Ponnuviji N P

@rmkcet.ac.in

Associate Professor/Department of Computer Science and Engineering
R.M.K. College of Engineering and Technology

Ponnuviji N P


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https://researchid.co/ponnuvijiparthiban

RESEARCH INTERESTS

Cloud Computing, Network Security, Machine Learning
19

Scopus Publications

Scopus Publications

  • AI-Driven Adaptive AML Framework with Real-Time Anomaly Detection and Deep Learning-Based Risk Profiling
    N.P. Ponnuviji, K. Venkatesh Guru, P. Palanisamy, J. Nirmala Gandhi
    Journal of Internet Services and Information Security, 2026
    The elusive development of financial crime requires new Anti-Money Laundering (AML) systems. In this paper, the authors introduce the concept of an AI-Driven Adaptive AML Framework, which combines real-time detection of anomalies and deep learning-based risk profiling. Legacy rule-based systems are ineffective and not adaptable to new money laundering schemes; they produce an intolerable number of false positives (estimated at 90-95 per cent of alerts), and they cannot evolve with new schemes. The methodology provides an improvement over the traditional thresholds by using Mahalanobis distance for unsupervised real-time anomaly detection, which immediately marks transactions that are not in line with the norms. At the same time, adaptive risk profiling, which is based on deep neural networks, assigns customers and transactions with granular risk scores based on the identification of complex connections in behavioral data. The performance and performance comparison with traditional rule-based systems prove the high efficacy of the framework. The AI-based concept is much more flexible and efficient in terms of operations, with a high score of 9/10 versus 3/10 in the case of legacy systems. What is more, the framework has an impressive false positive reduction potential (scoring 8/10 vs 4/10), which results in a greater true-positive ratio and is able to adapt AML systems to emerging threats continuously. The end goal of this AI-enhanced system is to increase the accuracy of detection, decrease the cost of operations, and create a safer, more secure financial system, but with high compliance.
  • Cloud-Based Intrusion Detection With TFSEA: Utilizing Graylevel Radial Component Analysis and Threshold-Based Kernel Extreme Learning Machine
    Saravanan Selvaraj, K. Lalitha Devi, N. P. Ponnuviji, Santhi Subbaian
    Transactions on Emerging Telecommunications Technologies, 2026
    The emergence of cloud computing has revolutionized business operations by providing effective scalability and flexibility. Security concerns have intensified due to the vast amount of data processed and stored in the cloud; hence protecting cloud infrastructure from cyber threats is crucial. Intrusion detection system plays a pivotal role in seamless monitoring of network traffic for exhibiting unauthenticated or malicious attempts. Recent advancements in IDS highlight certain issues such as low classification accuracy, high false positive rate, as well as overfitting when processing various network data. The feature extraction uses graylevel radial component analysis (GRCA) to extract salient features, while dimensionality reduction is performed by introducing the radial basis function principal component analysis. In this work, the crossover boosted dynamic cheetah optimization algorithm is employed in the feature selection process, which integrates Cheetah Optimization with dynamic evolutionary strategies to improve the overall search efficiency and tackle local optimal issues. The detection and classification of intrusion are performed by proposing a novel threshold‐based kernel extreme learning machine, which uses different thresholds to enhance generalization capability. Extensive experimental and statistical analysis is carried out, and the results exhibit that the proposed framework achieves a classification accuracy, precision, recall, F 1 score, and security rate of 98.84%, 97.22%, 97%, 97.2%, and 98.85%, respectively, compared to all other existing models. Finally, the classified data is stored in cloud infrastructure that allows third‐party monitoring services to assess and analyze critical intrusions and also provide threat analysis.
  • Strategic Business Transformation Through IoT: Enhancing Operational Efficiency and Business Model Innovation
    Sankar, P. V. Raja Suganya, R. Abitha, N. P. Ponnuviji, K. Saravanan, A. Maheshwari, V. Nivaskumar
    Iot Driven Business Transformation Strategy Data Trust and Security, 2026
    The Internet of Things (IoT) is transforming the strategic landscape of modern enterprises by enabling enhanced operational efficiency and driving innovation in business models. This chapter explores the foundational components of IoT, its integration into enterprise systems, and its role in real-time monitoring, automation, and predictive analytics. It highlights how IoT empowers organizations to shift from product-centric to service-oriented models, create new revenue streams, and improve customer engagement. The chapter also examines cross-industry applications, including manufacturing, healthcare, retail, and agriculture, while addressing key challenges such as data privacy, interoperability, and organizational resistance. Looking ahead, the convergence of IoT with AI, blockchain, and 5G points to a future of autonomous, ethical, and sustainable deployments.
  • Safeguarding confidentiality and privacy in cloud-enabled healthcare systems with spectrasafe encryption and dynamic k-anonymity algorithm
    D. Dhinakaran, N. Jagadish Kumar, N.P. Ponnuviji, B. Praveen kumar
    Expert Systems with Applications, 2025
  • Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach
    N. P. Ponnuviji, G. Nirmala, M. L. Sworna Kokila, S. Indra Priyadharshini
    Journal of the Institution of Engineers India Series B, 2025
  • Enhancing real-time health monitoring with hybrid recurrent long short-term tyrannosaurus search for menstrual cups
    S Indra Priyadharshini, D Shiny Irene, J. Rene Beulah, N.P Ponnuviji
    Biomedical Signal Processing and Control, 2025
  • EHECA-HSWS: A Holistic Method for Improving Menstrual Health App User Experience
    D. Shiny Irene, N. P. Ponnuviji, A. Kalaivani
    IETE Journal of Research, 2025
    Menstrual health and hygiene apps play a crucial role in empowering users to manage their reproductive health. This paper introduces a novel approach, the Ensemble Hybrid Extreme Convolutional Autoencoder-based Harbor Seal Whisker Search algorithm for enhancing menstrual health and hygiene management. The Extreme Gradient Boosting is applied for user segmentation, a Swin Transformer for issue extraction from user reviews, a hybrid Convolutional Neural Network for menstrual pattern tracking, and Autoencoders for anomaly detection. The weighted average ensemble combines outputs, and the Harbor Seal Whiskers Optimization algorithm enhances hyperparameter optimization. Evaluation metrics, including Area Under the Receiver Operating Characteristic Curve, precision, recall, accuracy, F1-score, Mean Squared Error, Mean Absolute Error, computational time, and specificity, demonstrate the model's effectiveness. Comparative analysis with existing methods affirms the superior performance of the Ensemble Hybrid Extreme Convolutional Autoencoder-based Harbor Seal Whisker Search Algorithm in analyzing user behavior and feedback data, ultimately improving the overall user experience during menstruation.
  • Leveraging Random Forest for Intelligent IoT Systems in Industrial Environments
    N. P. Ponnuviji, A. Sumaiya Begum, T. Gnanasekaran, M. Rajendiran, S. Shenbaga Ezhil
    Communications in Computer and Information Science, 2025
  • Securing cloud-based medical data: an optimal dual kernal support vector approach for enhanced EHR management
    M. L. Sworna Kokila, E. Fenil, N. P. Ponnuviji, G. Nirmala
    International Journal of System Assurance Engineering and Management, 2024
  • IoT-enabled assistive technologies approach for personalized geriatric health monitoring and safety
    N. P. Ponnuviji, G. Elangovan, K. Sujatha, Umamageswaran Jambulingam, Indumathi Ganesan, S. D. Lalitha
    Future of AI in Biomedicine and Biotechnology, 2024
    IoT is revolutionizing healthcare, especially for geriatric individuals in smart homes, prioritizing personalized, preventive, and comprehensive treatment. This research aims to create an intelligent environment for adaptable living, incorporating cutting-edge assistive technologies. The system includes features like medicine prompts, schedulers, fitness monitors, and improved fall detection, operating efficiently for up to seven days without battery replacement. To safeguard patient information, an ECDH module reduces latency by 77.78% compared to alternatives, ensuring security and efficiency. With user-friendly interfaces and adaptive functionalities, seamless user experience and accessibility are prioritized.
  • Real-time event detection and predictive analytics using IoT and deep learning
    Indumathi Ganesan, N. P. Ponnuviji, A. Siva Kumar, M. Nithya, Umamageswaran Jambulingam, S. D. Lalitha
    Industry Applications of Thrust Manufacturing Convergence with Real Time Data and AI, 2024
  • Technological Advancements in Menstrual Health: The Role of Generative Pre-Trained Transformer and Bees Algorithm
    D. Shiny Irene, S. Indra Priyadharshini, N. P. Ponnuviji, A. Kalaivani
    IETE Journal of Research, 2024
  • ALRN-RCS: Advanced Approach to Network Intrusion Detection Using Attention Long-Term Recurrent Networks and Chaotic Optimization
    Ponnuviji N P, Nirmala E, Mary Harin Fernandez F, Anitha K
    IETE Journal of Research, 2024
  • OpenCV Based Real-Time Traffic Analyzer
    C Pandi, N.P Ponnuviji, R. Srinivasan, C. Parthasarathy, P. Karthick, Rohith Kumar V
    Iccds 2024 International Conference on Computing and Data Science, 2024
  • AI-Powered Business Intelligence for Transforming Finance and Education
    Ponnuviji N. P., P. Murugan, Esakkiammal S, Sunil Kumar, Vishal KS, S. Meenakshi
    2024 2nd International Conference on Disruptive Technologies Icdt 2024, 2024
  • Analysis of Non-Linear Dynamic Characteristics of Fuzzy-Based SRM Drive for Electric Vehicle Applications
    N Padmavathi
    Communications on Applied Nonlinear Analysis, 2024
  • Enhancing lifestyle and health monitoring of elderly populations using CSA-TkELM classifier
    R. Anto Arockia Rosaline, Ponnuviji N.P., Subbu Lakshmi T.C., Manisha G.
    Knowledge Based Systems, 2023
  • Efficient Intrusion Detection and Prevention Model in Cloud Environment Using Sgd-LSTM and C2HA
    Ponnuviji NAMAKKAL PONNUSAMY, Vigilson Prem MONICKARAJ, Ezhumalai PERIYATHAMBI
    Studies in Informatics and Control, 2022
  • An IPSO-KELM based malicious behaviour detection and SHA256-RSA based secure data transmission in the cloud paradigm
    N. P. Ponnuviji, M. V. Prem
    Ksii Transactions on Internet and Information Systems, 2021