Dr. Palaniappan Bagavathi Sivakumar has joined Amrita in 1996 as Lecturer in the department of Computer Science & Engineering. He is currently with Coimbatore campus of AmritaVishwa Vidyapeetham University and holds the position of Associate Professor and Vice Chairperson of the Department of Computer Science and Engineering. He has guided over 75 projects both at under graduate and graduate levels. He has published 20 technical papers at various refereed international conferences and journals. He has delivered talks at various forums and seminars. He has visited University of Milan, Italy as part of a collaborative project. He has recently visited Hong Kong Polytechnic University and presented his invited paper there in 4th International symposium on Recurrence Plots. His name is included in the 2009-2015 editions of Marquis who’s who in the world. He has received the “Outstanding Educator & Scholar Award” for impeccable contribution to Teaching & Research from National Foundation for Entrepreneurship Development during the 5th Teacher’s Day Awards & Celebrations-2014.

He has been an active member of the following Technical Societies IEEE, IEEE Computer Society, IEEE Computational Intelligence Society, Institution of Electronics and Telecommunications Engineers (IETE), Computer Society of India (CSI), Institution of Engineers, India and Indian Society for Technical Education (ISTE).

His areas of interests include Predictive Analytics, Time Series Analysis and Forecasting, Data Mining, Machine Learning, Pattern Recognition, Computer Vision and Image Processing Applications, Internet of Things and Artificial & Computational Intelligence.

Honors and Awards

 “Outstanding Educator & Scholar Award” for impeccable contribution to Teaching & Research   from National Foundation for Entrepreneurship Development during the 5th Teacher’s Day Awards & Celebrations-2014.

Membership of Scientific and Professional Societies

  • Member, IEEE, USA.
  • Member, IEEE Computer Society
  • Member, IEEE Computational Intelligence Society
  • Member, IEEE Communications Society
  • Life Member CSI – Computer Society of India
  • Life Member ISTE – Indian Society for Technical Education
  • Member IE – India.


  • UG
  • PG
  • Object Oriented Analysis & Design
  • Software Engineering
  • Computer Architecture (Advanced)
  • AI & Expert systems
  • System Software
  • Engineering Economics & Management
  • Digital Electronics
  • Analysis & Design of algorithms
  • Digital Logic Design & Computer Organization
  • Computer Programming
  • Basic Engineering
  • Principles of Management
  • Pattern Recognition
  • Data Mining
  • Soft computing
  • Principles of Management
  •  Software Project Management


Publication Type: Journal Article
Year of Publication Publication Type Title
2016 Journal Article C. Vishal, V., R. Shivnesh, Kumar, V. Romil, Anirudh, M., P. Bhagavathi Sivakumar, Velayutham, C. S., Suresh, L. P., and Panigrahi, B. K., “A crowdsourcing-based platform for better governance”, Proceedings of the International Conference on Soft Computing Systems, Advances in Intelligent Systems and Computing, in L.P. Suresh and B.K. Panigrahi (eds), vol. 397, pp. 519-527, 2016.[Abstract]

The world’s population has been increasing as every year passes by, and Governments across the world face a stupendous challenge of governing each country. These challenges include providing proper sanitation facilities, efficient disaster management techniques, effective resource allocation and management, etc. Crowdsourcing methodologies, which empower the common man to provide valuable information for better decision making, have gained prominence recently to tackle several challenges faced by several governments. In this paper, we introduce a crowdsourcing-based platform that makes use of information provided by the common man for better governance. We illustrate how this platform can be used in several instances to attend to the problems faced by people. © Springer India 2016.

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2016 Journal Article G. R. Ramya and P. Bhagavathi Sivakumar, “Advocacy Monitoring of Women and Children Health through Social Data”, vol. 9, no. 6, 2016.[Abstract]

Background/Objective:To classify the extracted women and children health data from the social media and to utilize it for advocacy monitoring.;Methods/Statistical Analysis:Advocacy monitoring can be performed by extracting the social data related to women and children health. A keyword based search technique is used for this purpose. The children health details like the nutrition deficiencies, lack of vaccination, diseases like pneumonia, diarrhea and malaria that affect new born children and the women health data like maternal weight loss, maternal mortality rate, sanitation and antenatal care during maternity can be gathered from the social media using keyword based search technique. The extracted data are needed to be analyzed and classified into related data groups using Decision tree C4.5 and Support Vector Machine (SVM).Findings:Decision tree C4.5 algorithm classifies the data based on the concept of information entropy. The data are classified at each node of the tree after analyzing the attribute of the data. SVM analyzes the extracted data and uses the health parameters listed to group the related data. The approach is of two stages: training and testing. The training dataset is build using the health data representing the listed search words. This training set is used to classify the test data. The data are tested with the training set and only women and child health data are stored in classes that help in advocacy monitoring in an efficient way.;Applications/Improvements:Advocacy monitoring is required to define the socio-economic status of a region. The proposed approach efficiently classifies the extracted social data of women and children health and aids in effective advocacy monitoring. More »»
2015 Journal Article K. Nithin D. and P. Bhagavathi Sivakumar, “Generic Feature Learning in Computer Vision”, Procedia Computer Science, vol. 58, pp. 202 - 209, 2015.[Abstract]

Abstract Current Machine learning algorithms are highly dependent on manually designing features and the Performance of such algo- rithms predominantly depend on how good our representations are. Manually we might never be able to produce best and diverse set of features that closely describe all the variations that occur in our data. Understanding this, vision community is moving towards learning the optimum features itself instead of learning from the features. Traditional hand engineered features lack in generalizing well to other domains/Problems, are time consuming, expensive, requires expert knowledge on the problem domain and doesn’t facilitate learning from previous learnings/Representations(Transfer learning). All these issues are resolved in learning deep representations. Since 2006 a wide range of representation learning algorithms has been proposed but by the recent success and breakthroughs of few deep learning models, the representation learning algorithms have gained the spotlight. This paper aims to give short overview of deep learning approaches available for vision tasks. We also discuss their applicability (With respect to their properties) in vision field.

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2013 Journal Article H. Renjini and P. Bhagavathi Sivakumar, “Comparison of Automatic and Interactive Image Segmentation Methods”, International Journal of Engineering Research & Technology (IJERT), vol. 2, no. 6, pp. 3162-3170, 2013.[Abstract]

Image segmentation is a challenging research topic and a fundamental problem in the field of image processing. Image segmentation is the process of dividing an image into homogeneous regions. There are two approaches for segmentation: Automatic and Interactive. In Automatic image segmentation there is no need of user interaction whereas in interactive image segmentation it requires a minimal user interaction and can achieve better results than automatic segmentation. This work aims at the study, comparison and implementation of automatic and interactive image segmentation. In this work, different algorithms for segmentation are applied after obtaining the user inputs. Level set algorithm, watershed algorithm and Gaussian mixture model is used. Performance of different segmentation algorithms are evaluated and compared.

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2010 Journal Article P. Bhagavathi Sivakumar and Mohandas, V. Pb, “Performance comparison of attribute set reduction algorithms in stock price prediction - A case study on Indian stock data”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6466 LNCS, pp. 567-574, 2010.[Abstract]

Stock price prediction and stock trend prediction are the two major research problems of financial time series analysis. In this work, performance comparison of various attribute set reduction algorithms were made for short term stock price prediction. Forward selection, backward elimination, optimized selection, optimized selection based on brute force, weight guided and optimized selection based on the evolutionary principle and strategy was used. Different selection schemes and cross over types were explored. To supplement learning and modeling, support vector machine was also used in combination. The algorithms were applied on a real time Indian stock data namely CNX Nifty. The experimental study was conducted using the open source data mining tool Rapidminer. The performance was compared in terms of root mean squared error, squared error and execution time. The obtained results indicates the superiority of evolutionary algorithms and the optimize selection algorithm based on evolutionary principles outperforms others. © 2010 Springer-Verlag. More »»
2010 Journal Article P. Bhagavathi Sivakumar and V.P., M., “Performance Analysis of Hybrid Forecasting models with Traditional ARIMA Models - A Case Study on Financial Time Series Data”, International Journal of Computer Information Systems and Industrial Management Applications (IJCISIM), vol. 2, pp. 187-211, 2010.
Publication Type: Conference Proceedings
Year of Publication Publication Type Title
2015 Conference Proceedings S. Sharad, P. Bhagavathi Sivakumar, and V. Narayanan, A., “A Novel IoT-Based Energy Management System for Large Scale Data Centers”, Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems. ACM, New York, pp. 313-318, 2015.[Abstract]

The high energy consumption in data centers is becoming a major concern because it leads to increased operating costs and also, pollution, as fuel is burnt to produce the required energy. While many techniques and methods have been proposed by various organizations and researchers to minimize the energy consumption, there has been considerably less work done in making a smart-energy management system that is capable of collecting the data available and make decisions based on the energy consumption patterns. In this work, a smart system is proposed that uses Internet of Things to gather data and a machine learning algorithm for decision making.

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2014 Conference Proceedings D. Bhuvana and P. Bhagavathi Sivakumar, “Brain Tumor Detection and Classification in MRI Images using Probabilistic Neural Networks”, In Proceedings of the Second International Conference on Emerging Research in Computing, Information, Communication and Applications (ERCICA-14). Elsevier, pp. 796-801, 2014.
2013 Conference Proceedings N. S. Babu and P. Bhagavathi Sivakumar, “Fusion Techniques For Thermal And Visual Face Images”, International Journal of Engineering Research and Technology, vol. 2(6). ESRSA Publications, pp. 3157-3161, 2013.[Abstract]

Image Fusion is the process of combining relevant information from two or more images into a single image. This study presents a concept of image pixel fusion of visual and thermal faces, which helps to improve the overall performance of a face recognition system. Several factors are there which affect the face recognition performance such as pose variations, facial expression changes, occlusions etc. So, image pixel fusion of thermal and visual images is a solution to overcome the drawbacks present in the individual thermal and visual face images. Human face recognition is a challenging task and its domain of applications is very vast, covering different areas like security systems, defense applications, and intelligent machines.

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2012 Conference Proceedings T. Shajina and P. Bhagavathi Sivakumar, “Human gait recognition and classification using time series Shapelets”, Proceedings - 2012 International Conference on Advances in Computing and Communications, ICACC 2012. Cochin, pp. 31-34, 2012.[Abstract]

<p>Human gait is the main activity of daily life. Gait can be used for applications like human identification (in medical field etc). Since gait can be perceived from a distance it can be used for human identification. Gait recognition means identifying the person with his/her gait. Human identification using gait can be used in surveillance. A method is proposed for gait recognition using a technique which uses time series shapelets. First, for a gait video a preprocessing is done to extract the silhouette images from the video. From these silhouette images features like joint angle and swing distance are extracted which can be represented as the time series data. From this time series data, time series shapelets are extracted. Shapelets are subsequence of time series data which can discriminate between classes. Shapelets are maximally representative of the class. These time series shapelets can be used to identify human by their gait. Shapelets can also be used for classification. After extracting the shapelets, the prediction is done using the decision tree. In that it can be used for classifying normal and abnormal human gait. © 2012 IEEE.</p>

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2011 Conference Proceedings P. Bhagavathi Sivakumar and V.P., M., “Recurrence plot and recurrence quantification analysis as a tool for determining determinism in time series data: An empirical study on Indian stock data”, Proceedings - 4th International symposium on Recurrence Plots. Hong Kong Polytechnic University, Hong Kong, 2011.
2008 Conference Proceedings R. Rupi and P. Bhagavathi Sivakumar, “Gender Identification Using Fingerprint Images”, Proceedings of International Conference on Recent Trends in Computational Science (ICRTCS – 2008) . Toc H Institute of Science and Technology, Kochi, 2008.
2008 Conference Proceedings V. P. Vijina and P. Bhagavathi Sivakumar, “Hybrid Techniques for Image Retrieval”, Proceedings of International Conference on Recent Trends in Computational Science (ICRTCS ). Toc H Institute of Science and Technology, Kochi, 2008.
Publication Type: Book Chapter
Year of Publication Publication Type Title
2015 Book Chapter J. Anusha, V. Rekha, S., and P. Bhagavathi Sivakumar, “Artificial Intelligence and Evolutionary Algorithms in Engineering Systems”, in A Machine Learning Approach to Cluster the users of Stack Overflow Forum, vol. 325, P. L. Suresh, Dash, S. Subhransu, and Panigrahi, K. Bijaya New Delhi: Springer India, 2015, pp. 411–418.[Abstract]

Online question and answer (Q&A) forums are emerging as excellent learning platforms for learners with varied interests. In this paper, we present our results on the clustering of Stack Overflow users into four clusters, namely naive users, surpassing users, experts, and outshiners. This clustering is based on various metrics available on the forum. We use the X-means and expectation maximization clustering algorithms and compare the results. The results have been validated using internal, external, and relative validation techniques. The objective of this clustering is to be able to trace and predict the activity of a user on this forum. According to our results, majority of users (71 % of 40,000 users under consideration) fall in the ‘experts’ category. This indicates that the users in Stack Overflow are of high quality thereby making the forum an excellent platform for users to learn about computer programming.

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2007 Book Chapter P. Bhagavathi Sivakumar and Mohandas, V. P., “Evaluating the Predictability of Financial Time Series - A case study on SENSEX data”, in Innovations and Advanced Techniques in Computer and Information Sciences and Engineering - Edited by Tarek Sobh, Springer, 2007, pp. 99–104.[Abstract]

A discrete- time signal or time series is set of observations taken sequentially in time, space, or some other independent variable. Examples occur in various areas including engineering, natural sciences, economics, social sciences and medicine. Financial time series in particular are very difficult to model and predict, because of their inherent nature. Hence, it becomes essential to study the properties of signal and to develop quantitative techniques. The key characteristics of a time series are that the observations are ordered in time and that adjacent observations are related or dependent. In this paper a case study has been performed on the BSE and NSE index data and methods to classify the signals as Deterministic, Random or Stochastic and White Noise are explored. This pre-analysis of the signal forms the basis for further modeling and prediction of the time series. More »»
Publication Type: Conference Paper
Year of Publication Publication Type Title
2015 Conference Paper K. Nithin.D and P. Bhagavathi Sivakumar, “Learning of Generic Vision Features Using Deep CNN”, in 2015 Fifth International Conference on Advances in Computing and Communications (ICACC), Kochi, 2015.[Abstract]

Eminence of learning algorithm applied for computer vision tasks depends on the features engineered from image. It's premise that different representations can interweave and ensnare most of the elucidative genes that are responsible for variations in images, be it rigid, affine or projective. Hence researches give at most attention in hand-engineering features that capture these variations. But problem is, we need subtle domain knowledge to do that. Thereby making researchers elude epitome of representations. Hence learning algorithms never reach their full potential. In recent times there has been a shift from hand-crafting features to representation learning. The resulting features are not only optimal but also generic as in they can be used as off the shelf features for visual recognition tasks. In this paper we design and experiment with a basic deep convolution neural nets for learning generic vision features with an variant of convolving kernels. They operate by giving importance to individual uncorrelated color channels in a color model by convolving each channel with channel specific kernels. We were able to achieve considerable improvement in performance even when using smaller dataset. More »»
2014 Conference Paper D. V. Sagar, P. Bhagavathi Sivakumar, and Anand, R. V., “Random forest and change point detection for root cause localization in large scale systems”, in IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2014 (Best paper), Coimbatore, 2014.[Abstract]

Identification of root causes of a performance problem is very difficult in case of large scale IT environment. A model which is scalable and reasonably accurate is required for such complex scenarios. This paper proposes a hybrid model using random forest and statistical change point detection, for root cause localization. Based on impurity measure and change in error rates, random forest identifies the features which can be a potential cause for the problem. Since it is a tree based approach, it does not require any prior information about the measured features. To reduce the number of false classifications, a second level of selection using change point analysis is done. The ability of random forest to work well on very large dataset makes the solution scalable and accurate. Proposed model is applied and verified by identifying the root causes for Service Level Objective Violations in enterprise IT systems.

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2014 Conference Paper S. V. Rekha, Divya, N., and P. Bhagavathi Sivakumar, “A Hybrid Auto-tagging System for StackOverflow Forum Questions”, in In Proceedings of the International Conference on Interdisciplinary Advances in Applied Computing (ICONIAAC '14), New York, NY, USA, 2014.
2009 Conference Paper P. Bhagavathi Sivakumar and P., M. V., “Modeling and predicting stock returns using the ARFIMA-FIGARCH”, in Proceedings - 8th International Conference on Computer Information Systems & Industrial Management Applications, 2009.[Abstract]

Modeling of real world financial time series such as stock returns are very difficult, because of their inherent characteristics. ARIMA and GARCH models are frequently used in such cases. It is proven of late that, the traditional models may not produce the best results. Lot of recent literature says the successes of hybrid models. The modeling and forecasting ability of ARFIMA-FIGARCH model is investigated in this study. It is believed that data such as stock returns exhibit a pattern of long memory and both short term and long term influences are observed. Empirical investigation has been made on closing stock prices of S&P CNX NIFTY. The obtained statistical result shows the modeling power of ARFIMA-FIGARCH. The performance of this model is compared with traditional Box and Jenkins ARIMA models. It is proven that, by combining several components or models, one can account for long range dependence found in financial market volatility. The results obtained illustrate the need for hybrid modeling. More »»
1997 Conference Paper P. Bhagavathi Sivakumar, K., P., and R., S., “An Expert system to test the space craft based Onboard Computer”, in Sixth symposium on Intelligent systems, organized by IEEE, Bangalore Section, India, 1997.
1996 Conference Paper P. Bhagavathi Sivakumar and G., G., “Automatic Grammar Acquisition towards autoprogramming”, in First international conference on Educational Computing, EDUCOMP’96, TTTI (Technical Teachers Training Institute), Chandigarh, India, 1996.
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