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

Dr. G. Jeyakumar received his B. Sc. degree in Mathematics in 1994, M.C.A. degree (under the faculty of Engineering) in 1998 from Bharathidasan University, and Ph. D. degree (Computer Science and Engineering) in 2013, from Amrita Vishwa Vidyapeetham University, Tamil Nadu, India. He is currently an Associate Professor and Vice-Chairperson in the department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham University, Tamil Nadu, India. He joined Amrita in the year 2000.

His research interest includes Evolutionary Algorithms and its applications, Artificial Intelligence Techniques and Human Modeling. He has published numerous papers in reputed journals and conference proceedings, out of which majority of the publications are indexed in SCOPUS. He has got the best paper awards for few of his publications. He is reviewer for many reputed international journals (IEEE Transactions, Springer, Elsevier, IOS Press and Taylor&Francis). He has guided many student projects/thesis belong to the courses B.Tech., M.Tech., M.C.A. and M.Phil. and currently guiding Ph.D. scholars, many UG and PG students.

Publications

Publication Type: Conference Proceedings

Year of Publication Title

2020

A. M. and Dr. Jeyakumar G., “Performance Enhancement of Differential Evolution Algorithm with Modified Mutation and Crossover Components”, Proceedings of 4th IEEE International Conference on Computing Methodologies and Communication (ICCMC), Erode. pp. 13-20, 2020.

2020

K. P. V. S. M. S and Dr. Jeyakumar G., “A Deep Learning based System to Predict the Noise (Disturbance) in Audio Files”, Proceedings of 3rd International Conference on Emerging Current Trends in Computing and Expert Technologies (COMET-2020), Chennai, vol. 37. Advances in Parallel Computing, Intelligent System and Computer Technology, pp. 154-160, 2020.

2020

U. Subbiah and Dr. Jeyakumar G., “Soft Computing Approach to Determine Students’ Level of Comprehension Using a Mamdani Fuzzy System”, Intelligent Systems, Technologies and Applications, vol. 1148. Springer Singapore, Singapore, 2020.[Abstract]


Comprehension is a measure of one’s understanding a given piece of information, however extensive or summarized, complex or simple. Self-evaluation of one’s level of understanding is a difficult task, with imprecise boundaries and vague ranges. Typically, educational institutions use examinations to evaluate a student’s understanding of a subject or topic, assigning a mark (i.e., test score) or a grade to the student. Nevertheless, this approach calls for an evaluation prior to an estimation of the extent of student’s understanding. In contrast, this treatise proposes a method to self-evaluate one’s level of understanding, before an assessment, with an intent to highlight the deficient areas in one’s grasp of the concepts that need to be improved, before taking an examination. Using a simple questionnaire, to obtain inputs from the student, and a Mamdani fuzzy inference system (FIS) to process the inputs, the proposed model determines the level of understanding of a student, based on a three-level comprehension guide.

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2020

K. Harsha Saketh and Dr. Jeyakumar G., “Comparison of Dynamic Programming and Genetic Algorithm Approaches for Solving Subset Sum Problems”, Computational Vision and Bio-Inspired Computing, vol. 1108. Springer International Publishing, Cham, 2020.[Abstract]


Albeit Evolutionary Algorithms (EAs) are prominent, proven tools for resolution of optimization problems in the real world, appraisal of their appropriateness in solving wide variety of mathematical problems, from simple to complex, continues to be an active research area in the domain of Computer Science. This paper portrays an evaluation of the relevance of Genetic Algorithm (GA) in addressing the Subset Sum Problem (SSP) of Mathematics and providing empirical results with discussions. A GA with pertinent mutation and crossover operators is designed and implemented to solve SSP. Design of the proposed algorithm are clarified in detail. The results obtained by the proposed GA are assessed among different instances with different initial population by the intermediary solutions obtained and the execution time. This study also adapted the traditional Dynamic Programming (DP) approach, pursuing a bottom-up strategy, to solve the SSP. The findings revealed that the GA approach would be unpreferred on account of its longer execution time.

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2020

K. Vinaya Sree and Dr. Jeyakumar G., “A Computer Vision Based Fall Detection Technique for Home Surveillance”, Computational Vision and Bio-Inspired Computing, vol. 1108. Springer International Publishing, Cham, 2020.[Abstract]


In this modern era where the population and life expectancy are continuously increasing, the demand for an advanced healthcare system is increasing at an unprecedented rate. This paper presents a novel and cost-effective fall detection system for home surveillance which uses a surveillance video to detect the fall. The advantage of the proposed system is that it doesn’t need the person to carry or wear a device. The proposed system uses background subtraction to detect the moving object and marks it with a bounding box. Furthermore, few rules are based on the measures extracted from the bounding box and contours around the moving object. These rules are used with the transitions of a finite state machine (FSM) to detect the fall. It is done using the posture and shape analysis with two measures viz height and speed of falling. An alarm is sent when the fall is confirmed. The proposed approach is tested on three datasets URD, FDD and multicam. The obtained results show that proposed system works with an average accuracy of 97.16% and excels the previous approaches.

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2020

S. Abhishek, Emmanuel, S. Coreya, Rajeshwar, G., and Dr. Jeyakumar G., “A Genetic Algorithm Based System with Different Crossover Operators for Solving the Course Allocation Problem of Universities”, New Trends in Computational Vision and Bio-inspired Computing: Selected works presented at the ICCVBIC 2018, Coimbatore, India. Springer International Publishing, Cham, pp. 149 - 160, 2020.[Abstract]


Applying the popularly known technologies to solve real world problems are common practice among student researcher community, as it brings deeper understanding of the underlying technology for its further study and improvement. This paper aims at applying the Genetic Algorithm (GA) to solve the course allocation problem of educational institutions. The course allocation problem comprises of p number choices given by n numbers of students for m number of courses. Assigning the maximum number of students with their first or second choice of their courses is a cumbersome task. It is a typical optimization problem, which can be solved in ease by the Evolutionary Algorithms (EAs) such as GA. This paper proposes an automated system which uses GA (with five different crossover operators and three different mutation operators) to solve the course allocation system. A comparative study on the results obtained for different crossover operators is performed. The obtained results are verified with a real time data set collected from our University and validated the superiority of the proposed system.

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2019

Dhanya M. Dhanalakshmy, Dr. Jeyakumar G., and C. Velayutham, S., “Analytical Study and Empirical Validations on the Impact of Scale Factor Parameter of Differential Evolution Algorithm”, Pattern Recognition and Machine Intelligence, vol. 11941. Springer International Publishing, Cham, pp. 328-336, 2019.[Abstract]


Differential Evolution (DE) is a popular optimization algorithm in the repository of Evolutionary Algorithm (EAs). The DE algorithm is known for its simple algorithmic structure, which has minimal number (only three) of control parameters. A propitious avenue for enhancement of DE’s performance is making it a self-adaptive algorithm. There exist many algorithms for self-adapting one or more of DE parameters. The self-adaptiveness of any parameter needs critical analysis on the impact of that parameter. This paper analyzes and presents the impact of the parameter - mutation scale factor (F) of DE. Including empirical evidences for understanding the effect of F on the nature of convergence of DE at solving a problem is the novelty of this paper. The experiment includes implementing a set of benchmark functions, with diversified features, using different variants of DE, in order to critically analyze the role of F.

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2017

N. Rubini, Prasanthi, C. V., Subanidha, S., Dr. Vidhya Balasubramanian, and Dr. Jeyakumar G., “An Optimization Framework for Solving RFID Reader Placement Problem Using Differential Evolution Algorithm”, Communication and Signal Processing (ICCSP), 2017 International Conference on. IEEE, Adiparasakthi College of Engineering, 2017.[Abstract]


A RFID (Radio-Frequency Identification) system comprises of an arrangement of RFID readers and RFID tags. A RFID system is known for its quick identification of objects to which the RFID tags are attached. RFID system has been used mainly in industries for identifying and tracking important assets. The most essential property of a RFID system is deploying RFID readers to ensure total coverage of all the RFID tags in an area. The total cost of an RFID system relies mostly on the number of readers. Thus, finding an optimal number of readers and their positions to cover all tags is a standout amongst the most vital issues in a RFID system. This paper proposes a simple simulation strategy for optimal RFID reader placement for a system with RFID readers of circular coverage. This simulation is carried with Differential Evolution (DE) algorithm, which has achieved 100% coverage with optimal number of RFID readers. The experimental set up and the results are explained in this paper.

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2016

Dr. Jeyakumar G., K, R., N, P., D, K., and B, A., “A Prototype for Student Learning Style Modelling Using Felder-Silverman Learning Style Model”, International Conference on Smart Structures & Systems (ICSSS-2016). Chennai, India, pp. 178 – 182, 2016.

2015

S. Nambiar and Dr. Jeyakumar G., “Co-operative Co-evolution Based Hybridization of Differential Evolution and Particle Swarm Optimization Algorithms in Distributed Environment”, Emerging Research in Computing, Information, Communication and Applications: ERCICA 2015, vol. 2. Springer India, New Delhi, pp. 175–187, 2015.[Abstract]


Evolutionary computing algorithms play a great role in solving real time optimization problems. One of the evolutionary computing algorithm is Particle Swarm Optimization algorithm (PSO). The aim of this paper is to propose a model to improve the performance of PSO algorithm. Hybrid models of Particle Swarm Optimization (PSO) algorithm and Differential Evolution (DE) has already proved to be one of the better approaches for solving real world complex, dynamic and multimodal optimization problems. But these models hybridize PSO and DE to form a new serial algorithm. In these serial hybridization models, we are losing the originality of both DE and PSO algorithms since the structure of both the algorithms is being modified to get the hybridized PSO and DE algorithm. In this paper, we develop a model for PSO in distributed environment with improved performance in terms of speed and accuracy. The proposed model is a hybridized distributed mixing of DE and PSO (dm-DEPSO) which improves the performance of PSO algorithm. In this model, algorithms are implemented in a cluster environment to perform co-operative co-evolution. Better solutions are migrated from one node to another in the cluster environment. Co-operative co-evolving model shows better performance in terms of speed and accuracy. The algorithm is applied to a set of eight benchmarking functions and their performance are compared by mean of objective function values, standard deviation of objective function values, success rate, probability of convergence and quality measure.

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2011

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Experimental Study on Recent Advances in Differential Evolution Algorithm”, Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation, vol. 2, In Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation vol. 2011.[Abstract]


The Differential Evolution DE is a well known Evolutionary Algorithm EA, and is popular for its simplicity. Several novelties have been proposed in research to enhance the performance of DE. This paper focuses on demonstrating the performance enhancement of DE by implementing some of the recent ideas in DE's research viz. Dynamic Differential Evolution dDE, Multiple Trial Vector Differential Evolution mtvDE, Mixed Variant Differential Evolution mvDE, Best Trial Vector Differential Evolution btvDE, Distributed Differential Evolution diDE and their combinations. The authors have chosen fourteen variants of DE and six benchmark functions with different modality viz. Unimodal Separable, Unimodal Nonseparable, Multimodal Separable, and Multimodal Nonseparable. On analyzing distributed DE and mixed variant DE, a novel mixed-variant distributed DE is proposed whereby the subpopulations islands employ different DE variants to cooperatively solve the given problem. The competitive performance of mixed-variant distributed DE on the chosen problem is also demonstrated. The variants are well compared by their mean objective function values and probability of convergence.

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2011

Dr. Jeyakumar G. and Shanmugavelayutham, C., “Empirical measurements on the convergence nature of differential evolution variants”, Advances in Computer Science and Information Technology, vol. 131. Springer, pp. 472–480, 2011.[Abstract]


In this paper, we present an empirical study on convergence nature of Differential Evolution (DE) variants to solve unconstrained global optimization problems. The aim is to identify the convergence behavior of DE variants and compare. We have chosen fourteen benchmark functions grouped by feature: unimodal separable, unimodal nonseparable, multimodal separable and multimodal nonseparable. Fourteen variants of DE were implemented and tested on these problems for dimensions of 30. The variants are well compared by their Convergence Speed, Quality Measure and Population Convergence Measure.

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2010

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Analyzing the Explorative power of Differential Evolution Variants on Different Classes of Problems”, Lecture Notes in Computer Science (LNCS-6466), Springer-Verlag, vol. 6466. pp. 95-102, 2010.[Abstract]


This paper is focusing on comparing the performance of Differential Evolution (DE) variants, in the light of analyzing their Explorative power on a set of benchmark function. We have chosen fourteen different variants of DE and fourteen benchmark functions grouped by feature: Unimodal Separable, Unimodal NonSeparable, Multimodal Separable and Multimodal NonSeparable. Fourteen variants of DE were implemented and tested on these fourteen functions for the dimension of 30. The explorative power of the variants is evaluated and analyzed by measuring the evolution of population variance, at each generation. This analysis provides insight about the competitiveness of DE variants in solving the problem at hand.

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2010

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “A comparative study on theoretical and empirical evolution of population variance of differential evolution variants”, Simulated Evolution and Learning. Springer, pp. 75–79, 2010.[Abstract]


In this paper we derive theoretical expressions to compute expected population variance for Differential Evolution (DE) variants – DE/best/1/bin, DE/rand/2/bin and DE/best/2/bin by directly extending Zaharie’s work on DE/rand/1/bin. The study includes comparing the theoretical and empirical evolution of population variance of three DE variants. This work provides insight about the explorative power of the variants and explains their behavior.

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2009

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “An empirical comparison of differential evolution variants on different classes of unconstrained global optimization problems”, World Congress on Nature & Biologically Inspired Computing, NaBIC. IEEE, Coimbatore, pp. 866-871, 2009.[Abstract]


This paper presents an empirical analysis of the performance of differential evolution (DE) variants on different classes of unconstrained global optimization benchmark problems. This analysis has been undertaken to identify competitive DE variants which perform reasonably well on a range of problems with different features. Towards this, fourteen DE variants were implemented and tested on 14 high dimensional benchmark functions grouped by their modality and decomposability viz., unimodal separable, unimodal nonseparable, multimodal separable and multimodal nonseparable. This extensive performance analysis provides some insight about the competitiveness of DE variants in solving test problems with representative landscape features such as modality and decomposability. More »»

2009

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “A comparative performance analysis of differential evolution and dynamic differential evolution variants”, Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on. IEEE, Coimbatore, pp. 464-468, 2009.[Abstract]


In this paper we present an empirical, comparative performance, analysis of fourteen variants of Differential Evolution (DE) and Dynamic Differential Evolution (DDE) algorithms to solve unconstrained global optimization problems. The aim is to compare DDE, which employs a dynamic evolution mechanism, against DE and to identify the competitive variants which perform reasonably well on problems with different features. The fourteen variants of DE and DDE are benchmarked on 6 test functions grouped by features - unimodal separable, unimodal nonseparable, multimodal separable and multimodal non-separable. The analysis identifies the competitive variants and shows that DDE variants consistently outperform their classical counter parts. More »»

2005

Dr. Jeyakumar G. and T., S., “High Performance Query Processing and Optimization on Parallel Machine”, Proceedings - National conference on “New Trends in Distributed Computing and Networking, organized by the department of CSE. Tagore Engineering College, Chennai, 2005.

Publication Type: Conference Paper

Year of Publication Title

2020

N. Krishnnan, S. Ahmed, Ganta, T., and Dr. Jeyakumar G., “A Video Analytics Based Solution for Detecting the Attention Level of the Students in Class Rooms”, in 2020 10th International Conference on Cloud Computing, Data Science Engineering (Confluence), 2020.[Abstract]


Classroom surveillance, using video cameras, affords enhanced understanding of student behavior. This paper proposes a new algorithmic framework to evaluate the attention level of students, from classroom videos. The live video of a class room, when a teacher is delivering the lecture, is the input to the proposed framework. This framework identifies the key frames from the video and then detects the attention level of a particular student. The paper perused the Structural Similarity Index Method (SSIM) to discern key frames in a video. Detection of drowsiness is then performed to deduce whether or not the student is sleepy. Scrutiny of facial expressions is carried out, to perceive the psychological state of the student in the classroom. Finally, detection of gaze is carried out to examine whether or not the student's attention is on the black board. The algorithmic design for the proposed approach, the results obtained and the sample test cases are presented in this paper.

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2020

K. S. Raj, Nishanth, M., and Dr. Jeyakumar G., “Design of Binary Neurons with Supervised Learning for Linearly Separable Boolean Operations”, in Computational Vision and Bio-Inspired Computing, Cham, 2020.[Abstract]


Though the Artificial Neural Network is used as a potential tool to solve many of the real-world learning and adaptation problems, the research articles revealing the simple facts of how to simulate an artificial neuron for most popular tasks are very scarce. This paper has in its objective presenting the details of design and implementation of artificial neurons for linearly separable Boolean functions. The simple Boolean functions viz AND and OR are taken for the study. This paper initially presents the simulation details of artificial neurons for AND and OR operations, where the required weight values are manually calculated. Next, the neurons are added with learning capability with perceptron learning algorithm and the iterative adaptation of weight values are also presented in the paper.

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2019

T. Aravind, Reddy, B. S., Avinash, S., and Dr. Jeyakumar G., “A Comparative Study on Machine Learning Algorithms for Predicting the Placement Information of Under Graduate Students”, in 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2019.[Abstract]


As Machine Learning (ML) algorithms are becoming popular to solve challenging and interesting real world prediction problems around us, the interest level of student community has been increased in learning the principles of ML and its different algorithms. This includes by implementing the commonly known machine learning algorithms and tests them by solving simple prediction problems around the student community present in educational system. In this line, this paper proposes to solve the student placement prediction problem using linear regression model, K-neighbor regression model, decision tree regression model, XGBoost regression model, gradient boost regression model, light GBM regression model and random tree classifier model. This work is carried out in two phases. The Phase 1 is done on a simple data set and the Phase 2 is done with an extended data set with added additional features about the students. This research work presents the comparative performance analysis of these seven models by implementing them with these two data sets. The performance measurements considered in this study are prediction accuracy and the root mean square error (RMSE).

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2019

D. Srivatsa, Teja, T. P. V. Kris, Prathyusha, I., and Dr. Jeyakumar G., “An Empirical Analysis of Genetic Algorithm with Different Mutation and Crossover Operators for Solving Sudoku”, in Pattern Recognition and Machine Intelligence, Cham, 2019.[Abstract]


Prospective optimization tools such as Evolutionary Algorithms (EAs), are widely used to tackle optimization problems in the real world. Genetic Algorithm (GA), one of the instances of EAs, has potential research avenues of testing its applicability in real-world problems and improving its performance. This paper presents a study on the capability of the Genetic Algorithm (GA) to solve the classical Sudoku problem. The investigation includes various mutations and crossover schemes to unravel the Sudoku problem. A comparative study on the performance of GA with these schemes was conducted involving Sudoku. The findings reveal that GA is ineffective to deal with the Sudoku problem, as compared to other classical algorithms, as it often fails to disengage itself from some local optimum condition. On a positive note, GA was able to solve the Sudoku problems much faster, only the Sudoku had very few unfilled elements. A critical appraisal of the observed behavior of GA is presented in this paper, covering combinations of two mutations and three crossovers schemes.

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2019

S. S. Shinde, Dr. Thangavelu S., and Dr. Jeyakumar G., “Evolutionary Computing Approaches for Solving Multi-Objective and Many-Objective Optimization Problems: A Review”, in 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA), 2019.[Abstract]


In today world, there exists different algorithmic approaches in Computer Science to solve the complex real-world optimization problems around us. Based on the number of objectives they are classified as single-objective, multi-objective or many-objective optimization problems. Though many approaches are available for solving single-objective optimization problems, they are not directly scalable to solve Multi-objective and Many-objective optimization problems. However, the Evolutionary Computing (EC) based approaches are becoming most popular and common to solve all the types of optimization problems, due to their simplicity and wide applicability. This paper presents the details of the Evolutionary Algorithms (EAs) to solve the optimization problem with the category of EAs for Multi-objective and EAs for Many-Objective optimization problems. Insight about the Hybridization of EAs is also added. The details about the algorithmic components required for designing EAs is also presented along with the Benchmarking Problems and Performance Metrics available to validate the newly designed EAs.

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2018

K. Gokul, Pooja, R., and Dr. Jeyakumar G., “Empirical Evidences to Validate the Performance of Self-Switching Base Vector Based Mutation of Differential Evolution Algorithm”, in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2018.[Abstract]


There exist many tools in Computer Science to solve the optimization problems around us. One such tool is the set of algorithms known as Evolutionary Algorithms (EAs) which is under the Evolutionary Computing (EC) field. The Differential Evolution (DE) algorithm in the set of EAs is known for its unique mutation scheme. There are many research works in the literature to further study and modify this scheme to propose new mutation schemes. This paper presents detailed and extensive empirical evidences for the Self-Switching Base Vector Selection base mutation scheme (termed as DE/randorbest/1) found in the literature. The results for our validation are obtained by running the DE algorithm for all possible values of its control parameters (Mutation Step Size (F) and Crossover Rate (Cr)) on a Benchmarking-Function suite. The obtained results are compared by the performance metrics: Average Solution Accuracy (ASA) and Success Rate (SR).

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2018

Dr. Jeyakumar G. and Nagarajan, R., “Algorithmic Approaches for Solving RFID Reader Positioning Problem with Simulated and Real-time Experimental Setups”, in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2018.[Abstract]


The RFID (Radio Frequency Identification System) systems consist of two components - RFID readers and tags. The RFID systems are generally used to locate and track people and assets, in and around a building. The cost of installation of a RFID system and its efficiency in locating and tracking mainly depends on positioning required number of RFID readers at appropriate positions. This positioning problem is an optimization problem, as it involved finding a strategy (number of readers and positions) to place minimum number of readers to cover all the tags. This paper proposes to solve the RFID sensor placement problem as an optimization problem designing an optimization framework uses two algorithmic approaches: Evolutionary Approach and Greedy Approach. Both the algorithmic approaches are, initially, implemented in a simulated environment and finally verified in a real-time RFID system used for a class-room application in our University. The results and observations from the simulation and experimental studies are reported in this paper.

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2018

K. Devika and Dr. Jeyakumar G., “Solving Multi-Objective Optimization Problems using Differential Evolution Algorithm with Different Population Initialization Techniques”, in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2018.[Abstract]


The researchers of Evolutionary Computing (EC) community proposing new and different algorithmic strategies to tackle the increasing issues in handling optimization problems. As the number of objectives in an optimization problem increases the algorithmic complexity in solving the problem also increases. The way the initial population for an optimization problem generated is greatly affecting the performance of the Evolutionary Algorithms (EAs). This paper investigates the performance of Differential Evolution (DE) in solving Mutli-Objective optimization problems (MOOP) with two different population initialization (PI) techniques. The performance of different instances of DE is compared based on the solution accuracy obtained. The results obtained shows that DE shows different performance for different PI techniques.

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2018

Dr. Jeyakumar G. and K, S., “Personalized Courseware Construction using Association Rules with Differential Evolution Algorithm”, in In proceeding of 2018 International Conference on Advances in Computer Science, Engineering and Technology, Kalasalingam University, Madurai, 2018.[Abstract]


The learning style of individual students differs from each other. Hence the common way of teaching a course for all the students with same course material, in the existing education system, becoming unsuccessful for the present generation of student community. As a solution to this problem constructing different courseware based on the learning style of the students is an open and challenging research problem. This paper proposes an approach for personalized courseware construction by integrating the Evolutionary Computing (EC) approach with a Data Mining (DM) technique. This proposed approach uses Differential Evolution (DE) algorithm for generating association rules for student learning style models so that relevant materials can be provided for the courseware based on the students‟ requirements and interest. The sample data from FelderSilverman learning style is used for forming rules using DE to extract useful information for courseware recommendation. This paper presents this proposed approach in detail.

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2017

S. Raju, Dr. Jeyakumar G., Mukherji, A., and Thanki, J. K., “Time synchronized diagnostic event data recording based on AUTOSAR”, in 2017 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Bhubaneswar, India, 2017.[Abstract]


An in-vehicle fault diagnostics system includes several data logging devices that are distributed across the vehicular network. For an off-board analysis, it is important to know the trigger time of the faults. This paper provides a mechanism to establish a temporal relationship of the logged fault data across the vehicle, thereby enabling offline analysis. To support this, the fault data needs to be timestamped with an accurate system-wide reference time. For the purpose of establishing a global reference time within the vehicular network, the time synchronization mechanism as defined by Automotive Open System Architecture (AUTOSAR) is conceptualized in this paper. A reference implementation of the proposed system is analyzed and validated, where a sample diagnostic event is timestamped with the global time base.

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2017

K. Thomas Abraham, Ashwin, M., Sundar, D., Ashoor, T., and Dr. Jeyakumar G., “Empirical Comparison of Different Key Frame Extraction Approaches with Differential Evolution Based Algorithms”, in The International Symposium on Intelligent Systems Technologies and Applications, Manipal University, 2017.[Abstract]


Key frame extraction is an integral part of video analytics. The extracted key frames are used for video summarization and information retrieval. There exist many approaches for solving key frame extraction problem in video analytics. The focus of this paper is to extend the strategy of integrating Evolutionary Computing technique with a conventional key frame extraction approach, which is proposed by the authors in their previous work, with two other conventional approaches. The conventional approaches considered in this study are SSIM (Structural Similarity Index Method) Method, Entropy Method and Euclidean Distance method. This paper also proposes a new approach for key frame extraction by integrating the Euclidean Distance method with Differential Evolution algorithm. The proposed approach is compared with all the existing approaches by its speed and accuracy. It is found from the comparison that the proposed approach outperforms other approaches. The results and discussion related to this experiment study are presented in this paper.

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2017

K. Gokul, Pooja, R., Gowtham, K., and Dr. Jeyakumar G., “A self-switching base vector selection mechanism for differential mutation of differential evolution algorithm”, in Communication and Signal Processing (ICCSP), 2017 International Conference on, Chennai, India, 2017.[Abstract]


The Evolutionary Computing (EC) field of Computer Science has a pool of potential optimization algorithms. They are collectively termed as Evolutionary Algorithms (EAs). The Differential Evolution (DE) algorithm is added recently to this pool. It is known for its simplicity and applicability. DE differs from other EAs by its Differential Mutation logic. There exist many strategies for this mutation logic. This paper proposes a new mutation strategy which employs a self-switching base vector selection mechanism. This self-switching mechanism uses the diversity in the population as a measure to select the base vector either randomly or the best candidate in the population. It is found from the results of the experiments carried out that the proposed mechanism works better on a standard set of benchmarking functions solving unimodal optimization problems.

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2017

K. Thomas Abraham, Ashwin, M., Sundar, D., Ashoor, T., and Dr. Jeyakumar G., “An evolutionary computing approach for solving key frame extraction problem in video analytics”, in Communication and Signal Processing (ICCSP), 2017 International Conference on, Chennai, India, 2017.[Abstract]


Key frame extraction is a very important task in video analytics. The extracted key frames are used for video summarization and event detection for the given videos. This paper proposes a new algorithmic framework which combines the conventional SSIM (Structural Similarity Index) approach with the state-of-the-art Differential Evolution (DE) algorithm for key frame extraction from video data. The proposed algorithm is implemented to extract key frames from three different videos and its performance is compared with the conventional approach using the average SSIM values. The results show that DE based approach gives comparatively better results when compared to the naive SSIM approach.

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2017

N. Rubini, Prasanthi, C., Subanidha, S., Vamsi, T. N. S., and Dr. Jeyakumar G., “An optimization framework for solving RFID reader placement problem using greedy approach”, in 2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017, Manipal University, 2017, vol. 2017-January, pp. 900-905.[Abstract]


A RFID (Radio-Frequency Identification) system consists of RFID readers and tags.RFID technology uses electromagnetic fields to identify the location of the objects attached with the tag and track them continuously. The tags contain electronically stored information about the objects. The most essential requirement for setting up a RFIDenabled system is to deploy (locating and positioning) the RFID readers in order to ensure total coverage of all the RFID tags in an area. Finding an optimal number of readers required and their positions to cover all the tags is an important problem to be solved. Hence, this paper proposes a simple simulation strategy for finding optimal number of RFID readersand their positions for circular coverage using Greedy approach with 100% coverage of tags. The experimental set up and the results are explained in this paper. © 2017 IEEE.

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2017

K. C. Haritha, Dr. Jeyakumar G., and Dr. Thangavelu S., “Image fusion using evolutionary algorithms: A survey”, in 2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017, Sri Eeshwar College of Engineering, 2017.[Abstract]


This paper is mainly intended to compare image fusion method using different evolutionary algorithms and a comparison between these methods. The survey focuses on region-based fusion techniques, which is a major area of research. The paper compares image fusion processes using various evolutionary algorithms and illustrates the advantages and disadvantages of these algorithms. This survey illustrates that a method of image fusion can also be included in the DE optimization stage with the block size optimization. Finally, it is concluded that spatial frequency can be used as the sharpness criterion and Evolutionary algorithms perform better in block size optimization. © 2017 IEEE.

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2016

K. Sreenath and Dr. Jeyakumar G., “Evolutionary algorithm based rule(s) generation for personalized courseware construction in educational data mining”, in 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, India, 2016.[Abstract]


Evolutionary Computing for Educational Data Mining is a research field which with the applications of Evolutionary Algorithms (EAs) to mine, analyze and modify educational data. This paper presents the most relevant studies conducted in this research area. The paper also describes different EAs used for implementing different data mining techniques. It goes on to list how these algorithms are utilized by different educational users to carry out different tasks. Finally, a new combination of EA, Educational User and data mining technique is suggested for implementation. As a part of that a personalized courseware construction technique is proposed and a sample courseware is constructed using the proposed technique. The details about the rule construction and the data mining process involved in the courseware construction techniques are also explained. More »»

2016

V. Seshadri, Sudheesh, P., Dr. Jeyakumar G., and Dr. Jayakumar M., “Tracking the Variation of Tidal Stature using Kalman Filter”, in Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2016, 2016.[Abstract]


The intent of this paper is to track the height of a tidal wave, using the Kalman filter. By using the Kalman filter algorithm, mathematical expressions are derived to determine the height of a tidal wave. By placing buoy sensors at specific locations in the sea, the real tidal wave height is measured. The buoy sensor is placed at a particular distance from the shore. The sensors continuously record data at that particular position at different time intervals and then transmit the data to the receiver on the shoreline. By continuously evaluating this data, the height of the next wave is being estimated. Since a buoy cannot be placed at every point of the wave, this method provides an easy estimation of replicating the process. These sensors are used to simulate the proposed method of tracking the height of a tidal wave and hence giving a warning in advance in case of a wave height which is more than normal. This warning helps people living in coastal areas to vacate the place in advance, therefore avoiding fatality. This tracking of the tidal wave height is useful particularly in the case of a tsunami. By adding Gaussian white noise to the input data from the buoy sensors, a prediction of the next wave height is possible.

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2015

and Dr. Jeyakumar G., “Control Parameter Adaptation Strategies for Mutation and Crossover Rates of Differential Evolution Algorithm – An Insight”, in In Proceedings of IEEE International Conference on Computational Intelligence and Computing Research (ICCIC-2015), , 2015.

2014

S. .V, R, R. R., and Dr. Jeyakumar G., “Learning Interest Curve generation using PECS Agent based Model”, in International Conference on Communication and Computing (ICC2014), Proceedings : Computer Networks and Security, BANGALORE, India, 2014.

2009

Dr. Jeyakumar G. and Shanmugavelayutham, C., “An empirical comparison of differential evolution variants for high dimensional function optimization”, in Intelligent Agent & Multi-Agent Systems, 2009. IAMA 2009. International Conference on, Chennai, 2009.[Abstract]


In this paper, we present an empirical comparison of some differential evolution (DE) variants to solve high dimensional optimization problems. The aim is to identify the behavior and scalability of DE variants. Most studies on DE are obtained using low-dimensional problems (smaller than 100) , which are relatively small for many real-world problems. We have chosen four problems grouped by feature: unimodal and separable, unimodal and nonseparable, multimodal and separable, and multimodal and nonseparable. Fourteen variants were implemented and tested on four benchmark problems for dimensions of 30, 100, 500 and 1000. The value for the parameter CR is decided based on a bootstrap test conducted for 30 dimensions, and the same CR value is adopted for the dimensions 100, 500 and 1000 also. The analysis is done based on the results obtained for 100 runs, for each variant-function-dimension combination. More »»

2007

Dr. Jeyakumar G. and S., T., “An Optimized Approach for Solving The 8 Queens Problem Using the Methodology Of Evolutionary Algorithms”, in Proceedings – the 3rd national conference on High Performance Computing, CSE, Government College of Engineering, Tirunelveli, 2007.

2005

S. T. and Dr. Jeyakumar G., “Network Based Secured Architecture for Electronic Commerce Using Agents”, in Proceedings - National conference on Network Engineering, organized by TIFAC-CORE, Arulkigu Kalasalingam College of Engineering , Krishnankoil, 2005.

2005

R. V., Dr. Jeyakumar G., and T., S., “VLSI Based Implementation of Web Enabled Camera Using Embedded Web Server”, in Proceedings - National Conference on Network Security, CSI, Rajagiri College of Social Science, Kochi, 2005.

Publication Type: Journal Article

Year of Publication Title

2020

Dhanya M. Dhanalakshmy, Akhila, M. S., Vidhya, C. R., and Dr. Jeyakumar G., “Improving the search efficiency of differential evolution algorithm by population diversity analysis and adaptation of mutation step sizes”, International Journal of Advanced Intelligence Paradigms, vol. 15, no. 2, p. 119, 2020.[Abstract]


The aim of this research work is to improve the efficiency of differential evolution (DE) algorithm, at the cases of its unsuccessful searches. Initially, this work discusses and compares different methods to measure the population diversity of DE algorithm implemented for DE/rand/1/bin variant for a set of benchmarking functions. A method which well demonstrates difference in population diversity evolution at successful and unsuccessful cases of DE search is identified based on comparison. This work is then extended to detect unsuccessful searches in advance using the evolution of population diversity measured by the identified method. On detecting a search as unsuccessful, a parameter adaptation strategy to adapt the mutation step size (F) is added to DE algorithm to recover from it. The improved DE algorithm, which comprises of the logic of adapting F value based on the population diversity, is compared with its classical version and found outperforming. The comparison results are reported in this paper.

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2020

K. Sree and Dr. Jeyakumar G., “An Evolutionary Computing Approach to Solve Object Identification Problem for Fall Detection in Computer Vision-Based Video Surveillance Applications”, Journal of Computational and Theoretical Nanoscience, vol. 17, no. 1, pp. 1-18, 2020.[Abstract]


In the given image identifying the existence of a required object is the concern of the object detection process. This is quite natural for Human without any effort, however making a machine to detect an object in image is tedious. To make machines to recognize the objects, the feature descriptor algorithms are to be implemented. The general object detection approaches use collection of local and global descriptors to represent an image. Difficulties arise during this process when there is variation in lightening, positioning, rotation, mirroring, occlusion, scaling etc., of the same object in different image scenes. To overcome these difficulties, we need combination of features that detects the object in the image scene. But there exist lot of descriptors that can be used. Hence, finding the required number of feature descriptors for object detection is a crucial task. The question that comes out here is how to select the optimum number of descriptors to achieve optimum accuracy? The answer for the question is an optimization algorithm, which can be employed to select the best combination of the descriptors with maximum detection accuracy. This paper proposing an Evolutionary Computation (EC) based approach with the Differential Evolution (DE) algorithm to find the optimal combination of feature descriptors to achieve optimal object detection accuracy. The proposed approach is implemented and its superiority is verified with four different images and results obtained are presented in this paper.

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2019

S. S. Shinde, K., D., Dr. Thangavelu S., and Dr. Jeyakumar G., “Multi-objective evolutionary algorithm based approach for solving rfid reader placement problem using weight-vector approach with opposition-based learning method”, International Journal of Recent Technology and Engineering, vol. 7, pp. 177-184, 2019.[Abstract]


For smart building applications, identifying and tracking the objects and people in and around a building is an inevitable problem. There exist many approaches for solving this problem. Nowadays, the RFID network based approaches have become most popular for its speed and accuracy. However, placing the RFID readers at optimal places in a building to cover all the areas in order to identify and track the objects and people is a cumbersome task. This paper proposes a model in which the RFID reader placement problem is formulated as a multi-objective optimization problem and also proposes an algorithmic framework to solve the same. The proposed algorithmic frame work consists of a multi-objective Differential Evolution algorithm which adds weights to each of the objective and also follows the opposition-based learning approach for initializing the populations. The results obtained in solving the RFID reader placement problem with proposed algorithmic framework is studied and reported in details for individual objectives, combined objectives with different schemes and for two different population initialization techniques.

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2019

S. Ahmed, Krishnnan, N., Ganta, T., and Dr. Jeyakumar G., “A Video Analytics System for Class Room Surveillance Applications”, International Journal of Recent Technology and Engineering (IJRTE), vol. 7, no. 5S3, 2019.[Abstract]


Using video analytics to give insights about events happening in classroom is a very important task in classroom surveillance systems. This paper proposes a new algorithmic frameworkto identify abrupt changes in a class room video and thenevaluate the attention level of students.The proposed algorithm is implemented with and without video key frame extraction approaches. The SSIM (Structural Similarity Index) approach for key frame extraction is used in this study. After extracting the key frames, the detection of face and upper body of the students to evaluate their attention level is performed on the key frames. The results comparing thealgorithms with and without SSIM reveals that the SSIM based algorithm gives better results. The algorithmic design of the proposed approach, the results obtained and sample cases are presented in this paper.

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2019

A. Sruthi, Dr. Anbuudayasankar S. P., and Dr. Jeyakumar G., “Energy efficient green vehicle routing problem”, International Journal of Information Systems and Supply Chain Management, vol. 12, no. 4, pp. 27-41, 2019.[Abstract]


The greenhouse gas emissions from the transportation sector are one of the major contributors to global warming today. Freight share to GHG emissions is likely to increase 2-fold by 2050. This makes it critical for CO2 emissions to be reduced through an optimized transportation strategy. Vehicle routing, when done efficiently, can reduce these emissions across countries. In this attempt, the traditional distance minimization objective of the vehicle routing problem has been replaced with an energy-emission-centric objective. A model is formulated taking energy and emissions into simultaneous consideration and a typical VRP problem has been evaluated using a genetic algorithm. The application of the proposed model is observed to reduce emissions significantly compared to conventional models. Considering the possibility of increase in carbon tax in future, energy-emission minimized routing would not only aid “green logistics,” but also reduce the environmental costs incurred.

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2019

C. Pragadeesh, Jeyaraj, R., Siranjeevi, K., Abishek, R., and Dr. Jeyakumar G., “Hybrid feature selection using micro genetic algorithm on microarray gene expression data”, Journal of Intelligent and Fuzzy Systems, vol. 36, pp. 2241-2246, 2019.[Abstract]


Research has proved that DNA Microarray data containing gene expression profiles are potentially excellent diagnostic tools in the medical industry. A persistent problem with regard to accessible microarray datasets is that the number of samples are much lesser than the number of features that are present. Thus, in order to extract accurate information from the dataset, one must use a robust technique. Feature selection (FS) has proved to be an effective way by which irrelevant and noisy data can be discarded. In FS, relevant features are picked, and result in commendable classification accuracy. This paper proposes a model that employs a compounded hybrid feature selection technique (Filter + Wrapper) to classify microarray cancer data. Initially, a filter method called Information Gain (IG) to eliminate redundant features that will not contribute significantly to the final classification is used. Following to that, an evolutionary computing technique (micro Genetic Algorithm (mGA)) to find the best minimal subset of required features is employed. Then the features are classified using a traditional Support Vector Classifier and also cross validated to obtain high classification accuracy, using a minimal number of features. The complexity of the model is reduced significantly by adding mGA, as opposed to already existing models that use various other feature selection algorithms. © 2019 - IOS Press and the authors.

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2018

Dr. Shunmuga Velayutham C. and Dr. Jeyakumar G., “Heterogeneous Mixing of Dynamic Differential Evolution Variants in Distributed Frame work for Global Optimization Problems”, International Journal of Advanced Intelligence Paradigms, vol. 1, p. 1, 2018.[Abstract]


Differential Evolution (DE) is a real parameter optimization algorithm added to the pool of algorithms under Evolutionary Computing field. DE is well known for simplicity and robustness. The Dynamic Differential Evolution (DDE) was proposed in the literature as an extension to DE, to alleviate the static population update mechanism of DE. Since the island based distributed models are the natural extension of DE to parallelize it with structured population, they can also be extended for DDE. This paper, initially, implements distributed versions for 14 variants of DDE and also proposes an algorithm hmDDEv (heterogeneous mixing of dynamic differential evolution variants) to mix different DDE variants in island based distributed model. The proposed hmDDEv algorithm is implemented and validated against a well defined benchmarking suite with 14 benchmarking functions, by comparing it with its constituent DDE variants. The efficacy of hmDDEv is also validated with two state-of-the-art distributed DE algorithms.

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2018

D. K. and Dr. Jeyakumar G., “Theoretical Analysis and Empirical Comparison of Different Population Initialization Techniques for Evolutionary Algorithms”, Indonesian Journal of Electrical Engineering and Computer Science, vol. 12, no. 1, pp. 87-94, 2018.[Abstract]


Evolutionary Algorithms (EAs) are the potential tools for solving optimization problems. The EAs are the population based algorithms and they search for the optimal solution(s) from an initial set of candidates solutions known as population. This population is to be initialized at first before the evolution of the algorithm starts. There exists different ways to initialize this population. Understanding and choosing the right population initialization technique for the given problem is a difficult task for the researchers and problem solvers. To alleviate this issue, this paper is framed with two objectives. The first objective is to present the details of various Population Initialization (PI) techniques of EAs, for the readers to give brief description of all the PI techniques. The second objective is to present the steps and empirical comparison of the results of two different PI techniques implemented for Differential Evolution (DE) algorithm. Theoretical insights and empirical results of the PI techniques are presented in this paper.

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2018

Dr. Jeyakumar G. and M. Siva, S., “Universal Automobile Headlight Control Module for High Beam Adaptation during Night Vision Travel an Embedded Design Approach”, International Journal of Reconfigurable and Embedded Systems (IJRES), , vol. 7, no. 1, pp. 34-42, 2018.[Abstract]


Road accidents during night travel increases day by day and vision impairment due to high beam contributes to the majority of the total fatalities. Headlights of vehicles pose a great danger during night driving. [1] The drivers of most vehicles use high/bright beam while driving at night. This causes a discomfort to the person travelling from the opposite direction. The driver experiences a sudden glare caused due to the high intense headlight beam from the other vehicle coming towards him from the opposite direction. We are expected to dim the headlight to avoid this glare. This glare causes a temporary blindness to a person resulting in road accidents during the night. To avoid such incidents, an embedded prototype of Automatic Headlight adaptor module is proposed. This embedded module automatically switches the high beam to low beam and returns backs to high beam, thus reducing the sudden glare effect. It also eliminates the requirement of manual switching by the driver to switch back to low beam Universal Headlight adaptor module is a unique solution to achieve the above objective, the headlight intensity of the incoming vehicles causing the glare is automatically attenuated to low beam wirelessly by the nearby vehicles affected by high beam. The interconnected modules at every vehicle independently takes the decision on the head light control of the source vehicle causing the glare by evaluating various parameters like vehicle speed, current GPS location, direction of vehicle etc.

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2016

Dhanya M. Dhanalakshmy, Pranav, P., and Dr. Jeyakumar G., “A Survey on Adaptation Strategies for Mutation and Crossover Rates of Differential Evolution Algorithm”, International Journal on Advanced Science, Engineering and Information Technology (IJASEIT) (Scopus) , vol. 6, no. 5, pp. 613–623, 2016.[Abstract]


Differential Evolution (DE), the well-known optimization algorithm, is a tool under the roof of Evolutionary Algorithms (EAs) for solving non-linear and non-differential optimization problems. DE has many qualities in its hand, which are attributing to its popularity. DE also is known for its simplicity in solving the given problem with few control parameters: the population size (NP), the mutation rate (F) and the crossover rate (Cr). To avoid the difficulty involved in setting of suitable values for NP, F and Cr many parameter adaptation strategies are proposed in the literature. This paper is to present the working principle of the parameter adaptation strategies of F and Cr. The adaptation strategies are categorized based on the logic used by the authors, and clear insights about all the categories are presented. More »»

2016

R. Raghu and Dr. Jeyakumar G., “Mathematical Modelling of Migration Process to Measure Population Diversity of Distributed Evolutionary Algorithms”, Indian Journal of Science and Technology, vol. 9, no. 31, 2016.[Abstract]


Background/Objectives: Evolutionary Algorithms (EAs) have a major role in solving optimization problems. Distributed Evolutionary Algorithms (dEAs) improve the performance of classical EAs. In dEAs, the initial population is divided into a number of subpopulations and an independent as well as cooperative coevolution happens among the subpopulations. Methods/Statistical Analysis: The success of dEAs is mainly attributed to the migration process they follow, during the evolution. The migration process alters the diversity of the subpopulations. The contribution of the migration process over the success of dEAs can be better understood and/or improved in the light of changes it brings in the diversity of subpopulations. Three methodologies used in the modelling process are the theoretical approach, statistical approach and the empirical approach. Findings: This paper is to analyze and design a mathematical model of the migration process, for its better understanding. A statistical equation to measure the diversity changes in the subpopulation during the migration process is also derived. The derived equation is validated on different types of populations. Application/Improvement: The derived equation can be applied to study and improve the performance of distributed evolutionary algorithms. More »»

2016

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Hybridizing differential evolution variants through heterogeneous mixing in a distributed framework”, Studies in Computational Intelligence, vol. 611, pp. 107-151, 2016.[Abstract]


While hybridizing the complementary constituent soft computing techniques has displayed improved efficacy, the hybridization of complementary characteristics of different Differential Evolution (DE) variants (could as well be extended to evolutionary algorithms variants in general) through heterogeneous mixing in a distributed framework also holds a great potential. This chapter proposes to mix competitive DE variants with diverse characteristics in a distributed framework as against the typical distributed (homogeneous) Differential Evolution (dDE) algorithms found in DE literature. After an empirical analysis of 14 classical DE variants on 14 test functions, two heterogeneous dDE frameworks dDE_HeM_best and dDE_HeM_worst obtained by mixing best DE variants and worst DE variants, respectively, have been realized, implemented and tested on the benchmark optimization problems. The simulation results have validated the robustness of the heterogeneous mixing of best variants. The chapter also hybridized DE and dynamic DE variants in a distributed framework. The robustness of the resulting framework has been validated by benchmarking it against the state-of-the-art DE algorithms in the literature.

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2015

M. S. Akhila, Vidhya, C. R., and Dr. Jeyakumar G., “Population diversity measurement methods to analyze the behavior of differential evolution algorithm”, International Journal of Control Theory and Applications, vol. 8, pp. 1709-1717, 2015.[Abstract]


Differential Evolution (DE), the real parameter optimization algorithm for population based optimization problem, has proved its superiority over variety benchmarking and real time problems. Measuring and visualizing the changes in the diversity of DE population during its search is one of the ways to understand the algorithmic behavior of DE. This helps to provide better insight for proper tuning of control parameters of DE. Hence, an extensive study to describe various possible ways to measure the population diversity of DE algorithm would be a useful tool for the researchers and practitioners of DE. Towards this research direction, this paper presents variety of population diversity measurement methods available for population based algorithm (in general). As well as, as an initial attempt, three methods out of all the identified methods are implemented for DE/rand/1/bin algorithm for a benchmarking function suite with four different functions. The results recorded are presented and discussed in this paper. More »»

2015

R. Raghu and Dr. Jeyakumar G., “Empirical analysis on the population diversity of the sub-population in distributed differential evolution algorithm”, Control theory & applications, vol. 8, no. 5, pp. 1809-1816, 2015.[Abstract]


The Distributed Differential Evolution (dDE) algorithm is a natural extension of the Differential Evolution (DE) algorithm, which is a recent addition to the Evolutionary Algorithms (EAs) pool, in the Evolutionary Computing (EC) field of computer science. The algorithmic novelty of the dDE algorithm is well evident in the literature. However, the theoretical studies on the performance of the dDE algorithms are scarcely reported. This paper is an attempt to analyze the performance of the dDE algorithm with a theoretical study. A theoretical equation, to measure the population diversity of the sub-population of the dDE algorithm, after migration, is derived and the validity of the same is verified with a simple distributed framework of dDE with two sub-population.

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2015

R. R. Reddy and Dr. Jeyakumar G., “Differential evolution with added components for early detection and avoidance of premature convergence in solving unconstrained global optimization problems”, International Journal of Applied Engineering Research, vol. 10, no. 5, pp. 13579-13594, 2015.[Abstract]


Differential Evolution (DE) is a recent addition to the repository of Evolutionary Algorithms (EAs) under Evolutionary Computing Techniques. As similar to other EAs, DE also used for optimization based on population of members. During population optimization the classical DE faces major problem of premature convergence, which causes the sample members to converge early to a local optimum though there is a global optimum in the search space. This paper presents methods to reduce the effect premature convergence and achieve better optimal solutions. There are few works in the literature for same reason by altering the control parameters of DE viz., scaling factor (F) and crossover rate (CR). However, we propose methods to make suitable amendments in the population level directly after detecting premature convergence during the search of DE. These methods can be added as an additional component to DE algorithm. The proposed components are to replace the population members with distinct highest objective function values with random members, to replace the population members with distinct lowest objective function values with random members, to replace members in random fashion and to increase the population size dynamically to counter the early convergence of the population. The above mentioned techniques are implemented and added with classical DE. The performance efficacy of DE with added components is verified on implementing it over a set of Benchmarking Function Suite with functions of different characteristics. The experimental results proved that DE with above components added is able to achieve better optimum values than the classical DE. More »»

2015

K. .Roshini, .Bavya, B., and Dr. Jeyakumar G., “RoBaJe – A Simulated Computational Model for Human Memory to Illustrate Encoding and Decoding of Information”, International Journal of Applied Engineering Research, Research India Publications, vol. 9, pp. 26957-26970, 2015.[Abstract]


Modeling a human brain plays a vital part in the simulation of human being behavior. To model human behavior we need to understand the thought process of individuals. Thought process is influenced by the factors that affect the permanent storage of memory. Objective of this paper is to present the details of the experiments to create a simulation of human brain, in particular the storage and retrieval of memory with visual perceptions. It is critical to understand how the human memory works, without a suitable memory model. Currently there are inadequate models to simulate and understand the human brain. This paper presents design and implementation of a simple simulation model for human brain, in particular to simulate the memory process with visual perception. This simple model of human brain will serve as a basic prototype for future enhancements.

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2015

Dr. Thangavelu S., Dr. Jeyakumar G., and Dr. Shunmuga Velayutham C., “Population variance based empirical analysis of the behavior of differential evolution variants”, Applied Mathematical Sciences, vol. 9, pp. 3249-3263, 2015.[Abstract]


Differential Evolution (DE) is a simple but efficient Evolutionary Algorithm (EA) for stochastic real parameter optimization. With various types of mutation and crossover applicable to DE, there exist many variants of DE. The empirical comparisons between the performances of these variants on chosen benchmarking problems are well reported in literature. However, attempts to analyze the reason for such identified behavior of the variants are scarce. As an attempt in this direction, this paper empirically analyzes the performance as well as the reason for such performance of 14 classical DE variants on 4 benchmarking functions with different modality and decomposability. The empirical analysis is carried out by measuring the mean objective function values (MOV), success rate (Sr), probability of convergence (Pc), quality measure (Qm) and empirical evolution of the variance of the population (Evar). The study also includes reporting evidences for the variants suffering with stagnation and/or premature convergence. © 2014 S. Thangavelu, G. Jeyakumar and C. Shunmuga Velyautham.

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2015

M. Sanu and Dr. Jeyakumar G., “Empirical performance analysis of distributed differential evolution for varying migration topologies”, International Journal of Applied Engineering Research, vol. 10, pp. 11919-11932, 2015.[Abstract]


Distributed Differential Evolution, advancement in Differential Evolution (DE) algorithm, is based on the principle of cooperation and co-evolution. It provides multiple search space perspectives, alternate search paths and a more balanced exploitation and exploration capabilities to DE. Altogether it improves solution quality and prevents premature convergence and stagnation to a greater extent, compared to serial DE. The solution quality of Distributed DE is based on the choice of migration parameters. One of the most influencing migration parameter is the topology used. The aim of this work is to empirically analyze the performance of Distributed DE for varying migration topologies. Migration topologies differ from each other on the basis of degree and interconnectivity of nodes. This paper empirically analyzes the performance difference of distributed differential evolution algorithm with varying migration topologies, on a set of benchmarking problems. The migration topologies used in our experiments are basic ring and its variants, star, cartwheel, torus and mesh. Experimental results have shown that no single topology can said to be good for all optimization problems. It depends on the complexity and type of objective function to be optimized. Experimental analyses have also exposed the influence of DE variant, employed in different islands, on the performance ordering of topologies. The influence of other parameters like the selection policy and replacement policy are also found to be crucial. © Research India Publications.

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2014

K. Roshini, Bavya, B., and Dr. Jeyakumar G., “RoBaJe–A Simulated Computational Model for Human Memory to Illustrate Encoding and Decoding of Information”, International Journal of Applied Engineering Research, vol. 9, pp. 26957–26970, 2014.[Abstract]


Modeling a human brain plays a vital part in the simulation of human being behavior. To model human behavior we need to understand the thought process of individuals. Thought process is influenced by the factors that affect the permanent storage of memory. Objective of this paper is to present the details of the experiments to create a simulation of human brain, in particular the storage and retrieval of memory with visual perceptions. It is critical to understand how the human memory works, without a suitable More »»

2014

G. K. Deivanayagam, Gayathiri, D., Manikandan, A., Karthik, K. R. Raghul, Dr. Jeyakumar G., and Kriti, N., “Learning to identify bad coding practice”, International Journal of Applied Engineering Research, vol. 9, pp. 6747-6755, 2014.[Abstract]


Conventional code evaluation systems focus on output matching, with little importance being given to evaluating programming style and practice. However, judgement of coding practice is vital to aid the process of learning how to program. We hence propose to define a framework that evaluates source code by judging good coding practice, rather than by matching the output with predetermined test cases. Since the scope of the problem is large, we plan to implement it for a particular programming platform and paradigm. Our proposed approach is to use well established code metrics in order to evaluate training data, which can be fed to a supervised learning framework. The major challenge that we have identified so far is to come up with a definition of parameters that indicates 'good' coding style. We plan to resolve this issue by training our framework to learn to recognize the optimal values and combination of code metrics in order to comprehensively evaluate coding style. © Research India Publications.

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2014

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Distributed heterogeneous mixing of differential and dynamic differential evolution variants for unconstrained global optimization”, Soft Computing – Springer, vol. 18, pp. 1949-1965, 2014.[Abstract]


This paper proposes a novel distributed differential evolution framework called distributed mixed variants (dynamic) differential evolution ( {Mathematical expression}. This novel framework is a heterogeneous mix of effective differential evolution (DE) and dynamic differential evolution (DDE) variants with diverse characteristics in a distributed framework to result in {Mathematical expression}. The {Mathematical expression}, discussed in this paper, constitute various proportions and combinations of DE/best/2/bin and DDE/best/2/bin as subpopulations with each variant evolving independently but also exchanging information amongst others to co-operatively enhance the efficacy of {Mathematical expression} as whole. The {Mathematical expression} variants have been run on 14 test problems of 30 dimensions to display their competitive performance over the distributed classical and dynamic versions of the constituent variants. The {Mathematical expression}, when benchmarked on a different 13 test problems of 500 as well as 1,000 dimensions, scaled well and outperformed, on an average, five existing distributed differential evolution algorithms.

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2013

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Distributed mixed variant differential evolution algorithms for unconstrained global optimization”, Memetic Computing, vol. 5, pp. 275-293, 2013.[Abstract]


<p>This paper proposes a novel distributed differential evolution algorithm called Distributed Mixed Variant Differential Evolution (dmvDE). To alleviate the time consuming trial-and-error selection of appropriate Differential Evolution (DE) variant to solve a given optimization problem, dmvDE proposes to mix effective DE variants with diverse characteristics in a distributed framework. The novelty of dmvDEs lies in mixing different DE variants in an island based distributed framework. The 19 dmvDE algorithms, discussed in this paper, constitute various proportions and combinations of four DE variants (DE/rand/1/bin, DE/rand/2/bin, DE/best/2/bin and DE/rand-to-best/1/bin) as subpopulations with each variant evolving independently but also exchanging information amongst others to co-operatively enhance the efficacy of the distributed DE as a whole. The dmvDE algorithms have been run on a set of test problems and compared to the distributed versions of the constituent DE variants. Simulation results show that dmvDEs display a consistent overall improvement in performance than that of distributed DEs. The best of dmvDE algorithms has also been benchmarked against five distributed differential evolution algorithms. Simulation results reiterate the superior performance of the mixing of the DE variants in a distributed frame work. The best of dmvDE algorithms outperforms, on average, all five algorithms considered. © 2013 Springer-Verlag Berlin Heidelberg.</p>

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2012

Dr. Jeyakumar G. and ShunmugaVelayutham, C., “Differential evolution and dynamic differential evolution variants for unconstrained global optimization – An Empirical Comparative Study”, International Journal of Computers and Applications (IJCA), vol. 34, pp. 135-144, 2012.

2012

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Differential evolution and dynamic differential evolution variants - An empirical comparative performance analysis”, International Journal of Computers and Applications, vol. 34, pp. 135-144, 2012.[Abstract]


In this paper, we extend the dynamicity of differential evolution (DE) proposed for DE/rand/1/bin and DE/best/1/bin to five more variants DE/rand/2, DE/best/2, DE/current-to-rand/1, DE/current-to-best/1 and DE/rand-to-best/1. We present an empirical, comparative performance, analysis of 14 variants of DE and dynamic differential evolution (DDE) algorithms (7 variants with two crossovers - binomial and exponential) to solve unconstrained global optimization problems. The aim of this paper is to identify competitive DE and DDE variants which perform well on ifferent problems, and to compare the performance of DDE variants with DE variants. The performance of 14 variants of DE and DDE are analyzed by implementing them on 14 test functions. The analysis (done based on mean objective function value, probability of convergence and success performance) shows the superiority of DDE variants and identifies the competitive DE and DDE variants.

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2011

J. Vasudha, Iyshwarya, G., A Selvi, T., Iniyaa, S., and Dr. Jeyakumar G., “Application of Computer-Aided Music Composition in Music Therapy”, International Journal of Innovation, Management and Technology, vol. 2, pp. 55–57, 2011.[Abstract]


Music Therapy is the use of a selected music to obtain the same expected changes and hormonal alterations in the body, played uninterrupted for a while, to obtain the desired positive effect. In this project we try to implement computerized composition of Carnatic Music for curing the ailments. There is a growing awareness that ragas could complement or even be a safe alternative for many medical interventions. For this purpose, it is necessary to design a system which can generate music given the user needs and specifications. Our project aims at implementing this idea by giving the swaras of raagas as input and generating pleasant music using genetic algorithm. This application can be used by the medical practitioners by selecting a raga for playing after giving the patient details and disease as input. Formulating the fitness criteria is a herculean task in order to satisfy coherency, variety, harmony, rhythm and to reduce redundancy. The history of the therapy should also be stored which can be used as a constraint for fitness evaluation. This application is developed using Java. A Java API, called JFugue is used to support music programming. More »»

2011

Dr. Jeyakumar G. and Shanmugavelayutham, C., “Convergence Analysis of Differential Evolution Variants on Unconstrained Global Optimization Functions”, Arxiv preprint arXiv:1105.1901, 2011.

2010

Dr. Jeyakumar G. and ShunmugaVelayutham, C., “An Empirical Comparative Performance Analysis of Differential Evolution, Distributed and Mixed-Variants Distributed Differential Evolution Variants”, International Journal of Computational Intelligence Research, vol. 6, pp. 735–742, 2010.

2010

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Differential Evolution and Dynamic Differential Evolution for High Dimensional Function Optimization – An Empirical Scalability Study”, International Journal of Computer Science and Engineering (IJCSE), vol. 2, pp. 2932-2941, 2010.

2010

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “An empirical performance analysis of differential evolution variants on unconstrained global optimization problems”, International Journal of Computer Information Systems and Industrial Management Applications (IJCISIM), vol. 2, pp. 77–86, 2010.[Abstract]


In this paper we present an empirical, comparative performance, analysis of fourteen Differential Evolution (DE) variants on different classes of unconstrained global optimization benchmark problems. This analysis has been undertaken, with an objective, to compare and to identify competitive DE variants which perform reasonably well on problems with different features. Towards this, fourteen variants of DE are benchmarked on 14 high dimensional unconstrained test functions grouped by their modality and decomposability viz. unimodal separable, unimodal nonseparable, multimodal separable and multimodal nonseparable. The analysis identifies the overall competitive variants as well as the feature based performances of all the variants. More »»

2010

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Empirical study on migration topologies and migration policies for island based distributed differential evolution variants”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6466 LNCS, pp. 29-37, 2010.[Abstract]


In this paper we present an empirical performance analysis of fourteen variants of Differential Evolution (DE) on a set of unconstrained global optimization problems. The island based distributed differential evolution counterparts of the above said 14 variants have been implemented with mesh and ring migration topologies and their superior performance over the serial implementation has been demonstrated. The competitive performance of ring topology based distributed differential evolution variants on the chosen problem has also been demonstrated. Six different migration policies are experimented for ring topology, and their performances are reported. © 2010 Springer-Verlag. More »»

2009

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Performance and Scalability Analysis of Differential Evolution Variants on a Suite of High Dimensional Benchmark Functions”, “Mathematical and Computational Models – Recent Trends”, p. Page–No, 2009.

Publication Type: Book

Year of Publication Title

2017

Prashant R. Nair, Dr. Shriram K Vasudevan, V, S., and Dr. Jeyakumar G., Software Engineering. Alpha Science Publishers, United Kingdom, 2017.

2017

Prashant R. Nair, Dr. Shriram K Vasudevan, V, S., and Dr. Jeyakumar G., Software Engineering. Narosa Publishers, India, 2017.

Publication Type: Book Chapter

Year of Publication Title

2015

Dr. Thangavelu S., Dr. Jeyakumar G., Balakrishnan, R. M., and Dr. Shunmuga Velayutham C., “Theoretical Analysis of Expected Population Variance Evolution for a Differential Evolution Variant”, in Computational Intelligence in Data Mining (In Smart Innovation, Systems and Technologies), vol. 32, Springer, 2015, pp. 403–416.[Abstract]


In this paper we derive an analytical expression to describe the evolution of expected population variance for Differential Evolution (DE) variant—DE/current-to-best/1/bin (as a measure of its explorative power). The derived theoretical evolution of population variance has been validated by comparing it against the empirical evolution of population variance by DE/current-to-best/1/bin on four benchmark functions.

More »»

2010

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Empirical study on migration topologies and migration policies for island based distributed differential evolution variants”, in Swarm, Evolutionary, and Memetic Computing, Springer, 2010, pp. 29–37.

2009

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “A comparative performance analysis of multiple trial vectors differential evolution and classical differential evolution variants”, in Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, Springer, 2009, pp. 470–477.[Abstract]


In this paper we present an empirical , comparative performance, analysis of fourteen variants of Differential Evolution (DE) and Multiple Trial Vectors Differential Evolution algorithms to solve unconstrained global optimization problems. The aim is (1) to compare Multiple Trial Vectors DE, which allows each parent vector in the population to generate more than one trial vector, against the classical DE and (2) to identify the competitive variants which perform reasonably well on problems with different features. The DE and Multiple Trial Vectors DE variants are benchmarked on 6 test functions grouped by features – unimodal separable, unimodal nonseparable, multimodal separable and multimodal non-separable. The analysis identifies the competitive variants and shows that Multiple Trial Vectors DE compares well with the classical DE. More »»

Publication Type: Journal

Year of Publication Title

2011

J. Vasudha, Iniya, S., Iyshwarya, G., and Dr. Jeyakumar G., “Computer Aided Music Generation Using Genetic Algorithm and Its Potential Applications ”. 2011.[Abstract]


Music speaks what cannot be expressed, soothes the mind and gives it rest, heals the heart and makes is whole. Not everyone is gifted with a good voice but almost everyone has good ears when it comes to pleasant music.This music ranges from strictly organized compositions, through improvisational music to aleatoric forms which makes it tough to automate music generation. In this paper we try to bring in the idea of automated Carnatic music generation using genetic algorithm and its applications. Firstly a variety of compositions on specific ragas can be generated which can be improvised to deliver a good quality musical concert. This concept of automated music generation can also be applied to build an ‘online musical instrument tutorials’ by which users get to learn any instrument level by level. The work can also be modified to produce music of same fitness as any pre existing composition by giving the latter’s notes as input. The application of computerized composition can be used in Music Therapy which is the use of a selected music to obtain the same expected changes and hormonal alterations in the body,played uninterrupted for a while, to obtain the desired positive effect. The above mentioned field of music generation can be used by the medical practitioners by selecting a raga for playing after giving the patient details and disease as input. The application is developed using Java. A Java API, called JFugue is used to support music programming. More »»

Workshops Attended

  1. Attended the National Workshop on “Computer Vision, Graphics and Image Processing” organized by Thiagarajar College of Engineering and Indian Unit of Pattern Recognition and Artificial Intelligence (IUPRAI) from February 15-16, 2002.
  2. Completed the course in “Digital Image Processing and Its Application“, conducted by M.S.Ramaiah School of Advanced studies, from November 24-29, 2003.
  3. Attended STTP on “Intelligent Information Agents” from October 24- November 6, 2004, Organized by the department of CSE, PSG College of Technology, Coimbatore.
  4. Participated the training programme on MATLAB jointly organized by Department of Computational Engineering and Networking (CEN), held at Amrita Vishwa Vidyapeetham, Coimbatore from November 17- December 9, 2004.
  5. Attended the “Doctoral Research Symposium” Conducted by Amrita Vishwa Vidyapeetham held during December 18-19, 2004.
  6. Attended the Workshop on “High Performance Computing “, conducted by the CEN, Amrita Vishwa Vidyapeetham, held during December 22 – 27, 2004.
  7. Participated in the 2-day National Conference on “Network Security” organized in association with CSI held at Rajagiri College of social science, Kochi, from February 11-12, 2005.
  8. Participated in the National level conference on “New Trends In Distributed Computing and Networking” conducted by Dept of CSE, Tagore Engineering college, Chennai from March 18-19, 2005.
  9.  Participated in the workshop on “Research Methodology In Experimental and Social Sciences” (May 16-18, 2005) organized by SAHITHI (An Institute for Research and Extension of Education).
  10. Participated in the Workshop on “Data Mining and Data Warehousing” Jointly organized by L&T Infotech Limited and Eswari Engineering College, Chennai from July 21- 22, 2006.
  11. Participated in the Workshop on “Heuristic Optimization Techniques” organized by PSG College of Technology, Coimbatore on September 8-9, 2007.
  12. Participated in the Workshop on “Computational Techniques for Power Systems” Organized by EEE Department, Amrita School of Engineering, Ettimadai, Coimbatore, December 13-15, 2010.
  13. Attended the international workshop of “Differential Evolution and its Engineering Applications” (DEEA 2010), organized by EEE Department, SRM University, Chennai, December 16, 2010.
  14. Attended “PSG-IBM Collaborative Workshop on Parallel Architectures and Programming in HPC”, during August 11-14, 2011, at PSG College of Technology, Coimbatore.
  15. Attended one day workshop on “High Performance Computing” conducted by Dr K.V.Shriram on August 8, 2015.  Presented by Calligo Soft, Bangalore at Amrita Vishwa Vidyapeetham University, Coimbatore.
  16. Attended five days’ workshop “Universal Human Value” conducted by AICTE, from February 1- 5, 2021, and received the certificate.  
  17. Attended five days’ workshop “Machine Learning for Health-Care Applications” conducted by MEPCO Engineering College, Sivakasi, Tamil Nadu, February 8-12, 2021.  

Workshops / FDPs/Events Organized

  1. Organizing member for A2CWIC (Amrita ACM-W Celebration of Women in Computing) conducted by Amrita Vishwa Vidyapeetham from December 16-17, 2010.
  2. Coordinator for ICONIAAC'14 (International Conference on Interdisciplinary Advances in Applied Computing) conducted from October 10-11, 2014.
  3. Organized a three days’ workshop on “Publishing your research” for PG students at Amrita University, in May 2014.
  4. Organized Orientation Program for MCA 2014-2016 batch.
  5. Organized Placement Training for MCA 2013-2015 batch.
  6. Organized a student workshop on android app development (SWAD) for two years (SWAD-2014 and SWAD-2015).
  7. University Level Coordinator - Anoka'14 – A student symposium conducted at Amrita Vishwa VidyaPeetham University, 2014.
  8. Organized a series of bimonthly faculty research seminar and Dept of CSE, Amrita Vishwa Vidyapeetham, in the Academic Year 2013-2014.
  9. Organized Entrance Examination for MCA-2014 Batch and successfully completed the Admission process.
  10. Organizing member for the workshop NWCVIPT’2016 from March 18-19, 2016 at Amrita Vishwa Vidyapeetham.

Visits and Talks

  1. Contributed as a Resource person for the AICTE-ISTE sponsored STTP on “Wavelets for Computer Graphics” organized during January 21-30, 2003 by the Department of CSE, Amrita Vishwa Vidyapeetham.
  2. Contributed as a Resource person for the AICTE-ISTE sponsored STTP on “Information Retrieval and Security” organized during November 22 - December 3 2004 by the Department of CSE, Amrita Vishwapeetham.
  3. Delivered a Lecture on “Role Of Parents In Children’s Education” in the special seminar conducted by The Central Board for Workers Education (Ministry of Labour and Employment, Government of India) for unorganized laborers at Navakarai from July 8-9, 2004.
  4. Delivered a Lecture on “Developing Programming Skill” in the seminar conducted by Sankara College of Science and Commerce on July 22, 2006.
  5. Acted as Faculty (Resource Person) for Infosys Campus Connect Programme during the month of November’2007
  6. Delivered a Lecture on “Using VC++ for Real Time Applications” in the seminar conducted by Sri Narayana Guru College of Arts and Science on February 11, 2008.
  7. Delivered a Lecture on “Recent Trends in IT World” in the seminar conducted by Sri Narayana Guru College of Arts and Science on October 6, 2008.
  8. Presented a Session on “Artificial Intelligence: Basics to Research”, in the workshop organized by Shri Muthayammal College of Engineering, Rasipuram on October 23, 2010.
  9. Presented a session on “Database Management System Fundamentals” in the orientation program conducted by Amrita Vishwa Vidyapeetham for MCA students, from August 7 to 11, 2014.
  10. Presented a session on “Publication Based on Research” in the research workshop conducted by Amrita Vishwa Vidyapeetham on “Publishing your research”, May 26-28, 2014
  11. Delivered a talk on “Evolutionary computing - What it is?” on the NWCVIPT’2016 on March 19, 2016, at Amrita Vishwa Vidyapeetham.
  12. Expert Lecture on “Computer Graphics” – for Engineering Graduates at Erode Sengunther Engineering College, Erode, Tamil Nadu, September 9, 2016.

Awards & Honors

  1. Got university third rank and Gold medal for MCA (1995 -1998) from Bharathidasan University, Trichy.
  2. Got Best Staff '2000 award from my previous working place "Girvani Degree College", Chittoor, Andra Pradesh.
  3. Got best paper awards in the international conferences - IAMA 2009, ICSCS 2016, ICCBVIC 2019, COMET-2020 and SocPros-2020.

Reviewers in Journals and Conferences

Reviewer - International Journals

  1. IEEE Transactions on Network Science and Engineering.
  2. IEEE Transactions on Systems, Man and Cybernetics: Systems
  3. IEEE Access.
  4. International Journal of Machine Learning and Cybernetics – Springer.
  5. Journal of Neural Computing and Applications – Springer.
  6. Soft computing - Springer.
  7. Memetic Computing – Springer.
  8. Springer Nature – Applied Sciences – Springer.
  9. Neural Computing and Applications – Springer.
  10. International Journal of Swarm and Evolutionary Computation – Elsevier.
  11. Journal of Intelligent & Fuzzy Systems – IOS Press
  12. International Journal of Applied Evolutionary Computing – IGI.
  13. Journal of Experimental and Theoretical Artificial Intelligence -Taylor&Francis.
  14. Engineering Optimization - Taylor&Francis.
  15. Complexity – Hindawi.
  16. International Journal on Advanced Science, Engineering and Information Technology – Insight Society.

Reviewer - International Conferences

  1. CNSA 2011, NECOM 2011, WEST 2011, WiMON 2011, AICTY 2011.
  2. AICWIC2013
  3. ICCCI2014, ICES2014, ICONIAAC'14, ICCIDM'2014
  4. ICSGT 2015
  5. FSDM 2016, FACT 2016.
  6. FISITA 2017, ICONIAC 2018
Faculty Research Interest: