Nallarasan V

@srmist.edu.in

Assistant Professor, Engineering & Technology
SRM institute of science and Technology

Nallarasan V


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https://researchid.co/nallarav

RESEARCH, TEACHING, or OTHER INTERESTS

Multidisciplinary, Computer Engineering, Computer Networks and Communications
21

Scopus Publications

149

Scholar Citations

6

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • Blockchain-integrated intrusion detection system with optimized cosine CNN for enhanced privacy and security in cloud computing
    N.R. Rejin Paul, V. Nallarasan, Nallam Krishnaiah, L. Guganathan
    Information Sciences, 2026
  • A label-enhanced graph neural network optimized by Elk Herd optimization for heart disease detection
    Tawfeeq Alghazali, Rupali S. Kamathe, K. Mohanambal, Abhay Chaturvedi, Jitendra Kumar Seth, Mohit Tiwari, Vivekanandan G, V. Nallarasan
    Smart Science, 2026
    Early detection of heart disease is essential for minimizing mortality, particularly in resource-constrained health-care environments. Recent models have shown accurate performance for medical diagnosis; however, they often struggles from limited interpretability and sensitivity to missing clinical data. To address these challenges, this paper proposes A Label-Enhanced Graph Neural Network Optimized by Elk Herd Optimization for Heart Disease Detection (HDD–LEGNN–EHOA). The input data is taken from Cleveland Heart disease data collection. Initially, data are pre-processed using Generalized Correntropy Sparse Gauss–Hermite Quadrature Filter (GCSGQF) to impute missing values. Then, the processed data is given to Secretary Bird Optimization Algorithm (SBOA) to select optimal characteristics. Then, the selected attributes fed to Label-Enhanced Graph Neural Network (LEGNN) to detect heart disease as heart disease present and heart disease absent. Elk Herd Optimization Algorithm (EHOA) to fine-tune the weight parameter of LEGNN. The newly suggested HDD–LEGNN–EHOA framework is assessed based on some metrics like accuracy, sensitivity, and computational time. Finally, the performance of HDD–LEGNN–EHOA method provides attains 25.68%, 22.54%, and 31.24% higher accuracy when compared to the existing models, respectively.
  • Integrated and Secure Medical Care Using Blockchain Technology
    Yeduguri Geethika, Subbara Arun Kumar, Neeli Hari Kiran, Nallarasan V
    2nd International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering Rmkmate 2025, 2025
    In an era of swift technological progress in healthcare, there is an urgent need for solutions that elevate data security, enhance transparency, and streamline patient management. This study proposes an innovative healthcare system underpinned by blockchain technology to guarantee robust data integrity, protect patient privacy, and secure medical transactions. The architecture employs the Flask framework for web development, PostgreSQL for database handling, and the Ethereum blockchain with Web3 integration for maintaining decentralized records. Among its core features are fortified user authentication, tamper-proof prescription storage, a comprehensive hospital search tool, and one-time password (OTP) verification. Leveraging the decentralized nature of blockchain, the platform effectively thwarts unauthorized modifications and fraudulent practices, thereby cultivating a climate of trust between patients and healthcare providers. The paper further details the system’s design, the methodology for implementation, and suggests avenues for future enhancements.
  • Automatic classification of pancreatic cancer from urinary biomarkers using equivariant quantum convolutional neural networks with hybrid optimization algorithm
    Vivekanandan G, Soma Prathibha, Suganthi P, Sankaranarayanan R, Nallarasan V, Ravikumar Sethuraman
    Computer Methods in Biomechanics and Biomedical Engineering, 2025
    Early detection of Pancreatic Ductal Adenocarcinoma (PDAC) is crucial to improve survival rates. This study proposes an automated classification framework using equivariant quantum convolutional neural networks (EQCNNs) optimized with a Hybrid Adam-Dingo and Quantum Artificial Hummingbird Algorithm (HybADOQAHA). Urinary biomarkers – creatinine, lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1), regenerating islet-derived protein 1 beta (REG1B), and trefoil factor 1(TFF1) were analyzed from 590 samples comprising healthy, benign, and PDAC cases. Pre-processing with dual-feature filtering and feature extraction via lifted Euler characteristic transform enhanced data quality. The experimental results demonstrate better accuracy, precision, recall, specificity, and Area Under the Curve (AUC) compared with baseline models, establishing the proposed method as a promising non-invasive diagnostic tool for early PDAC detection.
  • Design of blockchain models for the identification of harmful attack activities in industrial internet of things (IIoT)
    V. Nallarasan, Rajat Dubey, Karumuru Venkat Reddy, Sai Srujan Gutlapalli, Mohit Tiwari, Tripti Tiwari
    Interdisciplinary Approaches to AI Internet of Everything and Machine Learning, 2024
    Industrial Internet-of-Things (IIoT) is a subsequent phase in the emergence of organizations, where connected machines are used to collect, analyze, record, and control all activities of the various entities in real time with improved quality and low production costs. Although there are many brilliant cross-management schemes in place in the present systems, there are still a number of issues with such setups in the businesses. Additionally, by integrating every gadget with the Internet, the advent of the Internet of Things (IoT) inside the sectors is expanding the range of applications. Yet, such IoT devices are vulnerable to numerous attacks from outsiders, which impacts the sector by, among other things, lowering output and increasing manufacturing costs.
  • Efficient predefined time adaptive neural network for motor execution EEG signal classification based brain-computer interaction
    Jose N N, Deipali Gore, Vivekanandan G, Nithya E, Nallarasan V, Krishnakumar K
    Knowledge Based Systems, 2024
  • Detection of Real Time Malicious Intrusions using GAN (Generative Adversarial Networks) in Cyber Physical System
    Sanjay Reddy Gunnam, Sameer Kumar Vepuri, Nallarasan V
    2024 5th International Conference for Emerging Technology Incet 2024, 2024
    The project understands that computer viruses, malware, and aggressive strikes on computer networks are a constant danger. This shows how important breach detection is as a proactive defense technology. Deep learning is used to detect and correct cyber security gaps and breaches in IoT-driven cyber-physical systems to improve security. The goal of the project is to make intruder detection better than what current systems can do by focusing on things like accuracy, efficiency, and lowering false positives. This draws attention to the progress and new ideas in defense. A generative adversarial network, a cutting-edge deep learning technique, is used by the method to reach the project's goals. It also stands out by comparing uncontrolled and deep learning-based methods to discrimination, showing a complete and effective way to handle privacy. In our project, we successfully used an ensemble method to improve the accuracy of our predictions by combining several separate models. It's worth mentioning that it has a mixed design that combines CNN and LSTM, which stands for Congressional Neural Networks and Long Short-Term Memory. When applied to the KDD-Cup dataset, this hybrid model got an amazing accuracy of 99%. This shows that our ensemble method works well for finding intrusions in IoT-based defense systems.
  • Chronic Kidney Disease Detection using Deep Learning Models
    Rohan Thaney, Shivani Shivani, Jothikumar C, V. Nallarasan
    International Conference on Distributed Systems Computer Networks and Cybersecurity Icdscnc 2024, 2024
    Chronic Kidney Disease (CKD) is a pervasive health challenge necessitating accurate and timely diagnosis to mitigate its progression and associated complications. This study proposes an innovative framework for CKD detection, integrating advanced deep learning methodologies with fuzzy logic principles to capitalize on both medical imaging data and longitudinal patient records. The model incorporates Convolutional Neural Networks (CNNs) for robust image analysis, recurrent neural networks (RNNs), including long short-term memory (LSTM) networks, for sequential data processing, And Fuzzy Hybrid Neural Networks (FHNNs) for integrating fuzzy logic with deep learning. By synergistically harnessing the capabilities of CNNs in extracting spatial features from medic15al images, RNNs in analysing temporal patterns from patient records, and FHNNs in incorporating fuzzy logic for uncertainty handling and interpretability, the proposed methodology aims to achieve unparalleled accuracy and robustness in CKD diagnosis. Additionally, the research introduces a novel evaluation metric, the Renal Impairment Index (RII), amalgamating multiple clinical parameters to provide a comprehensive assessment of CKD severity. Extensive experimentation on diverse datasets validates the efficacy and clinical relevance of the proposed methodology, demonstrating promising performance in early CKD detection &staging.
  • A Neural Network-Based Approach to Forecasting Bankruptcy Using Feature Importance Analysis
    Aditi Aatmaja, Dhruv Bhagwat, B. Prakash, V. Nallarasan
    Proceedings 2024 3rd International Conference on Sentiment Analysis and Deep Learning Icsadl 2024, 2024
    This research work addresses the critical problem of developing a reliable bankruptcy prediction model. Accurately predicting bankruptcy is essential to the stability of the economy, and existing methods have limitations that may lead to business failure. In this research, we aim to overcome these limitations and develop a more accurate bankruptcy forecasting method. To do this, we discuss the issue of unequal class sizes and the relative importance of various forms of misclassification. After identifying the most important financial characteristics using feature importance analysis, we employ neural networks to predict if a company will fail in the next years. To improve the model's performance, we adjust the parameters using Grid Search Cross Validation. The accuracy of bankruptcy predictions might be greatly increased with our suggested methodology, which would also increase economic stability.
  • Data Privacy and Ethics in Data Analytics
    Rajasegar R. S., Gouthaman P., Vijayakumar Ponnusamy, Arivazhagan N., Nallarasan V.
    Studies in Big Data, 2024
    Recent innovations performed on data analytics technologies within the last two decades have steered towards a new level of data-driven decision-making in different industries. This chapter elucidates the significant aspects of data privacy with ethics under the dominion of data analytics. Firstly, the chapter details how imperative is protecting an individual's personal information. Secondly, discusses the legal frameworks, namely, GDPR (General Data Protection Regulation) and different data protection laws around the world, which have greatly influenced for bringing awareness on data privacy. Thirdly, how ethical considerations do compliment the outcome when these regulations are complied with. Finally, this chapter also offers information on how the organizations and its professionals must meticulously put efforts towards building a world on how to handle data ethically. In this regard, the chapter provides various instances from projects, case studies and real-world scenarios to support and discuss how data analytics do create positive and negative impacts amongst an individual and the society. To conclude, this chapter focuses on the vital aspects of mixing data privacy and ethics when working with data analytics. Furthermore, how organizations can follow holistic approaches wherein a blend of technology safety, legal frameworks and ethical awareness can be infused into their work culture when their employees are dealing with data in various projects in the future.
  • Artifcial Intelligence, Computer Vision and Robotics for Industry 5.0
    P. Gouthaman, Rajasegar Rajendhiran Shanthi, Vijayakumar Ponnusamy, N. Arivazhagan, V. Nallarasan
    Industry 4 0 Smart Manufacturing and Industrial Engineering Challenges and Opportunities, 2024
  • Modern Real-World Applications Using Data Analytics and Machine Learning
    Vijayakumar Ponnusamy, Nallarasan V., Rajasegar R. S., Arivazhagan N., Gouthaman P.
    Studies in Big Data, 2024
  • Prediction of Kidney Disease Utilizing a Hybrid Deep Learning Methodology
    V. Nallarasan, Vijayakumar Ponnusamy, R. Lakshminarayanan, Sona k, S. Vigneshwari, R. Vinoth
    2024 2nd International Conference on Computer Communication and Control Ic4 2024, 2024
  • Revolutionizing Face Detection: VisionaryNet's Breakthrough in Unraveling Facial Camplexity
    V. Nallarasan, N. Arivazhagan, R. Lakshminarayanan, D Sharmila, Nagaveni B Sangolgi, Anand M
    2024 2nd International Conference on Computer Communication and Control Ic4 2024, 2024
  • Cognitive Radio-based Context-Aware Link Adaptation for Coverage Extension of Narrow Band Internet of Things
    Nallarasan V, Kottilingam Kottursamy
    International Journal of Engineering Trends and Technology, 2022
  • Cognitive Radio Jamming Attack Detection Using an Autoencoder for CRIoT Network
    V. Nallarasan, Kottilingam Kottursamy
    Wireless Personal Communications, 2022
  • Jamming Attack Mitigation in CR-IoT Using Game Theory
    Nallarasan Venkatachalam, Kottilingam Kottursamy
    Revue D Intelligence Artificielle, 2022
  • Energy Efficiency and Throughput Analysis of Cognitive based Internet of Things
    V Nallarasan, Kottilingam Kottursamy
    Proceedings 1st International Conference on Smart Technologies Communication and Robotics Stcr 2021, 2021
  • Spectrum Management Analysis for Cognitive Radio IoT
    V. Nallarasan, K. Kottilingam
    2021 International Conference on Computer Communication and Informatics Iccci 2021, 2021
  • Driver profiling-based anti-theft system
    L. Anand, V. Nallarasan, M. B. Mukesh Krishnan, S. Jeeva
    Aip Conference Proceedings, 2020
  • Fuzzy logic based reliable spectrum sensing-a SDR implementation
    Vijayakumar P, Nallarasan V, A. Joshua Jafferson
    Journal of Advanced Research in Dynamical and Control Systems, 2019

RECENT SCHOLAR PUBLICATIONS

  • Blockchain-integrated intrusion detection system with optimized cosine CNN for enhanced privacy and security in cloud computing
    NRR Paul, V Nallarasan, N Krishnaiah, L Guganathan
    Information Sciences 735, 123015 , 2026
    2026
    Citations: 2
  • A label-enhanced graph neural network optimized by Elk Herd optimization for heart disease detection
    T Alghazali, RS Kamathe, K Mohanambal, A Chaturvedi, JK Seth, ...
    Smart Science, 1-13 , 2026
    2026
  • Automatic classification of pancreatic cancer from urinary biomarkers using equivariant quantum convolutional neural networks with hybrid optimization algorithm
    G Vivekanandan, S Prathibha, P Suganthi, R Sankaranarayanan, ...
    Computer methods in biomechanics and biomedical engineering, 1-15 , 2025
    2025
  • Sentence Classification Using Transfer Learning with BERT
    A Verma, V Nallarasan
    2025
  • Design of Blockchain Models for the Identification of Harmful Attack Activities in Industrial Internet of Things (IIoT)
    V Nallarasan, R Dubey, KV Reddy, SS Gutlapalli, M Tiwari, T Tiwari
    Interdisciplinary Approaches to AI, Internet of Everything, and Machine … , 2025
    2025
  • Harnessing Generative AI in Education: From Theory to Real-World Impact
    A Verma, V Nallarasan
    OSF , 2025
    2025
  • Efficient predefined time adaptive neural network for motor execution EEG signal classification based brain-computer interaction
    NN Jose, D Gore, G Vivekanandan, E Nithya, V Nallarasan, ...
    Knowledge-Based Systems 303, 112270 , 2024
    2024
    Citations: 35
  • Artificial Intelligence, Computer Vision and Robotics for Industry 5.0
    P Gouthaman, RR Shanthi, V Ponnusamy, N Arivazhagan, V Nallarasan
    Industry 4.0, Smart Manufacturing, and Industrial Engineering, 196-216 , 2024
    2024
  • Detection of Real Time Malicious Intrusions Using GAN (Generative Adversarial Networks) in Cyber Physical System
    SR Gunnam, SK Vepuri
    2024 5th International Conference for Emerging Technology (INCET), 1-7 , 2024
    2024
    Citations: 4
  • Data privacy and ethics in data analytics
    RS Rajasegar, P Gouthaman, V Ponnusamy, N Arivazhagan, ...
    Data analytics and machine learning: Navigating the big data landscape, 195-213 , 2024
    2024
    Citations: 13
  • Modern real-world applications using data analytics and machine learning
    V Ponnusamy, V Nallarasan, RS Rajasegar, N Arivazhagan, ...
    Data Analytics and Machine Learning: Navigating the Big Data Landscape, 215-235 , 2024
    2024
    Citations: 6
  • A Neural Network-Based Approach to Forecasting Bankruptcy Using Feature Importance Analysis
    A Aatmaja, D Bhagwat, B Prakash, V Nallarasan
    2024 3rd International Conference on Sentiment Analysis and Deep Learning … , 2024
    2024
  • Prediction of kidney disease utilizing a hybrid deep learning methodology
    V Nallarasan, V Ponnusamy, R Lakshminarayanan, S Vigneshwari, ...
    2024 2nd International Conference on Computer, Communication and Control … , 2024
    2024
    Citations: 11
  • Revolutionizing Face Detection: VisionaryNet’s Breakthrough in Unraveling Facial Camplexity
    V Nallarasan, N Arivazhagan, R Lakshminarayanan, D Sharmila, ...
    2024 2nd International Conference on Computer, Communication and Control … , 2024
    2024
  • Cognitive radio jamming attack detection using an autoencoder for CRIoT network
    V Nallarasan, K Kottursamy
    Wireless Personal Communications 127 (3), 2267-2283 , 2022
    2022
    Citations: 23
  • Energy efficiency and throughput analysis of cognitive based internet of things
    V Nallarasan, K Kottursamy
    2021 Smart Technologies, Communication and Robotics (STCR), 1-6 , 2021
    2021
    Citations: 3
  • Spectrum management analysis for cognitive radio IoT
    V Nallarasan, K Kottilingam
    2021 international conference on computer communication and informatics … , 2021
    2021
    Citations: 28
  • Tailored Medicine for Patients based on Data Mining and Machine Learning Techniques
    JG Jeshurun, V Nallarasan, D Cumar
    Annals of the Romanian Society for Cell Biology 25 (4), 2695-2703 , 2021
    2021
  • Driver profiling-based anti-theft system
    L Anand, V Nallarasan, MBM Krishnan, S Jeeva
    AIP Conference Proceedings 2282 (1), 020042 , 2020
    2020
    Citations: 24
  • Enhanced security in IoT Networks using ensemble learning methods-A Cognitive Radio Approach
    V Nallarasan, K Kottilingam
    International Journal 8 (8) , 2020
    2020

MOST CITED SCHOLAR PUBLICATIONS

  • Efficient predefined time adaptive neural network for motor execution EEG signal classification based brain-computer interaction
    NN Jose, D Gore, G Vivekanandan, E Nithya, V Nallarasan, ...
    Knowledge-Based Systems 303, 112270 , 2024
    2024
    Citations: 35
  • Spectrum management analysis for cognitive radio IoT
    V Nallarasan, K Kottilingam
    2021 international conference on computer communication and informatics … , 2021
    2021
    Citations: 28
  • Driver profiling-based anti-theft system
    L Anand, V Nallarasan, MBM Krishnan, S Jeeva
    AIP Conference Proceedings 2282 (1), 020042 , 2020
    2020
    Citations: 24
  • Cognitive radio jamming attack detection using an autoencoder for CRIoT network
    V Nallarasan, K Kottursamy
    Wireless Personal Communications 127 (3), 2267-2283 , 2022
    2022
    Citations: 23
  • Data privacy and ethics in data analytics
    RS Rajasegar, P Gouthaman, V Ponnusamy, N Arivazhagan, ...
    Data analytics and machine learning: Navigating the big data landscape, 195-213 , 2024
    2024
    Citations: 13
  • Prediction of kidney disease utilizing a hybrid deep learning methodology
    V Nallarasan, V Ponnusamy, R Lakshminarayanan, S Vigneshwari, ...
    2024 2nd International Conference on Computer, Communication and Control … , 2024
    2024
    Citations: 11
  • Modern real-world applications using data analytics and machine learning
    V Ponnusamy, V Nallarasan, RS Rajasegar, N Arivazhagan, ...
    Data Analytics and Machine Learning: Navigating the Big Data Landscape, 215-235 , 2024
    2024
    Citations: 6
  • Detection of Real Time Malicious Intrusions Using GAN (Generative Adversarial Networks) in Cyber Physical System
    SR Gunnam, SK Vepuri
    2024 5th International Conference for Emerging Technology (INCET), 1-7 , 2024
    2024
    Citations: 4
  • Energy efficiency and throughput analysis of cognitive based internet of things
    V Nallarasan, K Kottursamy
    2021 Smart Technologies, Communication and Robotics (STCR), 1-6 , 2021
    2021
    Citations: 3
  • Blockchain-integrated intrusion detection system with optimized cosine CNN for enhanced privacy and security in cloud computing
    NRR Paul, V Nallarasan, N Krishnaiah, L Guganathan
    Information Sciences 735, 123015 , 2026
    2026
    Citations: 2
  • A label-enhanced graph neural network optimized by Elk Herd optimization for heart disease detection
    T Alghazali, RS Kamathe, K Mohanambal, A Chaturvedi, JK Seth, ...
    Smart Science, 1-13 , 2026
    2026
  • Automatic classification of pancreatic cancer from urinary biomarkers using equivariant quantum convolutional neural networks with hybrid optimization algorithm
    G Vivekanandan, S Prathibha, P Suganthi, R Sankaranarayanan, ...
    Computer methods in biomechanics and biomedical engineering, 1-15 , 2025
    2025
  • Sentence Classification Using Transfer Learning with BERT
    A Verma, V Nallarasan
    2025
  • Design of Blockchain Models for the Identification of Harmful Attack Activities in Industrial Internet of Things (IIoT)
    V Nallarasan, R Dubey, KV Reddy, SS Gutlapalli, M Tiwari, T Tiwari
    Interdisciplinary Approaches to AI, Internet of Everything, and Machine … , 2025
    2025
  • Harnessing Generative AI in Education: From Theory to Real-World Impact
    A Verma, V Nallarasan
    OSF , 2025
    2025
  • Artificial Intelligence, Computer Vision and Robotics for Industry 5.0
    P Gouthaman, RR Shanthi, V Ponnusamy, N Arivazhagan, V Nallarasan
    Industry 4.0, Smart Manufacturing, and Industrial Engineering, 196-216 , 2024
    2024
  • A Neural Network-Based Approach to Forecasting Bankruptcy Using Feature Importance Analysis
    A Aatmaja, D Bhagwat, B Prakash, V Nallarasan
    2024 3rd International Conference on Sentiment Analysis and Deep Learning … , 2024
    2024
  • Revolutionizing Face Detection: VisionaryNet’s Breakthrough in Unraveling Facial Camplexity
    V Nallarasan, N Arivazhagan, R Lakshminarayanan, D Sharmila, ...
    2024 2nd International Conference on Computer, Communication and Control … , 2024
    2024
  • Tailored Medicine for Patients based on Data Mining and Machine Learning Techniques
    JG Jeshurun, V Nallarasan, D Cumar
    Annals of the Romanian Society for Cell Biology 25 (4), 2695-2703 , 2021
    2021
  • Enhanced security in IoT Networks using ensemble learning methods-A Cognitive Radio Approach
    V Nallarasan, K Kottilingam
    International Journal 8 (8) , 2020
    2020