Qualification: 
Ph.D, MS
pbsk@cb.amrita.edu

Dr. Palaniappan Bagavathi Sivakumar had joined Amrita in 1996 as Lecturer in the Department of Computer Science & Engineering. He is currently with Coimbatore campus of Amrita Vishwa Vidyapeetham 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 undergraduate and graduate levels. He has published 20 technical papers at various refereed international conferences and journals, delivered talks at various forums and seminars, and 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 also 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.

TEACHING

  • 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
  • Neural Networks
  • Data Mining
  • Software Engineering
  • Software Quality Assurance & Testing
  • Digital Computer Fundamentals
  • Software project Management
  • Internet & Java Programming (Web Tech)
  • Computer Architecture
  • Network Protocols, Management & Security
  • Principles of Management and Object Oriented Analysis & Design. 
  • Pattern Recognition
  • PR & Machine Learning
  • Machine Learning
  • Modern Computer Architecture

Publications

Publication Type: Conference Proceedings

Year of Publication Publication Type Title

2019

Conference Proceedings

K. P. and Dr. Bhagavathi Sivakumar P., “Online Model for Suspension Faults Diagnostics Using IoT and Analytics”, International Conference on Advanced Computing Networking and Informatics. Advances in Intelligent Systems and Computing, vol. 870. Springer, Singapore, pp. 145-154, 2019.

2019

Conference Proceedings

V. S., Dr. Bhagavathi Sivakumar P., and V., A., “Efficient Real-Time Decision Making Using Streaming Data Analytics in IoT Environment”, International Conference on Advanced Computing Networking and Informatics. Advances in Intelligent Systems and Computing, vol. 870. Singapore, pp. 165-173, 2019.

2017

Conference Proceedings

S. Sharad, Dr. Bhagavathi Sivakumar P., and Anantha Narayanan V., “The smart bus for a smart city - A real-time implementation”, 2016 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2016. Institute of Electrical and Electronics Engineers Inc., 2017.[Abstract]


The need for a real-time public transport information system is growing steadily. People want to plan their city commutes and do not like waiting for long hours, nor take a long route to reach their destination. The proposed hardware solution in this paper computes the shortest path to reach the destination in real time and gives that information to the bus driver. Artificial Neural Networks (ANN) is used to give an accurate estimate of the arrival time (ETA) to the commuter by means of an application. ETA to the next stop is communicated to the commuter using the MQTT (Message Queuing Telemetry Transport) protocol, by the hardware mounted on the bus. The proposed solution also adds a fleet management console to the administrators, making them manage and monitor the fleet of buses in real time. The prototype thus developed makes sure the commuting in cities is pleasant, and hassle free. © 2016 IEEE.

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2017

Conference Proceedings

Dr. Shunmuga Velayutham C., S, S., S, R., S, G., Dr. Bhagavathi Sivakumar P., and S, V., “Bayesian nonparametric Multiple Instance Regression”, Proceedings - International Conference on Pattern Recognition. Institute of Electrical and Electronics Engineers Inc., Cancun, Mexico, pp. 3661-3666, 2017.[Abstract]


Multiple Instance Regression jointly models a set of instances and its corresponding real-valued output. We present a novel multiple instance regression model that infers a subset of instances in each bag that best describes the bag label and uses them to learn a predictive model in a unified framework. We assume that instances in each bag are drawn from a mixture distribution and thus naturally form groups, and instances from one of this group explain the bag label. The largest cluster is assumed to be correlated with the label. We evaluate this model on the crop yield prediction and aerosol depth prediction problems. The predictive accuracy of our model is better than the state of the art MIR methods.

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2016

Conference Proceedings

V. Anand R., Dr. Bhagavathi Sivakumar P., and V., S. Dhan, “Forecasting the Stability of the Data Centre Based on Real-Time Data of Batch Workload Using Times Series Models”, 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. 398. Springer India, pp. 579-589, 2016.[Abstract]


Forecasting has diverse range of applications in many fields like weather, stock market, etc. The main highlight of this work is to forecast the values of the given metric for near future and predict the stability of the Data Centre based on the usage of that metric. Since the parameters that are being monitored in a Data Centre are large, an accurate forecasting is essential for the Data Centre architects in order to make necessary upgrades in a server system. The major criteria that result in SLA violation and loss to a particular business are peak values in performance parameters and resource utilization; hence it is very important that the peak values in performance, resource and workload be forecasted. Here, we mainly concentrate on the metric batch workload of a real-time Data Centre. In this work, we mainly focused on forecasting the batch workload using the auto regressive integrated moving average (ARIMA) model and exponential smoothing and predicted the stability of the Data Centre for the next 6 months. Further, we have performed a comparison of ARIMA model and exponential smoothing and we arrived at the conclusion that ARIMA model outperformed the other. The best model is selected based on the ACF residual correlogram, Forecast Error histogram and the error measures like root mean square error (RMSE), mean absolute error (MAE), mean absolute scale error (MASE) and p-value of Ljung-Box statistics. From the above results we conclude that ARIMA model is the best model for forecasting this time series data and hence based on the ARIMA models forecast result we predicted the stability of the Data Centre for the next 6 months.

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2015

Conference Proceedings

S. Sharad, Dr. Bhagavathi Sivakumar P., and Anantha Narayanan V., “A novel IoT-Based energy management system for large scale data centers”, e-Energy 2015 - Proceedings of the 2015 ACM 6th International Conference on Future Energy Systems. Association for Computing Machinery, Inc (ACM New York NY USA), 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. © 2015 ACM.

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2014

Conference Proceedings

D. Bhuvana and Dr. Bhagavathi Sivakumar P., “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 Dr. Bhagavathi Sivakumar P., “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 Dr. Bhagavathi Sivakumar P., “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

Dr. Bhagavathi Sivakumar P. 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

V. P. Vijina and Dr. Bhagavathi Sivakumar P., “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.

2008

Conference Proceedings

R. Rupi and Dr. Bhagavathi Sivakumar P., “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.

Publication Type: Journal Article

Year of Publication Publication Type Title

2018

Journal Article

P. Vijai and Dr. Bhagavathi Sivakumar P., “Performance comparison of techniques for water demand forecasting”, Procedia Computer Science, vol. 143, pp. 258 - 266, 2018.[Abstract]


There is an ever growing demand of water due to the factors like global warming, urbanization and population growth. The situation demands to use more efficient planning which can be attained by technological advancement like Internet of things and smart systems. The cost related to water management system can be optimized by using prediction. The future demand for water could be better modeled with forecasting techniques. A collection of techniques (Artificial Neural Network (ANN), Deep Neural Network (DNN), Extreme Learning Machines (ELM), Least Square Support Vector Machine (LSSVM), Gaussian process regression (GPR), Random Forest (RF), multiple regression have been applied to analyze the performance in water demand forecasting using the common evaluation criteria. The work is aimed at short term prediction using hourly and daily intervals. A good performance was obtained through the ANN model for all short term predictions.

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2017

Journal Article

K. A. Maheshwari and Dr. Bhagavathi Sivakumar P., “Wireless IoT Based Smart Parking Solutions using Sensor Technology in the Context of Planned Smart Cities in India”, Journal of Engineering and Applied Sciences, vol. 12, no. 8, pp. 8302-8308, 2017.[Abstract]


One of the major issues faced in day-to-day life is parking of the vehicles in offices, malls, complexes, multiplexes and other public places. It becomes a tedious task to find first of all where the parking area is and then to find whether parking slots are available or not. As the number of vehicle increases every day, nearly people spent 30-35 min in finding the parking area during peak hours. Smart parking is an envisioned solution for this. With the advancements in wireless internet of things technology it becomes easier to design and implement cost effective solutions for realizing automated smart parking systems especially in developing countries. This becomes extremely relevant especially in the context of planned smart cities in India. This research is motivated on this. A low cost wireless internet of things based smart parking solution has been designed and implemented. The system through a user friendly interface helps in finding the vacant slots. The status of slots are administered and monitored through a server.

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2016

Journal Article

S. Sharad, Dr. Bhagavathi Sivakumar P., and Ananthanarayanan, V., “An Automated System to Mitigate Loss of Life at Unmanned Level Crossings”, Procedia Computer Science, vol. 92, pp. 404 - 409, 2016.[Abstract]


Every life is precious and is worth saving. This paper proposes the design and implementation of a system to mitigate the loss of life at unmanned railway level crossings. This system uses the advancements in Communication, Embedded Systems and Internet of Things to develop a real-time, early warning system for unmanned level crossings across India. The outcome of this work is to provide an audio-visual indication to the commuter warning about an approaching train. The need for such systems and its design implementation and feasibility is discussed in this paper.

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2016

Journal Article

G. R. Ramya and Dr. Bhagavathi Sivakumar P., “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 »»

2016

Journal Article

C. Vishal, V., R. Shivnesh, Kumar, V. Romil, Anirudh, M., Dr. Bhagavathi Sivakumar P., Dr. Shunmuga Velayutham C., 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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2015

Journal Article

K. Nithin D. and Dr. Bhagavathi Sivakumar P., “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 Dr. Bhagavathi Sivakumar P., “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

Dr. Bhagavathi Sivakumar P. 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.

2010

Journal Article

Dr. Bhagavathi Sivakumar P. 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 »»

Publication Type: Conference Paper

Year of Publication Publication Type Title

2018

Conference Paper

K. A. Maheshwari and Dr. Bhagavathi Sivakumar P., “Use of Predictive Analytics Towards Better Management of Parking Lot Using Image Processing”, in Computational Vision and Bio Inspired Computing, Cham, 2018, vol. 28, pp. 774-787.[Abstract]


As more and more smart cities are planned in India, there is a growing need for smart parking and smart transportation. Parking has been identified as a major challenge to traffic network and urban life quality. Already most of the cities are facing the problem of pollution. Due to drivers struggling for finding the parking area, 30{%} of traffic congestion occurs according to industry data. There is also a need for secure, efficient, intelligent and reliable systems that can be used for searching the unoccupied parking facilities, guide towards the parking facilities, and negotiate the parking fee. This would help in the proper management of the parking facility. There is no publically available data on parking in India. This work would be useful in creation of such datasets. Image based model has been proposed to identify the slot occupancy status. A prediction model has also been incorporated in the system to predict the occupancy rate and thereby help the management in better management of parking lots. One of the machine learning method, linear regression is used for predicting the number of car parked every hour. A slot based approach was used and the performances of prediction algorithms were compared.

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2016

Conference Paper

P. Vijai and Dr. Bhagavathi Sivakumar P., “Design of IoT Systems and Analytics in the Context of Smart City Initiatives in India”, in Procedia Computer Science, 2016, vol. 92, pp. 583-588.[Abstract]


The rapid growth of population and industrialization has paved way for the use of technologies like the Internet of Things which gave rise to the concept of smart cities. India as a developing country has a great prospect in developing technologies to make the cities smart. As urbanization occurs the demand for resources and efficient servicing will increase. To achieve this in a smart and efficient way, connected device (IoT) could be used. The possible design of an IoT system based on surveys performed on similar smart solutions implemented has been discussed in this paper. Urbanization and population growth has led to higher demand for resources like water which are of scarce. There is a keen interest from the organizations and government to make proper usage of water. The same can be achieved by proper monitoring and management of water distribution systems. The paper discusses the use of Machine learning techniques to smart city management aspects like smart water management which include water demand forecasting, water quality monitoring and anomaly detection. © 2016 The Authors. Published by Elsevier B.V.

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2016

Conference Paper

M. K. Shahanas and Dr. Bhagavathi Sivakumar P., “Framework for a Smart Water Management System in the Context of Smart City Initiatives in India”, in Procedia Computer Science, 2016, vol. 92, pp. 142-147.[Abstract]


The Internet, invention of the century, has completely revolutionized the world and brought people closer to each other than ever before. The advancement in technologies of computing, communication brings the next generation of Internet, Internet of Things. As the population and urbanization increases, the cities have to transform to Smart Cities which can be achieved with the help of Internet of Things. Water is one of the vital resource for existence of human life and so Smart water management system has a key role in smart city. The paper reviewed different technologies and platforms that are required for a smart environment. An architecture design for Smart water management is proposed and an implementation detail of Smart water monitoring system is discussed. © 2016 The Authors. Published by Elsevier B.V.

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2015

Conference Paper

K. Nithin.D and Dr. Bhagavathi Sivakumar P., “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

V Smrithi Rekha, Divya, N., and Dr. Bhagavathi Sivakumar P., “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.

2014

Conference Paper

D. V. Sagar, Dr. Bhagavathi Sivakumar P., 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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2009

Conference Paper

Dr. Bhagavathi Sivakumar P. 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

Dr. Bhagavathi Sivakumar P., 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

Dr. Bhagavathi Sivakumar P. 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.

Publication Type: Book Chapter

Year of Publication Publication Type Title

2018

Book Chapter

S. Birindha, Ananthanarayanan, V., and Dr. Bhagavathi Sivakumar P., “Smart Energy Management System Based on Image Analytics and Device Level Analysis”, in Computational Vision and Bio Inspired Computing. Lecture Notes in Computational Vision and Biomechanics, vol. 28, Springer, Cham, 2018.[Abstract]


Large educational institutes, organization, and industries face large challenges on energy utilities, consumption and its management strategies. But smart energy management technology solutions help the high energy consumption complexities during while putting the best and greenest foot forward. Smart Energy Management technology solutions, improve and respond quickly to power spikes at times of demand, expedite data gathering, reporting and regulatory compliance, automate services to control operating costs and enable to save energy. Connecting smart meters to data stores requires a reliable, intelligent network. The Smart Energy Management System introduced here deals with a device level analysis that gives information of the device that has caused the peak rise in the total power consumption of the organization. Predictive analysis technique is used on the database to predict the future maximum demand and load balancing technique is applied to reduce the consumption of power from generator source. Therefore the total power consumption from exceeding the maximum demand can be avoided and the maximum demand of the power supply for the organization can be maintained. Further, on application of AI techniques this system control becomes fully automated.

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2015

Book Chapter

J. Anusha, V Smrithi Rekha, and Dr. Bhagavathi Sivakumar P., “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

Dr. Bhagavathi Sivakumar P. 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 »»