Dr. Venkataraman D. currently serves as Assistant Professor at Department of Computer Science and Engineering, School of Engineering, Coimbatore Campus. His areas of research include Algorithms, Bioinformatics and Free and Open Source Software Technologies.






Publication Type: Journal Article
Year of Conference Publication Type Title
2016 Journal Article V. Gangothri, Saranya, S., D. Venkataraman, and Panigrahi, B. K., “Engender product ranking and recommendation using customer feedback”, Advances in Intelligent Systems and Computing, vol. 397, pp. 851-859, 2016.[Abstract]

In our day-to-day life we tend to buy products on the Internet. There are plenty of consumer reviews on the Internet. If a customer wants to know about a product, he sees the review and rating of the product given by the product users. In this case we come to know about the importance of rating and review of the product which impacts the product’s market value. This article proposes a framework for calculating an accurate rating using customer feedback. In particular, we first take the consumer review as an input then remove all common words by using the information retrieval concepts like stop word removal and stemming. The next step is parts of speech tagging and finding the opinion word extraction to the rest of the phrases. Then we have to match the keywords with the ontology and finally we develop a probabilistic aspect ranking algorithm to rank the product. We see elaborately about our concept in this article. © Springer India 2016.

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2016 Journal Article D. Venkataraman and Nair, R. Bb, “An approach to identify the depressed people using tweets”, International Journal of Control Theory and Applications, vol. 9, pp. 1087-1093, 2016.[Abstract]

Depression exists as one of the most common form of psychological disorder. It is seen in most of the individuals at some point of time in life. People who are depressed will feel sad, anxious, hopeless, sleepless etc. It creates an impact on both physical and mental health. The outcomes of depression can turn out to be severe if it is left untreated. It can lead an individual to risky behaviour such as drug or alcohol addiction. It can also ruin the relationships, affect the life in the work environment and also make it difficult for an individual to return back to the usual lifestyle. In this paper we provide a brief study on the data that is obtained from twitter and analyze whether the person is depressed or not. © International Science Press.

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2015 Journal Article E. Shehnaz and D. Venkataraman, “Classification of Images Using Active Learning”, International Journal of Applied Engineering Research, vol. 10, pp. 21185-21197, 2015.[Abstract]

Nowadays image classification has become an evolving area. Various methods are used for different classification. In image classification, there are many different machine learning algorithms used. In the database the retrieve images which are stored that is used to find the resemblance in the query image, then the CBIR allows the user to represent a query image. From image database it will retrieve all the images which belongs to a particular category and occurs problem in the search category. To achieve higher image accuracy within less execution time, classification of images is an intricate process which is essential to classify, organize and access them using an efficient, faster and easy way. The main motivation is to examine the performance of algorithm and check whether that algorithm is better suited for classification. By finding the accuracy of the classification, time and cost complexity can be low. The features can be extracted for both training and testing process in different set of images. The visual features of image such as texture, shape, color, etc. is a technique used by CBIR in which the user will search the image from large image database and represent the image in the form of a query according to the request of the user. The main objective is to classify images using active learning. By analyzing the active learning contribution in CBIR, different classification strategies are explained and compared. © Research India Publications.

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2015 Journal Article K. V. Kumar, Reddy, R. R., Balasubramanian, R., Sridhar, M., Sridharan, K., and D. Venkataraman, “Automated recommendation system with feedback analysis”, International Journal of Applied Engineering Research, vol. 10, pp. 22201-22210, 2015.[Abstract]

In today‟s world with the increasing e-commerce and online shopping involved recommendation systems have become a major part of decision making. In domains such as automobiles there are many websites but most of them are not having enhanced recommendation systems to enable easy decision making. Thus we have taken the onus of building a dataset with multiple parameters based on a survey of the communities needs and created a recommendation system using user based and item based collaborative filtering. To take into account the vast majority of people and their opinions we have added internal and external feedback analysis. Feedback analysis is the classification of textual data (comments) and analyzing the sentiment derived from it. We have proposed it at two levels external that is gathering comments from public platforms social media and automobile websites and internal i. e. the feedback taken from users who have been recommended items. We have developed the prototype for the proposed architecture and preliminary evaluation has been done. © Research India Publications.

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2015 Journal Article N. Francis and D. Venkataraman, “Evaluation of local detectors in detection of repetitive patterns in relief and facade images”, International Journal of Applied Engineering Research, vol. 10, pp. 400-404, 2015.[Abstract]

Local feature detectors are playing important role in many computer vision applications. There are number of measures for evaluating the efficiency of local feature detectors. This paper provides a performance evaluation of feature detectors and compares their efficiency in detection of repetitive pattern accurately in relief and façade images. Detection of repetitive patterns become a more challenging task in pattern recognition as well as in computer vision. Accuracy of segmentation of repeating patterns depends on accurate feature detection and matching. In application like 3D reconstruction and image retrieval detection of repetitive structure helps in various ways. Feature detection and pairwise feature matching are the initial steps in the detection of repetitive patterns found in relief and façade images. This paper gives an accurate keypoint detector which makes the detection and segmentation of repetitive patterns easier and robust. Here comparison is done on images using c/c++ language and opencv library. © Research India Publications.

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2015 Journal Article D. Venkataraman, V, G., and S, S., “A Comprehensive review of Recommender system”, International Journal of applied Engineering Research, vol. 10, pp. 13909 – 13919, 2015.
2015 Journal Article E. shenaz and D. Venkataraman, “Performance Analysis of Active learning in image classification”, International Journal of applied Engineering Research, vol. 10, pp. 1957 – 1962, 2015.
2015 Journal Article D. Venkataraman and Nair, R. B., “Big data Initiative: Analysing, Estimating the scarcity of food and determining the diseases”, International Journal of applied Engineering Research, vol. 10, pp. 2053 – 2056, 2015.
2015 Journal Article D. Venkataraman, Harinarayanan, S., Vinay, N., Tallam, V., and Reddy, S. Teja, “GPS and GSM Toll Collection System (GGTCS) for Indian Toll Booths”, International Journal of applied Engineering Research, vol. 10, pp. 2226 – 2229, 2015.
2014 Journal Article L. V. L and D. Venkataraman, “3D Modelling from uncalibrated images – A comparative study”, Indian Journal of Computer Science and Engineering, vol. 5, pp. 15 - 17, 2014.
2014 Journal Article G. Gautam, Sumanth, G., Karthikeyan, K. C., Sundar, S., and D. Venkataraman, “Eye movement based electronic wheel chair for physically challenged persons”, International Journal of Scientific & Technology Research, vol. 3, 2014.[Abstract]

A powered wheel chair is a mobility-aided device for persons with moderate/severe physical disabilities or chronic diseases as well as the
elderly. In order to take care for different disabilities, various kinds of interfaces have been developed for powered wheelchair control; such as joystick
control, head control and sip-puff control. Many people with disabilities do not have the ability to control powered wheel chair using the above mentioned
interfaces. The proposed model is a possible alternative. In this paper, we use the optical-type eye tracking system to control powered wheel chair.
User‘s eye movement are translated to screen position using the optical type eye tracking system. When user looks at appropriate angle, then computer
input system will send command to the software based on the angle of rotation of pupil i.e., when user moves his eyes balls up (move forward), left
(move left), right (move right) in all other cases wheel chair will stop. Once the image has been processed it moves onto the second part, our
microprocessor. The microprocessor will take a USB output from the laptop and convert the signal into signals that will be sent to the wheelchair wheels
for movement. Also, the pressure and object detection sensors will be connected to our microprocessor to provide necessary feedback for proper
operation of the wheelchair system. The final part of the project is the wheelchair itself. The rear wheels will provide forward. The front two wheels will be
used for steering left and right. All four wheels will be connected to our microprocessor that will send signals to control the wheels and thus the overall

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2013 Journal Article S. S. Kecheril, D. Venkataraman, Suganthi, J., and Sujathan, K., “Automated lung cancer detection by the analysis of glandular cells in sputum cytology images using scale space features”, Signal, Image and Video Processing, vol. 9, pp. 851–863, 2013.[Abstract]

Out of all various types of lung cancers, adenocarcinoma is increasing at an alarming rate mainly due to the increased rate of smoking. This work aims at developing a sputum cytology image analysis system which identifies benign and malignant glandular cells. In our proposed system, we develop an automated lung cancer detection system which segments the cell nuclei and classifies the glandular cells from the given sputum cytology image using a novel scale space catastrophe histogram (SSCH) feature. Catastrophe points occur when pairwise annihilation of extrema and saddle happens in scale space. Unusual nuclear texture shows the presence of malignancy in cells, and SSCH-based texture feature extraction from nuclear region is done. From the input high-resolution image, the cellular regions are localized using maximization of determinant of Hessian, nuclei regions are segmented using K-means clustering, and SSCH features are extracted and classified using support vector machine and color thresholding. The experimental results show that the proposed method obtained an accuracy of 87.53 % which is better than Gabor filter-based gray-level co-occurrence features, local binary pattern, and complex Daubechies wavelet-based features. The results obtained are in accordance with the dataset classified by medical experts.

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2013 Journal Article M. S. Sreelakshmi and D. Venkataraman, “Image compression using anti-forensics method”, International Journal of Computer Science Engineering and Application, vol. 3, pp. 81 – 89, 2013.[Abstract]

A large number of image forensics methods are available which are capable of identifying image tampering. But these techniques are not capable of addressing the anti-forensics method which is able to hide the trace of image tampering. In this paper anti-forensics method for digital image compression has been proposed. This anti-forensics method is capable of removing the traces of image compression. Additionally, technique is also able to remove the traces of blocking artifact that are left by image compression algorithms that divide an image into segments during compression process. This method is targeted to remove the compression fingerprints of JPEG compression.

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2013 Journal Article A. Ajay and D. Venkataraman, “A survey on sensing methods and feature extraction algorithms for {SLAM} problem”, CoRR, vol. abs/1303.3605, 2013.[Abstract]

This paper is a survey work for a bigger project for designing a Visual SLAM robot to generate 3D dense map of an unknown unstructured environment. A lot of factors have to be considered while designing a SLAM robot. Sensing method of the SLAM robot should be determined by considering the kind of environment to be modeled. Similarly the type of environment determines the suitable feature extraction method. This paper goes through the sensing methods used in some recently published papers. The main objective of this survey is to conduct a comparative study among the current sensing methods and feature extraction algorithms and to extract out the best for our work.

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2012 Journal Article S. Krishnan and D. Venkataraman, “Restoration of video by removing rain”, International Journal of Computer Science, Engineering and Applications, vol. 2, pp. 19-28, 2012.[Abstract]

The objective is to remove rain from videos without blurring the object. The algorithm helps to devise the system which removes rain from videos to facilitate video surveillance, and to improve the various visionbased algorithms. Rain is a noise that impairs videos and images. Such weather conditions will affect stereo correspondence, feature detection, segmentation, and object tracking and recognition. In video surveillance if any problem is found due to weather conditions the object cannot be tracked well. In this paper we have considered only rain falling in static environment, i.e., the object is not moving.

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2012 Journal Article S. S Kecheril, D. Venkataraman, Suganthi, J., and Sujathan, K., “Segmentation of lung glandular cells using multiple color spaces”, 2, no. 3, pp. 147 – 158, 2012.[Abstract]

Early detection of lung cancer is a challenging problem, the world faces today. Prior to classify glandular cells as malignant or benign a reliable segmentation technique is required. In this paper we present a novel lung glandular cell segmentation technique. The technique uses a combination of multiple color spaces and
various clustering algorithms to automatically find the best possible segmentation result. Unsupervised clustering methods of K-means and Fuzzy C-means were used on multiple color spaces such as HSV, LAB, LUV, xyY. Experimental results of segmentation using various color spaces are provided to show the
performance of the proposed system.

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Publication Type: Conference Paper
Year of Conference Publication Type Title
2015 Conference Paper V. Kavinkumar, Reddy, R. R., Balasubramanian, R., Sridhar, M., Sridharan, K., and D. Venkataraman, “A hybrid approach for recommendation system with added feedback component”, in 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, 2015, pp. 745-752.[Abstract]

With the increasing E-Commerce and online shopping there is a need for recommendation systems which help the customers in decision making and to suggest potential goods of purchase. In domains such as automobiles there are many websites but most of them are not having enhanced recommendation systems to enable easy decision making. Thus we have taken the initiative of building a dataset with multiple parameters based on a survey of the communities needs using potential blogs and created a recommendation system using user based and item based collaborative filtering. In addition to the combined collaborative filtering techniques we propose a framework which includes a feedback analysis to improve the recommendation system. The enhanced model AIDS the customers in decision making. We have proposed the feedback system at two levels. One is external feedback where the comments are gathered from public platforms like social media and automobile websites. The other is internal feedback i.e. the feedback is taken from users who have been provided with recommended items. The opinions extracted from such varied comments broadens the system and results. Our proposed hybrid model with feedback analysis has improvised the current system by providing better suggestions to customers. © 2015 IEEE.

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Publication Type: Conference Proceedings
Year of Conference Publication Type Title
2015 Conference Proceedings S. Neethu and D. Venkataraman, “Stroke Detection in Brain Using CT Images”. Springer India, New Delhi, pp. 379–386, 2015.[Abstract]

Computed tomographic (CT) images are widely used in the diagnosis of stroke. The objective is to find the stoke area from a CT brain image and also improve the visual quality. The proposed algorithm helps to detect the stoke part in the absence of radiologist or doctors. Seed region growing (SRG) technique is the most popular method for segmentation of medical images because of high-level knowledge of anatomical structures in seed selection process. The proposed method consists of three steps: preprocessing, feature extraction, and segmentation. Feature extraction is done based on texture using the Gabor filter, and segmentation is done using SRG algorithm.

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2015 Conference Proceedings K. Bharath Kumar and D. Venkataraman, “Object Detection Using Robust Image Features”, Springer International conference on artificial Intelligence and Evolutionary Algorithms in engineering systems. Springer India, New Delhi, pp. 285–295, 2015.
2012 Conference Proceedings D. Venkataraman, kecheril, S., Suganthi, J., and Sujathan, K., “Scale space based feature Extraction and classification of lung glandular cells”, International conference on Recent Development in Engineering and Technology. pp. 1-4, 2012.
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