Qualification: 
Ph.D
s_sachinkumar@cb.amrita.edu

Dr. Sachin Kumar S currently serves as Assistant Professor at center for Computational Engineering and Networking (CEN), Amrita Vishwa Vidyapeetham, Coimbatore

Professional Experience:

Assistant Professor

August 2020 – till date

Centre for Excellence in Computational Engineering and Networking

Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu

Faculty Associate

March 2020 – August 2020

Centre for Excellence in Computational Engineering and Networking

Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu

Data Science Consultant

(Natural Language Processing, Machine Vision System, Bio-Signal Analysis)

August 2019 – Feb 2020

Data Science Engineer

February 2019 – July 2019

Founding Minds, Technopark, Trivandrum, Kerala

Research Assistant

April 2015 – December 2018

Amrita Centre for Cyber Security,

Amrita Vishwa Vidyapeetham, Kollam, Kerala

Research Associate

August 2012 – March 2015

Centre for Excellence in Computational Engineering and Networking

Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu

Junior Research Associate

Sept 2011 – Feb 2012

Centre for Excellence in Computational Engineering and Networking

Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu

Junior Training Officer

August 2009 – November 2009

Centre for Corporate Relations, Amrita Vishwa Vidyapeetham, Kollam, Kerala

Software Engineer

August 2007 – July 2009

Amrita E-learning Research Initiatives, Amrita Vishwa Vidyapeetham, Kollam, Kerala

 

 

Publications

Publication Type: Book Chapter

Year of Publication Title

2021

S. Akshay, Dr. Soman K. P., Dr. Neethu Mohan, and Sachin Kumar S., “Dynamic Mode Decomposition and Its Application in Various Domains: An Overview”, in Applications in Ubiquitous Computing, R. Kumar and Paiva, S., Eds. Cham: Springer International Publishing, 2021, pp. 121–132.[Abstract]


The unprecedented availability of high-fidelity data measurements in various disciplines of engineering and physical and medical sciences reinforces the development of more sophisticated algorithms for data processing and analysis. More advanced algorithms are required to extract the spatiotemporal features concealed in the data that represent the system dynamics. Usage of advanced data-driven algorithms paves the way to understand the associated dominant dynamical behavior and, thus, improves the capacity for various tasks, such as forecasting, control, and modal analysis. One such emerging method for data-driven analysis is dynamic mode decomposition (DMD). The algorithm for DMD is introduced by Peter J. Schmid in 2010 based on the foundation of Koopman operator (Schmid. J Fluid Mech 656:5–28, 2010). It is basically a decomposition algorithm with intelligence to identify the spatial patterns and temporal features of the data measurements. DMD has recently gained improved interest due to its dominant ability to mine meaningful information from available measurements. It has revolutionized the analysis and modeling of physical systems like fluid dynamics, neuroscience, financial trading markets, multimedia, smart grid, etc. The ability to recognize the spatiotemporal patterns makes DMD as prominent among other similar algorithms. DMD algorithm merges the characteristics of proper orthogonal decomposition (POD) and Fourier transform.

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2020

M. Raj Sn, Sachin Kumar S., S, R., and Dr. Soman K. P., “Resolving Polysemy in Malayalam Verbs Using Context Similarity”, in Resolving Polysemy in Malayalam Verbs Using Context Similarity, vol. Applications in Ubiquitous Computing (pp.133-155), 2020.[Abstract]


Generally, verbs are polysemous in any language as their number is lesser than other categories including nouns. Mostly, the meaning of a verb is decided by the words with which it collocates. The contextual dependency of the verbs reduces the number of verbs in most of the languages or all languages. So by default, verbs become highly polysemous. In textual context or spoken context, the polysemy will not create problems as one can infer the correct meaning of a verb by the context of its occurrence. But in computational context, polysemy will be a problem. As machine does not have the knowledge which human brain has, it must be given knowledge by some means to interpret the meaning of a verb correctly. Polysemy is a problem in the interpretation of Malayalam verbs too. Resolving polysemy in Malayalam verb is needed for any NLP activity in Malayalam including machine translation. In machine translation, ambiguity due to polysemy is a crucial problem. This chapter explores all sorts of ambiguity focusing mainly on ambiguity due to polysemy in verbs. It will also explore resolving polysemy in Malayalam verbs using context similarity.

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2020

Dr. Soman K. P., Sachin Kumar S., and Neethu Mohan, “Modern Signal Processing with Linear Algebra and Optimization”, in Modern Signal Processing with Linear Algebra and Optimization, vol. Vol 1 – Linear Algebra for Signal Processing , 2020.

2020

A. S, Soman, K. P., Neethu Mohan, and Sachin Kumar S., “Dynamic Mode Decomposition and Its Application in Various Domains”, Springer, Cham, 2020.[Abstract]


The unprecedented availability of high-fidelity data measurements in various disciplines of engineering and physical and medical sciences reinforces the development of more sophisticated algorithms for data processing and analysis. More advanced algorithms are required to extract the spatiotemporal features concealed in the data that represent the system dynamics. Usage of advanced data-driven algorithms paves the way to understand the associated dominant dynamical behavior and, thus, improves the capacity for various tasks, such as forecasting, control, and modal analysis. One such emerging method for data-driven analysis is dynamic mode decomposition (DMD). The algorithm for DMD is introduced by Peter J. Schmid in 2010 based on the foundation of Koopman operator (Schmid. J Fluid Mech 656:5–28, 2010). It is basically a decomposition algorithm with intelligence to identify the spatial patterns and temporal features of the data measurements. DMD has recently gained improved interest due to its dominant ability to mine meaningful information from available measurements. It has revolutionized the analysis and modeling of physical systems like fluid dynamics, neuroscience, financial trading markets, multimedia, smart grid, etc. The ability to recognize the spatiotemporal patterns makes DMD as prominent among other similar algorithms. DMD algorithm merges the characteristics of proper orthogonal decomposition (POD) and Fourier transform.

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2019

Sachin Kumar S., M. Kumar, A., and Dr. Soman K. P., “Identifying Sentiment of Malayalam Tweets Using Deep Learning”, vol. Digital Business (pp.391-408), 2019.[Abstract]


The current chapter focus on providing a comparative study for identifying sentiment of Malayalam tweets using deep learning methods such as convolutional neural net (CNN), long short-term memory units (LSTM). The baseline methods used to compare are support vector machines (SVM), regularized least square classification with random kitchen sink mapping (RKS-RLSC). Malayalam is a low resource language spoken in Kerala state, India. Due to the unavailability of data, tweets were collected and labeled manually based on its polarity as neutral, negative and positive. RKS mapping is a well explored approach in which data are nonlinearly mapped to higher dimension where linear classifier can be used. The evaluation measure chosen for the experiments are F1-score, recall, accuracy and precision. The experiments also provide a comparison with classical methods such as logistic regression (LR), adaboost (Ab), random forest (RF), decision tree (DT), k-nearest neighbor (KNN) on the basis of accuracy as the measure. For the experiments using CNN and LSTM, we report the effectiveness of activation functions such as rectified linear units (ReLU), exponential linear units (ELU) and scaled exponential linear units (SELU) for the sentiment identification of Malayalam tweets over SVM and RKS-RLSC.

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2019

Neethu Mohan, Sachin Kumar S., and Dr. Soman K. P., “Modern Time-Frequency Techniques for Signal Analysis and its Applications”, vol. Studies in Computational Intelligence, Springer, 2019 , 2019.

2019

Dr. Soman K. P., Sachin Kumar S., Neethu Mohan, and Prabaharan P., “Modern Methods for Signal Analysis and Its Applications”, vol. Studies in Computational Intelligence book series (SCI, volume 823), 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.

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Publication Type: Journal Article

Year of Publication Title

2020

V. Mt, Sachin Kumar S., and Dr. Soman K. P., “Offensive Language Detection: A Comparative Analysis”, 2020.[Abstract]


Offensive behaviour has become pervasive in the Internet community. Individuals take the advantage of anonymity in the cyber world and indulge in offensive communications which they may not consider in the real life. Governments, online communities, companies etc are investing into prevention of offensive behaviour content in social media. One of the most effective solution for tacking this enigmatic problem is the use of computational techniques to identify offensive content and take action. The current work focuses on detecting offensive language in English tweets. The dataset used for the experiment is obtained from SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The dataset contains 14,460 annotated English tweets. The present paper provides a comparative analysis and Random kitchen sink (RKS) based approach for offensive language detection. We explore the effectiveness of Google sentence encoder, Fasttext, Dynamic mode decomposition (DMD) based features and Random kitchen sink (RKS) method for offensive language detection. From the experiments and evaluation we observed that RKS with fastetxt achieved competing results. The evaluation measures used are accuracy, precision, recall, f1-score.

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2020

E. Toth, Sachin Kumar S., Chaitanya, G., Riley, K., Pati, S., and Balasubramanian, K., “Machine learning approach to detect focal-onset seizures in the human anterior nucleus of the thalamus”, Journal of Neural Engineering, vol. 17(6), 2020.[Abstract]


Objective: There is an unmet need to develop seizure detection algorithms from brain regions outside the epileptogenic cortex. The study aimed to demonstrate the feasibility of classifying seizures and interictal states from local field potentials (LFPs) recorded from the human thalamus- a subcortical region remote to the epileptogenic cortex. We tested the hypothesis that spectral and entropy-based features extracted from LFPs recorded from the anterior nucleus of the thalamus (ANT) can distinguish its state of ictal recruitment from other interictal states (including awake, sleep). Approach: Two supervised machine learning tools (random forest and the random kitchen sink) were used to evaluate the performance of spectral (discrete wavelet transform-DWT), and time-domain (multiscale entropy-MSE) features in classifying seizures from interictal states in patients undergoing stereo EEG evaluation for epilepsy surgery. Under the supervision of IRB, field potentials were recorded from the ANT in consenting adults with drug-resistant temporal lobe epilepsy. Seizures were confirmed in the ANT using line-length and visual inspection. Wilcoxon rank-sum (WRS) method was used to test the differences in spectral patterns between seizure and interictal (awake and sleep) states. Main Results: 79 seizures (10 patients) and 158 segments (approx. 4 hours) of interictal stereo EEG data were analyzed. The mean seizure detection latencies with line length in the ANT varied between seizure types (range 5-34 seconds). However, the DWT and MSE in the ANT showed significant changes for all seizure types within the first 20 seconds after seizure onset. The random forest (accuracy 93.9 % and false-positive 4.6%) and the random kitchen sink (accuracy 97.3% and false-positive 1.8%) classified seizures and interictal states. Significance: These results suggest that features extracted from the thalamic LFPs can be trained to detect seizures that can be used for monitoring seizure counts and for closed-loop seizure abortive interventions.

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2020

E. Toth, Sachin Kumar S., Ganne, C., Riley, K. O., Balasubramanian, K., and Pati, S., “Machine learning approach to detect focal-onset seizures in the human anterior nucleus of the thalamus.”, J Neural Eng, 2020.[Abstract]


OBJECTIVE: There is an unmet need to develop seizure detection algorithms from brain regions outside the epileptogenic cortex. The study aimed to demonstrate the feasibility of classifying seizures and interictal states from local field potentials (LFPs) recorded from the human thalamus- a subcortical region remote to the epileptogenic cortex. We tested the hypothesis that spectral and entropy-based features extracted from LFPs recorded from the anterior nucleus of the thalamus (ANT) can distinguish its state of ictal recruitment from other interictal states (including awake, sleep).

APPROACH: Two supervised machine learning tools (random forest and the random kitchen sink) were used to evaluate the performance of spectral (discrete wavelet transform-DWT), and time-domain (multiscale entropy-MSE) features in classifying seizures from interictal states in patients undergoing stereo EEG evaluation for epilepsy surgery. Under the supervision of IRB, field potentials were recorded from the ANT in consenting adults with drug-resistant temporal lobe epilepsy. Seizures were confirmed in the ANT using line-length and visual inspection. Wilcoxon rank-sum (WRS) method was used to test the differences in spectral patterns between seizure and interictal (awake and sleep) states.

MAIN RESULTS: 79 seizures (10 patients) and 158 segments (approx. 4 hours) of interictal stereo EEG data were analyzed. The mean seizure detection latencies with line length in the ANT varied between seizure types (range 5-34 seconds). However, the DWT and MSE in the ANT showed significant changes for all seizure types within the first 20 seconds after seizure onset. The random forest (accuracy 93.9 % and false-positive 4.6%) and the random kitchen sink (accuracy 97.3% and false-positive 1.8%) classified seizures and interictal states.

SIGNIFICANCE: These results suggest that features extracted from the thalamic LFPs can be trained to detect seizures that can be used for monitoring seizure counts and for closed-loop seizure abortive interventions.

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2020

M. T. Vyshnav, Sachin Kumar S., Dr. Neethu Mohan, and Dr. Soman K. P., “Random fourier feature based music-speech classification”, Journal of Intelligent & Fuzzy Systems, vol. 38, pp. 6353 - 6363, 2020.[Abstract]


The present paper proposes Random Kitchen Sink based music/speech classification. The temporal and spectral features such as spectral centroid, Spectral roll-off, spectral flux, Mel-frequency cepstral coefficients, entropy, and Zero-crossing rate are extracted from the signals. In order to show the competence of the proposed approach, experimental evaluations and comparisons are performed. Even though both speech and music signals differ in their production mechanisms, those share many common characteristics such as a common spectrum of frequency and are comparatively non-stationary which makes the classification difficult. The proposed approach explicitly maps the data to a feature space where it is linearly separable. The evaluation results shows that the proposed approach provides competing scores with the methods in the available literature.

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2019

O. K. Sikha, Soman, K. P., and Sachin Kumar S., “VMD-DMD coupled data-driven approach for visual saliency in noisy images”, 2019.[Abstract]


Human visual system is endowed with an innate capability of distinguishing the salient regions of an image. It do so even in the presence of noise and other natural disturbances. Conventional8 computational saliency models in the literature assume that the input images are clean, though an explicit treatment of noise is missing. In this paper, we propose a coupled data-driven approach for estimating saliency map for a noisy input using Variational Mode Decomposition (VMD) and Dynamic Mode Decomposition(DMD. Variational Mode Decomposition (VMD) is a well received technique explored for denoising in the literature. VMD modes with high entropy (randomness) are removed and the residual modes are employed to generate a scalar valued saliency map. The proposed method is compared against seven state-of-the-art methods over a wide range of noise strengths. The submitted approach furnished comparable results with respect to state-of-the art methods for clean and noisy images in terms of various benchmark performance measures.

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2019

S. N. Mohan Raj, Sachin Kumar S., S. Rajendran, and Soman, K. P., “Word sense disambiguation of malayalam nouns”, Studies in Computational Intelligence, vol. 823, pp. 291-314, 2019.[Abstract]


The present study on word sense disambiguation of Malayalam aims at to understand the causes for lexical ambiguity and finding was to resolve the lexical ambiguity. It has been understood that homonymy and polysemy are the reason for creating ambiguity. Here we are concerned with ambiguity due to homonymy. To resolve the ambiguity we propose two approaches: cluster and deep learning approaches. Certain number of ambiguous words is collected with their occurrence in sentences. Cluster approach is a supervised approach involving POS tagging, lemmatization and sense annotation. The context words are identified for each sense of the experimental ambiguous words. A collocational dictionary is prepared based on this. WSD is implemented using the collocational dictionary. The neural network approach is based on deep learning. It is a corpus driven approach in which the necessary information for disambiguating homonymous words is extracted from the corpus itself. The quantity of the corpus used for WSD decides the accuracy of this approach. © Springer Nature Switzerland AG 2019.

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2018

Sachin Kumar S., Kumar, M. A., and Dr. Soman K. P., “Deep Learning Based Part-of-Speech Tagging for Malayalam Twitter Data (Special Issue: Deep Learning Techniques for Natural Language Processing)”, Journal of Intelligent Systems, 2018.[Abstract]


The paper addresses the problem of part-of-speech (POS) tagging for Malayalam tweets. The conversational style of posts/tweets/text in social media data poses a challenge in using general POS tagset for tagging the text. For the current work, a tagset was designed that contains 17 coarse tags and 9915 tweets were tagged manually for experiment and evaluation. The tagged data were evaluated using sequential deep learning methods like recurrent neural network (RNN), gated recurrent units (GRU), long short-term memory (LSTM), and bidirectional LSTM (BLSTM). The training of the model was performed on the tagged tweets, at word level and character level. The experiments were evaluated using measures like precision, recall, f1-measure, and accuracy. During the experiment, it was found that the GRU-based deep learning sequential model at word level gave the highest f1-measure of 0.9254; at character-level, the BLSTM-based deep learning sequential model gave the highest f1-measure of 0.8739. To choose the suitable number of hidden states, we varied it as 4, 16, 32, and 64, and performed training for each. It was observed that the increase in hidden states improved the tagger model. This is an initial work to perform Malayalam Twitter data POS tagging using deep learning sequential models. © 2018 Walter de Gruyter GmbH, Berlin/Boston 2018.

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2018

R. Vinayakumar, Dr. Soman K. P., Poornachandran, P., and Sachin Kumar S., “Evaluating deep learning approaches to characterize and classify the DGAs at scale”, Journal of Intelligent and Fuzzy Systems, vol. 34, pp. 1265-1276, 2018.[Abstract]


In recent years, domain generation algorithms (DGAs) are the foundational mechanisms for many malware families. Mainly, due to the fact that DGA can generate immense number of pseudo random domain names to associate to a command and control (C2) infrastructures. This paper focuses on to detect and classify the pseudo random domain names without relying on the feature engineering or any other linguistic, contextual or semantics and statistical information by adopting deep learning approaches. A deep learning approach is a complex model of traditional machine learning mechanism that has received renewed interest by solving the long-standing tasks in artificial intelligence (AI) related to the field of natural language processing, image recognition, speech processing and many others. They have immense capability to extract optimal feature representations by taking input as in the form of raw input texts. To leverage this and to transfer the performance enhancement in aforementioned areas towards characterize, detect and classify the DGA generated domain names to a specific malware family, this paper adopts deep learning mechanisms with a known one million benign domain names from Alexa, OpenDNS and a corpus of malicious domain names generated from 17 DGA malware families in real time for training in character and bigram level and a trained model has been evaluated on the OSNIT data set in real-time. Specifically, to understand the effectiveness of various deep learning mechanisms, we used 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. Additionally, to find out an optimal architecture, experiments are done with various configurations of network parameters and network structures. All experiments run up to 1000 epochs with a learning rate set in the range [0.01-0.5]. Overall, deep learning approaches, particularly family of recurrent neural network and a hybrid network (where the first layer is CNN and a subsequent layer is LSTM) have showed significant performance with a highest detection rate 0.9945 and 0.9879 respectively. The main reason is deep learning approaches have inherent mechanisms to capture hierarchical feature extraction and long range-dependencies in sequence inputs. © 2018 - IOS Press and the authors. All rights reserved.

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2018

R. Vinayakumar, Dr. Soman K. P., Poornachandran, P., and Sachin 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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2017

M. Raj, Sachin Kumar S., and Rajendran, S., “Resolving Polysemy in Malayalam Verbs”, Language in India, , vol. vol. 17, 2017.[Abstract]


Polysemy in verbs is a challenging problem in linguistics as well as in natural languageprocessing (NLP). Verbs are the most polysemous words among all the grammatical categories.The polysemy leads to word sense ambiguity. Resolving polysemy in verbs requires certain stepsleading to word sense disambiguation (WSD). The paper makes use of the methodologyproposed by Rumshisky Anna (2008). The result of the method is encouraging. Furtherimprovement can be done by making use of other knowledge sources like wordNet, dictionaryand onto-thesaurus.

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2017

O. K. Sikha, Sachin 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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2016

B. Premjith, Sachin Kumar S., 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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2012

Dr. Soman K. P., Sachin Kumar S., Sowmya, and Shajeesh, K. U., “Computational Thinking with Spreadsheet: Convolution, High-Precision Computing and Filtering of Signals and Images”, International Journal of Computer Applications, vol. 60, pp. 1-7, 2012.[Abstract]


Modern day innovations in sciences and engineering are direct outcome of human’s capacity for abstract thinking thereby creating effective computational models of the problems that can be solved efficiently by the number crunching and massive data handling capabilities of modern networked computers. Survival of any economy now depends on innovating-capacity of its citizens. Thus capacity for computational thinking has become an essential skill for survival in the 21st century. It is necessitating a fundamental change in our curriculum in schools. Computational thinking need to be introduced incrementally along with standard content in a way that makes the standard content easier to learn and vice versa. When learners successfully combine disciplinary knowledge and computational methods they develop their identity as Computational Thinkers. The need for trainers, training content and training methodology for imparting computational thinking has become subject of discussion in many international forums. In this article the use of spreadsheet as a tool for developing computational-thinking -capabilities by integrating it with existing curricula is explored. Concept of convolution which everybody uses when one does any multiplication is taken as a vehicle to develop exercises that enhance computational thinking. It is shown how convolution is visualized and implemented and also discussed a wide variety of computational experiments that students at various levels can do with the help of spreadsheet

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