before. Consequently, the quality of ground water is affected by fertilizers, effluents run off from industries, chemical dumping sites, domestic sewage, etc. Hence, it is necessary to constantly monitor ground water quality as it has a serious impact on human health. In this paper, we have analyzed ground water quality of Thiruvannamalai district of Tamil Nadu, India. The ground water samples are taken from 13 locations per area. Water Quality Index (WQI) is estimated for each area to ascertain for the potability of water. The physicochemical parameters like pH, Electrical Conductivity (EC), nitrates, fluorides, and chlorides sample data are compared against World Health Organization (WHO) standards. Geographical information system (GIS), an effi- cient tool for estimating water quality is used both in spatial and temporal domain. The results are useful in efficient monitoring and assessment of ground water and thus, for taking relevant measures to curb unrestrained exploitation. © Springer India 2016.

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2015

Sachin Kumar S, Neethu Mohan, Prabaharan Poornachandran, and Dr. Soman K. P., “Condition Monitoring in Roller Bearings using Cyclostationary Features”, in the Third International Symposium, 2015.[Abstract]


Proper machine condition monitoring is really crucial for any industrial and mechanical systems. The efficiency of mechanical systems greatly relies on rotating components like shaft, bearing and rotor. This paper focuses on detecting different fault in the roller bearings by casting the problem as machine learning based pattern classification problem. The different bearing fault conditions considered are, bearing-good condition, bearing with inner race fault, bearing with outer race fault and bearing with inner and outer race fault. Earlier the statistical features of the vibration signals were used for the classification task. In this paper, the cyclostationary behavior of the vibration signals is exploited for the purpose. In the feature space the vibration signals are represented by cyclostationary feature vectors extracted from it. The features thus extracted were trained and tested using pattern classification algorithms like decision tree J48, Sequential Minimum Optimization (SMO) and Regularized Least Square (RLS) based classification and provides a comparison on accuracies of each method in detecting faults.

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2015

A. Viswanath, K. Jose, J., Krishnan, N., S. Kumar, S., and Dr. Soman K. P., “Spike Detection of Disturbed Power signal using VMD”, in Procedia Computer Science, Bolgatty Palace and Island ResortKochi; India, 2015.[Abstract]


Most of the electronic equipments are susceptible to power disturbances. Transients are one of the most damaging power disturbances among them. In this paper, a modern adaptive signal decomposition technique called Variational Mode Decomposition (VMD) is used for the detection of impulsive transients or spikes from power signals. VMD decomposes the signal effectively in to several Intrinsic Mode Functions (IMF). Each IMF assumes to have a central frequency. The VMD algorithm focuses on finding these central frequencies and intrinsic mode functions using an optimization methodology called Alternating Direction Method of Multipliers (ADMM). In case of spike on a single tone signal like power signal, it is observed that the information about the original signal is dissolved in any of the modes and also observed that the energy of this mode will be higher when compared to other modes. Using the spectral information of this mode the signature of original signal is preserved. The proposed methodology is found to give good result in case of single tone signals. © 2015 The Authors.

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2015

C. Aneesh, Kumar, S., Hisham, P. M., and Dr. Soman K. P., “Performance comparison of Variational Mode Decomposition over Empirical Wavelet Transform for the classification of power quality disturbances using Support Vector Machine”, in International Conference on Information and Communication Technologies (ICICT 2014), Bolgatty Palace and Island ResortKochi; India, 2015.[Abstract]


This work considers the classification of power quality disturbances based on VMD (Variational Mode Decomposition) and EWT (Empirical Wavelet Transform) using SVM (Support Vector Machine). Performance comparison of VMD over EWT is done for producing feature vectors that can extract salient and unique nature of these disturbances. In this paper, these two adaptive signal processing methods are used to produce three Intrinsic Mode Function (IMF) components of power quality signals. Feature vectors produced by finding sines and cosines of statistical parameter vector of three different IMF candidates are used for training SVM. Validation for six different classes of power qualities including normal sinusoidal signal, sag, swell, harmonics, sag with harmonics, swell with harmonics is performed using synthetic data in MATLAB. Classification results using SVM shows that VMD outperforms over EWT for feature extraction process and the classification accuracy is tabled. © 2015 The Authors.

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2015

Vijay Krishna Menon, S. Rajendran, and Dr. Soman K. P., “A synchronised tree adjoining Grammar for English to Tamil Machine Translation”, in 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015 (Fourth International Symposium on Natural Language Processing (NLP'15)), SCMS Group of Institutions, Corporate Office CampusPrathap Nagar , Muttom, Aluva, Kochi, Kerala; India, 2015, pp. 1497-1501.[Abstract]


Tree adjoining Grammar (TAG) is a rich formalism for capturing syntax and some limited semantics of Natural languages. The XTAG project has contributed a very comprehensive TAG for English Language. Although TAGs have been proposed nearly 40 years ago by Joshi et al, 1975, their usage and application in the Indian Languages have been very rare, predominantly due to their complexity and lack of resources. In this paper we discuss a new TAG system and methodology of development for Tamil Language that can be extended for other Indian languages. The trees are developed synchronously with a minimalistic grammar obtained by careful pruning of XTAG English Grammar. We also apply Chomskian minimalism on these TAG trees, so as to make them simple and easily parsable. Furthermore we have also developed a parser that can parse simple sentences using the above mentioned grammar, and generating a TAG derivation that can be used for dependency resolution. Due to the synchronous nature of these TAG pairs they can be readily adapted for Formalism based Machine Translation (MT) from English to Tamil and vice versa. © 2015 IEEE. More »»

2015

H. B. Barathi Ganesh, Abinaya, N., Dr. M. Anand Kumar, Vinayakumar, R., and Dr. Soman K. P., “AMRITA - CEN@NEEL : Identification and linking of twitter entities”, in CEUR Workshop Proceedings, Florence; Italy, 2015, vol. 1395, pp. 64-65.[Abstract]


A short text gets updated every now and then. With the global upswing of such micro posts, the need to retrieve information from them also seems to be incumbent. This work focuses on the knowledge extraction from the micro posts by having entity as evidence. Here the extracted entities are then linked to their relevant DBpedia source by featurization, Part Of Speech (POS) tagging, Named Entity Recognition (NER) and Word Sense Disambiguation (WSD). This short paper encompasses its contribution to #Micropost2015 - NEEL task by experimenting existing Machine Learning (ML) algorithms. Copyright © 2015 held by author(s More »»

2015

C. Anjana, Sundaresan, S., Zacharia, T., Gandhiraj R., and Dr. Soman K. P., “An Experimental Study on Channel Estimation and Synchronization to Reduce error Rate in OFDM using GNU Radio”, in Procedia Computer Science, Bolgatty Palace and Island ResortKochi; India; 3, 2015, vol. 46, pp. 1056-1063.[Abstract]


Orthogonal Frequency Division Multiplexing (OFDM) is a particular case of multicarrier transmission in which, higher data rates are achieved using carriers that are densely packed. In this paper, implementation of OFDM communication system with channel estimation and synchronization is carried out and the bit error rate (BER) of OFDM system with and without channel estimation is observed and correspondingly a plot is traced. The choice has been made because of the advantages that OFDM and SDR has shown in terms of channel capacity and cost. Implementation of the prototype has been in GNU Radio; an open source software. © 2015 The Authors.

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2015

M. S., Dr. M. Anand Kumar, and Dr. Soman K. P., “Paraphrase Detection for Tamil language using Deep learning algorithms”, in International Conference on Big Data and Cloud Computing (ICBDCC-2015), 2015.

2015

, Dr. M. Anand Kumar, and Dr. Soman K. P., “Deep Belief Network based Part of Speech Tagger for Telugu Language”, in 2nd IC3T International Conference on Computer and Communication Technologies, 2015.

2014

Dr. Soman K. P., Sathiya, A., and Suganthi, N., “Classification of Stress of Automobile drivers using Radial Basis Function Kernel Support Vector Machine”, in International Conference on Information Communication and Embedded Systems (ICICES2014), 2014.[Abstract]


Classification of stress is imperative especially with regard to automobile drivers since stress level of the driver forms a major factor for accidents. This paper deciphered the classification of stress of automobile drivers using Radial Basis Function Kernel Support Vector Machine (SVM) classifier. The nonlinear separation of features in feature space was deciphered by this kernel trick. Pertinent feature extraction was done from ECG and EMG signals of the driver. Features extracted intuitively showed correlation with stress. This was made solid after getting a high classification accuracy of 100% using SVM using 10 fold cross validation. SVM performance was compared with that of kNN classifier and cross validation showed that kNN had only 81.26, 62.13 and 88.93% of classification rate, sensitivity and specificity where for SVM these parameters were 100%.

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2014

S. Santhosh, Abinaya, N., Rashmi, G., Sowmya V., and Dr. Soman K. P., “A novel approach for denoising coloured remote sensing image using Legendre Fenchel Transformation”, in 2014 International Conference on Recent Trends in Information Technology, ICRTIT 2014, Chennai, India, 2014.[Abstract]


Data acquired from remote sensing satellites are processed in order to retrieve the information from an image. Those images are preprocessed using image processing techniques such as noise removal. Satellite images are assumed to be corrupted with white Gaussian noise of zero mean and constant variance. Three planes of the noisy image are denoised separately through Legendre Fenchel Transformation. Later, these three planes are concatenated and compared with results obtained by Euler-Lagrange ROF model. Simulation results show that Legendre Fenchel ROF is highly convergent and less time consuming. To add evidence to the outcomes, quality metrics such as variance and PSNR for noisy and denoised images are calculated. The qualitative analysis of an image is analysed using MSSIM calculations, which clarifies the Structural Similarity between denoised images with original image. © 2014 IEEE.

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2014

D. Rajendra D. Kumar, Ambika, P. S., Anju, S., Gandhiraj R., and Dr. Soman K. P., “Simultaneous Access of RF Front End from more clients using GNU Radio for Low Cost Platform”, in International Confernece on Recent Trends in Engineering and Technology (ICRTET-2014), CSI Institue of Technolgy, Kanyakumari, 2014.

2014

M. Sreethivya, Dhanya, M. G., Nimisha, C., Gandhiraj R., and Dr. Soman K. P., “Radiation Pattern of Yagi-Uda antenna using GNU Radio Platform”, in International Conference on Trends in Technology for Convergence (TITCON '14), AVS Engineering College, Salem, 2014.

2014

S. Ravindranath, Ram, S. R. N., Subhashini, S., Reddy, A. V. S., Janarth, M., Aswathvignesh, R., Gandhiraj R., and Dr. Soman K. P., “Compressive sensing based image acquisition and reconstruction analysis”, in Proceeding of the IEEE International Conference on Green Computing, Communication and Electrical Engineering, ICGCCEE 2014, 2014.[Abstract]


Compressive sensing is a technique by which images are acquired and reconstructed from a relatively fewer measurements than what the Nyquist rate suggests. Compressive sensing is applicable when the signals under consideration are sparse, and most of the images are sparse in wavelet or frequency domain. In this paper, the mathematical formulation of compressive sensing is explained where in various notations and parameters like measurement matrices and sparsity-inducing matrices are dealt in detail. A deterministic measurement matrix, known as chess measurement matrix is implemented in an aperture assembly. Several reconstruction algorithms are analysed and the images reconstructed with PSNR plotted for every case. Based upon the results, it is proved that OMP is the efficient reconstruction algorithm among all. © 2014 IEEE More »»

2014

Dr. M. Anand Kumar, Rajendran, S., and Dr. Soman K. P., “AMRITA@ FIRE-2014: Morpheme Extraction for Tamil using Machine Learning (Working notes)”, in International Workshop: "MET shared Task" Forum for Information Retrieval Evaluation (FIRE- 2014), Bengaluru , 2014.

2014

, Anirudh Nair, Dr. M. Anand Kumar, and Dr. Soman K. P., “AMRITA@ FIRE-2014: Named Entity Recognition for Indian languages (Working notes)”, in International Workshop: "NER shared Task" Forum for Information Retrieval Evaluation (FIRE-2014), Bengaluru, 2014.

2014

P. Sanjanaashree, Dr. M. Anand Kumar, and Dr. Soman K. P., “Language learning for visual and auditory learners using scratch toolkit”, in 2014 International Conference on Computer Communication and Informatics: Ushering in Technologies of Tomorrow, Today, ICCCI 2014, https://www.scopus.com/record/display.uri?eid=2-s2.0-84911391150&origin=inward&txGid=0, 2014.[Abstract]


In recent years, with the development of technology, life has become very easy. Computers have become the life line of today's high-tech world. There is no work in our whole day without the use of computers. When we focus particularly in the field of education, people started preferring to e-books than carrying textbooks. In the phase of learning, visualization plays a major role. When the visualization tool and auditory learning comes together, it brings the in-depth understanding of data and their phoneme sequence through animation and with proper pronunciation of the words, which is far better than the people learning from the textbooks and imagining in their perspective and have their own pronunciation. Scratch with its visual, block-based programming platform is widely used among high school kids to learn programming basics. We investigated that in many schools around the world uses this scratch for students to learn programming basics. Literature review shows that students find it interesting and are very curious about it. This made us anxious towards natural language learning using scratch because of its interesting visual platform. This paper is based on the concept of visual and auditory learning. Here, we described how we make use of this scratch toolkit for learning the secondary language. We also claim that this visual learning will help people remember easily than to read as texts in books and the auditory learning helps in proper pronunciation of words rather than expecting someone's help. We have developed a scratch based tool for learning simple sentence construction of secondary language through primary language. In this paper, languages used are English (secondary language) and Tamil (primary language). This is an enterprise for language learning tool in scratch. This is applicable for other language specific exercises and can be adopted easily for other languages too. © 2014 IEEE. More »»

2013

Dr. Soman K. P., Alex, V., and Srinivas, C., “Analysis of Physiological Signals in Response to Stress using ECG and Respiratory Signals of Automobile Divers”, in 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), 2013.[Abstract]


This paper gives an analysis of variation of the physiological signals of a person with respect to the stress developed within him/her. The analysis was done using ECG and respiratory signals acquired from the automobile drivers who were made to drive on different road conditions to get different stress levels. As a part of analysis, we extracted two feature signals from the above said physiological signals. QRS power spectrum and the breathing rate were the two feature signals that were extracted from the mentioned physiological signals. Heart rate was used as the marker signal for analyzing the variations in the extracted physiological feature signals. The variations in the feature signals with respect to the stress were expressed in terms of correlation coefficients and were tabulated. The analysis clearly showed the changes in the feature signals with respect to the stress of the driver. It showed a direct proportionate relation in the QRS power and the breathing rate with respect to the stress of the driver. The analysis also showed that QRS power signal is a better feature signal for analyzing the stress since it showed more correlation with the heart rate marker signal. The analysis points out the fact that the physiological signals can be used as a metric for monitoring the stress of a person.

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2013

N. Prasannan, Xavier, G., Manikkoth, A., Gandhiraj R., Peter, R., and Dr. Soman K. P., “OpenBTS based microtelecom model: A socio-economic boon to rural communities”, in Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013, Kerala, 2013, pp. 856-861.[Abstract]


This paper proposes a low cost, low power, reconfigurable and flexible Open BTS (Base Transceiver Station) model based on SDR (Software Defined Radio) using USRP. A microtelecom model serves people at the "bottom of the pyramid" along with ensuring the ROI (Return on Investment) for MNO's (Mobile Network Operators). Thus a new telecom revolution that clubs Microtelecom business model with Open BTS concept would positively affect the socio-economic progress of rural communities, thereby ensuring the overall growth of developing nations. The success of a proposed model of this kind would encourage government to take initiatives and confidently invest on policies that would benefit the low-waged and less-privileged rural communities. © 2013 IEEE.

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2013

P. Sukanya, Suchithra, M., Sikha, O. K., Prabha, P., and Dr. Soman K. P., “Understanding CDMA in linear algebra point of view and its simulation in Excel”, in Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013, Kerala, 2013, pp. 78-83.[Abstract]


Code Division Multiple Access (CDMA) is one of the famous channel access method, mainly used in radio communication technologies. Unfortunately this concept is less understood by the student community due to the lack of understanding the mathematical rules behind it. This paper is intended to provide a linear algebra point of explanation of the concepts behind CDMA. The CDMA concept which was otherwise analyzed in spectral point of view is explained using the orthogonality of the bases. The Microsoft Excel Spread Sheet is used as an aid for the simulation since every one can go deep in to the basic concepts. © 2013 IEEE.

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2012

R. Anand, Xavier, G., Hariharan, V., Prasannan, N., Peter, R., and Dr. Soman K. P., “GNU radio based control system”, in Proceedings - 2012 International Conference on Advances in Computing and Communications, ICACC 2012, Cochin, 2012, pp. 259-262.[Abstract]


This paper is an attempt to reveal the untapped immense power of the GNU Radio - an open source software, in control and monitoring systems, both real time applications and class-room demonstrations. GNU Radio has already gained wide acceptance and glory in communication and signal processing. As a novel attempt to bring the controlling capability of GNU Radio to limelight, an experiment for temperature control, out of its innumerable applications, and the possibility of a revolution by this free open source software is predicted and explained through this paper. This paper describes the hardware and software requirements for the temperature control experiment with SBHS (Single Board Heater System) using GNU Radio and describes the recommended classroom demonstration. © 2012 IEEE.

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2010

Dr. Soman K. P. and Menon, A. G., “English to Tamil Machine Translation System”, in 9th Tamil Internet Conference (INFITT ), Chemmozhi Maanaadu, Coimbatore, India, 2010.

2010

S. Rajendran, Shivapratap, G., Dhanlakshmi, V., and Dr. Soman K. P., “Building a WordNet for Dravidian Languages”, in Proceedings of the Global WordNet Conference (GWC 10), Indian Institute of Technology, Mumbai, India, 2010.[Abstract]


This paper attempts to emphasize the need for a standalone and independent Dravidian WordNet. Since the morphology and lexical concepts of Dravidian languages are closer to each other than to a language from a different family, it is proposed to base the Dravidian WordNet on a Dravidian Language. A signifi-cant amount of work has already been done in Tamil language to understand the ontological structure and vocabulary. Based on the find-ings of these studies, it is proposed to build a Tamil WordNet first and then extend it to complete the Dravidian WordNet. A prototype model for the Tamil WordNet is also proposed in this paper. More »»

2010

Dr. M. Anand Kumar, Dhanalakshmi, V. V., Rajendran, S., Dr. Soman K. P., and Rekha, K. U., “A novel algorithm for Tamil morphological generator (Best Second Paper)”, in 8th International Conference on Natural Language Processing ( ICON2010), IIT Kharagpur, India, 2010.[Abstract]


Tamil is a morphologically rich language with agglutinative nature. Being agglutinative language most of the word features are postpositionally affixed to the root word. The morphological generator takes lemma, POS category and morpholexical description as input and gives a word-form as output. It is a reverse process of morphological analyzer. In any natural language generation system, morphological generator is an essential component in post processing stage. Morphological generator system implemented here is based on a new algorithm, which is simple, efficient and does not require any rules and morpheme dictionary. A paradigm classification is done for noun and verb based on S.Rajendran’s paradigm classification. Tamil verbs are classified into 32 paradigms with 1884 inflected forms. Like verbs, nouns are classified into 25 paradigms with 325 word forms. This approach requires only minimum amount of data. So this approach can be easily implemented to less resourced and morphologically rich languages. More »»

2010

V. P. Abeera, Aparna, S., Dhanalakshmi, V., M Kumar, A., Rajendran, S., Dr. Soman K. P., and Rekha, R. U., “Morphological Analyzer for Malayalam using Machine Learning”, in Second International Conference, ICDEM , Tiruchirappalli, India, 2010.[Abstract]


An efficient and reliable method for implementing Morphological Analyzer for Malayalam using Machine Learning approach has been presented here. A Morphological Analyzer segments words into morphemes and analyze word formation. Morphemes are smallest meaning bearing units in a language. Morphological Analysis is one of the techniques used in formal reading and writing. Rule based approaches are generally used for building Morphological Analyzer. The disadvantage of using rule based approaches are that if one rule fails it will affect the entire rule that follows, that is each rule works on the output of previous rule. The significance of using machine learning approach arises from the fact that rules are learned automatically from data, uses learning and classification algorithms to learn models and make predictions. The result shows that the system is very effective and after learning it predicts correct grammatical features even forwords which are not in the training set.

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2010

R. U. Rekha, M Kumar, A., Dhanalakshmi, V., Rajendran, S., and Dr. Soman K. P., “A Novel Approach to Morphological Generator for Tamil”, in 2nd International Conference on Data Engineering and Management (ICDEM 2010), Trichy, India, 2010.

2010

R. U. Rekha, M Kumar, A., Dhanalakshmi, V., Rajendran, S., and Dr. Soman K. P., “Morphological Generator for Tamil a New Data Driven Approach”, in 9th Tamil Internet Conference, Chemmozhi Maanaadu, Coimbatore, India, 2010.

2010

P. J. Antony, Mohan, S. P., and Dr. Soman K. P., “SVM Based Part of Speech Tagger for Malayalam”, in Recent Trends in Information, Telecommunication and Computing (ITC), 2010 International Conference on, Kochi, Kerala, 2010, pp. 339-341.[Abstract]


This paper presents the building of part-of-speech Tagger for Malayalam Language using Support Vector Machine (SVM). POS tagger plays an important role in Natural language applications like speech recognition, natural language parsing, information retrieval and information extraction. This supervised machine learning POS tagging approach requires a large amount of annotated training corpus to tag properly. At initial stage of POS-tagging for Malayalam, the model is trained with a very limited resource of annotated corpus. We tried to maximize the performance with this a substantial amount of annotated corpus. The objective of this project was to identify the ambiguities in Malayalam lexical items and develop an efficient and accurate POS Tagger. We have developed our own tagset for training and testing the POS-tagger generators. The present tagset consists of 29 tags. A corpus size of one hundred and eighty thousand words was used for training and testing the accuracy of the tagger generators. We found that the result obtained was more efficient and accurate compared with earlier methods for Malayalam POS tagging. More »»

2010

P. J. Antony, Ajith, V. P., and Dr. Soman K. P., “Kernel Method for English to Kannada Transliteration”, in Recent Trends in Information, Telecommunication and Computing (ITC), 2010 International Conference on, Kochi, Kerala, 2010, pp. 336-338.[Abstract]


Language transliteration is one of the important area in natural language processing. Accurate transliteration of named entities plays an important role in the performance of machine translation and cross-language information retrieval processes. The transliteration model must be design in such a way that the phonetic structure of words should be preserve as closely as possible. This paper addresses the problem of transliterating English to Kannada language using a publicly available structured output Support Vector Machines (SVM). The proposed transliteration scheme uses sequence labeling method to model the transliteration problem. This transliteration technique was demonstrated for English to Kannada Transliteration and achieved exact Kannada transliterations for 87.28% of English names. More »»

2010

K. Palanisamy, Kumar, A. Manoj, Chinnappa, M., M Manikandan, S., and Dr. Soman K. P., “Audio visual based pronunciation dictionary for Indian languages”, in Technology for Education (T4E), 2010 International Conference on, Mumbai, 2010, pp. 82-84.[Abstract]


The quality of pronunciation of a letter or word is most important for better conversations. We can accurately perceive the contextual information only when the speech sounds are produced clearly. The quality of any speech sound depends on the movements of organs of the human speech-production system. Nowadays, communication disorders are challenging the carrier development of individuals and growth of the nation. In this paper, we present an effective audio-visual based Tamil pronunciation dictionary by incorporating the visual actions of the organs for improving communication skills. More »»

2010

D. M. Chinnam, Madhusudhan, J., Nandhini, C., Prathyusha, S. N., Sowmiya, S., Dr. Ramanathan R., and Dr. Soman K. P., “Implementation of a Low Cost Synthetic Aperture Radar using Software Defined Radio”, in 2010 2nd International Conference on Computing, Communication and Networking Technologies, ICCCNT 2010, Karur, 2010.[Abstract]


GNU radio is a free open-source software toolkit for building software radios, in which software defines the transmitted waveforms and demodulates the received waveforms. In this paper an attempt has been made to explore the means to use a Software Defined Radio (SDR) to implement a basic radar system and then synthetic aperture radar. An experiment where in readings at two different scenarios (free environment and metal object) are taken into account and their plots are also given. This has been attempted keeping in mind the exponential increase in chip computing power and the ability to upgrade a radio transceiver via software updates with a marginal investment, the two features which makes such a foray attractive, technology wise and cost wise. This attempt also takes us a step closer to establishing the concept of a Cognitive radar which is software signal processing intensive.

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2010

P. J. Antony and Dr. Soman K. P., “Kernel based part of speech tagger for kannada”, in Machine Learning and Cybernetics (ICMLC), 2010 International Conference on, Qingdao, 2010, vol. 4, pp. 2139-2144.[Abstract]


The proposed paper presents the development of a part-of-speech tagger for Kannada language that can be used for analyzing and annotating Kannada texts. POS tagging is considered as one of the basic tool and component necessary for many Natural Language Processing (NLP) applications like speech recognition, natural language parsing, information retrieval and information extraction of a given language. In order to alleviate problems for Kannada language, we proposed a new machine learning POS tagger approach. Identifying the ambiguities in Kannada lexical items is the challenging objective in the process of developing an efficient and accurate POS Tagger. We have developed our own tagset which consist of 30 tags and built a part-of-speech Tagger for Kannada Language using Support Vector Machine (SVM). A corpus of texts, extracted from Kannada news papers and books, is manually morphologically analyzed and tagged using our developed tagset. The performance of the system is evaluated and we found that the result obtained was more efficient and accurate compared with earlier methods for Kannada POS tagging. More »»

2009

V. Prasanth, R., H. P., and Dr. Soman K. P., “Ticketing Solutions for Indian Railways Using RFID Technology”, in 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2009.[Abstract]


Modernization of Indian railways has always been a question in focus for the development of the basic infrastructure of our country. Since the railways represent one of the best modes of transport available to the common people, it would be impossible to just keep increasing the fares to meet costs incurred due to maintenance, the large workforce and the expansion activities. The railways should consider upgrading itself to cutting-edge technologies for better efficiency and cost reduction. One such up gradation is the role of information technology and e-ticketing which is achieved with the help of RFID technology. This RFID technology has been extensively used in the identification process these days with the help of a card and a reader. The idea has evolved from a systematic study of the computerization of railways and the loopholes in the present day system. A simple theoretical model is proposed which when implemented could result in an easier and better management of the tedious ticketing process.

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2009

R. Rajan, Sivan, R., Ravindran, R., and Dr. Soman K. P., “Rule Based Machine Translation from English to Malayalam”, in 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2009.[Abstract]


Here we propose a method for translating English sentences to Malayalam. This machine translation is done by rule based method. The core process is mediated by bilingual dictionaries and rules for converting source language structures into target language structures. The rules used in this approach are prepared based on the parts of speech (POS) tag and dependency information obtained from the parser. There are mainly two types of rules used here, one is transfer link rule and the other is morphological rules. In this method, the transfer link rules are used for generating target structure. Morphological rules are used for assigning morphological features. The bilingual dictionary used here is English, Malayalam bilingual dictionary. By using this approach, a given English sentence can be translated to its Malayalam equivalent.

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2009

N. Lalithamani and Dr. Soman K. P., “Towards Generating Irrevocable Key for Cryptography from Cancelable Fingerprints”, in Proceedings 2009-2nd IEEE International Conference on Computer Science and Information Technology(ICCSIT 2009), 2009, pp. pp.563-568.

2009

N. Lalithamani and Dr. Soman K. P., “An Efficient Approach for non-invertible Cryptographic Key Generation from Cancelable Fingerprint Biometrics”, in International Conference on Advances in Recent Technologies in Communication and Computing(ARTCom 2009), 2009, pp. pp.47-52, 2009.

2009

Va Dhanalakshmi, Padmavathy, Pa, M Kumar, A., Dr. Soman K. P., and Rajendran, Sb, “Chunker for Tamil”, in ARTCom 2009 - International Conference on Advances in Recent Technologies in Communication and Computing, Kottayam, Kerala, 2009, pp. 436-438.[Abstract]


This paper presents the chunker for Tamil using Machine learning techniques. Chunking is the task of identifying and segmenting the text into syntactically correlated word groups. The chunking is done by the machine learning techniques, where the linguistical knowledge is automatically extracted from the annotated corpus. We have developed our own tagset for annotating the corpus, which is used for training and testing the POS tagger generator and the chunker. The present tagset consists of thirty tags for POS and nine tags for chunking. A corpus size of two hundred and twenty five thousand words was used for training and testing the accuracy of the Chunker. We found that CRF++ affords the most encouraging result for Tamil chunker. © 2009 IEEE.

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2008

D. V., Dr. M. Anand Kumar, S., V. M., R., L., Dr. Soman K. P., and S., R., “Tamil Part-of-Speech Tagger based on SVM Tool”, in Proceedings of International Conference on Asian Language Processing 2008 (IALP 2008), Chiang Mai, Thailand, 2008.

1995

K. B. Misra and Dr. Soman K. P., “Multi State Fault Tree Analysis Using Fuzzy Probability Vectors and Resolution Identity”, in Reliability and Safety Analyses under Fuzziness, Heidelberg, 1995.[Abstract]


In this paper, we propose a method of estimating top event fuzzy probability of a fault tree in case of a system consisting of multistate elements. To the best of our knowledge, no such attempt has ever been made in this direction in the past. Beta fuzzy probability vectors, as proposed by Stein [9], are used to model the joint-possibility distribution of multistate elements. The use of resolution identity keeps the computational requirement at its minimum. However, the estimation procedure is based on Zadeh's extension principle.

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Publication Type: Journal Article

Year of Publication Title

2020

Neethu Mohan and Dr. Soman K. P., “A Data-driven Technique for Harmonics Monitoring in Emerging Power Grids using Noise-aware Dynamic Mode Decomposition”, Measurement Science and Technology, vol. 31, p. 015016, 2020.[Abstract]


A future power grid should be more robust, efficient, renewable, stable, reconfigurable, resilient, and distributed with more advanced control, protection and security schemes. It will combine a myriad of technologies such as information, communication, and power system engineering with computational intelligence. Because of the high proliferation of renewable energy sources, distributed generation systems and non-linear loads, grids are affected by several distortions and issues related to quality, stability, and control. Harmonics monitoring is a primary task in power grids for their safe and stable operation. It is essential for the protection and control of microgrid systems. Detection of harmonics, inter-harmonics and sub-harmonics improves the quality supply of power and protects the consumer equipments from failures. This paper investigates the effectiveness of a noise-aware dynamic mode decomposition algorithm, namely total-dynamic mode decomposition (TDMD), for harmonics monitoring in power grids. The ability of the TDMD algorithm to extract the hidden dynamic characteristics of time-series data is exploited for harmonics identification and its analysis. In the proposed method, multiple time-shifted copies of measured power signals are appended to create the initial data matrices. A singular value decomposition-based hard-thresholding is performed to avoid the ambiguities in the measured signal. Further, the eigendecomposition is performed using the TDMD algorithm and the corresponding frequencies and amplitudes are estimated. The performance advantage of the proposed method is verified by conducting several experiments using simulated and field measurements. The satisfactory performance certifies the practical applications of the proposed method for harmonics monitoring in emerging power grids

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2020

O. K. Sikha and Dr. Soman K. P., “Multi-resolution Dynamic Mode Decomposition-based Salient Region Detection in Noisy Images”, Signal, Image and Video Processing, 2020.[Abstract]


Detection of salient region in an image is a crucial problem in many cognition and computer vision applications like object detection, adaptive image compression, automatic image cropping, video and image analysis. A part of an image is considered as salient, if the set of pixels under consideration protrudes from the rest, in terms of features such as color, contrast and local orientations. Generally, computational models for saliency assume that the image under observation is clean and fails to deal with visual disturbances. This paper presents a robust method for the detection of salient regions, using the multi-resolution dynamic mode decomposition (MRDMD approach). Effectiveness of the proffered method for the detection of salient region within clean and noisy images was examined and successfully verified for a wide range of noise strengths.

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2019

Dr. Soman K. P., Vinayakumar, R., Poornachandran, P., and Menon, V. K., “A Deep-dive on Machine Learning for Cyber Security use Cases”, Machine Learning for Computer and Cyber Security, pp. 122-158, 2019.

2019

R. - V. Krishnamohan, S, S., Neethu Mohan, and Dr. Soman K. P., “Dynamic Mode Decomposition based feature for Image Classification”, 2019.[Abstract]


Irrespective of the fact that Machine learning has produced groundbreaking results, it demands an enormous amount of data in order to perform so. Even though data production has been in its all-time high, almost all the data is unlabelled, hence making them unsuitable for training the algorithms. This paper proposes a novel method of extracting the features using Dynamic Mode Decomposition (DMD). The experiment is performed using data samples from Imagenet. The learning is done using SVM-linear, SVM-RBF, Random Kitchen Sink approach (RKS). The results have shown that DMD features with RKS give competing results.

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2019

V. G. Sujadevi, Neethu Mohan, S. Kumar, S., Akshay, S., and Dr. Soman K. P., “A hybrid method for fundamental heart sound segmentation using group-sparsity denoising and variational mode decomposition”, Biomedical Engineering Letters, 2019.[Abstract]


Segmentation of fundamental heart sounds–S1 and S2 is important for automated monitoring of cardiac activity including diagnosis of the heart diseases. This pa-per proposes a novel hybrid method for S1 and S2 heart sound segmentation using group sparsity denoising and variation mode decomposition (VMD) technique. In the proposed method, the measured phonocardiogram (PCG) signals are denoised using group sparsity algorithm by exploiting the group sparse (GS) property of PCG signals. The denoised GS-PCG signals are then decomposed into subsequent modes with specific spectral characteristics using VMD algorithm. The appropriate mode for further processing is selected based on mode central frequencies and mode energy. It is then followed by the extraction of Hilbert envelope (HEnv) and a thresholding on the selected mode to segment S1 and S2 heart sounds. The performance advantage of the proposed method is verified using PCG signals from benchmark databases namely eGeneralMedical, Littmann, Washington, and Michigan. The proposed hybrid algorithm has achieved a sensitivity of 100%, positive predictivity of 98%, accuracy of 98% and detection error rate of 1.5%. The promising results obtained suggest that proposed approach can be considered for automated heart sound segmentation. © 2019, Korean Society of Medical and Biological Engineering.

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2019

Dr. Soman K. P., S. Kumar, S., Neethu Mohan, and Poornachandran, P., “Modern methods for signal analysis and its applications”, Studies in Computational Intelligence, vol. 823, pp. 263-290, 2019.[Abstract]


One of the objectives of signal processing is to extract features of the data which is considered as the first step toward data analysis. Number of oscillating components, the rate at which it oscillates, starting and ending time of the oscillation, duration of the oscillation, and strength of the oscillation are some of the features that help to make the decision for different problems such as classification, fault analysis, complex systems modeling, pattern recognition, condition monitoring etc. Many signals from natural or man-made dynamical systems are often composed of many different oscillations (or modes), with complex waveforms, time-varying amplitudes and frequencies. They carry valuable information about the originating system and are therefore worthy to conduct careful investigation. In this chapter, we look at signals with several components whose frequency varies with respect to time around a central frequency. We intend to explore various methods such as empirical mode decomposition (EMD), empirical wavelet transform (EWT), variational mode decomposition (VMD), synchrosqueezing transform (SST) for the analysis of signals such as electrocardiogram (ECG), electroencephalogram (EEG), phonocardiogram (PCG), machine vibrations etc. The most important keywords of this chapter are Hilbert transform, analytic signal, amplitude and frequency modulated signal and variational calculus. The code samples use the original authors packages for the methods. © Springer Nature Switzerland AG 2019.

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2019

R. Vinayakumar, Alazab, M., Dr. Soman K. P., Poornachandran, P., Al-Nemrat, A., and Venkatraman, S., “Deep Learning Approach for Intelligent Intrusion Detection System”, IEEE Access, vol. 7, pp. 41525-41550, 2019.[Abstract]


Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are continually changing and are occurring in very large volumes requiring a scalable solution. There are different malware datasets available publicly for further research by cyber security community. However, no existing study has shown the detailed analysis of the performance of various machine learning algorithms on various publicly available datasets. Due to the dynamic nature of malware with continuously changing attacking methods, the malware datasets available publicly are to be updated systematically and benchmarked. In this paper, a deep neural network (DNN), a type of deep learning model, is explored to develop a flexible and effective IDS to detect and classify unforeseen and unpredictable cyberattacks. The continuous change in network behavior and rapid evolution of attacks makes it necessary to evaluate various datasets which are generated over the years through static and dynamic approaches. This type of study facilitates to identify the best algorithm which can effectively work in detecting future cyberattacks. A comprehensive evaluation of experiments of DNNs and other classical machine learning classifiers are shown on various publicly available benchmark malware datasets. The optimal network parameters and network topologies for DNNs are chosen through the following hyperparameter selection methods with KDDCup 99 dataset. All the experiments of DNNs are run till 1,000 epochs with the learning rate varying in the range [0.01-0.5]. The DNN model which performed well on KDDCup 99 is applied on other datasets, such as NSL-KDD, UNSW-NB15, Kyoto, WSN-DS, and CICIDS 2017, to conduct the benchmark. Our DNN model learns the abstract and high-dimensional feature representation of the IDS data by passing them into many hidden layers. Through a rigorous experimental testing, it is confirmed that DNNs perform well in comparison with the classical machine learning classifiers. Finally, we propose a highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks.

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2019

R. Vinayakumar, Alazab, M., Dr. Soman K. P., Poornachandran, P., and Venkatraman, S., “Robust Intelligent Malware Detection Using Deep Learning”, IEEE Access, vol. 7, pp. 46717-46738, 2019.[Abstract]


Security breaches due to attacks by malicious software (malware) continue to escalate posing a major security concern in this digital age. With many computer users, corporations, and governments affected due to an exponential growth in malware attacks, malware detection continues to be a hot research topic. Current malware detection solutions that adopt the static and dynamic analysis of malware signatures and behavior patterns are time consuming and have proven to be ineffective in identifying unknown malwares in real-time. Recent malwares use polymorphic, metamorphic, and other evasive techniques to change the malware behaviors quickly and to generate a large number of new malwares. Such new malwares are predominantly variants of existing malwares, and machine learning algorithms (MLAs) are being employed recently to conduct an effective malware analysis. However, such approaches are time consuming as they require extensive feature engineering, feature learning, and feature representation. By using the advanced MLAs such as deep learning, the feature engineering phase can be completely avoided. Recently reported research studies in this direction show the performance of their algorithms with a biased training data, which limits their practical use in real-time situations. There is a compelling need to mitigate bias and evaluate these methods independently in order to arrive at a new enhanced method for effective zero-day malware detection. To fill the gap in the literature, this paper, first, evaluates the classical MLAs and deep learning architectures for malware detection, classification, and categorization using different public and private datasets. Second, we remove all the dataset bias removed in the experimental analysis by having different splits of the public and private datasets to train and test the model in a disjoint way using different timescales. Third, our major contribution is in proposing a novel image processing technique with optimal parameters for MLAs and deep learning architectures to arrive at an effective zero-day malware detection model. A comprehensive comparative study of our model demonstrates that our proposed deep learning architectures outperform classical MLAs. Our novelty in combining visualization and deep learning architectures for static, dynamic, and image processing-based hybrid approach applied in a big data environment is the first of its kind toward achieving robust intelligent zero-day malware detection. Overall, this paper paves way for an effective visual detection of malware using a scalable and hybrid deep learning framework for real-time deployments.

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2019

B. Premjith, Anand Kumar M., and Dr. Soman K. P., “Neural Machine Translation System for English to Indian Language Translation Using MTIL Parallel Corpus: Special Issue on Natural Language Processing”, Journal of Intelligent Systems, 2019.[Abstract]


Introduction of deep neural networks to the machine translation research ameliorated conventional machine translation systems in multiple ways, specifically in terms of translation quality. The ability of deep neural networks to learn a sensible representation of words is one of the major reasons for this improvement. Despite machine translation using deep neural architecture is showing state-of-the-art results in translating European languages, we cannot directly apply these algorithms in Indian languages mainly because of two reasons: unavailability of the good corpus and Indian languages are morphologically rich. In this paper, we propose a neural machine translation (NMT) system for four language pairs: English-Malayalam, English-Hindi, English-Tamil, and English-Punjabi. We also collected sentences from different sources and cleaned them to make four parallel corpora for each of the language pairs, and then used them to model the translation system. The encoder network in the NMT architecture was designed with long short-term memory (LSTM) networks and bi-directional recurrent neural networks (Bi-RNN). Evaluation of the obtained models was performed both automatically and manually. For automatic evaluation, the bilingual evaluation understudy (BLEU) score was used, and for manual evaluation, three metrics such as adequacy, fluency, and overall ranking were used. Analysis of the results showed the presence of lengthy sentences in English-Malayalam, and the English-Hindi corpus affected the translation. Attention mechanism was employed with a view to addressing the problem of translating lengthy sentences (sentences contain more than 50 words), and the system was able to perceive long-term contexts in the sentences. ©2019 Walter de Gruyter GmbH, Berlin/Boston 2019.

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2019

B. Premjith, Dr. Soman K. P., Anand Kumar M., and Jyothi Ratnam D., “Embedding linguistic features in word embedding for preposition sense disambiguation in english—Malayalam machine translation context”, Studies in Computational Intelligence, vol. 823, pp. 341-370, 2019.[Abstract]


Preposition sense disambiguation has huge significance in Natural language processing tasks such as Machine Translation. Transferring the various senses of a simple preposition in source language to a set of senses in target language has high complexity due to these many-to-many relationships, particularly in English-Malayalam machine translation. In order to reduce this complexity in the transfer of senses, in this paper, we used linguistic information such as noun class features and verb class features of the respective noun and verb correlated to the target simple preposition. The effect of these linguistic features for the proper classification of the senses (postposition in Malayalam) is studied with the help of several machine learning algorithms. The study showed that, the classification accuracy is higher when both verb and noun class features are taken into consideration. In linguistics, the major factor that decides the sense of the preposition is the noun in the prepositional phrase. The same trend was observed in the study when the training data contained only noun class features. i.e., noun class features dominates the verb class features. © Springer Nature Switzerland AG 2019.

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2019

Athira Gopalakrishnan, Dr. Soman K. P., and B. Premjith, “A Deep Learning-Based Named Entity Recognition in Biomedical Domain”, Lecture Notes in Electrical Engineering, vol. 545, pp. 517-526, 2019.[Abstract]


In the biomedical field, huge amounts of data have been produced day by day. These data drives the development of the biomedical area researches in so many ways. This paper mainly focusing on biomedical named entity recognition (NER) with the aim to enhance the performance through deep learning. Impressive results in natural language processing are made possible by deep learning techniques. Deep learning enables us to use them for NLP tasks and producing huge differences in accuracy compared to traditional methods. NER is a crucial initial step in information extraction in the biomedical domain. Here we use RNN, LSTM, and GRU on GENIA version 3.02 corpus and achieves an F score of 90%, which is better than the most state-of-the-art systems. © 2019, Springer Nature Singapore Pte Ltd.

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2019

A. Simon, Vinayakumar, R., Sowmya V., and Dr. Soman K. P., “Shallow Cnn with Lstm layer for tuberculosis detection in microscopic image”, International Journal of Recent Technology and Engineering, vol. 7, pp. 56-60, 2019.[Abstract]


Tuberculosis or TB, a disease mainly affecting lungs is infected by bacterium mycobacterium tuberculosis and diagnosed by careful examination of microscopic images taken from sputum specimen. Diagnosis of disease using microscopy and computer vision methods are applied for many previous practical problems. Recently, deep learning is playing major role in computer vision applications producing remarkable performance. But, computational complexity always remains as an obstacle in the application of deep learning in many aspects. So in this paper, a shallow CNN with LSTM layer is used for detecting the tubercle bacillus, mycobacterium tuberculosis, from the microscopic images of the specimen collected from the patients. The specified model is producing better performance than state of the art model and also have reduced number of learnable parameters, which requires comparatively less computation than the existing model. © BEIESP.

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2019

N. Damodaran, Sowmya V., Govind, D., and Dr. Soman K. P., “Scene classification using transfer learning”, Studies in Computational Intelligence, vol. 804, pp. 363-399, 2019.[Abstract]


Categorization of scene images is considered as a challenging prospect due to the fact that different classes of scene images often share similar image statistics. This chapter presents a transfer learning based approach for scene classification. A pre-trained Convolutional Neural Network (CNN) is used as a feature extractor for the images. The pre-trained network along with classifiers such as Support Vector Machines (SVM) or Multi Layer Perceptron (MLP) are used to classify the images. Also, the effect of single plane images such as, RGB2Gray, SVD Decolorized and Modified SVD decolorized images are analysed based on classification accuracy, class-wise precision, recall, F1-score and equal error rate (EER). The classification experiment for SVM was also done using a dimensionality reduction technique known as principal component analysis (PCA) on the feature vector. By comparing the results of models trained on RGB images with those grayscale images, the difference in the results is very small. These grayscale images were capable of retaining the required shape and texture information from the original RGB images and were also sufficient to categorize the classes of the given scene images. © Springer Nature Switzerland AG 2019.

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2019

N. Damodaran, Sowmya V., Govind, D., and Dr. Soman K. P., “Single-plane scene classification using deep convolution features”, Advances in Intelligent Systems and Computing, vol. 900, pp. 743-752, 2019.[Abstract]


Scene classification is considered as one of the challenging tasks of computer vision. Due to the availability of powerful graphics processing unit and millions of images, deep learning techniques such as convolutional neural networks (CNNs) have become popular in the image classification. This paper proposes the use of a pre-trained CNN model known as Places CNN, which was trained on scene-centric images. In this work, the pre-trained CNN is used as a feature extractor. The features are then used as input data for support vector machines (SVMs). The effect of grayscale images on the performance of pre-trained CNN-based scene classification system is analyzed by means of classification accuracy and equal error rate. The dataset used for this purpose is Oliva Torralba (OT) scene dataset, which consists of eight classes. The classification experiments are conducted using the feature vector from the ‘fc7’ layer of the CNN model for RGB, RGB2Gray, and SVD decolorized images. The classification experiment was also done using a dimensionality reduction technique known as principal component analysis (PCA) on the feature vector. The results obtained from classification experiments show that RGB2Gray and SVD decolorized images were able to give results similar to that of RGB images. The grayscale images were able to retain the required shape and texture information from the original RGB images and were also sufficient to categorize the classes of scene images. © Springer Nature Singapore Pte Ltd. 2019.

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2019

V. S. Mohan, Vinayakumar, R., Sowmya V., and Dr. Soman K. P., “Deep rectified system for high-speed tracking in images”, Journal of Intelligent and Fuzzy Systems, vol. 36, pp. 1957-1965, 2019.[Abstract]


Deep Rectified System for High-speed Tracking in Images (DRSHTI) is a unified open-source web portal developed for object detection in images. It aims to be a platform for the end user, where he/she can perform object detection on images without going through the hassles of debugging countless lines of code or setting up the right environment to perform computer vision tasks. By making the platform open-source, this work targets beginners in computer vision to form a basic understanding of object detection as an artificial intelligence task. This is made possible by releasing source codes, tools and tutorials on its usage via GitHub. This open-source portal offers two detection pipelines based on Faster-RCNN - a model to detect ground vehicles in aerial images and a model to detect everyday objects in 37 different classes in normal images. The former model is trained on VEDAI dataset, which gave 98.6% accuracy during testing and is offered as proof-of-concept that showcases the models ability to perform small target detections, but the latter model is trained on the PASCAL VOC dataset. Making the project open-source also aims at bringing in more development and tweaking to the existing vehicle detection module. The web portal can be accessed via https://drshti.github.io, where user can upload images and get annotations on objects present in it. Tutorials and source codes can be found at https://github.com/vyzboy92/Object-Detection-Net. © 2019 - IOS Press and the authors.

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2019

K. S. Charmisha, Sowmya V., and Dr. Soman K. P., “Dimensionally reduced features for hyperspectral image classification using deep learning”, Lecture Notes in Electrical Engineering, vol. 500, pp. 171-179, 2019.[Abstract]


Hyperspectral images (HSIs) cover a wide range of spectral bands in the electromagnetic spectrum with a very finite interval, and with high spectral resolution of data. The main challenges encountered with HSIs are those associated with their large dimensions. To overcome these challenges we need a healthy classification technique, and we need to be able to extract required features. This chapter analyzes the effect of dimensionality reduction on vectorized convolution neural networks (VCNNs) for HSI classification. A VCNN is a recently introduced deep-learning architecture for HSI classification. To analyze the effect of dimensionality reduction (DR) on VCNN, the network is trained with dimensionally reduced hyperspectral data. The network is tuned in accordance with the learning rate and number of iterations. The effect of a VCNN is analyzed by computing overall accuracy, classification accuracy, and the total number of trainable parameters required before and after DR. The reduction technique used is dynamic mode decomposition (DMD), which is capable of selecting most informative bands using the concept of eigenvalues. Through this DR technique for HSI classification using a VCNN, comparable classification accuracy is obtained using the reduced feature dimension and a lesser number of VCNN trainable parameters. © Springer Nature Singapore Pte Ltd. 2019.

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2019

A. Unnikrishnan, Sowmya V., and Dr. Soman K. P., “Deep learning architectures for land cover classification using red and near-infrared satellite images”, Multimedia Tools and Applications, 2019.[Abstract]


Classification of remotely sensed data requires the modelling of suitable image processing algorithms. The rise of machine learning systems upgraded the viability of satellite image applications. Using Convolutional Neural Networks (CNN), benchmark classification exactness can be accomplished for land cover grouping. Motivated by the concept of Normalized Difference Vegetation Index (NDVI), this paper utilizes only the red and near infrared (NIR) band information for classifying the publicly available SAT-4 and SAT-6 datasets. This is done, since NDVI computation requires only the two band (red and NIR) information and the classes involved in the dataset are types of vegetation. In this work, new deep learning architectures for three different networks (AlexNet, ConvNet, VGG) were proposed by hypertuning the network and the input as two band data. The modified architectures with the two band information along with reduced number of filters were trained and tested model manages to classify the images into different classes. The proposed architectures are compared against the existing architectures in terms of accuracy, precision and trainable parameters. The proposed architecture is found to perform equally efficient by retaining high accuracy with less number of trainable parameters, when compared against the the performance of benchmark deep learning architectures for satellite image classification. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

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2019

N. V. Jacob, Sowmya V., and Dr. Soman K. P., “Effect of denoising on hyperspectral image classification using deep networks and kernel methods”, Journal of Intelligent and Fuzzy Systems, vol. 36, pp. 2067-2073, 2019.[Abstract]


Hyperspectral Image (HSI) store the reflectance values of a single scene or object in several continuous bands of electromagnetic spectrum. When the image is recorded, the information in some of the spectral bands gets mixed with noise. The classification accuracy of hyperspectral image varies inversely with the quantity and nature of noise present in the cluster of spectral bands. Thus, denoising is a fundamental prerequisite in image processing applications like classification, unmixing, etc. In this paper, we compare the effect of denoising via classification using Vectorized Convolutional Neural Network (VCNN), kernel based Support Vector Machine (SVM) and Grand Unified Regularized Least Squares (GURLS) classifiers. The classifiers are provided with raw data (without denoising) and denoised data using spectral and spatial Least Square (LS) techniques. The data given to the network are in the form of pixels, so we call the convolutional neural network (CNN) as VCNN. The experiments are performed on three standard HSI datasets. The performance of the classifiers are evaluated based on overall and class-wise accuracy. © 2019 - IOS Press and the authors.

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2019

S. Emani, Dr. Soman K. P., Sajith Variyar V. V., and Adarsh, S., “Obstacle detection and distance estimation for autonomous electric vehicle using stereo vision and DNN”, Advances in Intelligent Systems and Computing, vol. 898, pp. 639-648, 2019.[Abstract]


Automation—replacement of humans with technology—is everywhere. It is going to become far more widespread, as industries are continuing to adapt to new technologies and are trying to find novel ways to save time, money, and effort. Automation in automobiles aims at replacing human intervention during the run time of vehicle by perceiving the environment around automobile in real time. This can be achieved in multitude of ways such as using passive sensors like camera and applying vision algorithms on their data or using active sensors like RADAR, LIDAR, time of flight (TOF). Active sensors are costly and not suitable for use in academic and research purposes. Since we have advanced computational platforms and optimized vision algorithms, we can make use of low-cost vision sensors to capture images in real time and map the surroundings of an automobile. In this paper, we tried to implement stereo vision on autonomous electric vehicle for obstacle detection and distance estimation. © Springer Nature Singapore Pte Ltd. 2019.

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2019

Sowmya V., Dr. Soman K. P., and Hassaballah, M., “Hyperspectral image: Fundamentals and advances”, Studies in Computational Intelligence, vol. 804, pp. 401-424, 2019.[Abstract]


Hyperspectral remote sensing has received considerable interest in recent years for a variety of industrial applications including urban mapping, precision agriculture, environmental monitoring, and military surveillance as well as computer vision applications. It can capture hyperspectral image (HSI) with a lager number of land-cover information. With the increasing industrial demand in using HSI, there is a must for more efficient and effective methods and data analysis techniques that can deal with the vast data volume of hyperspectral imagery. The main goal of this chapter is to provide the overview of fundamentals and advances in hyperspectral images. The hyperspectral image enhancement, denoising and restoration, classical classification techniques and the most recently popular classification algorithm are discussed with more details. Besides, the standard hyperspectral datasets used for the research purposes are covered in this chapter. © Springer Nature Switzerland AG 2019.

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2019

R. K. Renu, Sowmya V., and Dr. Soman K. P., “Pre-processed hyperspectral image analysis using tensor decomposition techniques”, Communications in Computer and Information Science, vol. 968, pp. 205-216, 2019.[Abstract]


Hyperspectral remote sensing image analysis has always been a challenging task and hence there are several techniques employed for exploring the images. Recent approaches include visualizing hyperspectral images as third order tensors and processing using various tensor decomposition methods. This paper focuses on behavioural analysis of hyperspectral images processed with various decompositions. The experiments includes processing raw hyperspectral image and pre-processed hyperspectral image with tensor decomposition methods such as, Multilinear Singular Value Decomposition and Low Multilinear Rank Approximation technique. The results are projected based on relative reconstruction error, classification and pixel reflectance spectrums. The analysis provides correlated experimental results, which emphasizes the need of pre-processing for hyperspectral images and the trend followed by the tensor decomposition methods. © 2019, Springer Nature Singapore Pte Ltd.

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2019

A. Unnikrishnan, Sowmya V., and Dr. Soman K. P., “A two-band convolutional neural network for satellite image classification”, Lecture Notes in Electrical Engineering, vol. 500, pp. 161-170, 2019.[Abstract]


The advent of neural networks has led to the development of image classification algorithms that are applied to different fields. In order to recover the vital spatial factor parameters, for example, land cover and land utilization, image grouping is most important in remote sensing. Recently, benchmark classification accuracy was achieved using convolutional neural networks (CNNs) for land cover classification. The most well-known tool which indicates the presence of green vegetation from multispectral pictures is the Normalized Difference Vegetation Index (NDVI). This chaper utilizes the success of the NDVI for effective classification of a new satellite dataset, SAT-4, where the classes involved are types of vegetation. As NDVI calculations require only two bands of information, it takes advantage of both RED- and NIR-band information to classify different land cover. The number and size of filters affect the number of parameters in convolutional networks. Restricting the aggregate number of trainable parameters reduces the complexity of the function and accordingly decreases overfitting. The ConvNet Architecture with two band information, along with a reduced number of filters, was trained, and high-level features obtained from a tested model managed to classify different land cover classes in the dataset. The proposed architecture, results in the total reduction of trainable parameters, while retaining high accuracy, when compared with existing architecture, which uses four bands. © Springer Nature Singapore Pte Ltd. 2019.

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2019

K. S. Charmisha, Sowmya V., and Dr. Soman K. P., “Dimensionality reduction by dynamic mode decomposition for hyperspectral image classification using deep learning and kernel methods”, Communications in Computer and Information Science, vol. 968, pp. 256-267, 2019.[Abstract]


Hyperspectral images are remotely sensed high dimension images, which capture a scene at different spectral wavelengths. There is a high correlation between the bands of these images. For an efficient classification and processing, the high data volume of the images need to be reduced. This paper analyzes the effect of dimensionality reduction on hyperspectral image classification using vectorized convolution neural network (VCNN), Grand Unified Recursive Least Squares (GURLS) and Support Vector Machines (SVM). Inorder to analyze the effect of dimensionality reduction, the network is trained with dimensionally reduced hyperspectral data for VCNN, GURLS and SVM. The experimental results shows that, one-sixth of the total number of available bands are the maximum possible reduction in feature dimension for Salinas-A and one-third of the total available bands are for Indianpines dataset that results in comparable classification accuracy. © 2019, Springer Nature Singapore Pte Ltd.

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2019

Sowmya V., Govind, D., and Dr. Soman K. P., “Significance of processing chrominance information for scene classification: a review”, Artificial Intelligence Review, 2019.[Abstract]


The primary objective of this paper is to provide a detailed review of various works showing the role of processing chrominance information for color-to-grayscale conversion. The usefulness of perceptually improved color-to-grayscale converted images for scene classification is then studied as a part of this presented work. Various issues identified for the color-to-grayscale conversion and improved scene classification are presented in this paper. The review provided in this paper includes, review on existing feature extraction techniques for scene classification, various existing scene classification systems, different methods available in the literature for color-to-grayscale image conversion, benchmark datasets for scene classification and color-to-gray-scale image conversion, subjective evaluation and objective quality assessments for image decolorization. In the present work, a scene classification system is proposed using the pre-trained convolutional neural network and Support Vector Machines developed utilizing the grayscale images converted by the image decolorization methods. The experimental analysis on Oliva Torralba scene dataset shows that the color-to-grayscale image conversion technique has a positive impact on the performance of scene classification systems. © 2019, Springer Nature B.V.

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2018

H. M, Dr. E. A. Gopalakrishnan, Vijay Krishna Menon, and Dr. Soman K. P., “NSE Stock Market Prediction Using Deep-Learning Models”, Procedia Computer Science, vol. 132, pp. 1351 - 1362, 2018.[Abstract]


The neural network, one of the intelligent data mining technique that has been used by researchers in various areas for the past 10 years. Prediction and analysis of stock market data have got an important role in today’s economy. The various algorithms used for forecasting can be categorized into linear (AR, MA, ARIMA, ARMA) and non-linear models (ARCH, GARCH, Neural Network). In this paper, we are using four types of deep learning architectures i.e Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for predicting the stock price of a company based on the historical prices available. Here we are using day-wise closing price of two different stock markets, National Stock Exchange (NSE) of India and New York Stock Exchange (NYSE). The network was trained with the stock price of a single company from NSE and predicted for five different companies from both NSE and NYSE. It has been observed that CNN is outperforming the other models. The network was able to predict for NYSE even though it was trained with NSE data. This was possible because both the stock markets share some common inner dynamics. The results obtained were com- pared with ARIMA model and it has been observed that the neural networks are outperforming the existing linear model (ARIMA).

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2018

S. Srivatsa, Sowmya V., and Dr. Soman K. P., “Least square based fast denoising approach to hyperspectral imagery”, Advances in Intelligent Systems and Computing, vol. 518, pp. 107-115, 2018.[Abstract]


The presence of noise in hyperspectral images degrades the quality of applications to be carried out using these images. But, since a hyperspectral data consists of numerous bands, the total time requirement for denoising all the bands will be much higher compared to normal RGB or multispectral images. In this paper, a denoising technique based on Least Square (LS) weighted regularization is proposed. It is fast, yet efficient in denoising images. The proposed denoising technique is compared with Legendre-Fenchel (LF) denoising, Wavelet-based denoising, and Total Variation (TV) denoising methods based on computational time requirement and Signal-to-Noise Ratio (SNR) calculations. The experimental results show that the proposed LS-based denoising method gives as good denoising output as LF and Wavelet, but with far lesser time consumption. Also, edge details are preserved unlike in the case of total variation technique. © Springer Nature Singapore Pte Ltd. 2018.

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2018

M. Swarna, Sowmya V., and Dr. Soman K. P., “Effect of dimensionality reduction on sparsity based hyperspectral unmixing”, Advances in Intelligent Systems and Computing, vol. 614, pp. 429-439, 2018.[Abstract]


Interpretation of hyperspectral data is challenging due to the lack of spatial resolution, which causes mixing of endmember information in each pixel. Hyperspectral unmixing aims at extracting the information related to the fractional abundance of each endmember present in every pixel. The unmixing problem can be carried out by considering that the spectral signature of each endmember is a linear combination of the pure spectral signatures known in prior. In this work, sparse unmixing techniques such as, Orthogonal Matching Pursuit and Alternating Directional Multiplier Methods are applied along with dimensionality reduction of the hyperspectral image. Dimensionality reduction is obtained using the Inter-Band Block Correlation followed by singular value and QR decomposition (SVD-QR). Furthermore, we analyze the effect of dimensionality reduction on two different unmixing algorithms. Our experimentation is carried out on two real hyperspectral datasets namely ‘samson’ and ‘jasper ridge’ and the results comprises of a comparison between hyperspectral unmixing before and after dimensionality reduction using the standard metrics such as root mean square error, classwise-accuracy and visual perception. This provides a new outlook for the unmixing process as abundance estimation can be done with only the most informative bands of the image instead of using the entire data by using the dimensionality reduction technique. © Springer International Publishing AG 2018.

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2018

R. Reshma, Sowmya V., and Dr. Soman K. P., “Effect of Legendre–Fenchel denoising and SVD-based dimensionality reduction algorithm on hyperspectral image classification”, Neural Computing and Applications, vol. 29, pp. 301-310, 2018.[Abstract]


This paper describes the importance of performing preprocessing techniques namely, denoising and dimensionality reduction to the hyperspectral data before classification. The two main problems faced in hyperspectral image processing are noise and higher dimension. Legendre–Fenchel transformation denoises each band in the data while preserving the edge information. To overcome the issue of high data volume, inter-band block correlation coefficient technique followed by singular value decomposition and QR decomposition is utilized to reduce the dimension of hyperspectral image without affecting the critical information. The preprocessed data are classified using kernel-based libraries, namely GURLS and LibSVM. Performance of these techniques is evaluated with accuracy assessment measures. The experiment was performed on five datasets. Experimental analysis shows that the proposed denoising technique increases the classification accuracy. In the case of Indian Pines data, with 10% of the training data, the classification accuracy is improved from 83.5 to 97.3%. And also, dimensionality reduction technique gives good classification accuracy even with 50% reduction in the number of bands. The classification accuracy of the Salinas-A and Pavia University data is 99.4 and 94.6% with the 50% dimensionally reduced (100 and 50 bands, respectively) number of bands. The bands extracted by the dimensionality reduction technique using the denoised hyperspectral data differ from that of the hyperspectral data without denoising. This emphasizes the importance of denoising the dataset before applying dimensionality reduction technique. In case of Pavia University, the band numbers above 50 (out of 100 bands) which were not informative bands before denoising are selected as informative bands by dimension reduction technique after denoising. © 2017, The Natural Computing Applications Forum.

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2018

R. Vinayakumar, Dr. Soman K. P., Poornachandran, P., Mohan, V. S., and Kumar, A. D., “ScaleNet: Scalable and Hybrid Framework for Cyber Threat Situational Awareness based on DNS, URL, and Email data Analysis”, Journal of Cyber Security and Mobility, vol. 8, pp. 189-240, 2018.[Abstract]


A computer virus or malware is a computer program, but with the purpose of causing harm to the system. This year has witnessed the rise of malware and the loss caused by them is high. Cyber criminals have continually advancing their methods of attack. The existing methodologies to detect the existence of such malicious programs and to prevent them from executing are static, dynamic and hybrid analysis. These approaches are adopted by anti-malware products. The conventional methods of were only efficient till a certain extent. They are incompetent in labeling the malware because of the time taken to reverse engineer the malware to generate a signature. When the signature becomes available, there is a high chance that a significant amount of damage might have occurred. However, there is a chance of detecting the malicious activities quickly by analyzing the events of DNS logs, Emails, and URLs. As these unstructured raw data contains rich source of information, we explore how the large volume of data can be leveraged to create cyber intelligent situational awareness to mitigate advanced cyber threats. Deep learning is a machine learning technique largely used by researchers in recent days. It avoids feature engineering which served as a critical step for conventional machine learning algorithms. It can be used along with the existing automation methods such as rule and heuristics based and machine learning techniques. This work takes the advantage of deep learning architectures to classify and correlate malicious activities that are perceived from the various sources such as DNS, Email, and URLs. Unlike conventional machine learning approaches, deep learning architectures don't follow any feature engineering and feature representation methods. They can extract optimal features by themselves. Still, additional domain level features can be defined for deep learning methods in NLP tasks to enhance the performance. The cyber security events considered in this study are surrounded by texts. To convert text to real valued vectors, various natural language processing and text mining methods are incorporated. To our knowledge, this is the first attempt, a framework that can analyze and correlate the events of DNS, Email, andURLsat scale to provide situational awareness against malicious activities. The developed framework is highly scalable and capable of detecting the malicious activities in near real time. Moreover, the framework can be easily extended to handle large volume of other cyber security events by adding additional resources. These characteristics have made the proposed framework stand out from any other system of similar kind.

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2018

Dr. M. Anand Kumar, B. Premjith, Shivkaran Singh, Sankaravelayuthan, R., and Dr. Soman K. P., “An Overview of the Shared Task on Machine Translation in Indian Languages (MTIL) - 2017”, Journal of Intelligent Systems, 2018.[Abstract]


In recent years, the multilingual content over the internet has grown exponentially together with the evolution of the internet. The usage of multilingual content is excluded from the regional language users because of the language barrier. So, machine translation between languages is the only possible solution to make these contents available for regional language users. Machine translation is the process of translating a text from one language to another. The machine translation system has been investigated well already in English and other European languages. However, it is still a nascent stage for Indian languages. This paper presents an overview of the Machine Translation in Indian Languages shared task conducted on September 7-8, 2017, at Amrita Vishwa Vidyapeetham, Coimbatore, India. This machine translation shared task in Indian languages is mainly focused on the development of English-Tamil, English-Hindi, English-Malayalam and English-Punjabi language pairs. This shared task aims at the following objectives: (a) to examine the state-of-the-art machine translation systems when translating from English to Indian languages; (b) to investigate the challenges faced in translating between English to Indian languages; (c) to create an open-source parallel corpus for Indian languages, which is lacking. Evaluating machine translation output is another challenging task especially for Indian languages. In this shared task, we have evaluated the participant's outputs with the help of human annotators. As far as we know, this is the first shared task which depends completely on the human evaluation. ©2018 Walter de Gruyter GmbH, Berlin/Boston 2018.

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2018

V. R. and Dr. Soman K. P., “DeepMalNet: Evaluating shallow and deep networks for static PE malware detection”, ICT Express, 2018.[Abstract]


This paper primarily evaluates the efficacy of shallow and deep networks to statically detect malicious windows portable executable (PE) files. This uses recently released, labeled and benchmark data set, EMBER malware benchmark data set. As deep networks are parameterized, the parameters are chosen based on comparing the performance of various network parameters and network topologies over various trials of experiments. The experiments of such chosen efficient configurations of deep models are run up to 1000 epochs with varying learning rates between 0.01 and 0.5. The observed results of deep networks are high compared to the shallow networks. © 2018 The Korean Institute of Communications and Information Sciences (KICS)

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2018

S. G., R., V., and Dr. Soman K. P., “Diabetes Detection using Deep Learning Algorithms”, ICT Express, 2018.[Abstract]


Diabetes is a metabolic disease affecting a multitude of people worldwide. Its incidence rates are increasing alarmingly every year. If untreated, diabetes-related complications in many vital organs of the body may turn fatal. Early detection of diabetes is very important for timely treatment which can stop the disease progressing to such complications. RR-interval signals known as heart rate variability (HRV) signals (derived from electrocardiogram (ECG) signals) can be effectively used for the non-invasive detection of diabetes. This research paper presents a methodology for classification of diabetic and normal HRV signals using deep learning architectures. We employ long short-term memory (LSTM), convolutional neural network (CNN) and its combinations for extracting complex temporal dynamic features of the input HRV data. These features are passed into support vector machine (SVM) for classification. We have obtained the performance improvement of 0.03% and 0.06% in CNN and CNN-LSTM architecture respectively compared to our earlier work without using SVM. The classification system proposed can help the clinicians to diagnose diabetes using ECG signals with a very high accuracy of 95.7%.

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2018

Neethu Mohan, Dr. Soman K. P., and S. Sachin Kumar, “A data-driven strategy for short-term electric load forecasting using dynamic mode decomposition model”, Applied Energy, vol. 232, pp. 229 - 244, 2018.[Abstract]


The electric load forecasting is extremely important for energy demand management, stability and security of power systems. A sufficiently accurate, robust and fast short-term load forecasting (STLF) model is necessary for the day-to-day reliable operation of the grid. The characteristics of load series such as non-stationarity, non-linearity, and multiple-seasonality make such prediction a troublesome task. This difficulty is conventionally tackled with model-driven methodologies that demand domain-specific knowledge. However, the ideal choice is a data-driven methodology that extracts relevant and meaningful information from available data even when the physical model of the system is unknown. The present work is focused on developing a data-driven strategy for short-term load forecasting (STLF) that employs dynamic mode decomposition (DMD). The dynamic mode decomposition is a matrix decomposition methodology that captures the spatio-temporal dynamics of the underlying system. The proposed data-driven model efficiently identifies the characteristics of load data that are affected by multiple exogenous factors including time, day, weather, seasons, social activities, and economic aspects. The effectiveness of the proposed DMD based strategy is confirmed by conducting experiments on energy market data from different smart grid regions. The performance advantage is verified using output quality measures such as RMSE, MAPE, MAE, and running time. The forecasting results are observed to be competing with the benchmark methods. The satisfactory performance suggests that the proposed data-driven model can be used as an effective tool for the real-time STLF task.

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2018

Dr. Geetha Srikanth, V, A., and Dr. Soman K. P., “DeepAirnet: Applying Recurrent Networks for Air Quality Prediction”, Procedia Computer Science, vol. 132, pp. 1394-1403, 2018.

2018

S. G, KP, S., R, V., and Dr. Soman K. P., “Automated Detection of Diabetes using CNN and CNN-LSTM Network and Heart Rate Signals”, Procedia Computer Science, vol. 132, pp. 1253 - 1262, 2018.[Abstract]


Diabetes mellitus, commonly known as diabetes, is a disease that affects a vast majority of people globally. Diabetes cannot be cured; it can only be kept under control. In this paper, diabetes is diagnosed by the analysis of Heart Rate Variability (HRV) signals obtained from ECG signals. We employed deep learning networks of Convolutional neural network (CNN) and CNN-LSTM (LSTM = Long short term memory) combination to automatically detect the abnormality. Unlike the conventional analysis methods so far followed, deep learning techniques do not require any feature extraction. We initially performed classification splitting the database into separate training and testing data. The maximum accuracy obtained for test data is 90.9% using CNN-LSTM. Using 5 fold cross-validation, CNN gave an accuracy of 93.6% while CNN-LSTM combination gave the maximum accuracy of 95.1%. As per our best knowledge, this is the first paper in which deep learning techniques are employed in distinguishing diabetes and normal HRV. The accuracy obtained using cross-validation is the maximum value achieved so far for the the automated detection of diabetes using HRV.

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2018

M. Swarna, Sowmya V., and Dr. Soman K. P., “Band selection using variational mode decomposition applied in sparsity-based hyperspectral unmixing algorithms”, Signal, Image and Video Processing, vol. 12, no. 8, pp. 1463-1470, 2018.[Abstract]


In this work, a frequency-based dimensionality reduction technique using variational mode decomposition (VMD) is proposed. Dimensionality reduction is a very important aspect of preprocessing in case of hyperspectral image (HSI) analysis where this step helps in elimination of the lesser informative bands, thereby reducing the size of the data and making its processing computationally less challenging. In contrast to the standard dimensionality reduction methods such as inter-band block correlation (IBBC) where bands are eliminated based on their similarity with the consecutive bands, the proposed method uses frequency information of each band to categorize it as a less or more informative band. In this way, only the topmost informative bands of HSI are selected to form the reduced dataset. In our experiment, in order to verify the efficiency of VMD as a dimensionality reduction technique, the hyperspectral unmixed results obtained for IBBC reduced dataset is compared with those obtained for VMD reduced dataset. From the parametric measures such as classification accuracy, root-mean-square error (RMSE) and visual results obtained after unmixing for both IBBC and VMD reduced datasets, it is noticed that the VMD reduced dataset performs better by achieving higher classification accuracy and lower RMSE than that of the existing IBBC method.

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2018

P. V. Veena, Dr. M. Anand Kumar, and Dr. Soman K. P., “Character embedding for language identification in Hindi-English code-mixed social media text”, Computacion y Sistemas, vol. 22, pp. 65-74, 2018.[Abstract]


Social media platforms are now widely used by the people to express their opinion or interest. The language used by the users in social media earlier was purely English. Code-mixed text, i.e., mixing of two or more languages, is commonly seen now. In code-mixed data, one language will be written using another language script. So to process such code-mixed text, identification of language used in each word is important for language processing. The main objective of the work is to propose a technique for identifying the language of Hindi-English code-mixed data used in three social media platforms namely, Facebook, Twitter, and WhatsApp. The classification of Hindi-English code-mixed data into Hindi, English, Named Entity, Acronym, Universal, Mixed (Hindi along with English) and Undefined tags were performed. Popular word embedding features were used for the representation of each word. Two kinds of embedding features were considered - word-based embedding features and character-based context features. The proposed method was done with the addition of context information along with the embedding features. A well-known machine learning classifier, Support Vector Machine was used to train and test the system. The work on Language Identification in code-mixed text using character-based embedding is a novel approach and shows promising results. © 2018 Instituto Politecnico Nacional. All rights reserved.

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2018

R. Vinayakumar, Dr. Soman K. P., and Poornachandran, P., “Detecting malicious domain names using deep learning approaches at scale”, Journal of Intelligent and Fuzzy Systems, vol. 34, pp. 1355-1367, 2018.[Abstract]


Threats related to computer security constantly evolving and attacking the networks and internet all the time. New security threats and the sophisticated methods that hackers use can bypass the detection and prevention mechanisms. A new approach which can handle and analyze massive amount of logs from diverse sources such as network packets, Domain name system (DNS) logs, proxy logs, system/service logs etc. required. This approach can be typically termed as big data. This approach can protect and provide solution to various security issues such as fraud detection, malicious activities and other advanced persistent threats. Apache spark is a distributed big data based cluster computing platform which can store and process the security data to give real time protection. In this paper, we collect only DNS logs from client machines in local area network (LAN) and store it in a server. To find the domain name as either benign or malicious, we propose deep learning based approach. For comparison, we have evaluated the effectiveness of various deep learning approaches such as recurrent neural network (RNN), long short-term memory (LSTM) and other traditional machine learning classifiers. Deep learning based approaches have performed well in comparison to the other classical machine learning classifiers. The primary reason is that deep learning algorithms have the capability to obtain the right features implicitly. Moreover, LSTM has obtained highest malicious detection rate in all experiments in comparison to the other deep learning approaches. © 2018 - IOS Press and the authors. All rights reserved.

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2018

V. Ankarao, Sowmya V., and Dr. Soman K. P., “Multi-sensor data fusion using NIHS transform and decomposition algorithms”, Multimedia Tools and Applications, vol. 77, pp. 30381–30402, 2018.[Abstract]


Multi-spectral image fusion is to enhance the details present in multi-spectral bands with the spatial information available in the panchromatic image. Fused images have the effect of spectral distortions and lack of structural similarity. To overcome these limitations, three methods are proposed using intensity, hue, saturation (IHS) and nonlinear IHS (NIHS) transform along with the Dynamic Mode Decomposition (DMD) and 2D-Empirical Mode Decomposition (2D-EMD or IEMD). An intensity plane is calculated from the NIHS transform. The modes are constructed using DMD by considering the variations between the intensity plane computed using NIHS transforms of a low resolution multi-spectral image and a panchromatic image. Similarly, 2D-EMD is also used for image fusion. Modes are subjected to weighted fusion rule to get an intensity plane with spatial and edge information. Finally, the calculated intensity plane is concatenated along with the hue and saturation plane of low-resolution multi-spectral image and transformed into RGB color space. Thus, the fused images have high spatial and edge information on spectral bands. The experiments and its quality assessment assure that proposed methods perform better than the existing methods.

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2018

R. Vinayakumar, Dr. Soman K. P., and Poornachandran, P., “Evaluating Deep Learning Approaches to Characterize and Classify Malicious URL's”, Journal of Intelligent and Fuzzy Systems, vol. 34, pp. 1333-1343, 2018.[Abstract]


Malicious uniform resource locator (URL), termed as malicious website is a foundation mechanisms for many of internet criminal activities such as phishing, spamming, identity theft, financial fraud and malware. It has been considered as a common and serious threat to the Cybersecurity. Blacklisting mechanism and many machine learning based solutions found by researchers with the aim to effectively signalize and classify the malicious URL's in internet. Blacklisting is completely ineffective at finding both variations of malicious URL or newly generated URL. Additionally, it requires human input and ends up as a time consuming approach in real-time scenarios. Machine learning based solutions implicitly rely on feature engineering phase to extract hand crafted features including linguistic, lexical, contextual or semantics, statistical information of URL string, n-gram, bag-of-words, link structures, content composition, DNS information, network traffic, etc. As a result feature engineering in machine learning based solutions has to evolve with the new malicious URL's. In recent times, deep learning is the most talked due to the significant results in various artificial intelligence (AI) tasks in the field of image processing, speech processing, natural language processing and many others. They have an ability to extract features automatically by taking the raw input texts. To leverage this and to transform the efficacy of deep learning algorithms to the task of malicious URL's detection, we evaluate various deep learning architectures specifically recurrent neural network (RNN), identity-recurrent neural network (I-RNN), long short-term memory (LSTM), convolution neural network (CNN), and convolutional neural network-long short-term memory (CNN-LSTM) architectures by modeling the real known benign and malicious URL's in character level language. The optimal parameter for deep learning architecture is found by conducting various experiments with various configurations of network parameters and network structures. All the experiments run till 1000 epochs with a learning rate in the range [0.01-0.5]. In our experiments, deep learning mechanisms outperformed the hand crafted feature mechanism. Specifically, LSTM and hybrid network of CNN and LSTM have achieved highest accuracy as 0.9996 and 0.9995 respectively. This might be due to the fact that the deep learning mechanisms have ability to learn hierarchical feature representation and long range-dependencies in sequences of arbitrary length. © 2018 - IOS Press and the authors.

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2018

R. Vinayakumar, Dr. Soman K. P., Poornachandran, P., and S. Kumar, S., “Detecting Android malware using Long Short-term Memory (LSTM)”, Journal of Intelligent and Fuzzy Systems, vol. 34, pp. 1277-1288, 2018.[Abstract]


Long Short-term Memory (LSTM) is a sub set of recurrent neural network (RNN) which is specifically used to train to learn long-term temporal dynamics with sequences of arbitrary length. In this paper, long short-term memory (LSTM) architecture is followed for Android malware detection. The data set for evaluation contains real known benign and malware applications from static and dynamic analysis. To achieve acceptable malware detection rates with low computational cost, various LSTM network topologies with several network parameters are used on all extracted features. A stacked LSTM with 32 memory blocks containing one cell each has performed well on detection of all individual behaviors of malicious applications in comparison to other traditional static machine learning classifier. The architecture quantifies experimental results up to 1000 epochs with learning rate 0.1. This is primarily due to the reason that LSTM has the potential to store long-range dependencies across time-steps and to correlate with successive connection sequences information. The experiment achieved the Android malware detection of 0.939 on dynamic analysis and 0.975 on static analysis on well-known datasets.

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2018

P. Megha, Sowmya V., and Dr. Soman K. P., “Effect of dynamic mode decomposition-based dimension reduction technique on hyperspectral image classification”, Lecture Notes in Electrical Engineering, vol. 490, pp. 89-99, 2018.[Abstract]


Hyperspectral imaging has become an interesting area of research in remote sensing over the past thirty years. But the main hurdles in understanding and analyzing hyperspectral datasets are the high dimension and presence of noisy bands. This work proposes a dynamic mode decomposition (DMD)-based dimension reduction technique for hyperspectral images. The preliminary step is to denoise every band in a hyperspectral image using least square denoising, and the second stage is to apply DMD on hyperspectral images. In the third stage, the denoised and dimension reduced data is given to alternating direction method of multipliers (ADMMs) classifier for validation. The effectiveness of proposed method in selecting most informative bands is compared with standard dimension reduction algorithms like principal component analysis (PCA) and singular value decomposition (SVD) based on classification accuracies and signal-to-noise ratio (SNR). The results illuminate that the proposed DMD-based dimension reduction technique is comparable with the other dimension reduction algorithms in reducing redundancy in band information. © Springer Nature Singapore Pte Ltd. 2018.

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2018

H. B. Barathi Ganesh, M. Kumar, A., and Dr. Soman K. P., “From Vector Space Models to Vector Space Models of Semantics”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10478 LNCS, pp. 50-60, 2018.[Abstract]


This paper assesses the performance of frequency and concept based text representation in Mixed Script Information Retrieval and Classification tasks. In text analytics, representation serves as an unresolved research problem to progress further towards different applications. In this paper observations from different text representation methods in text classification and information retrieval are presented. The data set from the Mixed Script Information Retrieval shared task is used in this experiment and the performance of final submitted model is evaluated by task organizers. It is observed that distributional representation performs better than the frequency based text representation methods. The final system attained first place in task 2 and was 3.89% lesser than the top scored system in task 1.

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2017

Dr. Soman K. P., Vinayakumar, R., and Poornachandran, P., “Evaluation of Recurrent Neural Network and its Variants for Intrusion Detection System (IDS)”, International Journal of Information System Modeling and Design (IJISMD) , vol. 8, no. 3, 2017.[Abstract]


This article describes how sequential data modeling is a relevant task in Cybersecurity. Sequences are attributed temporal characteristics either explicitly or implicitly. Recurrent neural networks (RNNs) are a subset of artificial neural networks (ANNs) which have appeared as a powerful, principle approach to learn dynamic temporal behaviors in an arbitrary length of large-scale sequence data. Furthermore, stacked recurrent neural networks (S-RNNs) have the potential to learn complex temporal behaviors quickly, including sparse representations. To leverage this, the authors model network traffic as a time series, particularly transmission control protocol / internet protocol (TCP/IP) packets in a predefined time range with a supervised learning method, using millions of known good and bad network connections. To find out the best architecture, the authors complete a comprehensive review of various RNN architectures with its network parameters and network structures. Ideally, as a test bed, they use the existing benchmark Defense Advanced Research Projects Agency / Knowledge Discovery and Data Mining (DARPA) / (KDD) Cup ‘99' intrusion detection (ID) contest data set to show the efficacy of these various RNN architectures. All the experiments of deep learning architectures are run up to 1000 epochs with a learning rate in the range [0.01-0.5] on a GPU-enabled TensorFlow and experiments of traditional machine learning algorithms are done using Scikit-learn. Experiments of families of RNN architecture achieved a low false positive rate in comparison to the traditional machine learning classifiers. The primary reason is that RNN architectures are able to store information for long-term dependencies over time-lags and to adjust with successive connection sequence information. In addition, the effectiveness of RNN architectures are shown for the UNSW-NB15 data set.

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2017

R. K. Rahul, Anjali, T., Menon, V. K., and Dr. Soman K. P., “Deep Learning for Network Flow Analysis and Malware Classification”, Communications in Computer and Information Science, vol. 746, pp. 226-235, 2017.[Abstract]


In this paper, we present the results obtained by applying deep learning techniques to classification of network protocols and applications using flow features and data signatures. We also present a similar classification of malware using their binary files. We use our own dataset for traffic identification and Microsoft Kaggle dataset for malware classification tasks. The current techniques used in network traffic analysis and malware detection is time consuming and beatable as the precise signatures are known. Deep learned features in both cases are not hand crafted and are learned form data signatures. It cannot be understood by the attacker or the malware in order to fake or hide it and hence cannot be bypassed easily.

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2017

O. K. Sikha, S. Kumar, S., and Dr. Soman K. P., “Salient Region Detection and Object Segmentation in Color Images using Dynamic Mode Decomposition”, Journal of Computational Science, 2017.[Abstract]


Estimation of visual saliency in images has become an important tool since it allows the processing of images without knowing the actual contents. In this paper we introduce a novel method to detect salient regions of an image using dynamic mode decomposition (DMD). The key idea is to utilize the analytical power of DMD, which is a powerful tool evolving in data science. The applicability of DMD in static image processing applications is made possible by developing a new way of image representation. The proposed algorithm utilizes color and luminance information to generate a full resolution saliency map. In order to model the non-linear behavior of human visual system we exploited the power of different color spaces including CIELab, YCbCr, YUV and RGB. The proposed method is computationally less expensive, simple and generates full resolution saliency maps.The effectiveness of the generated saliency map is evaluated and confirmed on three benchmark data sets across fourteen existing algorithms based on the standard performance measures such as F-measure, precision and recall curve, mean absolute error (MAE), area under ROC curve (AUC-Borji), normalized scanpath saliency (NSS) and Pearson's correlation coefficient (CC). We also propose a saliency driven transition region [SDTR] based segmentation to segment the salient object from images.

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2017

V. V. Pradeep, Sowmya V., and Dr. Soman K. P., “Application of M-band wavelet in pan-sharpening”, Special Issue in the Journal of Intelligent and Fuzzy Systems, IOS Press, Netherlands, vol. 32, no. 4, pp. 3151-3158, 2017.[Abstract]


Remote sensing satellites are proficient in taking earth images across various regions in visible part of electromagnetic spectrum. The images can be panchromatic image of a single band, multispectral image of three to seven different bands, and hyperspectral image taken from about 220 contiguous spectral bands. These images are used together or on its own, depending on the significance and usage of the preferred application. Pan-sharpening is one method which is used to improve the quality of a low resolution multispectral image by fusion with a high resolution panchromatic image. This paper proposes a method based on M-band wavelets for the pan-sharpening of a low resolution multispectral image. The method tries to improve the spatial characteristics while preserving the spectral quality of the data. The proposed technique uses weighted fusion rule and average fusion rule. The data used for the experiment were acquired by high resolution optical imagers onboard QuickBird, WorldView-3, WorldView-2 and GeoEye-1. A comparison with existing fusion techniques is done based on image quality metrics and visual interpretation. The experimental results and analysis suggests that the proposed pan-sharpening technique outperforms other compared pre-existing pan-sharpening methods. © 2017-IOS Press and the authors. All rights reserved.

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2017

Y. C. Nair, Kumar, S., and Dr. Soman K. P., “Real-time automotive engine fault detection and analysis using bigdata platforms”, Advances in Intelligent Systems and Computing, vol. 515, pp. 507-514, 2017.[Abstract]


This paper is aimed at diagnosing automotive engine fault in real-time utilizing BigData framework called spark. An automobile in the present day world is equipped with millions of sensors which are under the command of a central unit the ECU (Electronic Control Unit). ECU holds all information about the engine. A network of ECUs connected across the globe is a source tap of BigData. Leveraging the new sources of BigData by automotive giants boost vehicle performance, enhance loco driver experience, accelerated product designs. A piezoelectric transducer coupled to the ECU captures the vibration signals from the engine. The engine fault is detected by carving the problem into a pattern classification problem under machine learning after extracting cyclostationary features from the vibration signal. Spark-streaming framework, the most versatile BigData framework available today with immense computational capabilities is employed for engine fault detection and analysis. © Springer Nature Singapore Pte Ltd. 2017.

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2017

K. S. Gokul Krishnan, Pooja, A., Dr. M. Anand Kumar, and Dr. Soman K. P., “Character based bidirectional LSTM for disambiguating tamil part of speech categories”, International Journal of Control Theory and Applications, vol. 10, pp. 229-235, 2017.[Abstract]


Part of speech (POS) tagging is the process of labeling a part of speech tag to each and every word in the corpus. In this paper POS tagging for Tamil language is performed by using Bidirectional Long Short Term Memory. A C2W (character to word) model instead of traditional word lookup table for obtaining word embeddings using BLSTM is presented. The C2W model uses characters to form a vector representation of a word. The word embedding from C2W model is used by BLSTM to tag the words in the corpus. This method, when tested with 3723 words produced highest accuracy of 86.45%. © International Science Press. More »»

2017

K. Harikumar and Dr. Soman K. P., “Convex hyperspectral unmixing algorithm using parameterized non-convex penalty function”, Advances in Intelligent Systems and Computing, vol. 516, pp. 209-217, 2017.[Abstract]


Unmixing of hyperspectral data is an area of major research because the information it provides is utilized in plethora of fields. The year of 2006 witnessed the emergence of Compressed Sensing algorithm which was later used to spearhead research in umixing problems. Later, the notion of lp norms 0 < p < 1 and other non-smooth and non-convex penalty function were used in place of the traditional convex l1 penalty. Dealing with optimization problems with non-convex objective function is rather difficult as most methodologies often get stuck at local optima. In this paper, a parameterised non-convex penalty function is used to induce sparsity in the unknown.The parameters of penalty function can be adjusted so as to make the objective function convex, thus resulting in the possibility of finding a global optimal solution. Here ADMM algorithm is utilized to arrive at the final iterative algorithm for the unmixing problem. The algorithm is tested on synthetic data set, generated from the spectral library provided by US geological survey. Different parametric penalty functions like log and arctan are used in the algorithm and is compared with the traditional l1 penalties, in terms of the performance measures RSNR and PoS. It was observed that the non-convex penalty functions out-performs the l1 penalty in terms of the aforementioned measures. © Springer Nature Singapore Pte Ltd. 2017. More »»

2017

P. Poornachandran, Praveen, S., Ashok, A., Krishnan, M. R., and Dr. Soman K. P., “Drive-by-download malware detection in hosts by analyzing system resource utilization using one class support vector machines”, Advances in Intelligent Systems and Computing, vol. 516, pp. 129-137, 2017.[Abstract]


Drive-by-Download is an unintentional download of a malware on to a user system. Detection of drive-by-download based malware infection in a host is a challenging task, due to the stealthy nature of this attack. The user of the system is not aware of the malware infection occurred as it happens in the background. The signature based antivirus systems are not able to detect zero-day malware. Most of the detection has been performed either from the signature matching or by reverse engineering the binaries or by running the binaries in a sandbox environment. In this paper, we propose One Class SVM based supervised learning method to detect the drive-by-download infection. The features comprises of system RAM and CPU utilization details. The experimental setup to collect data contains machine specification matching 4 user profiles namely Designer, Gamer, Normal User and Student. The experimental system proposed in this paper was evaluated using precision, recall and F-measure. © Springer Nature Singapore Pte Ltd. 2017. More »»

2016

M. Swarna, Megha, P., Sowmya V., and Dr. Soman K. P., “Regularized least square approach for remote sensing image denoising using wavelet filters”, Indian Journal of Science and Technology, vol. 9, 2016.[Abstract]


Noise in remote sensing images (aerial and satellite) is caused due to various reasons such as atmospheric interference or lack of quality in sensors used to capture them. Removal of noise in an efficient way is a big challenge for researchers. In this paper, one dimensional signal denoising based on weighted regularized least square method is mapped to two dimensional image de noising. Objectives: This paper introduces a novel image denoising technique based on least square weighted regularization. Methods/Statistical Analysis: The proposed technique for image denoising based on Least Square (LS) approach is experimented on five different satellite and aerial images corrupted by gaussian noise with varying noise levels and regularization parameter lambda (λ) for different wavelet filter coefficients such as 'haar', 'symlet', 'daubechies' and coiflet. The effectiveness of the proposed method of image denoising is compared against the existing second order filter [based on LS] and conventional wavelet based image denoising technique based on the standard metric called Peak Signal to Noise Ratio (PSNR). Findings: From the experimental result analysis obtained it is inferred that the wavelet filters outperforms the second order filter and the conventional wavelet based image denoising. The complexity of the mathematics is low in our proposed method for image denoising. Applications/Improvements: The proposed denoising technique can be adopted as a faster pre-processing step in most of the image processing applications.

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2016

Sowmya V., Divu, G., and Dr. Soman K. P., “Significance of incorporating chrominance information for effective color-to-grayscale image conversion”, Signal, Image and Video Processing, vol. 11, 2016.[Abstract]


This paper provides an alternative framework for color-to-grayscale image conversion by exploiting the chrominance information present in the color image using singular value decomposition (SVD). In the proposed technique of color-to-grayscale image conversion, a weight matrix corresponds to the chrominance components is derived by reconstructing the chrominance data matrix (planes a* and b*) from the eigenvalues and eigenvectors computed using SVD. The final grayscale converted image is obtained by adding the weighted chrominance data to the luminous intensity which is kept intact for the CIEL*a*b* color space of the given color image. The effectiveness of the proposed grayscale conversion is confirmed by the comparative analysis performed on the color-to-gray benchmark dataset across 10 existing algorithms based on the standard objective measures, namely normalized cross-correlation, color contrast preservation ratio, color content fidelity ratio, E score and subjective evaluation. More »»

2016

B. Premjith, S. Sachin Kumar, R. Shyam, Dr. M. Anand Kumar, and Dr. Soman K. P., “A Fast and Efficient Framework for Creating Parallel Corpus”, Indian Journal of Science and Technology, vol. 9, 2016.[Abstract]


A framework involving Scansnap SV600 scanner and Google Optical character recognition (OCR) for creating parallel corpus which is a very essential component of Statistical Machine Translation (SMT). Methods and Analysis: Training a language model for a SMT system highly depends on the availability of a parallel corpus. An efficacious approach for collecting parallel sentences is the predominant step in an MT system. However, the creation of a parallel corpus requires extensive knowledge in both languages which is a time consuming process. Due to these limitations, making the documents digital becomes very difficult and which in turn affects the quality of machine translation systems. In this paper, we propose a faster and efficient way of generating English to Indian languages parallel corpus with less human involvement. With the help of a special type of scanner called Scansnap SV600 and Google OCR and a little linguistic knowledge, we can create a parallel corpus for any language pair, provided there should be paper documents with parallel sentences. Findings: It was possible to generate 40 parallel sentences in 1 hour time with this approach. Sophisticated morphological tools were used for changing the morphology of the text generated and thereby increase the size of the corpus. An additional benefit of this is to make ancient scriptures or other manuscripts in digital format which can then be referred by the coming generation to keep up the traditions of a nation or a society. Novelty: Time required for creating parallel corpus is reduced by incorporating Google OCR and book scanner.

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2016

A. Chandran, Neethu Mohan, and Dr. Soman K. P., “Non-convex group sparsity denoising for bearing fault diagnosis using SVM”, International Journal of Control Theory and Applications, vol. 9, pp. 4433-4443, 2016.[Abstract]


Bearings are the pivotal components in rotating machines whose failure can result in unpredicted loss in productivity. Hence the faults on bearing need to be rectified as early as possible. In this paper four conditions of a DC motor namely good condition, defect on inner of race, defect on outer of race and defect on both inner and outer of race are obtained and subjected to classification using statistical features after a preprocessing operation for denoising. The denoising algorithm employed for preprocessing is Overlapping Group Shrinkage (OGS) and SVM is the classifier used. The accuracy in classification is found to be more when statistical features of denoised signal are fed as inputs to the classifier. Later, a vibration signal modeling system and its denoising is studied. © International Science Press.

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2016

P. Megha, Swarna, M., K. Dixon, D. Merlin, Dr. Soman K. P., and Sowmya V., “Impact of least square denoising on kernel based hyperspectral image classification”, International Journal of Control Theory and Applications, vol. 9, pp. 4623-4630, 2016.[Abstract]


Hyperspectral sensors capture images in hundreds of spectral bands spanning almost all the regions in the electromagnetic spectrum. These images consists of many noisy bands. Hence, a strong diffusion scheme (denoising) is required to extract the meaningful spatial information present in the noisy spectral bands. In this paper, one dimensional signal denoising based on weighted regularized least square (LS) method is mapped to two dimensional hyperspectral image (HSI) denoising and the superiority of this method is determined on the basis of the classification accuracies of the hyperspectral image obtained using Grand Unified Regularized Least Square (GURLS) library and LibSVM, the support vector machines library. The proposed method brings out the efficiency of LS denoising based on the classification accuracies obtained on classifying the denoised image. The obtained results are also compared with the accuracy obtained on classifying the original image without denoising. The analysis is also extended to the classification of the images denoised using the conventional denoising techniques such as Total Variation (TV) and LF (Legendre Fenchel) based denoising. From the analysis, it is observed that the classification accuracies of the images denoised using the proposed method is much higher than the conventional denoising methods. Hence, showing that the LS based denoising is an efficient method which provides a denoised output almost similar to the original image. © International Science Press.

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2016

K. R. Rithu Vadhana, Neethu Mohan, and Dr. Soman K. P., “Convex denoising of hyperspectral images using non-convex tight frame regularization for improved sparsity based classification”, International Journal of Control Theory and Applications, vol. 9, pp. 4445-4451, 2016.[Abstract]


Hyperspectral images contain large spectral and spatial information's and hence it is widely used in the field of remote sensing for various application such as urban planning, disaster management and land use land cover classification. However, these images are usually corrupted by various kind of noises and which adversely affect the quality of images. In order to resolve thisissue, various preprocessing technique are exploited while dealing with hyperspectral images. convexdenoising using non-convex tight frame regularization technique is proposed as a preprocessing technique. After preprocessing, the images are classified using Orthogonal Matching Pursuit (OMP) algorithm. The classification results are evaluated interms of accuracy assessment measures. Also, the impact of the proposed preprocessing stageis compared with classification results of existing denoising techniques such as Total Variation(TV)denoising and wavelet based denoising. © International Science Press.

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2016

P. Poornachandran, B. Premjith, and Dr. Soman K. P., “A distributed approach for predicting malicious activities in a network from a streaming data with support vector machine and explicit random feature mapping”, IIOAB Journal, vol. 7, pp. 24-29, 2016.[Abstract]


Technology reduces human effort. However technological advancements always bring threat to personal as well as organizational security, mainly because we all are connected to the internet. Therefore, ensuring cyber security becomes the major topic of discussion. As the magnitude of activities over the internet is unimaginable, envisioning the characteristics of network activities whether it is malicious or good, coming from a stream of data in real time is really a tough task. To tackle this problem, in this paper, we propose a distributive approach based on Support Vector Machine (SVM) with explicit random feature mapping and features mapping is obtained using Compact random feature maps (CRAFTMaps) algorithm. Distributing the job achieves notable improvement in the total prediction time. © 2016, Institute of Integrative Omics and Applied Biotechnology. All rights reserved.

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2016

S. Archana, Dr. Shanmugha Sundaram G. A., and Dr. Soman K. P., “Analysis of precipitating clouds using precipitable water vapour”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 542-547, 2016.[Abstract]


The detection of precipitating clouds is necessary for weather prediction and climate researches. Precipitation occurs by condensation when the atmosphere is saturated with water vapour. The aim of the study is to discuss about the variations of Precipitable Water Vapour (PWV) in the case of such clouds. The brightness temperature data of 6.7ìm water vapour channel is primarily used in this study. The saturation mixing ratio can be calculated using temperature and pressure profile data. The study on PWV enhances the weather forecasting models and meteorological studies. In this study, the PWV for different precipitating clouds are estimated for a period of January-December 2014. More »»

2016

P. R. Sugatha Kumari, Dr. Shanmugha Sundaram G. A., and Dr. Soman K. P., “A case study on cirrus clouds using PWV measurement”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 559-564, 2016.[Abstract]


Cirrus (Ci) clouds are high level clouds at an altitude above 6 Km. Ci clouds are composed of ice crystals and responsible for optical phenomenon such as mock suns and halos. Though Ci clouds are non-precipitating in nature, it has considerable role in case of weather forecasting. This paper explores a study of Ci clouds as indicators of fair weather and high altitude wind direction based on Precipitable Water Vapor (PWV) measurement. Brightness Temperature (TB) values from data obtained using satellite water vapor channel, along with pressure and temperature from Global Forecast System (GFS) are used in this study for PWV calculation. Data collected over a period of one year from 1st of January, 2014 to 31st of December, 2014 is considered here. More »»

2016

S. S. Kumar, Dr. M. Anand Kumar, and Dr. Soman K. P., “Experimental analysis of malayalam pos tagger using epic framework in scala”, ARPN Journal of Engineering and Applied Sciences, vol. 11, pp. 8017-8023, 2016.[Abstract]


In Natural Language Processing (NLP), one of the well-studiedproblems under constant exploration is part-ofspeech tagging or POS tagging or grammatical tagging. The task is to assign labels or syntactic categories such as noun, verb, adjective, adverb, preposition etc. to the words in a sentence or in an un-annotated corpus. This paper presents a simple machine learning based experimental study for POS tagging using a new structured prediction framework known as EPIC, developed in scale programming language. This paper is first of its kind to perform POS tagging in Indian Language using EPIC framework. In this framework, the corpus contains labelled Malayalam sentences in domains like health, tourism and general (news, stories). The EPIC framework uses conditional random field (CRF) for building tagged models. The framework provides several parameters to adjust and arrive at improved accuracy and thereby a better POS tagger model. The overall accuracy were calculated separately for each domains and obtained a maximum accuracy of 85.48%, 85.39%, and 87.35% for small tagged data in health, tourism and general domain. More »»

2016

D. P. Kuttichira, Sowmya V., and Dr. Soman K. P., “Digit recognition using multiple feature extraction”, IIOAB Journal, vol. 7, pp. 37-43, 2016.[Abstract]


Digit Recognition is one of the classic problems in pattern classification. It has ten labels which are digits from 0-9 and each prototypes in the test set has to be classified under these labels. In this paper, we have used MNIST data for training and testing. MNIST database is a standard database for digit classification. A number of neural network algorithms have been used on MNIST to get high accuracy outputs. These algorithms are computationally costly. Here, we have used multiple feature extraction based on SVD and histogram to create testing and training matrix. To the feature vector formed by SVD, histogram values along x-axis and y-axis of an image is appended. These vectors are mapped to hyperplane using polynomial and Gaussian kernel. For classification open source software like GURLS and LIBSVM is used to obtain a fairly good accuracy. © 2016, Institute of Integrative Omics and Applied Biotechnology. All rights reserved.

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2016

S. Chandran, Sajith Variyar V. V., Prabhakar, T. V. Nidhin, and Dr. Soman K. P., “Aerial image classification using regularized least squares classifier”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 889-895, 2016.[Abstract]


The land cover classification and urban analysis of remotely sensed images has become a challenging problem, hence efficient classifiers are required in order to combat the problem of classifying the huge remote sensing aerial datasets. In this paper we have proposed the use of Random Kitchen Sink (RKS) algorithm and Regularized Least Squares (RLS) classifier for the classification of aerial image. The new machine learning algorithm RKS, primarily engages in mapping the feature data to a higher dimensional space and thereby generates random features. These randomized data are then adopted by RLS classifier for the classification task. It is observed that the randomization of the data reduces the computation time needed for training. The experiment is performed on five classes of the UC Merced Land Use Aerial Imagery Dataset. The efficiency of the proposed method is estimated by comparing the accuracy results with the conventional classifier namely, Support Vector Machine (SVM). Experimental result shows that the proposed method produces a high degree of classification accuracy i.e. 94.4%, when RBF kernel with LOO (Leave One Out) cross-validation was used, when compared to SVM. In this paper, statistical features show better precision and accuracy in classifying different set of classes, compared to textural features in both the classification approaches. Hence, better accuracies could be attained for multi class classification when compared to other classification technique like, SVM since, the random features reduces computation time and enhance the performance of kernel machines.

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2016

L. Prakash, Neethu Mohan, S. Kumar, S., and Dr. Soman K. P., “Accurate frequency estimation method based on basis approach and empirical wavelet transform”, Advances in Intelligent Systems and Computing, vol. 380, pp. 801-809, 2016.[Abstract]


Due to proliferating harmonic pollution in the power system, analysis and monitoring of harmonic variation in real-time have become important. In this paper, a novel approach for estimation of fundamental frequency in power system is discussed. In this method, the fundamental frequency component of the signal is extracted using Empirical Wavelet Transform. The extracted component is then projected onto fourier basis, where the frequency is estimated to a resolution of 0.001 Hz. The proposed approach gives an accurate frequency estimate compared with some existing methods. © Springer India 2016. More »»

2016

A. M, N, D., Sowmya V., Mahan, N., and Dr. Soman K. P., “Least Square Based approach for Image Inpainting”, Institute of Integrative Omics and Applied Biotechnology, vol. 7, pp. 44-59, 2016.[Abstract]


Images are widely used over various applications under the aegis of various domains like Computer vision, Biomedical, etc. The problem of missing data identification is of great concern in various fields involving image processing. Least square can be used for missing sample estimation for 1-D signals. The proposed system extends the missing sample estimation in 1-D using least square to 2-D, applied for image inpainting. The paper also draws a comparison between the Total Variation (TV) algorithm and the proposed method. The experiments were conducted on standard images and the standard metrics namely PSNR and SSIM are used to compare the image quality obtained using the proposed method (least square based) and TV algorithm. More »»

2016

H. T. Suseelan, Sudhakaran, S., Sowmya V., and Dr. Soman K. P., “Performance Evaluation of Sparse Banded Filter Matrices using content based image retrieval”, Institute of Integrative Omics and Applied Biotechnology, vol. 7, no. 3, pp. 11-18, 2016.[Abstract]


Content Based Image Retrieval (CBIR) is an extensively used application in the field of Image Processing. It is used to search through a massive database and retrieve the images that have similarity with the query image. In this paper, performance is evaluated for Sparse Banded Filter matrices (ABfilter) against the standard edge detection filters through Content Based Image Retrieval. Performance factor of ABfilter directly relates to its edge detection capabilities. Here, edge detection followed by the Singular Value Decomposition (SVD) is done for feature extraction for both the query and images in database. Query image feature and database image features are matched and those having similar values are retrieved. Similarity measurement is done by computing the distance between corresponding features. Experimental results indicate that retrieval results using ABfilter is much better than using standard edge detection filters for the same, which in turn establishes its superiority in edge detection.

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2016

V. P.V, G, R. Devi, Sowmya V., and Dr. Soman K. P., “Least Square based image denoising using wavelet filters”, Indian Journal of Science and Technology, vol. 9, no. 30, 2016.[Abstract]


Background/Objectives: Noise in a digital image, is unwanted information that degrades the quality of an image. The main aim of the proposed method is to denoise a noisy image based on least square approach using wavelet filters. Methods/ Statistical Analysis: One dimensional least square approach proposed by Selesnick is extended to two dimensional image denoising. In our proposed technique of least square problem formulation for image denoising, the matrix constructed using second order filter coefficients is replaced by wavelet filter coefficients. Findings: The method is experimented on standard digital images namely Lena, Cameraman, Barbara, Peppers and House. The images are subjected to different noise types such as Gaussian, Salt and Pepper and Speckle with varying noise level ranging from 0.01db to 0.5db. The wavelet filters used in the proposed approach of denoising are Haar, Daubechies, Symlet, Coiflet, Biorthogonal and Reverse biorthogonal. The outcome of the experiment is evaluated in terms of Peak Signal to Noise Ratio (PSNR). The analysis of the experiment results reveals that performance of the proposed method of least square based image denoising by wavelet filters are comparable to denoising using existing second order sparse matrix. Applications/Improvements: Digital images are often prone to noise; hence, proceeding with further processing of such an image requires denoising. This work can be extended in future to m-band wavelet filters.

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2016

Reshma R, Sowmya V., and Dr. Soman K. P., “Improvement in kernel based Hyperspectral image classification using legendre fenchel denoising”, Indian Journal of Science and Technology, vol. 9, no. 33, 2016.[Abstract]


Hyperspectral images have bulk of information which are widely used in the field of remote sensing. One of the main problems faced by these images is noise. This emphasizes the importance of denoising techniques for enhancing the image quality. In this paper, Legendre Fenchel Transformation (LFT) is used for preprocessing the Indian Pines Dataset. LFT reduces the noise of each band of the hyperspectral image without affecting the edge information. Signal to noise ratio is computed which helps to evaluate the performance of denoising. Further, the denoised image is classified using GURLS and LibSVM and the various accuracies are estimated. The experimental analysis shows that the overall and classwise accuracies are more for the preprocessed data classification when compared to the classification without preprocessing. The classification accuracy is improved with denoising of hyperspectral image.

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2016

B. G. Gowri, Kumar, S. S., Neethu Mohan, and Dr. Soman K. P., “A VMD based approach for speech enhancement”, Advances in Intelligent Systems and Computing, vol. 425, pp. 309-321, 2016.[Abstract]


This paper proposes a Variational Mode Decomposition (VMD) based approach for enhancement of speech signals distorted by white Gaussian noise.VMD is a data adaptive method which decomposes the signal into intrinsic mode functions (IMFs) by using the Alternating Direction Method of Multipliers (ADMM). Each IMF or mode will contain a center frequency and its harmonics. This paper tries to exploreVMDas a Speech enhancement technique. In the proposed method, the noisy speech signal is decomposed into IMFs using VMD. The noisy IMFs are enhanced using two methods; VMD based wavelet shrinkage (VMD-WS) and VMD based MMSE log STSA (VMD-MMSE). The speech signal distorted with different noise levels are enhanced using the VMD based methods. The level of noise reduction and speech signal quality are measured using the objective quality measures. © Springer International Publishing Switzerland 2016.

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2016

S. Se, Pradeep, D., Sowmya V., and Dr. Soman K. P., “Fourier Descriptor features for Shape Deformation Classification using Random Kitchen Sink”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 554-558, 2016.[Abstract]


This paper deals with the Fourier descriptor features for shape deformation classification using Random Kitchen Sink algorithm accessed through GURLS library. Shape recognition is an important method used in all industrial environments which are mostly concerned with robots. It is a highly essential task to make the robot understand the shape of an object. The object may have many deformed shapes and so it is necessary to train the classifier accordingly. Recognition methods based on polar coordinates and probabilistic models are already developed, but its accuracy for finding the deformed shape of the object is low. In this context, Random Kitchen Sink algorithm is used and the classification is done through GURLS in which, regularized least square method is used, which leads to better shape recognition. More »»

2016

N. Nechikkat, Sowmya V., and Dr. Soman K. P., “Low dimensional variational mode features for hyperspectral image classification”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 565-570, 2016.[Abstract]


High Dimensionality is always a great concern while working with hyperspectral images. The high dimension of hyperspectral image increases the computational complexity, creates data storage issues and decrease the performance and accuracy of hyperspectral image analysis algorithms. This paper focuses on low dimensional Variational Mode features for hyperspectral image classification. The proposed method consist of three stages: preprocessing using Inter Band Block Correlation (IBBC) technique, feature extraction using Variational Mode Decomposition (VMD) and dimensionality reduction using Singular Value Decomposition (SVD). The efficiency of the proposed method based on the low dimensional feature extraction using VMD is evaluated by one of the sparsity based classification algorithms namely Orthogonal Matching Pursuit (OMP). The proposed work is experimented on the standard dataset namely Indian pines acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The experimental analysis shows that our proposed technique produces 90.88% overall accuracy with 40% of training which is greater than the classification accuracy obtained without feature extraction. More »»

2016

Ca Anjana, Sundaresan, Sb, Dr. Shanmugha Sundaram G. A., and Dr. Soman K. P., “Impact Analysis of Wind Farms on Air Traffic Control Radar”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 580-584, 2016.[Abstract]


These days, wind vitality use gets to be gigantic which prompts an increment in number of wind turbine establishments. Because of these wind turbines, the electromagnetic waves get impedance and are scattered which brings about the loss of correspondence. In this paper, the unfriendly impacts of wind farms on radar framework is displayed and discussed for Air Traffic Control (ATC) Radar. Coimbatore domestic (ATC) radar working in S-band recurrence gets influenced by wind farms located in Palakkad gap area. Demonstrating of the wind turbines and estimation of Radar Cross-Section (RCS) is done utilizing high frequency EM solver viz., XGtd tool. The investigation of RCS dispersing plot, examination of improved RCS and air traffic issues revels that the wind farms exhibit in viewable pathway and those near to radar influences the framework and results in loss of information which leads to poor air traffic monitoring. More »»

2016

L. S. Kiran, Sowmya V., and Dr. Soman K. P., “Enhanced Variational Mode Features for Hyperspectral Image Classification”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 502-505, 2016.[Abstract]


Variational Mode Decomposition (VMD) is a recent method and is gaining popularity in the area of signal and image processing. The use of this decomposition technique in hyper spectral image classification is discussed in detail in this paper. The role of VMD as a feature extraction technique is exploited here. The proposed method includes an initial stage of dimensionality reduction so as to reduce the computational complexity. A final stage of recursive filtering is also added to further enhance the results. Results obtained by the proposed method on two hyper spectral image datasets 'Indian Pines and Salinas-A, suggests that VMD is a promising method in the area of image analysis and classification. Quality indices used for experimental analysis include overall accuracy (OA), average accuracy (AA) and kappa coefficient. Notable classification accuracy has been obtained for both the datasets and a final stage of recursive filtering has further improved the results (more than 98% accuracy in the case of Indian Pines). More »»

2016

M. Kavinandhini, Dr. Geetha Srikanth, and Dr. Soman K. P., “Climatic impacts and reliability of large scale wind farms in Tamil Nadu”, Indian Journal of Science and Technology, vol. 9, no. 6, 2016.[Abstract]


Objective: The main objective of this paper describes how the large scale windfarms affect the climate of south west monsoon region. Methods/Analysis: Method used for analysing climatic parameters of before and after installation of wind farms is Gaussian mixture model. ArcGIS and QGIS software is used for image and geo-information analysis. Data from the commercial wind turbine of south west monsoon region like temperature, relative humidity, precipitation, wind speed is used to find the climatic variation. Findings: Large scale wind farms significantly affect the various climatic parameters. These impacts depends on the static stability, increase or decrease in the climatic parameters. Conclusion/ Application: Improvements can be made by taking the ground temperature measured by satellite image and identify the warming effect of night and day time warming effect of large scale wind farm area of southwest monsoon regions.

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2016

Neethu Mohan, S. Kumar, S., Poornachandran, P., and Dr. Soman K. P., “Modified variational mode decomposition for power line interference removal in ECG signals”, International Journal of Electrical and Computer Engineering, vol. 6, pp. 151-159, 2016.[Abstract]


Power line interferences (PLI) occurring at 50/60 Hz can corrupt the biomedical recordings like ECG signals and which leads to an improper diagnosis of disease conditions. Proper interference cancellation techniques are therefore required for the removal of these power line disturbances from biomedical recordings. The non-linear time varying characteristics of biomedical signals make the interference removal a difficult task without compromising the actual signal characteristics. In this paper, a modified variational mode decomposition based approach is proposed for PLI removal from the ECG signals. In this approach, the central frequency of an intrinsic mode function is fixed corresponding to the normalized power line disturbance frequency. The experimental results show that the PLI interference is exactly captured both in magnitude and phase and are removed. The proposed approach is experimented with ECG signal records from MIT-BIH Arrhythmia database and compared with traditional notch filtering. Copyright © 2016 Institute of Advanced Engineering and Science. All rights reserved.

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2016

S. Moushmi, Sowmya, V., and Dr. Soman K. P., “Empirical wavelet transform for multifocus image fusion”, Advances in Intelligent Systems and Computing, vol. 397, pp. 257-263, 2016.[Abstract]


Image fusion has enormous applications in the fields of satellite imaging, remote sensing, target tracking, medical imaging, and much more. This paper aims to demonstrate the application of empirical wavelet transform for the fusion of multi- focus images incorporating the simple average fusion rule. The method proposed in this paper is experimented on benchmark datasets used for fusing images of different focuses. The effectiveness of the proposed method is evaluated across the existing techniques. The performance comparison of the proposed method is done by visual perception and assessment of standard quality metrics which includes root mean squared error, relative average spectral error, universal image quality index, and spatial information. The experimental result analysis shows that the proposed technique based on the empirical wavelet transform (EWT) outperforms the existing techniques. © Springer India 2016. More »»

2015

L. Prakash, P.R., S. Kumari, Chandran, S., Sachin Kumar S., and Dr. Soman K. P., “Self-sufficient Smart Prosumers of Tomorrow”, Procedia Technology, vol. 21, pp. 338 - 344, 2015.[Abstract]


Due to the abrupt rise in population, the demand for food and energy has increased tremendously. The current challenge is to meet all these demands in future. To tackle the problems of energy crisis and food adulteration, the only solution is to become self-reliant in all means. The spectacular technological innovations in the areas of information and communication have aided to design this lifestyle. This paper delivers the concept of building self sufficient smart homes by coupling the areas of i-energy, aquaponics and vertical farming. Also, provides a scope for developing self-sufficient homes in Kerala. This paper elucidates the architectural design perspective of a house, which is bound to satisfy all the basic needs and encourages each individual to turn to a prosumer.

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2015

Dr. Soman K. P., Prabaharan Poornachandran, S., A., and K., H., “Recursive Variational Mode Decomposition Algorithm for Real Time Power Signal Decomposition”, Procedia Technology, vol. 21, pp. 540 - 546, 2015.[Abstract]


Conventional methods of signal decomposition are observed to fail in power system applications and computationally intensive algorithms like EMD, VMD, EWT are found to give better performance. The heavy computations associated with them restricts their use in real time applications and stream processing. This paper presents a recursive block processing technique for real time signal decomposition. The use of recursive FFT and the clever initializations of the center frequencies in the existing VMD algorithm helps in reducing the computational complexity and hence speeds up the process. This low complexity algorithm was tested on synthetically generated power signals and the results were observed to be consistent with the existing VMD algorithm.

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2015

S. R., H.B., B. Ganesh, Sachin Kumar S., Prabaharan Poornachandran, and Dr. Soman K. P., “Apache Spark a Big Data Analytics Platform for Smart Grid”, Procedia Technology, vol. 21, pp. 171 - 178, 2015.[Abstract]


Smart grid is a complete automation system, where large pool of sensors is embedded in the existing power grids system for controlling and monitoring it by utilizing modern information technologies. The data collected from these sensors are huge and have all the characteristics to be called as Big Data. The Smart-grid can be made more intelligent by processing and deriving new information from these data in real time. This paper presents Apache spark as a unified cluster computing platform which is suitable for storing and performing Big Data analytics on smart grid data for applications like automatic demand response and real time pricing.

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2015

B. Premjith, Neethu Mohan, Prabaharan Poornachandran, and Dr. Soman K. P., “Audio Data Authentication with PMU Data and EWT”, Procedia Technology, vol. 21, pp. 596 - 603, 2015.[Abstract]


Digital forensics has become a flourishing research area. Electrical Network Frequency (ENF) plays an important role in assessing the authenticity of a digital recording such as audio. ENF criterion is a tool for extracting the embedded power line frequency from the recording. A cross correlation between a reference PMU data and extracted ENF signal can be done in order to determine the authenticity of an audio signal. In this paper, Empirical Wavelet Transform (EWT) is used for extracting the ENF from an audio signal. EWT decomposes signal into N modes. Hilbert Transform is used to compute the instantaneous frequency and amplitude of the extracted mode corresponding to ENF. EWT method is not able to capture the weak harmonics in a signal. This problem is resolved by fixing the frequency domain boundaries of each mode.

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2015

S. P. Sanjay, Dr. M. Anand Kumar, and Dr. Soman K. P., “AMRITA_CEN-NLP@ FIRE 2015: CRF Based Named Entity Extration for Twitter Microposts”, 2015.[Abstract]


This proposed method implements the Named Entity Recognition (NER) for four dialects Such as English, Tamil, Malayalam, and Hindi. The results obtained from this work are submitted to a research evaluation workshop Forum for Information Retrieval and Evaluation (FIRE 2015). It is single-layered problem which is divided into multi- layered this step is called pre-processing; it has three levels of named entity tags which are referred as BIO format. This format is trained using Condition Random field(CRF) are used for implementing in NER system , the results obtained are grouped back to single-label or single-tagged referred as Format converting. In FIRE 2015, we developed English, Tamil, Malayalam, and Hindi NER system using CRF. The FIRE estimated the average precision for all the four languages.

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2015

Sachin Kumar S., B. Premjith, Anand Kumar M., and Dr. Soman K. P., “AMRITA_CEN-NLP@SAIL2015: Sentiment analysis in indian language using regularized least square approach with randomized feature learning”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9468, pp. 671-683, 2015.[Abstract]


The present work is done as part of shared task in Sentiment Analysis in Indian Languages (SAIL 2015), under constrained category. The task is to classify the twitter data into three polarity categories such as positive, negative and neutral. For training, twitter dataset under three languages were provided Hindi, Bengali and Tamil. In this shared task, ours is the only team who participated in all the three languages. Each dataset contained three separate categories of twitter data namely positive, negative and neutral. The proposed method used binary features, statistical features generated from SentiWordNet, and word presence (binary feature). Due to the sparse nature of the generated features, the input features were mapped to a random Fourier feature space to get a separation and performed a linear classification using regularized least square method. The proposed method identified more negative tweets in the test data provided Hindi and Bengali language. In test tweet for Tamil language, positive tweets were identified more than other two polarity categories. Due to the lack of language specific features and sentiment oriented features, the tweets under neutral were less identified and also caused misclassifications in all the three polarity categories. This motivates to take forward our research in this area with the proposed method. © Springer International Publishing Switzerland 2015. More »»

2015

K. R. Rithu Vadhana, G. Swamynadhan, P. V. Neethu, B. Premjith, and Dr. Soman K. P., “Computational experiment of one class SVM in excel”, International Journal of Applied Engineering Research, vol. 10, no. 20, pp. 19356-19360, 2015.[Abstract]


Computational thinking is a strategic thought process that has led to spectacular achievements which ignited a technological boom across domains. Classification is one of the major task in machine learning. Support Vector Machine is one of the classification method in machine learning. One class SVM is a method for identifying outliers from a data set. In one class SVM, only target class information is taken into consideration and outliers information are not taken. Using this we minimize the chance of accepting outliers by optimizing the radius of hypersphere. This is a robust method against outliers. In this paper, the experiment of one class SVM on simulated data points is implemented in Excel. Excel is a powerful and easy tool that gives an opportunity for better understanding and ease of learning. It is the only platform which requires very less system requirements and programming skills. © Research India Publications. More »»

2015

A. C, Haridas, N., Sowmya V., and Dr. Soman K. P., “Effect of AB filter denoising on ADMM based Hyperspectral Image Classification”, International Journal of Applied Engineering Research (IJAER), vol. 10, no. 73, pp. 127-131, 2015.[Abstract]


In recent years, hyperspectral remote sensing has emerged as a prominent area of research. This has developed a lot of practical solutions to solve the various challenges faced in the field. Noise is one of such issues which deteriorate the quality of information present in the hyperspectral images. In order to address this problem, various preprocessing (denoising) techniques are applied prior to data analysis. In this paper, the proposed method evaluates the effect of Hyperspectral Image (HSI) denoising employing AB filter on optimization based classification which uses Basis Pursuit solved by Alternating Direction Method of Multipliers (ADMM). AVIRIS Indian Pines dataset is used for the experimental study. The efficiency of the proposed technique is proved by a comparative study with other existing preprocessing methods. The experimental result analysis based on visual interpretation and quantitative assessment shows that the proposed method provides better classification results compared to the existing methods. The classification results are assessed by Overall Accuracy, Average accuracy and Kappa coefficient.

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2015

N. Haridas, Sowmya V., and Dr. Soman K. P., “GURLS vs LIBSVM: Performance Comparison of Kernel Methods for Hyperspectral Image Classification”, Indian Journal of Science and Technology, vol. 8, no. 24, 2015.[Abstract]


Kernel based methods have emerged as one of the most promising techniques for Hyper Spectral Image classification and has attracted extensive research efforts in recent years. This paper introduces a new kernel based framework for Hyper Spectral Image (HSI) classification using Grand Unified Regularized Least Squares (GURLS) library. The proposed work compares the performance of different kernel methods available in GURLS package with the library for Support Vector Machines namely, LIBSVM. The assessment is based on HSI classification accuracy measures and computation time. The experiment is performed on two standard Hyper Spectral datasets namely, Salinas A and Indian Pines subset captured by AVIRIS (Airborne Visible Infrared Imaging Spectrometer) sensor. From the analysis, it is observed that GURLS library is competitive to LIBSVM in terms of its prediction accuracy whereas computation time seems to favor LIBSVM. The major advantage of GURLS toolbox over LIBSVM is its simplicity, ease of use, automatic parameter selection and fast training and tuning of multi-class classifier. Moreover, GURLS package is provided with an implementation of Random Kitchen Sink algorithm, which can easily handle high dimensional Hyper Spectral Images at much lower computational cost than LIBSVM.

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2015

C. Aswathy, Sowmya V., and Dr. Soman K. P., “ADMM based hyperspectral image classification improved by denoising using legendre fenchel transformation”, Indian Journal of Science and Technology, vol. 8, no. 24, 2015.[Abstract]


This paper discusses about a sparsity based algorithm used for Hyperspectral Image (HSI) classification where the test pixel vectors are sparsely represented as the linear combination of a few number of training samples from a well-organised dictionary matrix. The sparse vector is obtained using Basis Pursuit (BP) which is a constrained l4 minimization problem. This problem is solved by using a simple and powerful iterative algorithm known as Alternating Direction Method of Multipliers (ADMM) which significantly reduces the computational complexity of the problem and thereby speeds up the convergence. The classification accuracy is considerably improved by including efficient preprocessing techniques to remove the unwanted information (noise) present in Hyperspectral images. This paper uses a fast and reliable denoising technique based on Legendre Fenchel Transformation (LFT) to effectively denoise each band of HSI prior to ADMM based classification (proposed method). A comparison of proposed technique with one of the convex optimization tools namely, CVX is given to exhibit the fast convergence of the former method. The experiment is performed on standard Indian Pines dataset captured using AVIRIS sensor. The potential of the proposed method is illustrated by analyzing the classification indices obtained with and without applying any preprocessing methods. With only 10% training set, an overall accuracy of 96.76% is obtained for the proposed method at a much faster rate compared to computation time taken by CVX solver.

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2015

S. Vishvanathan, Neethu Mohan, and Dr. Soman K. P., “Sparse banded matrix filter for image denoising”, vol. 8, 2015.[Abstract]


Noise is one of the prime factors which degrade the quality of an image. Hence, image denoising is an essential image enhancement technique in the image processing domain. In this paper, we use low-pass sparse banded filter matrices for image denoising. Sparsity is the key concept in this filter design. We applied the designed low-pass filter both row-wise and column-wise to denoise the image. The proposed method is experimented on standard test images corrupted with different types of noises namely Gaussian, White Gaussian, Salt & Pepper and Speckle with noise level equals to 0.01, 0.05 and 0.1. The effectiveness of the proposed method of denoising is evaluated by the computation of standard quality metric known as Peak Signal-to-Noise Ratio (PSNR). The experimental result analysis shows that the proposed image denoising technique based on sparse banded filter matrices results in significant improvement in PSNR around 2dB to 8dB for different type of noises with noise level equal to 0.1 and is also aided by the visual analysis.

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2015

S. Athira, Harikumar, K., Sowmya V., and Dr. Soman K. P., “Parameter analysis of random kitchen sink algorithm”, International Journal of Applied Engineering Research, vol. 10, no. 20, pp. 19351-19355, 2015.[Abstract]


Classification is one of the main stream areas in machine learning. Many a time we have to deal with data sets that are not classifiable using a linear separator. This paper presents a theoretical and an experimental study of random kitchen sink algorithm which makes it possible for such data sets to become linearly separable by efficiently, effectively and explicitly mapping it to an appropriate higher dimensional space. It explores the dependency of various parameters of the algorithm, mainly the dimension and variance of the concerned random variable on the accuracy of classification, and how to fine tune these parameters to obtain the best of results. It also presents an intuitive understanding of how these parameters actually affect the accuracy of classification by connecting it with the shift invariant RBF kernel. The effect of choice of these parameters on a two class classification problem is also included. © Research India Publicatio

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2015

Y. C. Nair, Binsha, P., V. Pradeep, V., Sowmya V., and Dr. Soman K. P., “Spreadsheet implementation of random kitchen sink for classification”, International Journal of Applied Engineering Research, vol. 10, no. 20, pp. 19228-19233, 2015.[Abstract]


In order to excavate on large chunks of unstructured data to retrieve the nuggets of knowledge, a lot of technically sound algorithms have been developed. In this paper, the classification of data has been performed in an efficient manner by inheriting concepts from linear algebra and optimization theory. This paper demonstrates the implementation of the mathematical ideas behind mapping input data to higher dimension using Random Kitchen Sink (RKS) and implementation of classification algorithm using Regularized Least Square (RLS) estimation. In this context, computational thinking methodology is accomplished using the most versatile tool available for non-programmers, that is excel. This paper elucidates a number of excel utilities. © Research India Publications.

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2015

P. Maya, Dhivya, N., Kartikga, C., and Dr. Soman K. P., “Discrimination between inrush and internal fault currents in a power transformer using Variational Mode Decomposition Method”, International Journal of Applied Engineering Research, vol. 10, no. 55, pp. 3298-3301, 2015.[Abstract]


Transformers, which are critical and expensive components of a power system, require suitable measures for their protection to ensure reliable operation. Identification between in rush current and internal fault current is important in the design of transformer protection relay. Often nuisance tripping of protection relay occurs when inrush current flows in the system. Identification methods based on higher second harmonic content present in inrush current has limitations in its application. This work investigates the scope of classification method based on Variation Mode Decomposition (VMD) and Support Vector Machine (SVM) in distinguishing internal fault current and inrush current in a power transformer. Validation of this method is done using synthetic data from MATLAB/SIMULINK. Choice of various kernel functions for SVM for better accuracy is also investigated. © Research India Publications. More »»

2015

S. N. Vinithra, Dr. M. Anand Kumar, and Dr. Soman K. P., “Analysis of sentiment classification for Hindi movie reviews: A comparison of different classifiers”, International Journal of Applied Engineering Research, vol. 10, 2015.[Abstract]


To decide on anything in our day to day life, it is important to have an opinion. Every opinion has a sentiment which helps in carrying decisions easier. There is a huge amount of data on the web which needs to be mined in order to find its sentiment. This paper aims at classifying labelled textual Hindi movie reviews with different classifiers. The dataset has been segregated into positive and negative reviews before processing. The goal of this paper is to predict the sentiment of the online movie review which is in form of documents with varied size. A 10-fold-cross-validation is done in order to check the calibre of the classifier used. The test accuracy is checked using the F1 score considering both precision and recall. A detailed comparison of the unigram and bigram feature‟s accuracy of all the mentioned models is done. The proposed model is classified on the following classifiers Naïve Bayes, Logistic Regression and Random Kitchen Sink algorithm. Each one of these algorithms gave better accuracy when bigram was performed. Out of these four classifying algorithms, it is observed that Naive Bayes Multinomial model has the best accuracy with a 70.37%. Hence, this sentiment analysis model which is a developing big data application is suggested for industrial applications wherein predicting the sentiment is a vital component. More »»

2015

A. Muralidharan, Sugumaran, V., Dr. Soman K. P., and Amarnath, M., “Fault diagnosis of helical gear box using variational mode decomposition and random forest algorithm”, SDHM Structural Durability and Health Monitoring, vol. 10, pp. 55-80, 2015.[Abstract]


Gears are machine elements that transmit motion by means of successively engaging teeth. In purely scientific terms, gears are used to transmit motion. A faulty gear is a matter of serious concern as it affects the functionality of a machine to a great extent. Thus it is essential to diagnose the faults at an initial stage so as to reduce the losses that might be incurred. This necessitates the need for continuous monitoring of the gears. The vibrations produced by gears from good and simulated faulty conditions can be effectively used to detect the faults in these gears. The introduction of Variational Mode Decomposition (VMD) as a new signal pre-processing technique along with the different decision trees have provided good classification performance. VMD allows decomposition of the signal into various modes by identifying a compact frequency support around its central frequency, such that adding all the modes reconstructs the original signal. Alternating direction multiplier method (ADMM) is used by VMD to find the intrinsic mode functions on central frequencies. Meaningful statistical features can be extracted from VMD processed signals. J48 decision tree algorithm was used to identify the useful features and the selected features were used for classification using the decision trees namely, Random Forest, REP Tree and Logistic Model Tree algorithms. The performance analyses of various algorithms are discussed in detail. Copyright © 2014 Tech Science Press.

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2014

N. G. Resmi and Dr. Soman K. P., “Multiresolution analysis of source code using discrete wavelet transform”, International Journal of Applied Engineering Research, vol. 9, pp. 13341-13360, 2014.[Abstract]


In this paper, we propose a method to analyze source code files at multiple resolutions using Discrete Wavelet Transform (DWT) and hence detect plagiarisms in source code files written in C, C++ and Java. Multiresolution analysis of source code files using DWT helps to identify files which are highly similar. On applying DWT distinct clusters of potentially plagiarized files are identified. Further comparison of the potentially plagiarized files can be done using a more reliable and structure-based code similarity detection technique to isolate plagiarized files. Selection of proper wavelet and an optimum level of decomposition can improve the performance of the system to a great extent. © Research India Publications. More »»

2014

R. Jegadeeshwaran, Sugumaran, V., and Dr. Soman K. P., “Vibration based fault diagnosis of a hydraulic brake system using Variational Mode Decomposition (VMD)”, SDHM Structural Durability and Health Monitoring, vol. 10, pp. 81-97, 2014.[Abstract]


In automobile, brake system is an essential part responsible for control of the vehicle. Vibration signals of a rotating machine contain the dynamic information about its health condition. Many research papers have reported the suitability of vibration signals for fault diagnosis applications. Many of them are based on (Fast Fourier Transform) FFT, which have their own drawback with non-stationary signals. Hence, there is a need for development of new methodologies to infer diagnostic information from such non stationary signals. This paper uses vibration signals acquired from a hydraulic brake system under good and simulated faulty conditions for the purpose of fault diagnosis. A new approach called Variational mode decomposition (VMD) was used in this study. VMD decomposes the signal into various modes by identifying a compact frequency support around its central frequency, such that adding all the modes reconstructs the original signal. VMD finds intrinsic mode functions on central frequencies using alternating direction multiplier method (ADMM). Descriptive statistical features were extracted from VMD processed signals and classified using a machine learning algorithm. For classification J48 decision tree algorithm was used. The results were compared with the statistical features extracted from raw signal using decision tree classifier. Copyright © 2014 Tech Science Press. More »»

2014

P. Prabha, Sikha, O. K., Suchithra, M., and Dr. Soman K. P., “Accelerating the performance of DES on GPU and a visualization tool in Microsoft Excel Spreadsheet”, Advances in Intelligent Systems and Computing, vol. 246, pp. 405-411, 2014.[Abstract]


Graphic processing units (GPU) have attained a greater dimension based on their computational efficiency and flexibility compared to that of classical CPU systems. By utilizing the parallel execution capability of GPU, traditional CPU systems can handle complex computations effectively. In this work, we exploit the parallel structure of GPU and provide an improved parallel implementation for data encryption standard (DES), one of the famous symmetric key cryptosystems. We also developed a visualization tool for DES in Microsoft Excel Spreadsheet which helps the students to understand the primitive operations that constitute the DES cryptosystem clearly. The main objective of this work is to investigate the strength of parallel implementation, on the basis of execution time on GPU as well as on CPU systems. © Springer India 2014. More »»

2014

S. S. Kumar, Manjusha, K., and Dr. Soman K. P., “Novel SVD based character recognition approach for Malayalam language script”, Advances in Intelligent Systems and Computing, vol. 235, pp. 435-442, 2014.[Abstract]


The research on character recognition for Malayalam script dates back to 1990’s. Compared to other Indian languages the research and developments on OCR reported for Malayalam script is very less. The character level and word level accuracy of the existing OCR tools for Indian languages can be improved by implementing robust character recognition and post-processing algorithms. In this paper, we are proposing a character recognition procedure based on Singular Value Decomposition (SVD) and k- Nearest Neighbor classifier (k-NN). The proposed character recognition scheme tested with the dataset created from Malayalam literature books and it could classify 94% of character images accurately. © Springer International Publishing Switzerland 2014 More »»

2014

A. M. Kumar, Dhanalakshmi, V., Dr. Soman K. P., and S. Rajendran, “Factored statistical machine translation system for English to Tamil language”, Pertanika Journal of Social Science and Humanities, vol. 22, pp. 1045-1061, 2014.[Abstract]


This paper proposes a morphology based Factored Statistical Machine Translation (SMT) system for translating English language sentences into Tamil language sentences. Automatic translation from English into morphologically rich languages like Tamil is a challenging task. Morphologically rich languages need extensive morphological pre-processing before the SMT training to make the source language structurally similar to target language. English and Tamil languages have disparate morphological and syntactical structure. Because of the highly rich morphological nature of the Tamil language, a simple lexical mapping alone does not help for retrieving and mapping all the morpho-syntactic information from the English language sentences. The main objective of this proposed work is to develop a machine translation system from English to Tamil using a novel pre-processing methodology. This pre-processing methodology is used to pre-process the English language sentences according to the Tamil language. These pre-processed sentences are given to the factored Statistical Machine Translation models for training. Finally, the Tamil morphological generator is used for generating a new surface word-form from the output factors of SMT. Experiments are conducted with nine different type of models, which are trained, tuned and tested with the help of general domain corpora and developed linguistic tools. These models are different combinations of developed pre-processing tools with baseline models and factored models and the accuracies are evaluated using the well known evaluation metric BLEU and METOR. In addition, accuracies are also compared with the existing online "Google-Translate" machine translation system. Results show that the proposed method significantly outperforms the other models and the existing system. © Universiti Putra Malaysia Press More »»

2014

Gandhiraj R. and Dr. Soman K. P., “Modern analog and digital communication systems development using GNU Radio with USRP”, Telecommunication Systems, vol. 56, pp. 367-381, 2014.[Abstract]


In this modern world many communication devices are highly intelligent and interconnected between each other. Any up-gradation of the hardware in the existing communication devices is not easier one. Compatibility of the new hardware with existing hardware is highly essential. But the new protocols may or may not support the older one. The solution for these problems can be provided by using the reconfigurable hardware design. The hardware can be reprogrammed according to the new change in technology up-gradation. The cost of commercially available hardware and software requirements for setting up such a module is very high. This can be solved by using Open source hardware and software such as Universal Software Radio Peripheral (USRP) and GNU Radio. This work demonstrates how the modern analog communication system like Community Radio Schemes and Radio Data System (RDS) and digital communication systems such as Simple Digital Video Broadcasting (DVB) and OFDM based data communication can be developed using the Open source hardware USRP1. This work will be helpful even for first year level of engineering students to easily implement any communication and control applications with cheaper cost. © 2013 Springer Science+Business Media New York. More »»

2013

B. Premjith, S. Sachin Kumar, Akhil Manikkoth, T. V. Bijeesh, and Dr. Soman K. P., “Insight into Primal Augmented Lagrangian Multilplier Method”, Numerical Analysis, 2013.[Abstract]


We provide a simplified form of Primal Augmented Lagrange Multiplier algorithm. We intend to fill the gap in the steps involved in the mathematical derivations of the algorithm so that an insight into the algorithm is made. The experiment is focused to show the reconstruction done using this algorithm.

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2013

Gandhiraj R. and Dr. Soman K. P., “Modern analog and digital communication systems development using GNU Radio with USRP”, Telecommunication Systems, pp. 1-15, 2013.[Abstract]


In this modern world many communication devices are highly intelligent and interconnected between each other. Any up-gradation of the hardware in the existing communication devices is not easier one. Compatibility of the new hardware with existing hardware is highly essential. But the new protocols may or may not support the older one. The solution for these problems can be provided by using the reconfigurable hardware design. The hardware can be reprogrammed according to the new change in technology up-gradation. The cost of commercially available hardware and software requirements for setting up such a module is very high. This can be solved by using Open source hardware and software such as Universal Software Radio Peripheral (USRP) and GNU Radio. This work demonstrates how the modern analog communication system like Community Radio Schemes and Radio Data System (RDS) and digital communication systems such as Simple Digital Video Broadcasting (DVB) and OFDM based data communication can be developed using the Open source hardware USRP1. This work will be helpful even for first year level of engineering students to easily implement any communication and control applications with cheaper cost. © 2013 Springer Science+Business Media New York.

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2012

Jyothish Lal G., Prasannan, N., Das, R., and Dr. Soman K. P., “Software Redefined Communication System”, IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), vol. 4, no. 2, pp. 16-23, 2012.[Abstract]


“Beginning with practical difficulties in teaching communication systems in class room, this paper describes a set of innovative experimental demonstrations developed using SDR”. Communication engineering is one of the interesting, at the same time difficult subject to learn if the concept is not clear or well explained. Normal practice is that the faculties will just give a theoretical class on communication systems and for students the various communication processes like filtering, modulation, demodulation etc. are just imaginary things. Giving a clear idea about these things to a graduate and under graduate student is a little difficult task. The main aim of SDR is to create an attractive learning platform for the students where they are freed from the boring routine of theoretical learning. We are challenging current education system to think outside the “board”, where students will feel excited to learn something new so that they can physically feel and have a real time experience, which would be quite difficult to forget, for which the expense required would be quite high. So in order to better our possibilities, we suggest this software which would equip them to visualize a solid image of what they are learning about. Another fact is that as this software is so simple and is easy to learn and is feasible for everyone who would like to have basics in electronics and communication. This software is worth a stepping stone for those who would dream about being a “real” engineer. This will boost engineering students having great struggle to understand and learn communication subjects. The soul of education system should transform from “cramming as you learn” to “see as you learn’

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2012

J. Rajendran, Dr. Soman K. P., and Peter, R., “Design of Circular Polarized Microstrip Patch Antenna for L band”, International Journal of Electronics Signals and Systems, vol. 1, no. 3, pp. 47-50, 2012.[Abstract]


In this paper, we share our experience of designing a circularly polarized square patch antenna at L band. The antenna is designed using a relatively cheap substrate FR-4 with permittivity r = 4:4 and loss tangent tan = 0:02. The antenna has a gain of 5dB. Simulated response shows that the designed antenna has an input impedance(Zin) of 50 approximately. An efficiency of 65% is obtained for a single patch. It has a narrow bandwidth and a high Q factor. The design procedure, feed mechanism and simulation results are presented in this paper.

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2012

Dr. Soman K. P., Sowmya, V., Krishnan, P., and V.G, M. Unni, “Enhancing Computational Thinking with Spreadsheet and Fractal Geometry: Part 1”, International Journal of Computer Applications, vol. 55, pp. 1-8, 2012.

2012

G. Abraham, Dr. Soman K. P., Prasannan, N., S, S., and Neethu Mohan, “Two Stage Wavelet based Image Denoising”, International Journal of Computer Applications, vol. 56, no. 14, 2012.

2012

G. Xavier, Dr. Soman K. P., TVN, D., and Philip, T. Erlin, “An Efficient algorithm for the segmentation of Astronomical images”, IVSRJCE, 2012.

2012

, Dr. Soman K. P., Kurian, A. P., Kartha, M. M., and Mohan, L., “Modified Wavelet image fusion based on OSVD”, IJERT, 2012.

2012

Shravan Sriram, Gunturi Srivasta, Gandhiraj R., and Dr. Soman K. P., “Plug-ins for GNU Radio Companion”, International Journal of Computer Applications, vol. 52, pp. 11–16, 2012.[Abstract]


This paper gives an insight on how to develop plug-ins (signal processing blocks) for GNU Radio Companion. GRC is on the monitoring computer and does bulk of the signal processing before transmission and after reception. The coding done in order to develop any block is discussed. A block that performs Huffman coding has been built. Huffman coding is a coding technique that gives a prefix code. A block that performs convolution coding at any desired rate using any generator polynomial has also been built. Both Huffman and Convolution coding are done on data stored in file sources by these blocks. This paper thus describes the ease of signal processing that can be attained by developing blocks in demand by changing the C++ and PYTHON codes of the HOWTO package. Being an open source it is available to all, is highly cost effective and is a field with great potential.

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2012

M. S. Manikandan and Dr. Soman K. P., “A novel method for detecting R-peaks in electrocardiogram (ECG) signal”, vol. 7, no. 2, pp. 118 - 128, 2012.[Abstract]


The R-peak detection is crucial in all kinds of electrocardiogram (ECG) applications. However, almost all existing R-peak detectors suffer from the non-stationarity of both QRS morphology and noise. To combat this difficulty, we propose a new R-peak detector, which is based on the new preprocessing technique and an automated peak-finding logic. In this paper, we first demonstrate that the proposed preprocessor with a Shannon energy envelope (SEE) estimator is better able to detect R-peaks in case of wider and small QRS complexes, negative QRS polarities, and sudden changes in QRS amplitudes over that using the absolute value, energy value, and Shannon entropy features. Then we justify the simplicity and robustness of the proposed peak-finding logic using the Hilbert-transform (HT) and moving average (MA) filter. The proposed R-peak detector is validated using the first-channel of the 48 ECG records of the MIT-BITH arrhythmia database, and achieves average detection accuracy of 99.80%, sensitivity of 99.93% and positive predictivity of 99.86%. Various experimental results show that the proposed R-peak detection method significantly outperforms other well-known methods in case of noisy or pathological signals. More »»

2012

S. M Manikandan and Dr. Soman K. P., “A novel method for detecting R-peaks in electrocardiogram (ECG) signal”, Biomedical Signal Processing and Control, vol. 7, pp. 118–128, 2012.

2011

A. P. J and Dr. Soman K. P., “A Rule based Kannada Morphological Analyzer and Generator using Finite State Transducer”, International Journal of Computer Applications , vol. 27, no. 10, pp. 45-52, 2011.

2011

Dr. Soman K. P. and J, A. P., “Machine Transliteration for Indian Languages: A Literature Survey”, International Journal of Scientific & Engineering Research, IJSER, 2011.[Abstract]


This paper address the various developments in Indian language machine transliteration system, which is considered as a very important task needed for many natural language processing (NLP) applications. Machine transliteration is an important NLP tool required mainly for translating named entities from one language to another. Even though a number of different transliteration mechanisms are available for worlds top level languages like English, European languages, Asian languages like Chinese, Japanese, Korean and Arabic, still it is an initial stage for Indian languages. Literature shows that, recently some recognizable attempts have done for few Indian languages like Hindi, Bengali, Telugu, Kannada and Tamil languages. This paper is intended to give a brief survey on transliteration for Indianlanguages.

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2011

Dr. Soman K. P. and J, A. P., “Parts Of Speech Tagging for Indian Languages: A Literature Survey”, International Journal of Computer Applications , vol. 34, no. 8, pp. 0975-8887, 2011.[Abstract]


Part of speech (POS) tagging is the process of assigning the part of speech tag or other lexical class marker to each and every word in a sentence. In many Natural Language Processing applications such as word sense disambiguation, information retrieval, information processing, parsing, question answering, and machine translation, POS tagging is considered as the one of the basic necessary tool. Identifying the ambiguities in language lexical items is the challenging objective in the process of developing an efficient and accurate POS Tagger. Literature survey shows that, for Indian languages, POS taggers were developed only in Hindi, Bengali, Panjabi and Dravidian languages. Some POS taggers were also developed generic to the Hindi, Bengali and Telugu languages. All proposed POS taggers were based on different Tagset, developed by different organization and individuals. This paper addresses the various developments in POS-taggers and POS-tagset for Indian language, which is very essential computational linguistic tool needed for many natural language processing (NLP) applications

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2011

S. S. Prasad, Gandhiraj R., and Dr. Soman K. P., “Multi-User Spectrum Sensing based on Multi-Taper Method for Cognitive Environments”, International Journal of Computer Applications (IJCA), vol. 22, no. 9, pp. 2613–1093, 2011.[Abstract]


This paper gives a brief but comprehensive review of the Multitaper spectrum estimation method that uses the data tapers or windows in digital signal processing. Instead of using a single kind of window functions, here a cluster of window functions are mentioned, which is known as Slepian tapers. This taper family minimize leakage also, and computing them requires solving eigenvalue problems that are large for long time series. However, the eigenvalue problems have a special structure that makes a fast algorithm possible.Secondly, the enabling of the algorithmic method with Cognitive Radio (CR) Technology, More »»

2011

S. R Narayanan and Dr. Soman K. P., “DATA DRIVEN SUFFIX LIST AND CONCATENATION ALGORITHM FOR TELUGU MORPHOLOGICAL GENERATOR.”, International Journal of Engineering Science & Technology, vol. 3, 2011.

2011

P. Kathirvel, M Manikandan, S., and Dr. Soman K. P., “Automated Referee Whistle Sound Detection for Extraction of Highlights from Sports Video”, International Journal of Computer Applications, vol. 12, pp. 0975–8887, 2011.[Abstract]


This paper proposes a simple and automated referee whistle sound detection (RWSD) for sports highlights extraction and video summarization. The proposed method is based on preprocessor, linear phase bandpass finite impulse response (FIR) filter shorttime energy estimator and decision logic. At the processing stage the discrete audio sequence is divided into non-overlapping blocks and then amplitude normalization is performed. Then, a bandpass filter is designed to accentuate referee whistle sound and suppress other audio events. Then, the filtered signal is fed to short-time energy (STE) estimator which includes amplitude squarer and linear filter to obtain a positive signal. In this work, we use decision rules based on the amplitude-dependent threshold and time-dependent threshold for detecting of referee whistle sound regions. The performance of the proposed design is tested using a large scale audio database including American football, soccer, and basket ball. The total duration of the test audio signal is approximately 12 hours and 11 minutes. The proposed method results in time-instants of boundaries of whistle sounds and then time instants are used to automatically extract the sports highlights from the unscripted video. Then, audio perception of the extracted sound segments is performed to indentify the false positive (FP) and false negative (FN). The proposed method has a detection failure rate of 19.4 % (42 FP and 26 FN) and detects 324 whistle sounds successfully. The sensitivity and reliability of the proposed design are 92.5 % and 80.5%, respectively. More »»

2011

R. Harshawardhan, Augustine, M. Sara, and Dr. Soman K. P., “Phrase based English–Tamil Translation System by Concept Labeling using Translation Memory”, International Journal of Computer Applications (0975–8887), vol. 20, 2011.[Abstract]


In this paper, we present a novel framework for phrase based translation system using translation memory by concept labeling. The concepts are labeled on the input text, followed by the conversion of text into phrases. The phrase is searched throughout the translation memory, where the parallel corpus is stored. The translation memory displays all source and target phrases, wherever the input phrase is present in them. Target phrase corresponding to the output source phrase having the same concept as that of input source phrase, is chosen as the best translated phrase. The system is implemented for English to Tamil translation. More »»

2011

J. Amudha, Dr. Soman K. P., and S Reddy, P., “A Knowledge Driven Computational Visual Attention Model”, International Journal of Computer Science Issues, vol. 8, no. 3, 2011.[Abstract]


Computational Visual System face complex processing problems as there is a large amount of information to be processed and it is difficult to achieve higher efficiency in par with human system. In order to reduce the complexity involved in determining the saliency region, decomposition of image into several parts based on specific location is done and decomposed part is passed for higher level computations in determining the saliency region with assigning priority to the specific color in RGB model depending on application. These properties are interpreted from the user using the Natural Language Processing and then interfaced with vision using Language Perceptional Translator (LPT). The model is designed for a robot to search a specific object in a real time environment without compromising the computational speed in determining the Most Salient Region. More »»

2011

J. Amudha, Dr. Soman K. P., and Kiran, Y., “Feature Selection in Top-Down Visual Attention Model using WEKA.”, International Journal of Computer Applications, vol. 24, no. 4, pp. 38-43, 2011.

2011

P. Kathirvel, Manikandan, M. S., Prasanna, S. R. M., and Dr. Soman K. P., “An Efficient R-peak Detection Based on New Nonlinear Transformation and First-Order Gaussian Differentiator”, Cardiovascular Engineering and Technology, vol. 2, pp. 408-425, 2011.[Abstract]


In this paper, we present a reliable and efficient automatic R-wave detection based on new nonlinear transformation and simple peak-finding strategy. The detection algorithm consists of four stages. In the first stage, the bandpass filtering and differentiation operations are used to enhance QRS complexes and to reduce out-of-band noise. In the second stage, we introduce a new nonlinear transformation based on energy thresholding, Shannon energy computation, and smoothing processes to obtain a positive-valued feature signal which includes large candidate peaks corresponding to the QRS complex regions. The energy thresholding reduces the effect of spurious noise spikes from muscle artifacts. The Shannon energy transformation amplifies medium amplitudes and results in small deviations between successive peaks. Therefore, the proposed nonlinear transformation is capable of reducing the number of false-positives and false-negatives under small-QRS and wide-QRS complexes and noisy ECG signals. In the third stage, we propose a simple peak-finding strategy based on the firstorder Gaussian differentiator (FOGD) that accurately identifies locations of candidate R-peaks in a feature signal. This stage computes convolution of the smooth feature signal and FOGD operator. The resultant convolution output has negative zero-crossings (ZCs) around the candidate peaks of feature signal due to the anti-symmetric nature of the FOGD operator. Thus, these negative ZCS are detected and used as guides to find locations of real R-peaks in an original signal at the fourth stage. Unlike other existing algorithms, the proposed algorithm does not use search back algorithm and learning phase. The proposed algorithm is validated using the standard MIT-BIH arrhythmia database and achieves an average sensitivity of 99. 94% and a positive predictivity of 99.96%. Experimental results show that the proposed algorithm outperforms other existing algorithms in case of different QRS complex morphologies (negative, low-amplitude, wide), very big change in amplitudes of adjacent R-peaks, irregular heart rates, and noisy ECG signals.

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2010

Dr. Soman K. P., P, U., and J, A. P., “A Novel Approach for English to South Dravidian Language Statistical Machine Translation System ”, (IJCSE) International Journal on Computer Science and Engineering , vol. 2, 2010.[Abstract]


Development of a well fledged bilingual machine translation (MT) system for any two natural languages with limited electronic resources and tools is a challenging and demanding task. This paper presents the development of a statistical machine translation (SMT) system for English to South Dravidian languages like Malayalam and Kannada by incorporating syntactic and morphological information. SMT is a data oriented statistical framework for translating text from one natural language to another based on the knowledge extracted from bilingual corpus. Even though there are efforts towards building such an English to South Dravidian translation system ,unfortunately we do not have an efficient translation system till now. The first and most important step in SMT is creating a well aligned parallel corpus for training the system. Experimental research shows that the existing methodology for bilingual parallel corpus creation is not efficient for English to South Dravidian language in the SMT system. In order to increase the performance of the translation system, we have introduced a new approach in creating parallel corpus. The main ideas which we have implemented and proven very effective for English to south Dravidian languages SMT system are: (i) reordering the English source sentence according to Dravidian syntax, (ii) using the root suffix separation on both English and Dravidian words and iii) use of morphological information which substantially reduce the corpus size required for training the system. Since the unavailability of full fledged parsing and morphological tools for Malayalam and Kannada languages, sentence synthesis was done both manually and existing morph analyzer created by Amrita university. From the experiment we found that the performance of our systems are significantly well and achieves a very competitive accuracy for small sized bilingual corpora. The proposed ideas can be directly used for other south Dravidian languages like Tamil and Telugu with some minor changes.

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2010

Dr. Soman K. P., H, A., and Rajgopal, C., “A Simple Approach to Clustering in Excel”, International Journal of Computer Applications (0975 – 8887), vol. 11, 2010.[Abstract]


Data clustering refers to the method of grouping data into different groups depending on their characteristics. This grouping brings an order in the data and hence further processing on this data is made easier. This paper explains the clustering process using the simplest of clustering algorithms - the K-Means. The novelty of the paper comes from the fact that it shows a way to perform clustering in Microsoft Excel 2007 without using macros, through the innovative use of what-if analysis. The paper also shows that, image processing operations can be done in excel and all operations except displaying an image do not require a macro. The paper gives a solution to the problem of reading an image in excel by introducing a user defined add-in. The paper also has explained and implemented image segmentation as an application of clustering. This paper aims at showing that Microsoft Excel is a great tool as far as technical learning is concerned for the fact that, it can implement almost all algorithms and processes, and is very successful in providing the first hand exposure to an novice studen

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2010

Dr. Soman K. P. and Krishnamoorthy, S., “Implementation and Comparative Study of Image Fusion Algorithms”, International Journal of Computer Applications, vol. 9, no. 2, pp. 25-35, 2010.[Abstract]


Image Fusion is a process of combining the relevant information from a set of images, into a single image, wherein the resultant fused image will be more informative and complete than any of the input images. This paper discusses the implementation of three categories of image fusion algorithms – the basic fusion algorithms, the pyramid based algorithms and the basic DWT algorithms, developed as an Image Fusion Toolkit - ImFus, using Visual C++ 6.0. The objective of the paper is to assess the wide range of algorithms together, which is not found in the literature. The fused images were assessed using Structural Similarity Image Metric (SSIM) [10], Laplacian Mean Squared Error along with seven other simple image quality metrics that helped us measure the various image features; which were also implemented as part of the toolkit. The readings produced by the image quality metrics, based on the image quality of the fused images, were used to assess the algorithms. We used Pareto Optimization method to figure out the algorithm that consistently had the image quality metrics produce the best readings. An assessment of the quality of the fused images was additionally performed with the help of ten respondents based on their visual perception, to verify the results produced by the metric based assessment. Coincidentally, both the assessment methods matched in their raking of the algorithms. The Pareto Optimization method picked DWT with Haar fusion method as the one with the best image quality metrics readings. The result here was substantiated by the visual perception based method where it was inferred that fused images produced by DWT with Haar fusion method was marked the best 63.33% of times which was far better than any other algorithm. Both the methods also matched in assessing Morphological Pyramid method as producing fused images of inferior quality.

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2010

Dr. Ramanathan R., Dr. Soman K. P., and , “A Novel Methodology for Designing Linear Phase IIR Filters”, Aceee International Journal on Communication, vol. 1, 2010.[Abstract]


This paper presents a novel technique for designing an Infinite Impulse Response (IIR) Filter with Linear Phase Response. The design of IIR filter is always a challenging task due to the reason that a Linear Phase Response is not realizable in this kind. The conventional techniques involve large number of samples and higher order filter for better approximation resulting in complex hardware for implementing the same. In addition, an extensive computational resource for obtaining the inverse of huge matrices is required... More »»

2010

P. J. Antony, M Kumar, A., and Dr. Soman K. P., “A Paradigm based Morphological Analyzer for English to Kannada Using a Machine Learning Approach.”, Advances in Computational Sciences & Technology, Research India Publication(RIP), vol. 3, 2010.[Abstract]


The role of morphological analyzer is very significant in the field of natural language processing (NLP) applications like machine translation (MT), information extraction (IE), information retrieval (IR), spell checker, lexicography etc. So from a serious computational perspective the creation and availability of a morphological analyzer for a language is important. The morphological analyzer maps an inflected word into its stem, parts of speech and feature equations corresponding to inflectional information. The morphological structure of an agglutinative language is unique and capturing its complexity in a machine analyzable and generatable format is a challenging job. This paper presents a paradigm based morphological analyzer, for the complex agglutinative Kannada language using the machine learning approach. The proposed morphological analyzer is designed using sequence labeling approach and training, testing and evaluations are done by support vector method (SVM) algorithms. The system captures the various non-linear relationships and morphological features of Kannada language in a better and simpler way. We also compared the efficiency of our system with the existing morphological analyzers which are publically available in the internet. From the experiment we found that the performance of our system significantly outperforms the existing morphological analyzer and achieves a very competitive accuracy of 96.25% for Kannada verbs. More »»

2010

V. Dhanalakshmi, Rajendran, S., M Kumar, A., and Dr. Soman K. P., “Natural Language processing Tools for Tamil grammar Learning and Teaching”, International journal of Computer Applications (0975-8887), vol. 8, 2010.[Abstract]


Today we are living in the world of communication. The world of communication interlinks everyone through its various media. In this aspect Computers play a major role by bringing the world under the user's finger tip. Grammar is the legal advocacy to the human art of communication. But learners get annoyed with the language rules and the old teaching methodology. Interlinking the computer to the language through Natural language Processing (NLP) paves a way to solve this problem. The innovative NLP applications are used to generate language learning and teaching tools which enhance the teaching and learning of Grammar. In this paper we present the Grammar teaching tools for analyzing and learning character, word and sentence of Tamil Language. Tools like Character Analyzer for analyzing character, Morphological Analyzer and Generator and Verb Conjugator for the word level analysis and Parts of Speech Tagger, Chunker and Dependency parser for the sentence level analysis were developed using machine learning based technology. These tools are very useful for second language learners to understand the character, word and sentence construction of Tamil language in a non-conceptual way... More »»

2010

A. M Kumar, Dhanalakshmi, V., Dr. Soman K. P., and Rajendran, S., “A sequence labeling approach to morphological analyzer for Tamil language”, IJCSE) International Journal on Computer Science and Engineering, vol. 2, pp. 1944–195, 2010.[Abstract]


Morphological analysis is the basic process for any Natural Language Processing task. Morphology is the study of internal structure of the word. Morphological analysis retrieves the grammatical features and properties of a morphologically inflected word. Capturing the agglutinative structure of Tamil words by an automatic system is a challenging job. Generally rule based approaches are used for building morphological analyzer. In this paper we propose a novel approach to solve the morphological analyzer problem using machine learning methodology. Here morphological analyzer problem is redefined as classification problem. This approach is based on sequence labeling and training by kernel methods that captures the non linear relationships of the morphological features from training data samples in a better and simpler way. Keywords- morphology; morphological analyzer; machine learning; sequence labeling... More »»

2010

A. M Kumar, Rekha, R. U., Dr. Soman K. P., Rajendran, S., and Dhanalakshmi, V., “A Novel Data Driven Algorithm for Tamil Morphological Generator”, International Journal of Computer Applications, vol. 6, pp. 52–56, 2010.[Abstract]


Tamil is a morphologically rich language with agglutinative nature. Being agglutinative language most of the word features are postpositionally affixed to the root word. The morphological generator takes lemma, POS category and morpho-lexical description as input and gives a word-form as output. It is a reverse process of morphological analyzer. In any natural language generation system, morphological generator is an essential component in post processing stage. Morphological generator system implemented here is based on a new algorithm, which is simple, efficient and does not require any rules and morpheme dictionary. A paradigm classification is done for noun and verb based on Dr.S.Rajendran’s paradigm classification. Tamil verbs are classified into 32 paradigms with 1884 inflected forms. Like verbs, nouns are classified into 25 paradigms with 325 word forms. This approach requires only minimum amount of data. So this approach can be easily implemented to less resourced and morphologically rich languages. More »»

2010

S. M Manikandan and Dr. Soman K. P., “Robust Heart Sound Activity Detection in Noisy Environments”, Electronics letters, vol. 46, pp. 1100–1102, 2010.[Abstract]


A novel and robust method for heart sound activity detection (HSAD) is presented. In this method, a new discriminative feature is developed based on the lag-1 autocorrelation coefficient and feature smoothing is introduced to identify the endpoints of low-level heart murmurs. Several experiments on a large scale phonocardiogram database show that the method significantly outperforms the other HSAD methods under varying levels of heart sounds and different types of noise. For a signal-to-noise ratio of 5 dB, the method ... More »»

2009

Dr. Soman K. P. and J, A. P., “Computational Morphology and Natural Language Parsing for Indian Languages: A Literature Survey ”, International Journal of Scientific and Engineering Research 3 , 2009.[Abstract]


Computational Morphology and Natural Language Parsing are the two important as well as essential tasks required for a number of natural language processing application including machine translation. Developing well fledged morphological analyzer and generator (MAG) tools or natural language parsers for highly agglutinative languages is a challenging task. The function of morphological analyzer is to return all the morphemes and their grammatical categories associated with a particular word form. For a given root word and grammatical information, morphological generator will generate the particular word form of that word. On the other hand Parsing is used to understand the syntax and semantics of a natural language sentences confined to the grammar. This literature survey is a ground work to understand the different morphology and parser developments in Indian language. In addition, the paper also deals with various approaches that are used to develop morphological analyzer and generator and natural language parsers tools.

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2009

Dr. Soman K. P., MS, V., VP, A., and G, S., “English to Tamil Transliteration using WEKA”, International Journal of Recent Trends in Engineering, vol. 1, no. 1, p. 498, 2009.[Abstract]


Machine transliteration has gained prime importance as a supporting tool for Machine translation and cross language information retrieval especially when proper names and technical terms are involved. The performance of machine translation and cross-language information retrieval depends extremely on accurate transliteration of named entities. Hence the transliteration model must aim to preserve the phonetic structure of words as closely as possible. In this paper, the transliteration problem is modeled as classification problem and trained using C4.5 decision tree classifier, in WEKA Environment. The training was implemented with features extracted from a parallel corpus. This technique was demonstrated for English to Tamil Transliteration and achieved exact Tamil transliterations for 84.82% of English names. Possible equivalent transliterations were also generated by the model. It is found that the transliteration accuracy is increased when the top five ranked transliterations were considered

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2009

N. Lalithamani and Dr. Soman K. P., “An Effective Scheme for Generating Irrevocable Cryptographic Key from Cancelable Fingerprint Templates”, Int. J. Comput. Sci. Netw. Secur, vol. 9, no. 3, pp. 183-193, 2009.[Abstract]


Summary Unswerving information security mechanisms are the need of the hour for fighting the escalating enormity of identity theft in our society. Besides cryptography being a dominant tool in attaining information security, one of the key confronts in cryptosystems is to preserve the secrecy of the cryptographic keys. The incorporation of biometrics with cryptography will be an effective solution to this problem. Recently generating cryptographic key from biometrics has gained enormous

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2009

N. Lalithamani and Dr. Soman K. P., “Irrevocable Cryptographic Key Generation from Cancelable Fingerprint Templates: An Enhanced and Effective Scheme”, European Journal of Scientific Research, 2009.[Abstract]


The growing vastness of identity theft in our society has made the requirement of reliable information security mechanisms a priority. Even though information security can be accomplished with the help of a prevailing tool like cryptography, protecting the confidentiality of the cryptographic keys is one of the significant issues to be deal with. This predicament can be efficiently solved by the integration of biometrics with cryptography. Of late, the enhanced performance of cryptographic key generated from biometrics in terms of security has obtained massive reputation amongst the researchers and experimenters. However, there is a permanent association between the biometric and the user, in which modification is impossible. Hence, the biometric is gone eternally and possibly for all the applications which apply it, if there is a compromise of the biometric identifier. This may be solved by the construction of revocable biometric templates through cancelable biometrics. In this paper, we have proposed an approach to generate irrevocable cryptographic key from cancelable fingerprint templates, which performs in an efficient manner. Initially, the fingerprints are employed to extract the minutiae points which are transformed in an efficient manner to obtain deformed points. Subsequently, the deformed points are employed to generate the cancelable templates which are utilized for the extraction of irrevocable keys. It is highly impractical to acquire cancelable fingerprint templates and original fingerprints from the generated key owing to the fact that cryptographic key generated is irrevocable. We have utilized the fingerprints accessible from open sources to evaluate the efficacy of our approach. In addition, the protection analysis of the proposed method has been presented.

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2009

V. Dhanalakshmi, Shivapratap, G., Dr. Soman K. P., Rajendran, S., and M Kumar, A., “Tamil POS tagging using Linear Programming”, International Journal of Recent Trends in Engineering, vol. 1, 2009.[Abstract]


Part of speech (POS) tagging is the process of annotating syntactic categories for each word in a corpus. This paper presents an SVM methodology based on Linear Programming for implementing automatic Tamil POS tagger. We have designed our own tagset consisting of 32 tags for preparing the annotated corpus for Tamil. The features are extracted from a corpus of twenty five thousand sentences and trained with linear programming based SVM. This method, when tested with 10,000 sentences, gave an ... More »»

2007

S. K, R, L., CJ, S., V, A., and Dr. Soman K. P., “Multiclass Hierarchical SVM for Recognition of Printed Tamil Characters ”, 2007.[Abstract]


This paper presents an efficient method for recognizing printed Tamil characters exploring the interclass relationship between them. This is accomplished using Multiclass Hierarchical Support Vector Machines [Crammer et al., 2001; Weston et al., 1998], a new variant of Multi Class Support Vector Machine which constructs a hyperplane that separates each class of data from other classes. 126 unique characters in Tamil language have been identified. A lot of inter-class dependencies were found in them based on their shapes. This enabled the characters to be organized into hierarchies thereby enhancing the process of recognizing the characters. The System was trained using features extracted from the binary character sub-images of sample documents using Hu’s [Hu., 1962; Jain et al., 1996] moment invariant feature extraction method. The system fetched us promising results in comparison with other classifying algorithms like KNN, Bayesian Classifier and decision trees. An accuracy of 96.85% was obtained in the experiments using Multiclass Hierarchical SVM

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1993

Dr. Soman K. P. and Misra, K. B., “On Bayesian Estimation of System Reliability”, Microelectronics Reliability, vol. 33, pp. 1455 - 1459, 1993.[Abstract]


For a system with n s-independent components, the uncertainty regarding the reliability of components for a fixed point of time is expressed by a Bayesian probability distribution. Using the moments of these distributions, the exact moments of the system reliability distribution are derived, from which a discrete probability density function is obtained on the basis of the principle of maximum entropy. Taking this distribution as a prior distribution for system reliability, a posterior density function for the system reliability is constructed either using the data obtained from life tests conducted at a system level or from field data. For tracking the evolution of the reliability distribution over time, a modified Kalman filter technique, along with use of a Bayesian procedure, is proposed. This method is simple, elegant and easy to compute.

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1993

K. B. Misra and Dr. Soman K. P., “Fuzzy Fault Tree Analysis using Resolution Identity”, J Fuzzy Math, vol. 1, pp. 193-212, 1993.

1992

Dr. Soman K. P. and Misra, K. B., “A Least Square Estimation of Three Parameters of a Weibull Distribution”, Microelectronics Reliability, vol. 32, pp. 303 - 305, 1992.[Abstract]


The present paper applies a least square method to estimate parameters of a Weibull distribution, with the shape parameter lying in the range 0–3, where other methods like the maximum likelihood method are generally not applicable. Further, Fisher's F-test is employed to find the goodness of fit of a straight line. Two approximate methods have also been developed for the remaining range of the shape parameter. Illustrations are provided wherever necessary.

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Publication Type: Conference Proceedings

Year of Publication Title

2020

V. S. Vineeth, Sundaram, G. A. Shanmug, and Dr. Soman K. P., “Influence of Clutter on 5G Waveform Modulated RADAR Signals”, AIP Conference Proceedings, vol. 2222. p. 030006, 2020.[Abstract]


The primary objective of radar is to identify a target in various scenarios. Radar signal generation with various coding and modulation schemes are introduced from earlier times to recent times for this purpose. There are not much efficient waveforms available for a radar system to identify a target, hence a new waveform modulation is always needed. In the work discussed here, a stepped frequency waveform (SFW) is modulated with two waveforms such as cyclic prefix - orthogonal frequency division multiplexing (CP-OFDM) and discrete fourier transform-spread-orthogonal frequency division multiplexing (DFT-S-OFDM) in order to improve the complexity of the waveform and to obtain pulse compression. This CP-OFDM and DFT-S-OFDM are the new waveform candidates that are expected to use in 5G communication systems. The modulated waveforms are analyzed with a clutter model in order to test whether this waveform design can be used in a radar system for the purpose of target identification in the midst of clutter. The modulated waveforms are analyzed using ambiguity functions

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2018

Dr. Govind D., Sowmya V., Sachin, R., and Dr. Soman K. P., “Dependency of Various Color and Intensity Planes on CNN Based Image Classification”, Advances in Intelligent Systems and Computing, International Symposium on Signal Processing and Intelligent Recognition Systems SIRS 2017, vol. 678. Springer International Publishing AG 2018, Manipal, India, pp. 167-177, 2018.[Abstract]


Scene classification systems have become an integral part of computer vision. Recent developments have seen the use of deep scene networks based on convolutional neural networks (CNN), trained using millions of images to classify scenes into various categories. This paper proposes the use of one such pre-trained network to classify specific scene categories. The pre-trained network is combined with the simple classifiers namely, random forest and extra tree classifiers to classify scenes into 8 different scene categories. Also, the effect of different color spaces such as RGB, YCbCr, CIEL*a*b* and HSV on the performance of the proposed CNN based scene classification system is analyzed based on the classification accuracy. In addition to this, various intensity planes extracted from the said color spaces coupled with color-to-gray image conversion techniques such as weighted average, and singular value decomposition (SVD) are also taken into consideration and their effects on the performance of the proposed CNN based scene classification system are also analyzed based on the classification accuracy. The experiments are conducted on the standard Oliva Torralba (OT) scene data set which comprises of 8 classes. The analysis of classification accuracy obtained for the experiments conducted on OT scene data shows that the different color spaces and the intensity planes extracted from various color spaces and color-to-gray image conversion techniques do affect the performance of proposed CNN based scene classification system.

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2018

Dr. Soman K. P., B. Gowri, G., and Dr. Govind D., “Improved Epoch Extraction from Telehonic Speech Signals using Chebfun and zero frequency filtering”, Accepted for publication in INTERSPEECH 2018. INTERSPEECH 2018, Hyderabad, INDIA, 2018.

2017

Sowmya V., Dr. Govind D., and Dr. Soman K. P., “Significance of contrast and structure features for an improved color image classification system”, 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). pp. 210-215, 2017.[Abstract]


In general, the three main modules of color image classification systems are: color-to-grayscale image conversion, feature extraction and classification. The color-to-grayscale image conversion is the important pre-processing step which must incorporate the significant and discriminative contrast and structure information in the converted grayscale images as in the original color image. All the existing techniques for color-to-grayscale image conversion preserves the significant contrast and structure information in the converted grayscale images in different manners. Hence, the present work is to analyze the significant and discriminative contrast and structure information preserved in the converted grayscale images using two different decolorization techniques called rgb2gray and singular value decomposition based color-to-grayscale image conversion (SVD) applied in the color image classification systems using the three different proposed features. The three different features for color image classification systems are proposed based on the combination of the existing dense SIFT features and the contrast &amp; structure content computed using color-to-gray structure similarity index (C2G-SSIM) metric.

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2017

Sowmya V., Ashwini B, Neethu Mohan, Shriya se, and Dr. Soman K. P., “Performance Evaluation of Edge Feature Extracted using Sparse Banded Matrix Filter Applied for Face Recognition”, IEEE International Conference on Circuit, Power and Computing, ICCPCT-2017. Baselious Mathews II College of Engineering, Kerala, pp. 19-20, 2017.

2016

B. Ganesh, M, Akumar, and Dr. Soman K. P., “Amrita_CEN at SemEval-2016 Task Semantic Textual Similarity : Semantic Relation from Word Embeddings in Higher Dimension”, International Workshop on Semantic Evaluation (SemEval 2016). 2016.

2015

Sowmya V., Neethu Mohan, and Dr. Soman K. P., “Edge Detection Using Sparse Banded Filter Matrices”, Second International Symposium on Computer Vision and the Internet (VisionNet’15). Elsevier Procedia Computer Science Journal, SCMS School of Engineering, Aluva, Kochi , pp. 10–17, 2015.[Abstract]


Edges are intensity change, which occur on the boundary between two regions in an image. Edges can be used as feature descriptors of an object. Hence, edge detection plays an important role in computer vision applications. This paper presents the application of sparse banded filter matrices in edge detection. The filter design is formulated in terms of banded matrices. The sparsity property of the designed filter leads to efficient computation. In our proposed method, we applied sparse banded high-pass filter row-wise and column-wise to extract the vertical and the horizontal edges of the image respectively. The proposed technique is experimented on standard images and the results are compared with the state-of-the-art methods. The visual comparison of the experimental results shows that the proposed approach for edge extraction based on sparse banded filter matrices produces result comparable to the existing methods. The advantage of the proposed approach is that the continuous edges are attained without any parameter tuning.

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2013

Jyothish Lal G., Veena, V. K., and Dr. Soman K. P., “A Combined Crypto-steganographic Approach for Information Hiding in Audio Signals Using Sub-band Coding, Compressive Sensing and Singular Value Decomposition”, Security in Computing and Communications. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 52-62, 2013.[Abstract]


In this paper, a new method of audio data security system is proposed, which uses the complementary services provided by steganography and cryptography. Here the audio data to be send secretly is encoded using the compressive measurements of the same and the resultant data is embedded in the perceptible band of the cover audio data using the SVD based watermarking algorithm. Thus the combination of these two methods enhances the protection against most serious attacks when audio signals are transmitted over an open channel. Decryption stage uses SVD based watermark extraction algorithm and L1 optimization. Experimental results show that the combined system enhances the security of the audio data embedded.

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2013

Abirami M, Dr. Soman K. P., R, G., M.B, S., and V, H., “Exploiting GNU Radio and USRP: An economical Test Bed for Real Time Communication Systems”, Proceedings of Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). pp. 1-6, 2013.

2013

Dr. Padmavathi S., Dr. Soman K. P., and Aarthi, R., “Image restoration using knowledge from the image”, Advances in Intelligent Systems and Computing, vol. 177 AISC. Chennai, pp. 19-25, 2013.[Abstract]


There are various real world situations where, a portion of the image is lost or damaged which needs an image restoration. A Prior knowledge of the image may not be available for restoring the image, which demands for a knowledge derivation from the image itself. Restoring the lost portions of the image based on the knowledge obtained from the image area surrounding the lost area is called as Digital Image Inpainting. The information content in the lost area could contain structural information like edges or textural information like repeating patterns. This knowledge is derived from the boundary area surrounding the lost area. Based on this, the lost area is restored by looking at similar information in the same image. Experimentation have been done on various images and observed that the algorithm restores the image in a visually plausible way. © 2013 Springer-Verlag.

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2013

M. Suchithra, Sukanya, P., Prabha, P., Sikha, O. K., Sowmya V., and Dr. Soman K. P., “An experimental study on application of orthogonal matching pursuit algorithm for image denoising”, 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013. IEEE, Kochi, Kerala, pp. 729-736, 2013.[Abstract]


<p>Signal or image reconstruction has now become a common task in many applications. According to linear algebra perspective, the number of measurements made or the number of samples taken for reconstruction must be greater than or equal to the dimension of signal or image. Also reconstruction follows the Shanon's sampling theorem which is based on the Nyquist sampling rate. The reconstruction of a signal or image using the principle of compressed sensing is an exception which makes use of only few number of samples which is below the sampling limit. Compressive sensing also known as sparse recovery aims to provide a better data acquisition and reduces computational complexities that occur while solving problems. The main goal of this paper is to provide clear and easy way to understand one of the compressed sensing greedy algorithm called Orthogonal Matching Pursuit (OMP). The OMP algorithm involves the concept of overcomplete dictionary that is formulated based on different thresholding methods. The proposed method gives the simplified approach for image denoising by using OMP only. The experiment is performed on few standard image data set simulated with different types of noises such as Gaussian noise, salt and pepper noise, exponential noise and Poisson noise. The performance of the proposed method is evaluated based on the image quality metric, Peak Signal-to-Noise Ratio (PSNR). © 2013 IEEE.</p>

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2013

G. Aarthy, Amitha, P. L., Krishnan, T., Pillai, G. S., Sowmya V., and Dr. Soman K. P., “A comparative study of spike and smooth separation from a signal using different overcomplete dictionary”, 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013. IEEE, Kochi, Kerala, pp. 590-595, 2013.[Abstract]


Most of the natural signals are complex and are highly time varying, since they are non stationary in nature. In this paper, a comparative study for separating spikes and smooth signal components from a non-stationary signal are performed based on different overcomplete dictionaries. The experiment is evaluated using the sparse representation with different bases such as the Discrete Cosine Transform (DCT), Walsh-Hadamard, Orthogonal and Biorthogonal wavelet basis. The primary focus of this paper is to use L1 minimization for retrieving the smooth and spikes component of the signal using different overcomplete dictionary. The experimental results reveals out the dictionary that delivers a better separation without distorting temporal and spectral characteristics. © 2013 IEEE.

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2013

B. G. Gowri, Hariharan, V., Thara, S., Sowmya V., Kumar, S. S., and Dr. Soman K. P., “2D Image data approximation using Savitzky Golay filter - Smoothing and differencing”, 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013. IEEE, Kochi, Kerala, pp. 365-371, 2013.[Abstract]


Smoothing and differencing is one of the major important and necessary step in the field of signal processing, image processing and also in the field on analytical chemistry. The search for an efficient image smoothing and edge detection method is a challenging task in image processing sector. Savitzky Golay Filters are one among the widely used filters for analytical chemistry. Even though they have exceptional features, they are rarely used in the field of image processing. The designed filter is applied for image smoothing and a mathematical model based on partial derivatives is proposed to extract the edges in images. The smoothing technique of SG filter offers an extremely simple aid in extracting the edge information. An approach using SG filter which can be applied in preserving edge information is one of the major tasks involved in the classification process in the domain of Optical Character Recognition. The paper is focused on designing the Savitzky Golay filter by using the concepts of linear algebra. The main objective of the paper is to portray a clear cut idea about Savitzky Golay filter and to study the design of Savitsky Golay filters based on the concepts of Linear Algebra. © 2013 IEEE.

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2013

M. B. Sruthi, Abirami, M., Manikkoth, A., Gandhiraj R., and Dr. Soman K. P., “Low cost digital transceiver design for software defined radio using RTL-SDR”, Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013. Kerala, pp. 852-855, 2013.[Abstract]


The field of wireless communication has become the hottest area and Software Defined Radio (SDR) is revolutionizing it. By bringing much functionality as software, SDR reduces the cost of hardware maintenance and up-gradation. Open source hardware such as USRP (Universal Software Radio Peripheral) and software called GNU Radio-Companion are commonly used to do experiments in SDR. Since the cost of USRP is high, a low cost set up is needed which is affordable by the student community. In this paper a low cost alternative to USRP is proposed using RTL-SDR (Realtek Software Defined Radio) which is only used for reception. For transmitting purpose, a mixer circuit can be used to map the baseband signal to the band that can be received by RTL-SDR on the other end on Linux / Windows platform. Initially, the experiment is done in simulation. After that, it is tested with low cost hardware such as mixer and RTL-SDR. The cost for total transceiver system can be less than USD 100 which is 10 times less than the existing one. © 2013 IEEE.

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2013

Jyothish Lal G., Dr. Soman K. P., and V. K. Veena, “A cryptographic approach to video watermarking based on compressive sensing, arnold transform, sum of absolute deviation and SVD”, Emerging Research Areas and 2013 International Conference on Microelectronics, Communications and Renewable Energy (AICERA/ICMiCR. p. 1,5, 2013.[Abstract]


Video watermarking is relatively a new technology to ensure protection of intellectual property rights and to stop video piracy. The ownership information or watermark is normally hidden in the video sequences. In this paper, a novel video watermarking method is proposed to protect the ownership information in a robust way. This method encrypts the watermark by combining the strengths of Compressive Sensing and Arnold scrambling. The frames for watermark embedding are chosen by computing the Sum of Absolute Deviation between successive frames. The cipher watermark is then embedded into chosen frames based on SVD watermark embedding algorithm. The decryption stage performs SVD watermark extraction algorithm, Arnold inverse transform and L 1 optimization for retrieval of watermark. Experimental results show that proposed method enhances the security of the ownership information embedded.

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2012

A. M., Dr. Soman K. P., R., G., M.B., S., and Akhil Manikkoth, “Low cost digitaltransceiver design for software defined radio using Rtl-Sdr (2012)”, IBM Conference (poster presentation). 2012.

2012

Gandhiraj R., Ram, R., and Dr. Soman K. P., “Analog and digital modulation toolkit for software defined radio”, Procedia Engineering, vol. 30. Coimbatore, pp. 1155-1162, 2012.[Abstract]


This work is a small tutorial for the new users in the field of software defined radio. Applications are build up using graphical user interface called the GNU radio companion (GRC). The idea behind developing such a tool kit is to give practical exposure in the communication concepts like basic signal generations, signal operations, multi-rate concepts, analog and digital modulation schemes and finally multiplexing schemes with the help of GNU radio. Unlike MATLAB Simulink or Labview GNU radio is open source i.e. free of cost and the concepts can be easily reached to the normal people without much of programming concepts using the pre written blocks. And programmers also have the chance to write their own applications.

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2012

H. Aravind, Gandhiraj R., Dr. Soman K. P., Manikandan, M. S., and Peter, R., “Spectrum sensing implementations for software defined radio in simulink”, Procedia Engineering, vol. 30. Coimbatore, pp. 1119-1128, 2012.[Abstract]


The lack of spectrum for communication and for research is a bottleneck as far as technology and business development is considered. It is a fact that the availability of useful spectrum is limited by hardware constraints. The studies conducted by the Federal Communications Commission found that that there are many areas of the radio spectrum which are not fully utilized in different geographical areas of the country and FCC recommended locating and utilizing these unused spectrum spaces by other users. This is where spectrum sensing comes into use. From then on different spectrum sensing algorithms were developed. The paper implements four of those major sensing spectrum algorithms in MATLAB-Simulink and also does a performance comparison among them.

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2012

K. Lakshmi, Parvathy, R., Soumya, S., and Dr. Soman K. P., “Image denoising solutions using heat diffusion equation”, 2012 International Conference on Power, Signals, Controls and Computation, EPSCICON 2012. Thrissur, Kerala, 2012.[Abstract]


The idea of this paper is to model image denoising using an approach based on partial differential equations (PDE), which describes two dimensional heat diffusion. The two dimensional image function is taken to be the harmonic, when it can be obtained as the solution to the equation describing the the heat diffusion. To achieve this, image denoising is formulated as an optimization problem, in which a function with two terms is to be minimized. The first term is called the regularization term, which is some form of energy of the image (like Sobolev energy) and the second term is called the data fidelity term, which measures the similarity between the original image and the processed image. The two terms are combined using a control parameter whose value decides which term has to be minimized more. Image denoising problem could then be solved by a simple iterative equation, derived based on the Gradient Descent method. © 2012 IEEE.

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2012

Dr. Padmavathi S., Rajalaxmi, C., and Dr. Soman K. P., “Texel identification using K-means clustering method”, Advances in Intelligent and Soft Computing, vol. 167 AISC. New Delhi, pp. 285-294, 2012.[Abstract]


Identifying the smallest portion of the image that represents the entire image is a basic need for its efficient storage. Texture can be defined as a pattern that is repeated in a specific manner. The basic pattern that is repeated is called as Texel(Texture Element). This paper describes a method of extracting a Texel from the given textured image using K means clustering algorithm and validating it with the entire image. The number of gray levels in an image is reduced using a linear transformation function. The image is then divided in to sub windows of certain size. These sub windows are clustered together using K-means algorithm. Finally a heuristic algorithm is applied on the cluster labels to identify the Texel, which results in more than one candidate for Texel. The best among them is then chosen based on its similarity with the overall image. The similarity between the Texel and the image is calculated based on then Normalized Gray level co-occurrence matrix in the maximum gradient direction. Experiments are conducted on various texture images for various block sizes and the results are summarized. © 2012 Springer-Verlag GmbH.

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2012

P. K. Indukala, Lakshmi, K., Sowmya V., and Dr. Soman K. P., “Implementation of ℓ 1 magic and one bit compressed sensing based on linear programming using excel”, International Conference on Advances in Computing and Communications, ICACC 2012. IEEE, Kochi, Kerala, pp. 69-72, 2012.[Abstract]


<p>Compressed sensing helps in the reconstruction of sparse or compressible signals from small number of measurements. The sparse representation has great importance in modern signal processing. The main objective is to provide a strong understanding of the concept behind the theory of compressed sensing by using the key ideas from linear algebra. In this paper, the concept of compressed sensing is explained through an experiment formulated based on linear programming and solved using l1 magic and One bit compressed sensing methods in Excel. © 2012 IEEE.</p>

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2012

R. Anand, Prabha, P., Sikha, O. K., Suchithra, M., Dr. Soman K. P., and Sowmya V., “Visualization of OFDM using Microsoft Excel spreadsheet in Linear Algebra Perspective”, International Conference on Advances in Computing and Communications, ICACC 2012. IEEE, Kochi, Kerala, pp. 58-64, 2012.[Abstract]


Orthogonal Frequency Division Multiplexing (OFDM) is one of the leading technology that is ruling the communication field. But unfortunately, it is shrouded in mystery. A good knowledge in Linear Algebra is required to appreciate the technology in a better way. So the work focuses on explaining OFDM system from linear algebra point of view. Also, OFDM model communication system is simulated using Excel which makes ease for anyone experiment with OFDM and understand the underlying principle. The paper aims to provide strong foundation on the concept behind OFDM without the need of having much knowledge in electronics field. © 2012 IEEE.

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2012

P. Prabha, Sikha, O. K., Suchithra, M., Sukanya, P., Sowmya V., and Dr. Soman K. P., “Computation of continuous wavelet transform using microsoft excel spreadsheet”, Proceedings - 2012 International Conference on Advances in Computing and Communications, ICACC 2012. IEEE, Kochi, Kerala, pp. 73-77, 2012.[Abstract]


Wavelet theory has become an essential and significant tool for signal and image processing applied in the analysis of various real time signals. It is thus necessary to include wavelet transform and its application in multifractal analysis as a part of the engineering curriculum. In this paper, we present simple and effective way of computing Continuous Wavelet Transform (CWT) using Microsoft Excel Spreadsheet which serves as an user friendly mathematical tool for beginners. The motivation of this paper is to prove the computational power of excel, using which students can have better understanding of the basic concept behind the computation of Continuous Wavelet Transform. The plot of Continuous Wavelet Transform of Brownian signal computation in Excel is compared with that of the result in the Matlab Toolbox. The singularities present in the signal can be inferred from the wavelet modulus maxima plot. The visual interpretation proves that Excel tool provides computational power comparable to that of the Matlab software. The codes for the implementation of CWT in Excel are available on nlp.amrita.edu:8080/sisp/wavelet/cwt/cwt.xlsm, nlp.amrita.edu:8080/sisp/wavelet/cwt/modmax.xlsm, nlp.amrita.edu:8080/sisp/ wavelet/cwt/thermo.xlsm © 2012 IEEE.

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2012

A. V. Sreedhanya and Dr. Soman K. P., “Secrecy of cryptography with compressed sensing”, Proceedings - 2012 International Conference on Advances in Computing and Communications, ICACC 2012. Cochin, pp. 207-210, 2012.[Abstract]


This paper deals with a new image encryption scheme which employs both compressive sensing and Arnold scrambling method. The compressed sensing(CS) paradigm unifies sensing and compression of sparse signals in a simple linear measurement step. Compressed measurements are scrambled using Arnold transform. So this system provides more security to the data. © 2012 IEEE.

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2012

K. Syama, George, N., Sekhar, S., Neethu, C. S., Manikandan, M. S., and Dr. Soman K. P., “Performance study of active contour model based character segmentation with nonlinear diffusion”, Proceedings - 2012 International Conference on Advances in Computing and Communications, ICACC 2012. Cochin, pp. 118-121, 2012.[Abstract]


In this paper, we present the combined character segmentation algorithm based on the active contour model and nonlinear diffusion techniques. The active contour model is used to perform segmentation of printed characters. The coherence enhancing diffusion technique is proposed to smooth out artifacts and background noises without destroying the edges. The performance of the two character segmentation methods: i) the combined ACM-FGM and CED algorithm, and ii) the ACM-FGM algorithm have been validated using a large scale printed documents in Hindi, Malayalam and Telugu text. The combined algorithm achieves an average segmentation accuracy of 89.08% whereas the ACM-FGM algorithm alone had an average accuracy of 52.63%. The whole character segmentation process time is lesser than that of the ACM-FGM algorithm alone. Experiments show that the combined algorithm provides promising results under scanned documents with different font-size and fond-style characters, and the different artifacts and background noises caused by the aging of the paper and diffusion. © 2012 IEEE.

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2012

N. V. Varghees, Manikandan, M. S., J. Gini, R., and Dr. Soman K. P., “A New Framework to Automatically Select Noise Model for Rician Noise Estimation in MR Images”, Proceedings - 2012 International Conference on Advances in Computing and Communications, ICACC 2012. Cochin, pp. 82-85, 2012.[Abstract]


In this paper, we study a set of histogram and higher-order statistical (HOS) features for automatically identifying the presence of large background in the magnitude MR images. The robustness and discriminative power of each individual feature and combining feature sets are investigated using different MR images including brain, cardiac, breast, spine, stomach and noisy images corrupted by Rician noise with different standard deviations, σ={5,10,15,20,25,30,35}. The performances of the identification approaches are evaluated in terms of sensitivity, specificity, and accuracy. Experimental results obtained on 2544 MR images show that an approach based on the kurtosis and histogram peak ratio (HPR) features outperforms significantly as compared to that of other approaches reported in this work. The proposed approach can be used for selection of distribution model (Rayleigh or Gaussian) for accurate estimation of Rician noise level in MR images having large or little background regions. © 2012 IEEE.

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2012

V. K. Veena, Jyothish Lal G., S. Vishnu Prabhu, S. Sachin Kumar, and Dr. Soman K. P., “A robust watermarking method based on Compressed Sensing and Arnold scrambling”, 2012 International Conference on Machine Vision and Image Processing (MVIP). Publisher: IEEE, Taipei, Taiwan, 2012.[Abstract]


Watermarking is a technique for information hiding, which is used to identify the authentication and copyright protection. In this paper, a new method of watermarking scheme is proposed, which uses both Compressed Sensing and Arnold scrambling method for efficient data compression and encryption. Compressive sensing technique aims at the reconstruction of sparse signal using a small number of linear measurements. Compressed measurements are then encrypted using Arnold transform. The proposed encryption scheme is computationally more secure against investigated attacks on digital multimedia signals.

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2011

B. D Bhushan, Sowmya V., M Manikandan, S., and Dr. Soman K. P., “An Effective Pre-processing Algorithm for Detecting Noisy Spectral Bands in Hyperspectral Imagery”, International Symposium on Ocean Electronics, SYMPOL 2011. IEEE, Kochi, pp. 34 - 39, 2011.[Abstract]


In this paper, we present an effective pre-processing algorithm for band selection approach which is an essential task in hyperspectral image analysis. The pre-processing algorithm is developed based on the average inter-band block-wise correlation coefficient measure and a simple thresholding strategy. Here, the threshold parameter is found based on the standard deviation of the average inter-band block-wise correlation coefficients. The performance of the proposed algorithm is validated using the standard hyperspectral database created by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. By comparing the detected bands with ground-truth annotations, we observed that the proposed algorithm identifies the noisy and water absorption bands in the high-dimensional hyperspectral images. The proposed algorithm achieves the classification accuracy of 94.73%.

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2010

P. J. Antony, Ajith, V. P., and Dr. Soman K. P., “Statistical method for English to Kannada transliteration”, International Conference on Recent Trends in Business Administration and Information Processing, BAIP 2010. Springer, Trivandrum, Kerala, India, pp. 356–362, 2010.[Abstract]


Language transliteration is one of the important area in natural language processing. Machine Transliteration is the conversion of a character or word from one language to another without losing its phonological characteristics. It is an orthographical and phonetic converting process. Therefore, both grapheme and phoneme information should be considered. Accurate transliteration of named entities plays an important role in the performance of machine translation and cross-language information retrieval processes. The transliteration model must be design in such a way that the phonetic structure of words should be preserve as closely as possible. This paper address the problem of transliterating English to Kannada language using a publically available translation tool called Statistical Machine Translation (SMT).This transliteration technique was demonstrated for English to Kannada Transliteration and achieved exact Kannada transliterations for 89.27% of English names. The result of proposed model is compared with the SVM based transliteration system as well as Google Indic transliteration system. More »»

2010

B. D. Bhushan, Sowmya V., and Dr. Soman K. P., “Super resolution blind reconstruction of low resolution images using framelets based fusion”, International Conference on Recent Trends in Information, Telecommunication, and Computing ITC 2010. IEEE, Kochi, Kerala, pp. 100-104, 2010.[Abstract]


In this paper, we propose a fusion technique based on framelets to obtain super resolution image from sub-pixel shifted, noisy, blurred low resolution images. This method has high advantages over all existing methods. A Tight frame filter bank provides symmetry and has a redundancy that allows for approximate shift invariance which leads to clear edges, high spatial information with effective denoising which was lacked in critically sampled discrete wavelet transform. They are also shorter and results in smoother scaling and wavelet functions. The reconstructed super resolution image obtained by this technique has high peak signal to noise ratio (PSNR) and low mean square error (MSE) than that obtained by wavelet based fusion method, which is evident through the experimental results. © 2010 IEEE.

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2010

T. Arathi, Dr. Latha Parameswaran, and Dr. Soman K. P., “A study of reconstruction algorithms in computerized tomographic images”, Proceedings of the 1st Amrita ACM-W Celebration of Women in Computing in India, A2CWiC'10. ACM New York, NY, USA , Coimbatore, 2010.[Abstract]


Computerized tomography is extensively used in the medical imaging field. It has made a revolutionary impact in diagnostic medicine, helping doctors to view the internal organs of the human body to a very high precision, at the same time ensuring complete safety to the patient. This paper is a study of two such reconstruction algorithms, most commonly used in computerized tomography. They are the filtered backprojection algorithm and the fanbeam projection algorithm respectively. A comparison between the performances of the two methods has been carried out using a set of quality metrics. The experimental results and the conclusions drawn are also included. © 2010 ACM.

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2009

Dr. Soman K. P., Menon, A. G., Saravanan, S., and Loganathan, R., “Amrita Morph Analyzer and Generator for Tamil: A Rule based Approach”, Proceedings of Tamil Internet Conference. pp. 239-243, 2009.

2009

R. Peter and Dr. Soman K. P., “Evaluation of SVD and NMF Methods for Latent Semantic Analysis”, International Journal of Recent Trends in Engineering , vol. 1. p. 308, 2009.

2009

A. M Kumar, Dhanalakshmi, V., Dr. Soman K. P., and Rajendran, S., “A Novel Approach for Tamil Morphological Analyzer”, Proceedings of the 8th Tamil Internet Conference . Koeln, Germany, 2009.

Publication Type: Book Chapter

Year of Publication Title

2018

R. Vinayakumar, Poornachandran, P., and Dr. Soman K. P., “Scalable Framework for Cyber Threat Situational Awareness Based on Domain Name Systems Data Analysis”, in Big Data in Engineering Applications, S. Sekhar Roy, Samui, P., Deo, R., and Ntalampiras, S., Eds. Singapore: Springer Singapore, 2018, pp. 113–142.[Abstract]


There are myriad of security solutions that have been developed to tackle the Cyber Security attacks and malicious activities in digital world. They are firewalls, intrusion detection and prevention systems, anti-virus systems, honeypots etc. Despite employing these detection measures and protection mechanisms, the number of successful attacks and the level of sophistication of these attacks keep increasing day-by-day. Also, with the advent of Internet-of-Things, the number of devices connected to Internet has risen dramatically. The inability to detect attacks on these devices are due to (1) the lack of computational power for detecting attacks, (2) the lack of interfaces that could potentially indicate a compromise on this devices and (3) the lack of the ability to interact with the system to execute diagnostic tools. This warrants newer approaches such as Tier-1 Internet Service Provider level view of attack patterns to provide situational awareness of Cyber Security threats. We investigate and explore the event data generated by the Internet protocol Domain Name Systems (DNS) for the purpose of Cyber threat situational awareness. Traditional methods such as Static and Binary analysis of Malware are sometimes inadequate to address the proliferation of Malware due to the time taken to obtain and process the individual binaries in order to generate signatures. By the time the Anti-Malware signature is available, there is a chance that a significant amount of damage might have happened. The traditional Anti-Malware systems may not identify malicious activities. However, it may be detected faster through DNS protocol by analyzing the generated event data in a timely manner. As DNS was not designed with security in mind (or suffers from vulnerabilities), we explore how the vast amount of event data generated by these systems can be leveraged to create Cyber threat situational awareness. The main contributions of the book chapter are two-fold: (1). A scalable framework that can perform web scale analysis in near real-time that provide situational awareness. (2). Detect early warning signals before large scale attacks or malware propagation occurs. We employ deep learning approach to classify and correlate malicious events that are perceived from the protocol usage. To our knowledge this is the first time, a framework that can analyze and correlate the DNS usage information at continent scale or multiple Tier-1 Internet Service Provider scale has been studied and analyzed in real-time to provide situational awareness. Merely using a commodity hardware server, the developed framework is capable of analyzing more than 2 Million events per second and it could detect the malicious activities within them in near real-time. The developed framework can be scaled out to analyze even larger volumes of network event data by adding additional computing resources. The scalability and real-time detection of malicious activities from early warning signals makes the developed framework stand out from any system of similar kind

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1995

Dr. Soman K. P., “Fuzzy Sets and Probability Theory in Reliability and Safety Related Problem”, in Reliability and Safety Analyses under Fuzziness, 1995.

Publication Type: Book

Year of Publication Title

2018

D. Jyothi Ratnam, Dr. M. Anand Kumar, B. Premjith, Dr. Soman K. P., and S. Rajendran, Sense disambiguation of English simple prepositions in the context of English-Hindi machine translation system. Springer Singapore, 2018, pp. 245-268.[Abstract]


In the context of developing a Machine Translation System, the identification of the correct sense of each and every word in the document to be translated is extremely important. Adpositons play a vital role in the determination of the sense of a particular word in a sentence as they link NPs with the VPs. In the context of developing English to Hindi Machine Translation system, the transfer of the senses of each Preposition into the target langue needs done with much attention. The linguistic and grammatical role of a preposition is to express a variety of syntactic and semantic relationships between nouns, verbs, adjectives, and adverbs. Here we have selected the most important and most frequently used English simple prepositions such as ‘at’, ‘by’, ‘from’, ‘for’, ‘in’, ‘of’, ‘on’, ‘to’ and ‘with’ for the sake of contrast. A supervised machine learning approach called Support Vector Machine (SVM) is used for disambiguating the senses of the simple preposition ‘at’ in contrast with Hindi postpositions. © Springer Nature Singapore Pte Ltd. 2018.

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2010

Dr. Soman K. P. and Dr. K. I. Ramachandran, Insight into wavelets: From theory to practice. PHI Learning Pvt. Ltd., 2010.[Abstract]


Wavelet theory has matured and has entered into its second phase of development and evolution in which practitioners are finding newer applications in ever-widening scientific domains such as bio-informatics, computational drug discovery and nano-material simulation. Parallelly, the theory of wavelets got more and more demystified and has become an everyday tool for signal and image processing. Postgraduate courses in mathematics and physics now include a subject on wavelet theory either as a separate... More »»

2009

Dr. Soman K. P., Loganathan, R., and Ajay, V., Machine Learning with SVM and other Kernel methods. PHI Learning Pvt. Ltd., 2009.[Abstract]


Support vector machines (SVMs) represent a breakthrough in the theory of learning systems. It is a new generation of learning algorithms based on recent advances in statistical learning theory. Designed for the undergraduate students of computer science and engineering, this book provides a comprehensive introduction to the state-of-the-art algorithm and techniques in this field. It covers most of the well known algorithms supplemented with code and data. One Class, Multiclass and hierarchical SVMs are included which will help the students to... More »»

2006

Dr. Soman K. P., Dr. Shyam Diwakar, and Ajay, V., DATA MINING: THEORY AND PRACTICE . PHI Learning Pvt. Ltd., 2006.