Dr. (Col.) P. N. Kumar currently serves as Professor at the department of Computer Science and Engineering, Amrita School of Engineering and Head - Students Affairs, Coimbatore Campus. His areas of research include Data Analytics, Business intelligence and Agent based modeling of Financial Markets.


Publication Type: Journal Article
Year of Publication Publication Type Title
2016 Journal Article M. Anagha R., Aiswarya, V., Subathra, P., and Kumar, P. N., “Browsing Behavioral Analysis Using Topic Modeling”, International Conference Soft Computing Systems”, International Conference Soft Computing Systems”, ICSCS-2016, 2016.
2016 Journal Article A. N., Krishna, M. Vijay, and Kumar, P. N., “Cluster Computing Paradigms – A Comparative study of Evolving Frameworks”, International Conference Soft Computing Systems, ICSCS-2016 , 2016.
2016 Journal Article L. K. Devi, P. Subathra, and Kumar, P. N., “Performance evaluation of sentiment classification using query strategies in a pool based active learning scenario”, Advances in Intelligent Systems and Computing, vol. 412, pp. 65-75, 2016.[Abstract]

In order to perform Sentiment Classification in scenarios where there is availability of huge amounts of unlabelled data (as in Tweets and other big data applications), human annotators are required to label the data, which is very expensive and time consuming. This aspect is resolved by adopting the Active Learning approach to create labelled data from the available unlabelled data by actively choosing the most appropriate or most informative instances in a greedy manner, and then submitting to human annotator for annotation. Active learning (AL) thus reduces the time, cost and effort to label huge amount of unlabelled data. The AL provides improved performance over passive learning by reducing the amount of data to be used for learning; producing higher quality labelled data; reducing the running time of the classification process; and improving the predictive accuracy. Different Query Strategies have been proposed for choosing the most informative instances out of the unlabelled data. In this work, we have performed a comparative performance evaluation of Sentiment Classification in a Pool based Active Learning scenario adopting the query strategies—Entropy Sampling Query Strategy in Uncertainty Sampling, Kullback-Leibler divergence and Vote Entropy in Query By Committee using the evaluation metrics Accuracy, Weighted Precision, Weighted Recall, Weighted F-measure, Root Mean Square Error, Weighted True Positive Rate and Weighted False Positive Rate. We have also calculated different time measures in an Active Learning process viz. Accumulative Iteration time, Iteration time, Training time, Instances selection time and Test time. The empirical results reveal that Uncertainty Sampling query strategy showed better overall performance than Query By Committee in the Sentiment Classification of movie reviews dataset. © Springer Science+Business Media Singapore 2016.

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2015 Journal Article P. N. Kumar and P., B., “Agent based model for market making in emerging stock markets”, International Journal of Applied Engineering Research, vol. 10, pp. 8349-8364, 2015.[Abstract]

Emerging stock markets across the globe are seen to be volatile and also face liquidity problems, vis-à-vis the more matured markets of the developed world. In order to make markets more efficient and an attractive venue for international investors, regulators are required to adopt a suitable market making design. For the purpose of the study on emerging stock markets, the Indian stock market data is analyzed in this paper. Various studies and analyses on the Indian stock markets show that being an emerging market, the volatility of the market is relatively high when compared to that of the other matured developed stock markets like NASDAQ and NYSE. Higher the volatility of a market, higher is the risk involved for investors. Unlike the stock markets of NASDAQ and NYSE, absence of an electronic market maker in the Indian markets appears to be an obvious reason for the prevailing high volatility and this issue is investigated in this paper. The electronic market makers prevalent in the developed markets are generally seen to have a stabilizing effect on the market, apparently reducing volatility to a great extent. This paper demonstrates the suitability of Extended Glosten and Milgrom (EGM) market maker model as the electronic market maker for the Bombay Stock Exchange (BSE) of India. The market maker in the EGM model sets the bid-ask prices based on the orders placed by the traders. It is shown that in the EGM model, the market maker’s quotes reflect the intrinsic value of the stock and any change in the fundamental value (in case of jumps) causes fluctuations in the quotes that are very quickly resolved, thereby bringing stability in the market. The results of the experiments done on the real data from BSE show that, this model can be used as the market maker in the context of any emerging market. This would induce more stability and reduce the volatility, thereby making the market safe for genuine investors. © Research India Publications.

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2015 Journal Article S. Jaysri, Priyadharshini, J., P. Subathra, and Kumar, P. N., “Analysis and performance of collaborative filtering and classification algorithms”, International Journal of Applied Engineering Research, vol. 10, pp. 24529-24540, 2015.[Abstract]

Machine learning is a method which is used to learn from data without any human involvement. Recommendation systems come under Machine learning technique which has become one of the essential systems in our day to day e-commerce internet interaction. Many algorithms are proposed to effectively capture the taste of the users and to provide recommendations accurately. Collaborative filtering is one such successful method to provide recommendation to the users. Classification which also falls under Machine learning technique contains many algorithms which can classify text, numerical data, etc. In this paper, we demonstrate two Collaborative Filtering algorithms viz, User based and Item based recommender systems; and three Classification algorithms viz, Naive-Bayes, Logistic Regression and Random Forest Classification. We analysed the results based on evaluation metrics. Our experiment suggests that in Recommender systems, Item based scores over User based; and in Classification, Naive-Bayes emerges superior. © Research India Publications.

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2015 Journal Article U. Jaswanth and Kumar, P. N., “Big Data Analytics: A Supervised Approach for Sentiment Classification Using Mahout-An Illustration ”, International Journal of Applied Engineering Research (IJAER), vol. 10, pp. 13447-13457, 2015.
2015 Journal Article L. K. Devi, Amrita, R., P., S., and Kumar, P. N., “An Analysis on the Performance Evaluation of Collaborative Filtering Algorithms Using Apache Mahout”, International Journal of Applied Engineering Research (IJAER), vol. 10, pp. 14797-14812, 2015.[Abstract]

Recommendation systems are being widely adopted in many areas which include social networking, e-commerce etc. Long years of research have led to the proposal of many algorithms in order to effectively capture the real tastes of users and deliver the recommendations accurately. Collaborative filtering is considered to be one of the popular and successful approaches to provide recommendations. In this paper, we conduct a performance evaluation of three popular collaborative filtering algorithms viz. User based, Item based and Slope-one recommender. We illustrate a brief overview on the different approaches of collaborative filtering, their method of working, advantages and limitations. We demonstrate the results based on the evaluation metrics precision, recall, f-measure, fallout and reach. Our experiments revealed that the Slope-one approach outperformed the other two approaches based on the evaluation metrics. We also explored different kinds of similarity metrics and highlighted the effect of size of the neighbourhood on the evaluation metrics. Keywords: Collaborative Filtering (CF), Recommendation systems, Apache Mahout, User based CF, Item based CF, Slope one.

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2015 Journal Article S. Abijith, Renu, P., P. Sachin, S., and Kumar, P. N., “A Postulatory Study on Portfolio Optimization Algorithms: A Survey”, Research Journal of Applied Sciences, Engineering and Technology, (RJASET), vol. 11, pp. 988-993, 2015.[Abstract]

A stock represents the capital a company or corporation raises by issuing and subscribing shares. The stock market is a term used to describe the physical location where the buying and selling as well as overall market activity takes place. Companies issue stocks to acquire capital while investors buy them to own a portion of the company. Investors buy stocks with the belief that the company will grow continuously to raise the value of their shares. Every shareholder in a company will have a say on how the company runs. Making investment among various financial enterprises, industries and other categories is associated by a risk factor. Diversification is a technique that is used to mitigate the effects of such risks and creating a portfolio of stocks is the technique used in diversification. In this study, effort has been taken to describe three of the most important portfolio optimization algorithms viz. Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing.

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2010 Journal Article P. N. Kumar, G. Seshadri, R., Hariharan, A., Mohandas, V. P., and Balasubramanian, P., “Financial Market Analysis of Bombay Stock Exchange using an Agent Based Model”, International Journal of Imaging Science and Engineering, vol. 8, 2010.[Abstract]

Returns on stocks have traditionally been modelled by fitting a suitable statistical process to empirical returns. Studies on agent based models of stock market have been carried out by researchers, primarily on US markets. This paper analyzes the empirical features generated using historical data from the Bombay Stock Exchange (BSE), employing the concept of agent based model proposed by LeBaron[2,3,8]. Agent-based approach to stock market considers stock prices as arising from the interaction of a number of individual investors. These investors are modeled as intelligent agents, using differing lengths of past information, each trading with its own rules adapting and evolving over time, and this in turn determines the market prices. It is seen that the model generates some features that are similar to those from actual data of the BSE. More »»
2010 Journal Article P. N. Kumar, G. Seshadri, R., Hariharan, A., Mohandas, V. P., and Balasubramanian, P., “A Methodology for Aiding Investment Decision between Assets in Stock Markets Using Artificial Neural Network”, International Journal of Computer Science Issues (IJCSI ), vol. 7, no. 6, 2010.[Abstract]

This paper outlines a methodology for aiding the decision making process for investment between two financial market assets (eg a risky asset versus a risk-free asset), using neural network architecture. A Feed Forward Neural Network (FFNN) and a Radial Basis Function (RBF) Network have been evaluated. The model is employed for arriving at a decision as to where to invest in the next time step, given data from the current time step. The time step could be chosen on daily/weekly/monthly basis, based on the investment requirement. In this study, the FFNN has yielded good results over RBF. Consequently the FFNN developed enable us make a decision on investment in the next time step between a risky asset (eg the BSE Sensex itself or a single share) versus a riskfree asset (eg Securities like Govt Bonds, Public Provident Funds etc).The FFNN is trained with a set of data which helps in under standing the market behaviour. The input parameters or the information set consisting of six items is arrived at by considering important empirical features acting on real markets. These are designed to allow both passive and active, fundamental and technical trading strategies, and combinations of these. Using just six items simplifies the decision making process by extracting potentially useful information from the large quantity of historic data. The prediction made by the FFNN model has been validated from the actual market data. This model can be further extended to choose between any two categories of assets whose historical data is available. More »»
Publication Type: Conference Proceedings
Year of Publication Publication Type Title
2015 Conference Proceedings A. Ravindran, Kumar, P. N., and P. Subathra, “Similarity Scores Evaluation in Social Networking Sites”, Proceedings of the International Conference on Soft Computing Systems (ICSCS). Springer, 2015.[Abstract]

In today’s world, social networking sites are becoming increasingly popular. Often we find suggestions for friends, from such social networking sites. These friend suggestions help us identify friends that we may have lost touch with or new friends that we may want to make. At the same time, these friend suggestions may not be that accurate. To recommend a friend, social networking sites collect information about user’s social circle and then build a social network based on this information. This network is then used to recommend to a user, the people he might want to befriend. FoF algorithm is one of the traditional techniques used to recommend friends in a social network. Delta-SimRank is an algorithm used to compute the similarity between objects in a network. This algorithm is also applied on a social network to determine the similarity between users. Here, we evaluate Delta-SimRank and FoF algorithm in terms of the friend suggestion provided by them, when applied on a Facebook dataset. It is observed that Delta-SimRank provides a higher precise similarity score because it considers the entire network around a user.

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2010 Conference Proceedings P. N. Kumar, G. Seshadri, R., Hariharan, A., Mohandas, V. P., and Balasubramanian, P., “Agent based Modeling of Financial Markets”, IEEE International Conference on Computational Intelligence and Computing Research (Best paper award), vol. 2. IEEE Explore , 2010.
Publication Type: Conference Paper
Year of Publication Publication Type Title
2015 Conference Paper N. Binoy, Kumar, P. N., R., P. Sidharth, Mudra, S. Lopa, Vijayalakshmi, K., Sai, G. R., and J., R., “Forecasting Short-Term Stock Prices using Sentiment Analysis and Artificial Neural Networks”, in International Conference on Soft Computing in Applied Engineering & Sciences (ICSCASE), 2015.
2008 Conference Paper P. N. Kumar and Isha, T. B., “Inductance calculation of 8/6 switched reluctance motor”, in 2008 Joint International Conference on Power System Technology POWERCON and IEEE Power India Conference, POWERCON 2008, New Delhi, 2008.[Abstract]

This paper describes a method to obtain the magnetic characteristics of an 8/6 switched reluctance machine (SRM). Finite element method (FEM) using ANSYS 10.0 software is used for the purpose. Machine model is created by PRO ENGINEER software for the ANSYS environment. To model the machine using FEM, the geometry and mechanicalparameters of the machine are established. Then the model is developed to obtain the flux linkage, current and rotor position relationship, and inductance is computed. Further, this inductance was verified experimentally and found in agreement with each other. © 2008 IEEE. More »»
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