Gnanasekar J M

@rajalakshmi.org

Professor and Head Department of Artificial Intelligence and Data Science
Rajalakshmi Engineering College

Gnanasekar J M


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https://researchid.co/jmgsekar

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems, Computer Science Applications
27

Scopus Publications

1172

Scholar Citations

19

Scholar h-index

26

Scholar i10-index

Scopus Publications

  • Sentiment Influenced Deep Learning Model for Stock Market Prediction
    R. Gnanavel, J. M. Gnanasekar
    Computational Economics, 2026
  • An Innovative Sentiment Influenced Stock Market Prediction Based on Dual Scale Adaptive Residual Long Short Term Memory With Attention Mechanism
    R. Gnanavel, J. M. Gnanasekar
    Computational Intelligence, 2025
    The stock market is extremely unpredictable and impulsive because of a variety of reasons, including public opinion, economic conditions, and so on. Each second, many Petabytes of data emerge from various sources, impacting the stock marketplace. A fair and effective merging of those sources of information (factors) into knowledge is predicted to improve the precision of stock market predictions. However, combining these characteristics from multiple sources of data into a single dataset to supply market evaluation is considered difficult since they are presented in various formats. This paper recommends a deep learning framework for performing prediction in the stock market by considering the sentiment text and historical information from social media. Initially, the required sentiment text and data are collected from the social media platform. From the database, the historical data of the company and the sentiment text from the user uploaded in the social media and news articles are collected. After that, the collected sentiment texts are preprocessed to remove the unwanted data. The preprocessed sentiment texts are given to the Bidirectional Encoder Representations from Transformers (BERT) model for retrieving the first set of features from the positive and negative sentiments. On the other hand, the deep features are retrieved from the data using a One‐Dimensional Convolutional Neural Network (1DCNN), which is considered a second feature set from historical data. The two sets of features retrieved from the sentiment text and data are passed to the Dual Scale Adaptive Residual Long Short‐Term Memory with Attention Mechanism (DSAResLSTM‐AM) for stock market price prediction, where the attributes of the ResLSTM are tuned using Enhanced Deep Sleep Optimizer (EDSO). Here, the sentiment text having positive and negative sentiments helps to predict the stock market price of the company effectively to be less or high along with the analysis of previous data. The recommended model helps to perform the accurate stock market prediction, and it is used to enhance the return and reduce the investment. Finally, experimental validations are conducted to find the performance of the developed model in the stock market prediction.
  • A Comprehensive Survey of Blockchain and Reinforcement Learning in IoMT
    Ajoe Sweetlin Jeena A, Gnanasekar J M
    Proceedings of 3rd International Conference on Sustainable Computing and Data Communication Systems Icscds 2025, 2025
  • Fitness App: A Customized Workout Recommendations based on the Evaluation of the user's Fitness Objectives
    16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
  • Robust Object Detection Using Fire Hawks Optimizer with Deep Learning Model for Video Surveillance
    S. Prabu, J. M. Gnanasekar
    Journal of Circuits Systems and Computers, 2024
    In recent years, video surveillance has become an integral part of computer vision research, addressing a variety of challenges in security, memory management and content extraction from video sequences. This paper introduces the Robust Object Detection using Fire Hawks Optimizer with Deep Learning (ROD-FHODL) technique, a novel approach designed specifically for video surveillance applications. Combining object detection and classification the proposed technique employs a two-step procedure. Utilizing the power of the Mask Region-based Convolutional Neural Network (Mask-RCNN) for object detection, we optimize its hyperparameters using the Fire Hawks Optimizer (FHO) algorithm to improve its efficacy. Our experimental results on the UCSD dataset demonstrate the significant impact of the proposed work. It achieves an extraordinary RUNNT of 1.34[Formula: see text]s on the pedestrian-1 dataset, significantly outperforming existing models. In addition, the proposed system surpasses in accuracy, with a pedestrian-1 accuracy rate of 97.45% and Area Under the Curve (AUC) values of 98.92%. Comparative analysis demonstrates the superiority of the proposed system in True Positive Rate (TPR) versus False Positive Rate (FPR) across thresholds. In conclusion, the proposed system represents a significant advancement in video surveillance, offering advances in speed, precision and robustness that hold promise for enhancing security, traffic management and public space monitoring in smart city infrastructure and other applications.
  • Realtime Object Detection Through M-ResNet in Video Surveillance System
    S. Prabu, J. M. Gnanasekar
    Intelligent Automation and Soft Computing, 2023
  • A Conceptual Overview on Earlier Methodologies Focused on Stock Price Prediction
    R Gnanavel, J M Gnanasekar
    Proceedings 5th International Conference on Smart Systems and Inventive Technology Icssit 2023, 2023
    Data science, analytics, and mining are the subsets of data engineering, and they play a vital role in the majority of emerging applications. Stock Price Prediction (SPP) is one such application that has drawn the attention of investors ranging from individuals to large business houses. It has become the focus of study for many financial institutions that starve to park their capital in expectation of better returns. Of Late, much research has been done on this aspect, and this trend is expected to continue further. Many algorithms like Advanced conventional, AI, machine learning, and deep learning have focused on analyzing national and international stock data to predict the market movement. Sinoe the stock data is time-series data that is always volatile and unpredictable, accuracy in predicting the stock value has always been a topic of discussion in the global financial markets. This unpredictable nature of stock prices has misled genuine investors into investing in the wrong place and at the wrong time. There is now a dire need for an algorithm that could predict the stock movements as close as possible to the accurate price. Until now, research has been done on this topic using statistical methods combined with machine learning algorithms to predict stock movements. Still, they were far from giving accurate and satisfactory predictions that only resulted in diminishing returns. In an attempt to overcome this issue, this paper has analyzed various research works done so far and tried to come up with a solution in the form of a novel machine-learning algorithm. It uses historical data to make better predictions on the stock movements that are considerably nearer to the real value. This work has also aimed to highlight the challenges faced by previous researchers while framing an algorithm. It should help future researchers to get a better outline of the SPP.
  • Intelligent Tool for Persons with Visual Impairments: An Overview
    N. Deepika, J.M. Gnanasekar
    8th International Conference on Smart Structures and Systems Icsss 2022, 2022
    Generally, humans have been gifted with five senses namely vision, acoustic, aromatic, flavor, and touch. These five senses provide us with plenty of information about the world. According to the findings, vision contributes to nearly 84 percent of the overall data. This paper presents several existing pieces of literature-based tools for blind people within a mixed-mode approach. The survey was categorized into four parts 1. Reading Assistant 2. Object Detection 3. Face Recognition 4. Indoor and Outdoor Navigation. This survey examines the support process for visually impaired people.
  • A Review on Character Recognition and Information Retrieval from Ancient Inscriptions
    R Vijayalakshmi, J.M. Gnanasekar
    8th International Conference on Smart Structures and Systems Icsss 2022, 2022
    In today's digital age, there is a need to digitize ancient images and documents, the ancient periods from stone inscriptions reveals the cultural history and helps to preserve and emphasize our country's heritage A recognition system aids in the analysis and digitization of traditional cultures and heritages. In this case, digitization entails the recognition of ancient images in both online and offline modes. This paper discusses a variety of strategies and techniques for retrieving, classifying, and extracting information from ancient inscriptions in a variety of languages.
  • Light Fidelity (Li-Fi) technology: The future man-machine-machine interaction medium
    J.M. Gnanasekar, T. Veeramakali
    Human Communication Technology Internet of Robotic Things and Ubiquitous Computing, 2021
  • Preface
    M. Rajesh, J. Gnanasekar, R. Sitharthan
    Journal of Physics Conference Series, 2021
  • Multi-level SLA violation-based resource allocation in cloud using kernel fuzzy C means with aid of EABC
    A. Kannaki, J.M. Gnanasekar
    International Journal of Business Information Systems, 2021
  • Data Integrity and Recovery Management in Cloud Systems
    S. Gokulakrishnan, J.M. Gnanasekar
    Proceedings of the 4th International Conference on Inventive Systems and Control Icisc 2020, 2020
  • Efficient and privacy for data integrity and data replication in cloud computing
    S. Gokulakrishnan*, Dr. J.M. Gnanasekar, and
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • A novel prescriptive approach for health care management using predictive and descriptive analysis of data mining
    International Journal of Recent Technology and Engineering, 2019
  • Intelligent security algorithm for UNICODE data privacy and security in IOT
    Balajee Maram, J. M. Gnanasekar, Gunasekaran Manogaran, M. Balaanand
    Service Oriented Computing and Applications, 2019
  • M learning, data mining and data visualization methodologies for healthcare resource
    Journal of Advanced Research in Dynamical and Control Systems, 2018
  • Path Observation Based Physical Routing Protocol for Wireless Ad Hoc Networks
    M. Rajesh, J. M. Gnanasekar
    Wireless Personal Communications, 2017
  • Cloud computing overview, security threats and solutions-A survey
    A. Kannaki VasanthaAzhagu, J. M. Gnanasekar
    ACM International Conference Proceeding Series, 2016
  • Consistently neighbor detection for MANET
    M. Rajesh, J. M. Gnanasekar
    Proceedings of the International Conference on Communication and Electronics Systems Icces 2016, 2016
  • Efficient adaptive power control protocol for MANET
    International Journal of Applied Engineering Research, 2016
  • Path observation-based physical routing protocol for wireless ad hoc networks
    M. Rajesh, J.M. Gnanasekar
    International Journal of Wireless and Mobile Computing, 2016
  • Congestion control in heterogeneous WANET using FRCC
    Journal of Chemical and Pharmaceutical Sciences, 2015
  • GCC over heterogeneous wireless ad hoc networks
    Journal of Chemical and Pharmaceutical Sciences, 2015
  • Light weight cryptographic algorithm to improve avalanche effect for data security using prime numbers and bit level operations
    International Journal of Applied Engineering Research, 2015
  • A novel software environment for developing migrating internet applications based on fusion of mobile agent, web services and BPEL technologies
    J.M. Gnanasekar, Venkatesan D. Pillai
    Proceedings 2010 IEEE 7th International Conference on Services Computing Scc 2010, 2010
  • Integration of wireless sensor network with cloud
    V. Rajesh, J.M. Gnanasekar, R.S. Ponmagal, P. Anbalagan
    Itc 2010 2010 International Conference on Recent Trends in Information Telecommunication and Computing, 2010

RECENT SCHOLAR PUBLICATIONS

  • Sentiment Influenced Deep Learning Model for Stock Market Prediction
    R Gnanavel, JM Gnanasekar
    Computational Economics, 1-39 , 2026
    2026
  • A Comprehensive Survey of Blockchain and Reinforcement Learning in IoMT
    JM Gnanasekar
    2025 3rd International Conference on Sustainable Computing and Data … , 2025
    2025
  • An Innovative Sentiment Influenced Stock Market Prediction Based on Dual Scale Adaptive Residual Long Short Term Memory With Attention Mechanism
    R Gnanavel, JM Gnanasekar
    Computational Intelligence 41 (3) , 2025
    2025
    Citations: 1
  • Robust object detection using fire Hawks optimizer with deep learning model for video surveillance
    S Prabu, JM Gnanasekar
    Journal of Circuits, Systems and Computers 33 (13), 2450226 , 2024
    2024
    Citations: 4
  • AudioScene: Enhancing Visual Independence Through Scene Recognition
    S Vidhyalakshmi, Gnanasekar J,M
    International Research Journal on Advanced Engineering Hub 2 (6), 1776- 1783 , 2024
    2024
  • Realtime Object Detection Through M-ResNet in Video Surveillance System.
    S Prabu, JM Gnanasekar
    Intelligent Automation & Soft Computing 35 (2) , 2023
    2023
    Citations: 7
  • A conceptual overview on earlier methodologies focused on stock price prediction
    R Gnanavel, JM Gnanasekar
    2023 5th International Conference on Smart Systems and Inventive Technology … , 2023
    2023
    Citations: 1
  • A review on character recognition and information retrieval from ancient inscriptions
    R Vijayalakshmi, JM Gnanasekar
    2022 8th International Conference on Smart Structures and Systems (ICSSS), 1-7 , 2022
    2022
    Citations: 13
  • Intelligent Tool for Persons with Visual Impairments: An Overview
    N Deepika, JM Gnanasekar
    2022 8th International Conference on Smart Structures and Systems (ICSSS), 1-5 , 2022
    2022
    Citations: 2
  • Recent trends in intensive computing
    M Rajesh, K Vengatesan, M Gnanasekar, AB Pawar, PN Kalvadekar, ...
    SAGE Publications Limited , 2021
    2021
    Citations: 7
  • Light Fidelity (Li‐Fi) Technology: The Future Man–Machine–Machine Interaction Medium
    JM Gnanasekar, T Veeramakali
    Human Communication Technology: Internet of Robotic Things and Ubiquitous … , 2021
    2021
    Citations: 3
  • A novel multi-agent approach to control service level agreement violations in cloud computing
    A Kannaki, V Azhagu, JM Gnanasekar
    Turkish Journal of Computer and Mathematics Education 12 (12), 1431-1438 , 2021
    2021
    Citations: 6
  • Multi-level SLA violation-based resource allocation in cloud using kernel fuzzy C means with aid of EABC
    A Kannaki, JM Gnanasekar
    International Journal of Business Information Systems 37 (3), 376-399 , 2021
    2021
    Citations: 3
  • A study on image segmentation method for image processing
    S Prabu, JM Gnanasekar
    Recent Trends in Intensive Computing, 419-424 , 2021
    2021
    Citations: 15
  • Data integrity and recovery management under peer to peer convoluted fault recognition cloud systems
    S Gokulakrishnan, JM Gnanasekar
    Journal of Computational and Theoretical Nanoscience 17 (5), 2147-2150 , 2020
    2020
    Citations: 3
  • Data integrity and recovery management in cloud systems
    S Gokulakrishnan, JM Gnanasekar
    2020 Fourth International Conference on Inventive Systems and Control (ICISC … , 2020
    2020
    Citations: 15
  • Intelligent security algorithm for UNICODE data privacy and security in IOT
    B Maram, JM Gnanasekar, G Manogaran, M Balaanand
    Service Oriented Computing and Applications 13 (1), 3-15 , 2019
    2019
    Citations: 72
  • Efficient and privacy for data integrity and data replication in cloud computing
    S Gokulakrishnan, JM Gnanasekar
    Int. J. Innov. Technol. Explor. Eng , 2019
    2019
    Citations: 5
  • REAL time criminology detection and criminal identification (RCDCI) algorithm
    C Jayapratha, JM Gnanasekar
    Int J Adv Res Comput Eng Technol (IJARCET) 7 (5), 521-524 , 2018
    2018
    Citations: 1
  • Using Classification and Regression Tree Techniques for Predicting Length of Stay of Diabetes Patients
    MC Natarajan, JM Gnanasekar, MJ Hermia
    International Journal of Management, IT and Engineering 8 (8), 170-176 , 2018
    2018

MOST CITED SCHOLAR PUBLICATIONS

  • Path observation based physical routing protocol for wireless ad hoc networks
    M Rajesh, JM Gnanasekar
    Wireless Personal Communications 97 (1), 1267-1289 , 2017
    2017
    Citations: 123
  • Congestion control using aodv protocol scheme for wireless ad-hoc network
    M Rajesh, JM Gnanasekar
    Advances in Computer Science and Engineering 16 (1/2), 19 , 2016
    2016
    Citations: 86
  • Integration of wireless sensor network with cloud
    V Rajesh, JM Gnanasekar, RS Ponmagal, P Anbalagan
    2010 International Conference on Recent Trends in Information … , 2010
    2010
    Citations: 74
  • Intelligent security algorithm for UNICODE data privacy and security in IOT
    B Maram, JM Gnanasekar, G Manogaran, M Balaanand
    Service Oriented Computing and Applications 13 (1), 3-15 , 2019
    2019
    Citations: 72
  • Path observation-based physical routing protocol for wireless ad hoc networks
    M Rajesh, JM Gnanasekar
    International Journal of Wireless and Mobile Computing 11 (3), 244-257 , 2016
    2016
    Citations: 69
  • Gccover heterogeneous wireless ad hoc networks
    M Rajesh, JM Gnanasekar
    Journal of Chemical and Pharmaceutical Sciences 8 (2), 195-200 , 2015
    2015
    Citations: 69
  • Congestion control scheme for heterogeneous wireless ad hoc networks using self-adjust hybrid model
    M Rajesh, JM Gnanasekar
    International Journal of Pure and Applied Mathematics 116 (21), 519-536 , 2017
    2017
    Citations: 59
  • Congestion control in heterogeneous wireless ad hoc network using FRCC
    M Rajesh, JM Gnanasekar
    Australian Journal of Basic and Applied Sciences 9 (7), 698-702 , 2015
    2015
    Citations: 57
  • Evaluation of key dependent S-box based data security algorithm using Hamming distance and balanced output
    MK Balajee, JM Gnanasekar
    Tem Journal 5 (1), 67 , 2016
    2016
    Citations: 52
  • An optimized congestion control and error management system for OCCEM
    M Rajesh, JM Gnanasekar
    International Journal of Advanced Research in IT and Engineering 4 (4), 1-10 , 2015
    2015
    Citations: 48
  • Get-up-and-go efficientmemetic algorithm based amalgam routing protocol
    M Rajesh, JM Gnanasekar
    International Journal of Pure and Applied Mathematics 116 (21), 537-547 , 2017
    2017
    Citations: 42
  • Annoyed realm outlook taxonomy using twin transfer learning
    M Rajesh, JM Gnanasekar
    International Journal of Pure and Applied Mathematics 116 (21), 549-558 , 2017
    2017
    Citations: 39
  • Constructing Well-Organized Wireless Sensor Networks with Low-Level Identification
    M Rajesh, JM Gnanasekar
    Oriental Journal of Computer Science and Technology 9 (1), 17-23 , 2016
    2016
    Citations: 39
  • Consistently neighbor detection for MANET
    M Rajesh, JM Gnanasekar
    2016 International Conference on Communication and Electronics Systems … , 2016
    2016
    Citations: 28
  • Congestion control in heterogeneous WANET using FRCC
    M Rajesh
    2015
    Citations: 27
  • Sector Routing Protocol (SRP) in Ad-hoc Networks
    M Rajesh, JM Gnanasekar
    Control Network and Complex Systems 5 (7), 1-4 , 2015
    2015
    Citations: 27
  • A block cipher algorithm to enhance the avalanche effect using dynamic key-dependent S-box and genetic operations
    B Maram, JM Gnanasekar
    International Journal of Pure and Applied Mathematics 119 (10), 399-418 , 2018
    2018
    Citations: 22
  • Hop-by-hop channel-alert routing to congestion control in wireless sensor networks
    M Rajesh
    2015
    Citations: 20
  • Routing and Broadcast Development for Minimizing Transmission Interruption in Multi rate Wireless Mesh Networks using Directional Antennas
    M Rajesh, JM Gnanasekar
    Innovative Systems Design and Engineering 6 (7), 30-42 , 2015
    2015
    Citations: 19
  • Error-Lenient Algorithms for Connectivity Reinstallation in Wireless Adhoc Networks
    M Rajesh, JM Gnanasekar
    International Journal of Advanced Engineering Technology 7 (1), 270-278 , 2016
    2016
    Citations: 18