Lilly Sheeba S

Verified @gmail.com

Associate Professor, Computer Science and Engineering
SRM Institute of Science and Technology, Ramapuram

Lilly Sheeba S


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https://researchid.co/lillysheeba

EDUCATION

BE, ME, PhD

RESEARCH INTERESTS

Machine Learning, Cloud Computing, Data Science, Wireless Networks
20

Scopus Publications

115

Scholar Citations

6

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Enhanced Heterogeneous Vehicular Networks With Intelligent Congestion Avoidance Mechanism via Regularized Q-Value-Based Graph Generalized Neural Network Transformer
    S. Christalin Nelson, R. Beulah Jayakumari, S. Lilly Sheeba
    International Journal of Communication Systems, 2025
    The rapid development of heterogeneous vehicular networks (HetVNETs) has transformed the transportation industry by enabling vehicle‐to‐vehicle and vehicle‐to‐infrastructure communication. However, as traffic load increases, these networks face severe congestion, leading to unreliable and insecure communication. Congestion control in HetVNETs is challenging due to dynamic topologies, multiple communication protocols, and varying traffic intensities. Traditional congestion control techniques struggle to address these issues, necessitating an intelligent mechanism to detect and prevent data congestion in advance. This research introduces the enhanced heterogeneous vehicular networks with intelligent congestion avoidance mechanism via regularized Q‐value‐based graph generalized neural network transformer (RQ‐GGNN‐ArJ). The proposed hybrid framework integrates a graph‐based generalized neural network for modeling dynamic network topologies, a regularized Q‐value transformer for adaptive dedicated short‐range communications (DSRC) transmission power control to ensure real‐time congestion mitigation, and artificial jelly‐driven adaptive optimization (ArJ‐AO) for fine‐tuning weight parameters and loss functions. These components collectively form a highly efficient congestion avoidance mechanism with real‐time decision‐making capabilities. The proposed system achieves remarkable performance, with 99.8% prediction accuracy in identifying congestion patterns, a 99.6% reduction in packet loss, a 99.7% improvement in communication reliability, and 99.3% resource utilization. Therefore, the RQ‐GGNN‐ArJ framework establishes a new benchmark for intelligent congestion management in HetVNETs.
  • Tamil Handwritten Text Recognition using Deep Learning Technique
    Jayanthi V, Beulah Jayakumari R, Maya Eapen, Krishnaveni S, Darwin P, Lilly Sheeba S
    4th Wireless Antenna and Microwave Symposium Wams 2025, 2025
    Text recognition of handwritten Tamil words is a tedious process as the Tamil language contains a most of the characters are similar to each another as well as with complicated structures. A total of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 1 3}$</tex> characters are produced by the combination of 156 characters. The majority of the characters have curves, loops, and strokes, which makes the task of recognition difficult. When a character is incorrectly recognized, the word's meaning is modified. A Convolutional Recurrent Neural Network (CRNN) was developed to solve this challenge. This approach efficiently combines convolutional layers for extracting features with recurrent layers for modeling, allowing the system to accurately capture the nuances of Tamil script. The method was designed to capture the long-range dependencies that recognize the character without any errors and acquire a high level of accuracy, precision, recall, and F1 score.
  • The Role of Imaging Techniques in the Diagnosis and Management of Respiratory Problems
    International Journal of Computer Information Systems and Industrial Management Applications, 2024
  • COVID-19 Mental Health Impact Analysis using Ensemble-based Classifier
    Beulah Jayakumari R, Malathy Jawahar, Maya Eapen, Jani Anbarasi L, Vinayakumar Ravi, Lilly Sheeba S, Tahani Jaser Alahmadi
    Open Public Health Journal, 2024
    Introduction In the 21st century, human community witnessed a range of biological crises resulting in long-term consequences like loss of life, economic decline, trauma and social disruptions. COVID -19, named the SARs-CoV-2 virus by United Nations, was a similar outbreak in China in the year 2019, which later spread across the world. During the pandemic, as part of preventive measures, the government authorities introduced SOP (standard operating procedures) measures such as social distancing, lockdown, quarantining and closure of educational institutions imposing a great impact on mental health and well-being of humans, especially among the youth. Materials and Methods A study was performed on a public dataset containing survey records collected from 1182 students of different educational institutions. The survey data was based on age, region of residence, time spent online and health fitness. The method used in the proposed work is a classifier model based on an ensemble of decision trees called random forest to predict the consequences of online learning on student’s health. The optimum and promising features are selected by using Recursive feature elimination (RFE) method. Results Our findings reveal a notable enhancement in predicting human health during a pandemic, as indicated by a significant increase in validation accuracy based on confusion for various classifiers. Experimental validation of the developed classifier model is done through the confusion matrix and receiver operating characteristic (ROC) curve. Further, performance metrics such as accuracy, precision, recall, F1-score, specificity, and error rate were employed. The experimental results established the superiority of the proposed ensemble subspace discriminant classifier compared to traditional classifiers. Discussions The RFE feature selection method used in the proposed work helps to select the optimum features as well as more informative features. Moreover, the method employed hyper parameter tuning method to enhance the performance of the classifier model. Conclusion This study highlights the importance of taking care of the emotional and physical health of humans during any pandemic. Furthermore, our approach possesses the capacity to significantly influence the field of predicting health, facilitating the development of more effective and advanced prediction strategies in the future.
  • E-voting system using cloud-based hybrid blockchain technology
    Beulah Jayakumari, S Lilly Sheeba, Maya Eapen, Jani Anbarasi, Vinayakumar Ravi, A. Suganya, Malathy Jawahar
    Journal of Safety Science and Resilience, 2024
    With the invention of Internet-enabled devices, cloud and blockchain-based technologies, an online voting system can smoothly carry out election processes. During pandemic situations, citizens tend to develop panic about mass gatherings, which may influence the decrease in the number of votes. This urges a reliable, flexible, transparent, secure, and cost-effective voting system. The proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system, and it is carried out in three phases: the registration phase, vote casting phase and vote counting phase. A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting phases. Using smart contracts, third-party interventions are eliminated, and the transactions are secured in the blockchain network. Finally, to provide accurate voting results, the practical Byzantine fault tolerance (PBFT) consensus mechanism is adopted to ensure that the vote has not been modified or corrupted. Hence, the overall performance of the proposed system is significantly better than that of the existing system. Further performance was analyzed based on authentication delay, vote alteration, response time, and latency.
  • Counting the presence of the people in a real time contained area using convolutional neural network
    Ramya Ravindran, Lilly Sheeba Selvin, Christalin Nelson Selvin, Beulah Jayakumari Rajarathnam
    Aip Conference Proceedings, 2024
    Maintaining crowd and imparting social distancing remains a very challenging issue in overpopulated countries like India. In pandemic times like these days, social distancing is the most essential way to slow down COVID-19, failing of which might lead to increased number cases and end up with a massive community spread. The proposed method employs convolutional neural network to detect the number of people present in a specific space capable of limited occupancy. If the count of the people gathered exceeds the occupancy capacity of the location, it will give an alert to the concerned authorities for triggering subsequent actions as required. When the government announces certain rules for social distancing in public spaces, this proposed system will assist the corresponding authorities in supervising whether the provisioned rules are incorporated by the general public with minimal human supervision. This in turn will help to reduce the burden on health workers.
  • A Systematic Review of Various Deep Learning Techniques for Network Intrusion Detection System
    A. N. Sasikumar, Sheeba S. Lilly
    IFIP Advances in Information and Communication Technology, 2024
  • Precision Care in Addiction Treatment: A Bayesian-Based Machine Learning Analysis for Adults with Substance Use Disorders
    S. Ezra Vethamani, S. Lilly Sheeba
    Lecture Notes in Networks and Systems, 2024
  • Deep Learning Methodology for Detection of Multimodal Minority Class Sample Attacks
    Nanotechnology Perceptions, 2024
  • Study on Health Issue Identification Using Deep Learning and Convolutional Neural Networks
    Madhusoodanan Aparna, S. Lilly Sheeba
    Lecture Notes in Networks and Systems, 2024
  • EDULE: An AI-Enhanced Collaborative Learning Platform for Students
    S. Lilly Sheeba, Jayanth Srinivasan, M. Niranjanee, C. Nandhana
    Lecture Notes in Networks and Systems, 2024
  • An IoT-Based Deep Learning Approach for Online Fault Detection Against Cyber-Attacks
    Sujatha Rajkumar, S. Lilly Sheeba, R. Sivakami, S. Prabu, A. Selvarani
    SN Computer Science, 2023
  • A Robust and Novel Hybrid Deep Learning based Lung Nodule Identification on CT Scan Images
    A. Sivakumar, Lilly Sheeba S, M. Suganthy, B. Gunasundari, S. Sivamurugan, Sajiv G
    Proceedings of the International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering Iceconf 2023, 2023
  • Detection of Lung Cancer from CT Images Using Image Processing
    S. Lilly Sheeba, L. Gethsia Judin
    Lecture Notes in Networks and Systems, 2022
  • Enhanced cache sharing through cooperative data cache approach in MANET
    S. Lilly Sheeba, P. Yogesh
    International Journal of Biomedical Engineering and Technology, 2020
  • Collaborative Clustering for Cooperative Caching in Mobile Ad Hoc Networks
    S. Lilly Sheeba, P. Yogesh
    Wireless Personal Communications, 2017
  • Analysis of Bodily Fluids and Fomites in Transmission of Ebola Virus Using Bigdata
    C. Jemimah, S. Lilly Sheeba
    Procedia Computer Science, 2016
  • Push-pull cache consistency mechanism for cooperative caching in mobile ad hoc environments
    Lilly Sheeba SELVIN, Yogesh PALANICHAMY
    Turkish Journal of Electrical Engineering and Computer Sciences, 2016
  • A novel r-sip cache consistency mechanism for mobile wireless networks
    Journal of Theoretical and Applied Information Technology, 2014
  • An efficient HOT-B protocol for caching in mobile adhoc networks
    Sheeba S Lilly, P Yogesh
    International Conference on Recent Trends in Information Technology Icrtit 2012, 2012

RECENT SCHOLAR PUBLICATIONS

  • Study on Health Issue Identification Using Deep
    M Aparna¹, SL Sheeba
    Intelligent Systems Design and Applications: Smart Healthcare, Volume 1 1, 62 , 2024
    2024
  • A Bayesian-Based Machine Learning Analysis
    SE Vethamani, SL Sheeba
    Intelligent Systems Design and Applications: Smart Healthcare, Volume 1 1, 243 , 2024
    2024
  • E-voting system using cloud-based hybrid blockchain technology
    B Jayakumari, SL Sheeba, M Eapen, J Anbarasi, V Ravi, A Suganya, ...
    Journal of Safety Science and Resilience 5 (1), 102-109 , 2024
    2024
    Citations: 48
  • Precision Care in Addiction Treatment: A Bayesian-Based Machine Learning Analysis for Adults with Substance Use Disorders
    S Ezra Vethamani, S Lilly Sheeba
    International Conference on Intelligent Systems Design and Applications, 243-256 , 2023
    2023
  • Study on Health Issue Identification Using Deep Learning and Convolutional Neural Networks
    M Aparna, S Lilly Sheeba
    International Conference on Intelligent Systems Design and Applications, 62-69 , 2023
    2023
  • EDULE: An AI-Enhanced Collaborative Learning Platform for Students
    S Lilly Sheeba, J Srinivasan, M Niranjanee, C Nandhana
    International Conference on Intelligent Systems Design and Applications, 185-193 , 2023
    2023
  • An IoT-based deep learning approach for online fault detection against cyber-attacks
    S Rajkumar, SL Sheeba, R Sivakami, S Prabu, A Selvarani
    SN Computer Science 4 (4), 393 , 2023
    2023
    Citations: 15
  • Detection of lung cancer from ct images using image processing
    S Lilly Sheeba, L Gethsia Judin
    International Conference on Intelligent Systems Design and Applications, 686-695 , 2021
    2021
    Citations: 4
  • Time series model for stock market prediction utilising prophet
    SL Sheeba, N Gupta, RMA Ragavender, D Divya
    Turkish Journal of Computer and Mathematics Education 12 (6), 4529-4534 , 2021
    2021
    Citations: 16
  • GROUP EVENT RECOMMENDATIONS FRAMEWORK BASED ON DATA MINING
    SL Sheeba, BE Joshini, T Rushil
    Turkish Journal of Computer and Mathematics Education 12 (12), 4258-4263 , 2021
    2021
  • Enhanced cache sharing through cooperative data cache approach in MANET
    SL Sheeba, P Yogesh
    International Journal of Biomedical Engineering and Technology 32 (4), 384-399 , 2020
    2020
    Citations: 4
  • Collaborative clustering for cooperative caching in mobile Ad Hoc networks
    S Lilly Sheeba, P Yogesh
    Wireless Personal Communications 95 (2), 1087-1107 , 2017
    2017
    Citations: 3
  • Audit for Data Sharing & Retrieval from Revoked Users with Proof Verify
    SLSI R.Sowmya, R.Sindhuja
    International Journal of Advanced Research Methodology in Engineering … , 2017
    2017
  • Analysis on Drug based on Patient Reviews using Big data
    SMLSS Priyanka S
    International Journal of Science Technology & Engineering 2 (10), 62-64 , 2016
    2016
  • Emergency Alert for Disaster Management using Big Data Analytics
    KGLSS 6. Indhuja M
    International Journal of Science Technology & Engineering 2 (10), 65-67 , 2016
    2016
  • Enhanced Classification Analysis for Product Based Customer Reviews using Big Data
    SSLSS Shwetha RP
    International Journal of Science Technology & Engineering 2 (10), 59-61 , 2016
    2016
  • Push-pull cache consistency mechanism for cooperative caching in mobile ad hoc environments
    LS Selvin, Y Palanichamy
    Turkish Journal of Electrical Engineering and Computer Sciences 24 (5), 3459 … , 2016
    2016
    Citations: 6
  • Analysis of bodily fluids and fomites in transmission of ebola virus using bigdata
    C Jemimah, SL Sheeba
    Procedia Computer Science 92, 56-62 , 2016
    2016
    Citations: 8
  • A Survey on Cache Consistency Schemes Adopted in Manets
    SL Sheeba, R Udayakumar
    Indian Journal of Science and Technology 8, 32 , 2015
    2015
  • A Novel Context Aware Counter Based Cooperative Cache Replacement Strategy for Mobile Networks
    SLYP Sheeba
    International Journal of Applied Engineering Research 10 (5), 3974-3978 , 2015
    2015
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • E-voting system using cloud-based hybrid blockchain technology
    B Jayakumari, SL Sheeba, M Eapen, J Anbarasi, V Ravi, A Suganya, ...
    Journal of Safety Science and Resilience 5 (1), 102-109 , 2024
    2024
    Citations: 48
  • Time series model for stock market prediction utilising prophet
    SL Sheeba, N Gupta, RMA Ragavender, D Divya
    Turkish Journal of Computer and Mathematics Education 12 (6), 4529-4534 , 2021
    2021
    Citations: 16
  • An IoT-based deep learning approach for online fault detection against cyber-attacks
    S Rajkumar, SL Sheeba, R Sivakami, S Prabu, A Selvarani
    SN Computer Science 4 (4), 393 , 2023
    2023
    Citations: 15
  • Analysis of bodily fluids and fomites in transmission of ebola virus using bigdata
    C Jemimah, SL Sheeba
    Procedia Computer Science 92, 56-62 , 2016
    2016
    Citations: 8
  • A time index based approach for cache sharing in mobile adhoc networks
    S Lilly Sheeba, P Yogesh
    Proceedings of first international conference on computer science … , 2011
    2011
    Citations: 8
  • Push-pull cache consistency mechanism for cooperative caching in mobile ad hoc environments
    LS Selvin, Y Palanichamy
    Turkish Journal of Electrical Engineering and Computer Sciences 24 (5), 3459 … , 2016
    2016
    Citations: 6
  • Detection of lung cancer from ct images using image processing
    S Lilly Sheeba, L Gethsia Judin
    International Conference on Intelligent Systems Design and Applications, 686-695 , 2021
    2021
    Citations: 4
  • Enhanced cache sharing through cooperative data cache approach in MANET
    SL Sheeba, P Yogesh
    International Journal of Biomedical Engineering and Technology 32 (4), 384-399 , 2020
    2020
    Citations: 4
  • Collaborative clustering for cooperative caching in mobile Ad Hoc networks
    S Lilly Sheeba, P Yogesh
    Wireless Personal Communications 95 (2), 1087-1107 , 2017
    2017
    Citations: 3
  • A Novel Context Aware Counter Based Cooperative Cache Replacement Strategy for Mobile Networks
    SLYP Sheeba
    International Journal of Applied Engineering Research 10 (5), 3974-3978 , 2015
    2015
    Citations: 2
  • An efficient HOT-B protocol for caching in Mobile Adhoc Networks
    SS Lilly, P Yogesh
    2012 International Conference on Recent Trends in Information Technology … , 2012
    2012
    Citations: 1
  • Study on Health Issue Identification Using Deep
    M Aparna¹, SL Sheeba
    Intelligent Systems Design and Applications: Smart Healthcare, Volume 1 1, 62 , 2024
    2024
  • A Bayesian-Based Machine Learning Analysis
    SE Vethamani, SL Sheeba
    Intelligent Systems Design and Applications: Smart Healthcare, Volume 1 1, 243 , 2024
    2024
  • Precision Care in Addiction Treatment: A Bayesian-Based Machine Learning Analysis for Adults with Substance Use Disorders
    S Ezra Vethamani, S Lilly Sheeba
    International Conference on Intelligent Systems Design and Applications, 243-256 , 2023
    2023
  • Study on Health Issue Identification Using Deep Learning and Convolutional Neural Networks
    M Aparna, S Lilly Sheeba
    International Conference on Intelligent Systems Design and Applications, 62-69 , 2023
    2023
  • EDULE: An AI-Enhanced Collaborative Learning Platform for Students
    S Lilly Sheeba, J Srinivasan, M Niranjanee, C Nandhana
    International Conference on Intelligent Systems Design and Applications, 185-193 , 2023
    2023
  • GROUP EVENT RECOMMENDATIONS FRAMEWORK BASED ON DATA MINING
    SL Sheeba, BE Joshini, T Rushil
    Turkish Journal of Computer and Mathematics Education 12 (12), 4258-4263 , 2021
    2021
  • Audit for Data Sharing & Retrieval from Revoked Users with Proof Verify
    SLSI R.Sowmya, R.Sindhuja
    International Journal of Advanced Research Methodology in Engineering … , 2017
    2017
  • Analysis on Drug based on Patient Reviews using Big data
    SMLSS Priyanka S
    International Journal of Science Technology & Engineering 2 (10), 62-64 , 2016
    2016
  • Emergency Alert for Disaster Management using Big Data Analytics
    KGLSS 6. Indhuja M
    International Journal of Science Technology & Engineering 2 (10), 65-67 , 2016
    2016