Arjun Paramarthalingam

@aucev.edu.in

Assistant Professor, Department of Computer Science and Engineering
University College of Engineering Villupuram

Arjun Paramarthalingam


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https://researchid.co/arjunucev
Arjun Paramarthalingam currently works at the department of CSE, University College of Engineering Villupuram, Tamilnadu, India. Arjun does research in Computer Vision, Artificial Intelligence and IoT.

EDUCATION

B.Tech., M.E., Ph.D

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Vision and Pattern Recognition, Computer Science, Computer Science Applications, Artificial Intelligence
22

Scopus Publications

210

Scholar Citations

8

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • A Privacy-Preserving Approach to Health Insurance Fraud Detection Using Vertical Federated Learning
    Raghi K R, Arjun Paramarthalingam, Harini Kanthan, Mahalakshmi Karthiban
    Sensors, 2025
    In fraud detection, centralized approaches often face challenges related to data protection, security, and potential data breaches. Such methods require sensitive healthcare and insurance data to be pooled in one location, which increases vulnerability to misuse. This paper introduces FraudNetX, a privacy-preserving fraud detection framework, by utilizing Vertical Federated Learning (VFL) to address centralized system limitations. VFL enables models to be trained collaboratively while ensuring data privacy and security through quantifiable Differential Privacy (DP) guarantees (ε = 1.0, δ = 1 × 105). FraudNetX implements a noise injection based on Differential Privacy (DP) with Gaussian noise (s = 1.2) in the process of training the model to guarantee confidentiality of the personal data. This research entails two partner organizations, which are a hospital and an insurance company, in an actual VFL configuration. The model is trained on 10 communication rounds in this federated setup, and the local optimization of each client is followed by the global aggregation step. Hospitals and insurers can learn fraud patterns without sharing their data. The proposed FraudNetX is a hybrid architecture which is composed of Feedforward Neural Networks (FFNNs) and transformer encoders. An adaptive weighting strategy has been applied to handle category imbalance concern and enhance recall of a few categories which are hard to detect, especially in fraud involving minorities, balancing the recall performance. The framework also includes a decision model that uses hospital data and claim behavior to classify each claim as legitimate, under review, or fraudulent. The experimental evaluation on the real-world dataset demonstrates that FraudNetX enhances the accuracy and F1-score of fraud detection (accuracy = 99.91%, F1 = 99.94%, ROC-AUC = 0.98) but does not violate data privacy.
  • AI-Driven Chatbot for Mental Health Analysis Using Transformer Models
    Arjun Paramarthalingam, Amirthasaravanan Arivunambi, Ashokkumar Janarthanan, Sindhuja Sundaresan, Srinivas saravanan Ariyangavu
    Premier Journal of Science, 2025
    This Mental Wellness Chatbot is a software-based solution created for the purpose of solving the incomplete alleviation of depression symptoms in a person. This mental wellness Companion is a chatbot that act as a normal chatbot while mainly focus on the mental health of the user. To accomplish this, we are going to be use various techniques and algorithms to create this chatbot. We are going to use API token of a pre-trained hugging face model in order to create this chatbot rather than using the Large Language Model (LLM) locally since executing the chatbot locally requires a lot of GPU power in the device in order to create and embed the data that we are providing as a dataset. Mainly we are going to be using a pre-trained model from the open-source hugging face model and we are not going to be using OpenAI for completing it. For the database we are going to be using the vector database Facebook AI Similarity Search (FAISS) instead of Mangodb. The mental health Chatbot app is like a reliable friend in the pocket, always ready to lend an ear and offer support whenever you need it. Through gentle questions and personalized suggestions, it helps to navigate the emotions and find ways to feel better. By tracking persons progress over time, it creates a valuable record of emotional journey, empowering to understand oneself better. With 24/7 availability and a commitment to privacy and confidentiality, it provides a safe space for a person to express openly and honestly. Whether the person looking for coping strategies, educational insights, or connections to additional resources, the Chatbot app is there to guide a person on their path to emotional well-being.
  • Opinion Mining in Tamil YouTube Comments Using Machine Learning Approach
    Ashokkumar Janarthanan, Arjun Paramarthalingam, Banumathi Jayachandra, Amirthasaravanan Arivunambi
    Aip Conference Proceedings, 2025
  • Pneumonia Detection from Chest X-Ray Images with Inception-ResNetV2
    Arjun Paramarthalingam, Ashokkumar Janarthanan, Vidhya Athmalingam, Hariharan Saravanan
    Lecture Notes in Networks and Systems, 2025
  • AI-powered virtual mouse control through hand gestures with computer vision
    Arjun Paramarthalingam, Ashokkumar Janarthanan, Amirthasaravanan Arivunambi, Srinivas Saravanan Ariyangavu, Hariprasath Senthamaraikannan, Rekha Ganapathy
    Harnessing AI in Geospatial Technology for Environmental Monitoring and Management, 2024
    This article introduces an Artificial Intelligence (AI) enabled virtual mouse system that utilizes hand gestures and fingertip detection to operate computer mouse functions through AI and computer vision techniques. It serves as a convenient alternative to traditional physical mouse, offering users increased flexibility and accessibility in navigating and controlling their devices. In this article, the proposed work uses three modules such as OpenCV, MediaPipe and PyautoGUI to create the virtual mouse system. OpenCV library is used for its real-time computer vision functionality to help us to capture the hand using the web camera. The MediaPipe framework is used to detect the hand region using KLT Tracking Algorithm and then the K-cosine border tracking algorithm is used to identify various types of hand gesture movements to mimic the computer mouse cursor movement and scrolling operations. The PyAutoGUI module is used to perform appropriate mouse actions based on the recognized hand gestures.
  • A deep learning model to assist visually impaired in pothole detection using computer vision
    Arjun Paramarthalingam, Jegan Sivaraman, Prasannavenkatesan Theerthagiri, Balaji Vijayakumar, Vignesh Baskaran
    Decision Analytics Journal, 2024
    Visually impaired individuals encounter numerous impediments when traveling, such as navigating unfamiliar routes, accessing information, and transportation, which can limit their mobility and restrict their access to opportunities. However, assistive technologies and infrastructure solutions such as tactile paving, audio cues, voice announcements, and smartphone applications have been developed to mitigate these challenges. Visually impaired individuals also face difficulties when encountering potholes while traveling. Potholes can pose a significant safety hazard, as they can cause individuals to trip and fall, potentially leading to injury. For visually impaired individuals, identifying and avoiding potholes can be particularly challenging. The solutions ensure that all individuals can travel safely and independently, regardless of their visual abilities. An innovative approach that leverages the You Only Look Once (YOLO) algorithm to detect potholes and provide auditory or haptic feedback to visually impaired individuals has been proposed in this paper. The dataset of pothole images was trained and integrated into an application for detecting potholes in real-time image data using a camera. The app provides feedback to the user, allowing them to navigate potholes and increasing their mobility and safety. This approach highlights the potential of YOLO for pothole detection and provides a valuable tool for visually impaired individuals. According to the testing, the model achieved 82.7% image accuracy and 30 Frames Per Second (FPS) accuracy in live video. The model is trained to detect potholes close to the user, but it may be hard to detect potholes far away from the user. The current model is only trained to detect potholes, but visually impaired people face other challenges. The proposed technology is a portable option for visually impaired people.
  • Investigation and prediction of itemsets frequency using machine learning techniques
    Sameera Banu M., Samyuktha S. R., Gajalakshmi Duraikannu, Arjun Paramarthalingam
    Spectrum and Power Allocation in Cognitive Radio Systems, 2024
    Frequency of Itemsets plays a crucial role in analytics of retail industry, which delves into latent patterns in customer purchasing behavior. This paper presents an Apriori algorithm to extract associations among products in a given dataset, shedding light on frequently co-occurring items. By discerning these relationships, the business gains profound insights into customer preferences and tendencies, aiming not only to understand current purchasing behavior but also to identify potential cross-selling opportunities. As businesses rely on transactional data for insights, analysis reliability hinges on data quality. This study explores missing values, outliers, and data inconsistency, impacting market basket analysis accuracy. Leveraging the Apriori algorithm facilitates the revelation of robust product associations, enabling strategic optimization and heightened customer satisfaction. The gleaned insights inform targeted marketing, product placements, and inventory management, catalyzing more effective business optimization in the retail sector.
  • Brain Tumour Segmentation with a U-Net Based GAN Model Using Multi-Modal MRI Images
    Arjun Paramarthalingam, Ashokkumar Janarthanan, Amirthasaravanan Arivunambi, Thapashvin Rajamohan
    Aip Conference Proceedings, 2024
  • Proactive Detection of Mirai Botnet Threats: Leveraging XGBoost for Enhanced Cybersecurity
    Raghi K R, Arjun Paramarthalingam
    Iet Conference Proceedings, 2024
    Detecting the Mirai botnet is still a key challenge in cybersecurity because of the continuous growth of hostile tactics. Existing detection systems frequently rely on antiquated methodologies, resulting in low accuracy and efficiency. The study indicates a novel approach for detecting Mirai botnet activity that uses XGBoost, an advanced ensemble learning algorithm. The proposed system outperforms standard approaches in terms of performance measures by incorporating thorough data preparation, feature extraction, and selection methodologies, as well as efficient XGBoost model training. The results show a considerable improvement in accuracy (0.90), precision (0.89), recall (0.91), and F1 score (0.90), as well as a decrease in false positives (20) and false negatives (15). Furthermore, improved computational efficiency leads to shorter training and prediction timeframes, less memory utilization, and more responsive detection capabilities. Overall, the proposed system provides a strong framework for proactive detection and mitigation of Mirai botnet threats, which improves network security in real-world scenarios.
  • Curved Road Lanes Detection Using Fully Convolutional Neural Network
    Arjun Paramarthalingam, Amirthasaravanan Arivunambi, Ashokkumar Janarthanan
    Lecture Notes in Networks and Systems, 2024
  • A Study on Word Embeddings in Local LLM-based Chatbot Applications
    Hariprasath S, Arjun Paramarthalingam, Shanmugam Sundaramurthy, Stefano Cirillo
    2024 International Conference on Innovation and Intelligence for Informatics Computing and Technologies 3ict 2024, 2024
  • Privacy-Preserving Deep sigmoid NN Classification over Entropy based Cryptosystem in Cloud Environments
    Raghi K. R, Arjun Paramarthalingam
    2023 International Conference on Disruptive Technologies Icdt 2023, 2023
  • A Study on Two-Phase Monitoring Server for Ransomware Evaluation and Detection in IoT Environment
    Amirthasaravanan Arivunambi, Arjun Paramarthalingam
    Journal of Trends in Computer Science and Smart Technology, 2022
  • Real-time indoor air quality monitoring using the Internet of Things
    Ashokkumar Janarthanan, Arjun Paramarthalingam, Amirthasaravanan Arivunambi, P. M. Durai Raj Vincent
    Proceedings of the 2022 3rd International Conference on Intelligent Computing Instrumentation and Control Technologies Computational Intelligence for Smart Systems Icicict 2022, 2022
  • A Smart Virtual Brain System for Reliable EEG Sensing and Actuation in Intelligent Brain IOT Environment
    Amirthasaravanan Arivunambi, Arjun Paramarthalingam, P. Sanju, S. Uthayashangar, Keerthi. V. L
    2021 International Conference on System Computation Automation and Networking Icscan 2021, 2021
  • Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval
    Arjun Paramarthalingam, Mirnalinee Thankanadar
    Iet Image Processing, 2021
  • An Application-Driven IoT Based Rooftop Farming Model for Urban Agriculture
    Arjun Paramarthalingam, Amirthasaravanan Arivunambi, Sreedhar Thapasimony
    IFIP Advances in Information and Communication Technology, 2021
  • A study on IoT based smart street light systems
    P. Arjun, S. Stephenraj, N.Naveen Kumar, K.Naveen Kumar
    2019 IEEE International Conference on System Computation Automation and Networking Icscan 2019, 2019
  • An Efficient Image Retrieval System Based on Multi-Scale Shape Features
    P. Arjun, T. T. Mirnalinee
    Journal of Circuits Systems and Computers, 2018
  • Compact centroid distance shape descriptor based on object area normalization
    P. Arjun, T.T. Mirnalinee, M. Tamilarasan
    Proceedings of 2014 IEEE International Conference on Advanced Communication Control and Computing Technologies Icaccct 2014, 2015
  • Affine invariant compact centroid distance shape descriptor for image retrieval
    P. Arjun, T. T. Mirnalinee
    Applied Mathematical Sciences, 2015
  • Affine invariant shape descriptor using object area normalization
    P. Arjun, T. T. Mirnalinee, S. Sindhuja, G. Bharathi Raja
    Lecture Notes in Electrical Engineering, 2015

RECENT SCHOLAR PUBLICATIONS

  • Optimized U-net model for precise retinal blood vessel segmentation from colour fundus images
    J Sivaraman, A Paramarthalingam, A Thirunavukkarasu, A Vasudevan, ...
    Scientific Reports , 2026
    2026
  • Advancements in Image Captioning: A Comprehensive Survey on Techniques, Modalities, and Applications
    A Janarthanan, A Paramarthalingam, S Sundaramurthy
    2025 International Conference on Decision Aid Sciences and Applications … , 2025
    2025
  • A Generative Method for Steganography by Cover Synthesis with Secure System
    KR Raghi, A Paramarthalingam, AM Mathew, AM Mathew
    International Conference on Computing Science, Communication and Security, 57-68 , 2025
    2025
  • Patent-MACHINE LEARNING-BASED DIGITAL ELECTROCARDIOGRAPH DEVICE
    KR Shanmugam Sundaramurthy, Syed Anwar Hussainy F, Sugumar R, Arjun ...
    2025
  • Intelligent Drowsiness Detection using Haar Cascade Classifier and Convolutional Neural Network
    A Janarthanan, A Paramarthalingam, S Sundaresan, S Yokeshvaran
    Journal of Ubiquitous Computing and Communication Technologies 7 (2), 176-194 , 2025
    2025
    Citations: 2
  • Opinion mining in tamil YouTube comments using machine learning approach
    A Janarthanan, A Paramarthalingam, B Jayachandra, A Arivunambi
    AIP Conference Proceedings 3137 (1), 020001 , 2025
    2025
    Citations: 1
  • A Privacy-Preserving Approach to Health Insurance Fraud Detection Using Vertical Federated Learning
    A Paramarthalingam, H Kanthan, M Karthiban
    Sensors 25 (23), 7354 , 2025
    2025
  • AI-Driven Chatbot for Mental Health Analysis Using Transformer Models
    A Paramarthalingam, A Arivunambi, A Janarthanan, S Sundaresan, ...
    Science 15, 100133 , 2025
    2025
    Citations: 1
  • AI-Powered Virtual Mouse Control Through Hand Gestures With Computer Vision
    A Paramarthalingam, A Janarthanan, A Arivunambi, SS Ariyangavu, ...
    Harnessing AI in Geospatial Technology for Environmental Monitoring and … , 2025
    2025
    Citations: 2
  • Proactive detection of Mirai botnet threats: leveraging XGBoost for enhanced cybersecurity
    R KR, A Paramarthalingam
    IET Conference Proceedings CP900 2024 (23), 34-39 , 2024
    2024
    Citations: 3
  • A study on word embeddings in local LLM-based chatbot applications
    S Hariprasath, A Paramarthalingam, S Sundaramurthy, S Cirillo
    2024 International Conference on Innovation and Intelligence for Informatics … , 2024
    2024
    Citations: 3
  • A deep learning model to assist visually impaired in pothole detection using computer vision
    A Paramarthalingam, J Sivaraman, P Theerthagiri, B Vijayakumar, ...
    Decision Analytics Journal 12, 100507 , 2024
    2024
    Citations: 39
  • Brain tumour segmentation with a U-Net based GaN model using multi-modal MRI images
    A Paramarthalingam, A Janarthanan, A Arivunambi, T Rajamohan
    AIP Conference Proceedings 3075 (1), 020219 , 2024
    2024
    Citations: 3
  • Pneumonia Detection from Chest X-Ray Images with Inception-ResNetV2
    A Paramarthalingam, A Janarthanan, V Athmalingam, H Saravanan
    International Conference on Recent Trends in Computing, 341-355 , 2024
    2024
    Citations: 1
  • DESIGN - IOT BASED CANCER DETECTION DEVICE
    S Sundaramurthy, A Paramarthalingam, M Hemalatha, G Karthi, ...
    IN Patent 416407-001 , 2024
    2024
  • AI-driven exploration and prediction of company registration trends with Registrar of Companies (RoC)
    R Venkatakrishnan, M Sivagurunathan, L Siva, S Subramani, ...
    Journal of Ubiquitous Computing and Communication Technologies 6 (1), 64-75 , 2024
    2024
    Citations: 2
  • Investigation and Prediction of Itemsets Frequency Using Machine Learning Techniques
    SR Samyuktha, G Duraikannu, A Paramarthalingam
    Spectrum and Power Allocation in Cognitive Radio Systems, 113-126 , 2024
    2024
  • Curved Road Lanes Detection Using Fully Convolutional Neural Network
    A Paramarthalingam, A Arivunambi, A Janarthanan
    International Conference on Recent Trends in Computing, 633-644 , 2023
    2023
  • Privacy-Preserving Deep sigmoid NN Classification over Entropy based Cryptosystem in Cloud Environments
    R K.R, A Paramarthalingam
    2023 International Conference on Disruptive Technologies (ICDT), 306-312 , 2023
    2023
    Citations: 3
  • Real-time indoor air quality monitoring using the Internet of Things
    A Janarthanan, A Paramarthalingam, A Arivunambi, PMDR Vincent
    2022 Third International Conference on Intelligent Computing Instrumentation … , 2022
    2022
    Citations: 9

MOST CITED SCHOLAR PUBLICATIONS

  • A deep learning model to assist visually impaired in pothole detection using computer vision
    A Paramarthalingam, J Sivaraman, P Theerthagiri, B Vijayakumar, ...
    Decision Analytics Journal 12, 100507 , 2024
    2024
    Citations: 39
  • A study on IoT based smart street light systems
    P Arjun, S Stephenraj, NN Kumar, KN Kumar
    2019 IEEE international conference on system, computation, automation and … , 2019
    2019
    Citations: 37
  • Machine parts recognition and defect detection in automated assembly systems using computer vision techniques
    P Arjun, TT Mirnalinee
    Rev. Téc. Ing. Univ. Zulia 39 (1), 71-80 , 2016
    2016
    Citations: 30
  • A study on curvature scale space
    S Anand, M Tamilarasan, P Arjun
    International Journal of Innovative Research in Computer and Communication … , 2014
    2014
    Citations: 12
  • Intelligent slime mold algorithm for proficient jamming attack detection in wireless sensor network
    A Arivunambi, A Paramarthalingam
    Global Transitions Proceedings 3 (2), 386-391 , 2022
    2022
    Citations: 11
  • Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval
    A Paramarthalingam, M Thankanadar
    IET Image Processing 15 (5), 1093-1104 , 2021
    2021
    Citations: 11
  • Real-time indoor air quality monitoring using the Internet of Things
    A Janarthanan, A Paramarthalingam, A Arivunambi, PMDR Vincent
    2022 Third International Conference on Intelligent Computing Instrumentation … , 2022
    2022
    Citations: 9
  • An efficient image retrieval system based on multi-scale shape features
    P Arjun, TT Mirnalinee
    Journal of Circuits, Systems and Computers 27 (11), 1850174 , 2018
    2018
    Citations: 9
  • Affine invariant shape descriptor using object area normalization
    P Arjun, TT Mirnalinee, S Sindhuja, G Bharathi Raja
    Power Electronics and Renewable Energy Systems: Proceedings of ICPERES 2014 … , 2014
    2014
    Citations: 6
  • Compact centroid distance shape descriptor based on object area normalization
    P Arjun, TT Mirnalinee, M Tamilarasan
    2014 IEEE International Conference on Advanced Communications, Control and … , 2014
    2014
    Citations: 5
  • A Smart Virtual Brain System for Reliable EEG Sensing and Actuation In Intelligent Brain IOT Environment
    A Arivunambi, A Paramarthalingam, P Sanju, S Uthayashangar, K V. L
    2021 IEEE International Conference on System, Computation, Automation and … , 2021
    2021
    Citations: 4
  • An Application-Driven IoT Based Rooftop Farming Model for Urban Agriculture
    A Paramarthalingam, A Arivunambi, S Thapasimony
    International Conference on Computational Intelligence in Data Science, 52-63 , 2021
    2021
    Citations: 4
  • A survey on object recognition system
    P Arjun, J Anitha, AH Thahseen
    Int Journal Appl Eng Res 10 (72), 295-300 , 2015
    2015
    Citations: 4
  • Affine Invariant Compact Centroid Distance Shape Descriptor for Image Retrieval
    P Arjun, TT Mirnalinee
    Applied Mathematical Sciences 9 (47), 2325-2335 , 2015
    2015
    Citations: 4
  • Proactive detection of Mirai botnet threats: leveraging XGBoost for enhanced cybersecurity
    R KR, A Paramarthalingam
    IET Conference Proceedings CP900 2024 (23), 34-39 , 2024
    2024
    Citations: 3
  • A study on word embeddings in local LLM-based chatbot applications
    S Hariprasath, A Paramarthalingam, S Sundaramurthy, S Cirillo
    2024 International Conference on Innovation and Intelligence for Informatics … , 2024
    2024
    Citations: 3
  • Brain tumour segmentation with a U-Net based GaN model using multi-modal MRI images
    A Paramarthalingam, A Janarthanan, A Arivunambi, T Rajamohan
    AIP Conference Proceedings 3075 (1), 020219 , 2024
    2024
    Citations: 3
  • Privacy-Preserving Deep sigmoid NN Classification over Entropy based Cryptosystem in Cloud Environments
    R K.R, A Paramarthalingam
    2023 International Conference on Disruptive Technologies (ICDT), 306-312 , 2023
    2023
    Citations: 3
  • Intelligent Drowsiness Detection using Haar Cascade Classifier and Convolutional Neural Network
    A Janarthanan, A Paramarthalingam, S Sundaresan, S Yokeshvaran
    Journal of Ubiquitous Computing and Communication Technologies 7 (2), 176-194 , 2025
    2025
    Citations: 2
  • AI-Powered Virtual Mouse Control Through Hand Gestures With Computer Vision
    A Paramarthalingam, A Janarthanan, A Arivunambi, SS Ariyangavu, ...
    Harnessing AI in Geospatial Technology for Environmental Monitoring and … , 2025
    2025
    Citations: 2