Elankavi

@sietk.org

Associate Professor / CSE
SIDDHARTH INSTITUTE OF ENGINEERING & TECHNOLOGY

Elankavi


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https://researchid.co/elankavi

RESEARCH, TEACHING, or OTHER INTERESTS

Engineering, Artificial Intelligence, Human-Computer Interaction, Information Systems
55

Scopus Publications

401

Scholar Citations

7

Scholar h-index

7

Scholar i10-index

Scopus Publications

  • Efficient gastric tumor detection from endoscopic images using trans-mapped learning models
    I. Govindharaj, Gnanajeyaraman Rajaram, S. Ravichandran, J. Viswanath, R. Elankavi, J. Raja
    Biomedical Signal Processing and Control, 2026
    Gastric cancer has emerged as a major health concern in recent years, often attributed to improper or unhealthy dietary habits. Early detection remains challenging due to the lack of identifiable symptoms in its initial stages, emphasizing the need for intelligent computational diagnostic methods. This study introduces the Inflate Region-based Tumor Recognition (IRTR) scheme, a novel approach leveraging endoscopy images and trans-mapped learning to detect inflated tumor regions with precision. The proposed scheme employs trans-mapping layers, which are trained to analyze inputs and outputs for identifying high and low-intensity feature regions. By focusing on external boundaries with elevated trans-intensity levels, the scheme effectively identifies regions exhibiting significant differences across the image. These mapped features are then utilized to train a model that repetitively processes high-to-low and low-to-high intensity transitions across input and output layers, enhancing the recognition of inflated tumor regions. Boundary differentiation, a key component of this approach, further refines detection precision from early endoscopic inputs. Evaluation results demonstrate that the IRTR scheme achieves superior performance, with an accuracy improvement of 9.38%, a precision increase of 12.04%, 9.69% in specificity and a mean error reduction of 11.04% for maximum intensity rates. This study underscores the potential of trans-mapped learning in advancing early gastric tumor detection.
  • GENERATIVE AI FOR MULTICULTURAL MUSIC EDUCATION
    Vaibhav Kaushik, Ananta Narayana, Dayal Singh Satha, Joshila Grace, R. Elankavi, Gajanan Chavan
    Shodhkosh Journal of Visual and Performing Arts, 2025
    Generative Artificial Intelligence is quickly transforming music education with the ability to create new creative experiences, opportunities of cultural interaction, and tailored learning. In this paper, the authors explain how to design and implement a Generative AI framework on multicultural music education to assist in preserving, exploring, and pedagogically integrating various musical traditions. The proposed approach is based on ethnomusicology and cross-cultural learning theory, and it presents the models of symbolic, audio, and multimodal musical presentation to reflect the rhythm, melody, timbre, structure, and culturally specific motifs. A cultural conditioning layer is added to make the generative models move towards stylistic authenticity to avoid the homogenization process, instead promoting creative diversity. The model combines architectures created with transformers with carefully curated and ethically sourced datasets inferred with cultural context, performance practice and expressive intent. Methodologically, the paper describes the data preprocessing pipelines and style adaptation mechanisms as well as evaluation protocols which are an extension of the quantitative measures of motif similarity and tonal coherence measures with qualitative measures of cultural fidelity, creativity and perception by learners. Findings suggest that AI-generated compositions, when transparently created and pedagogically mediated can help increase the engagement of learners, their intercultural knowledge, and their confidence in their creative abilities without jeopardizing the conventional teaching.
  • MEASURING CREATIVITY IN CONTEMPORARY ART VIA AI MODELS
    Ashish Verma, YuvrajSinh Sindha, Rashmi Manhas, R. Elankavi, Saudagar Subhash Barde, Kunal Meher
    Shodhkosh Journal of Visual and Performing Arts, 2025
    The process of measuring creativity in modern art has mostly been based on subjective expert opinion, cultural background and qualitative interpretations, which although they may be valuable, are usually not scalable, consistent and repeatable. As digital art practices and art datasets continue to expand at a very high rate, there is an increasing demand to have computational systems that can enable the systematic evaluation of the features of creativity without compromising artistic subtleties. In the present paper, the author suggests an AI-enabled creativity measurement framework which is a combination of computer vision, natural language processing, and multimodal learning to measure creativity in modern artworks. The framework conceptualizes creativity as a multidimensional construct that entails visual novelty, stylistic deviance, conceptual richness, narrative novelty and contextual topicality. Deep convolutional and transformer-based vision models are used to extract features of visual analysis that include color harmony, compositional complexity, variation of texture and deviation of style. The conceptual and semantic levels are represented by the use of NLP models on the texts of artists, exhibition, and critical descriptions, which allows analyzing originality, metaphor density, and coherence of the theme. Multimodal visionlanguage models also match a visual and textual representation to generate an overall creativity score, one that is holistic that captures the perceptual as well as interpretative elements of art. The suggested approach is compared to the baseline statistical and single-modality models in terms of the quantitative indicators of novelty indices, semantic divergence scores, cross-modal coherence, and accuracy in the classification of creativity.
  • Advancing cardiac motion estimation with emerging AI techniques for enhanced echocardiographic image registration
    M. Rajesh, S. Balakrishnan, R. Elankavi
    Methodsx, 2025
    Monitoring and diagnosis of cardiovascular diseases rely on cardiac motion estimation. The methods used for registering echocardiographic images have drawbacks such as low resolution, noise, and distortion of the anatomy. In order to enhance the prediction of cardiac motion, this research presents an AI-powered architecture that makes use of Vision Transformers, Diffusion Models, and Neural Radiance Fields (NeRF). Adversarial and self-supervised contrastive learning enhance picture quality and generalisability across adult and foetal echocardiography, while a graph neural network (GNN)-based anatomical constraint maintains heart shape. Better, more accurate, more efficient real-time motion tracking without relying on massive labelled datasets is possible with the proposed approach. Cardiac motion analysis in a wide range of patient populations is now therapeutically viable, thanks to this innovative approach that improves echocardiographic picture registration.•Utilizes Vision Transformers, Diffusion Models, and NeRF for high-quality cardiac motion prediction.•Adversarial and self-supervised contrastive learning improve echocardiographic registration across demographics.•A GNN-based anatomical constraint ensures accurate heart morphology during motion analysis.
  • Enhanced leukemia prediction using hybrid ant colony and ant lion optimization for gene selection and classification
    Santhakumar D, Gnanajeyaraman Rajaram, Elankavi R, Viswanath J, Govindharaj I, Raja J
    Methodsx, 2025
    Gene selection plays a crucial role in the pre-processing of microarray data, aiming to identify a small set of genes that enhances classification accuracy and reduces costs. Traditional methods, such as Genetic Algorithms (GA) and Maximum Relevance Minimum Redundancy (MRMR), have been widely used, but bio-inspired algorithms like Ant Colony Optimization (ACO) and Ant Lion Optimizer (ALO) have shown promising results. These algorithms are based on natural processes: ACO mimics the foraging behavior of ants, while ALO models the hunting strategy of ant-lion larvae. However, both approaches face challenges like premature convergence and inefficient feature space mapping when used individually. To address these issues, this work introduces a hybrid ACO-ALO method, combining the strengths of both algorithms. The proposed hybrid approach enhances feature selection by improving accuracy, reducing computational complexity, and boosting classifier performance. The proposed model, which identifies the optimal feature set for classification using Support Vector Machine (SVM), has achieved an impressive prediction accuracy of 93.94 %. Results on microarray datasets for leukemia prediction show that the hybrid approach outperforms other methods in terms of both effectiveness and efficiency. This work demonstrates the potential of hybrid optimization techniques in bioinformatics for better gene selection and cancer diagnosis.•Hybrid ACO-ALO approach combines strengths of both algorithms for better feature selection.•Enhances classifier performance while reducing computational complexity.•Outperforms traditional methods on leukemia prediction datasets.
  • Hybrid YOLOv8 and ResNet-50 Framework for Plant Disease Detection
    Kalimuthu M, Arunkumar M, Elankavi R, Dhanalakshmi M, DhanishaWini G
    Proceedings of the 6th International Conference on Smart Electronics and Communication Icosec 2025, 2025
    Color changes in plant leaves are early signs of stress, lack of nutrients, or disease. This study presents a deep learning–based framework for automated plant disease detection using leaf imagery, integrating classification and object detection to improve diagnostic accuracy. The proposed system employs ResNet-50 for feature extraction and binary classification to separate healthy from diseased samples, followed by YOLOv8 for precise localization and identification of specific disease categories. The PlantVillage dataset, comprising multiple crops and disease types, was used for training and validation. Experimental results demonstrate high performance, achieving 91% accuracy, 0.90 precision, 0.90 recall, and an F1-score of 0.90, with consistent results across varying leaf shapes, orientations, and backgrounds. This accurate classification and precise detection helps farmers take early action and prevent big crop losses. Future work will focus on expanding the dataset with additional species, disease severity levels, and real-field imagery to improve robustness and field adaptability.
  • Self Replication Mode for Network Intrusion Detection of Imperative Node Evaluator
    B. Sarvesan, R. Elankavi, Potli Harshavardhanreddy, G Jahnavi, Penakalapati Harsha Vardhan, Bayatapalle Jahnavi
    13th International Conference on Intelligent Embedded Microelectronics Communication and Optical Networks Iemecon 2025, 2025
    Security is now a critical concern for modern systems due to network expansion. Keeping an eye out for anomalies in user behavior is a helpful fraud detection tactic. This is a plan for an intrusion detection system using techniques like self-replication and pattern matching. The system notifies other nodes to be on the lookout for potentially damaging event sequences by identifying potentially dangerous symptoms in the surrounding environment. The suggested model, Imperative Node Evaluator with Self Replication Code and Auto Triggering Mode (INE-SRC-ATM), is made to automatically trigger nodes to secure the network and lessen false alarms, as well as to automatically repair the network in the event of an intrusion. When there is a difference in characteristics that raise the bar for network security, the suggested model reacts instantly. Both the accuracy of self-replication triggering and the degrees of intrusion detection demonstrate good performance of this model in intrusion detection.
  • Forecasting Network Intrusions To Prevent Secrecy Leakage Using Machine Learning
    R. G. Kumar, R. Elankavi, Y. Suchitra, S. Sofiya, Himanshu Kumar Thakur, K. Shalini
    2024 5th IEEE Global Conference for Advancement in Technology Gcat 2024, 2024
    An internet site intrusion detection gadget (IDS) facilitates finding assaults over the business enterprises and the criminals can be arrested. Earlier, diverse Machine Learning (ML) Methods were implemented for IDS to try to improve the detection outcomes for improved accuracy of attackers. They advocated this article which was an approach to develop an effective IDS using most important aspect analysis (PCA) and random wooded area category set of rules where PCA can assist in organizing the facts set by decreasing the dimensionality of the statistics and making it random a wooded area. The results received indicate the scope of the technique it plays and greater efficiently in terms of accuracy than different such methods like SVM, Naive Base and Decision Tree. The results received via the proposed technique are Execution time values (min) are three.24 mins, to be actual (%) is 96 and mistakes fee (%) is 0.21%.
  • Identification of social media cyberbullying with the aid of machine learning
    R Elankavi, B. Geethavani, S.C. Meghana, Vemula Rekha, J. Sai Priya, Kommaddi Mohamad Lookmaan
    Proceeding of 2024 International Conference on Communication Computing and Energy Efficient Technologies I3ceet 2024, 2024
    The internet, a vast platform for communication and idea-sharing, hosts around 4 billion users on social media platforms. Unfortunately, this widespread digital landscape has witnessed a surge in online abuse, harassment, trolling, and cyberbullying. These issues have far-reaching consequences, including depression, self-harm, and, tragically, suicide. Cyberbullying, involving negative, false, or harmful content targeting individuals, has severe implications, especially for the mental health of the younger generation. We propose the CBSA (Cyberbullying with Sentiment Analysis) model, which leverages a range of features to enhance cyberbullying detection.
  • Gradient Based Insights Into Eye Disease Identification With AI
    R. Elankavi, R. Surekha, Urekha Nuthalapati, Vadala Chandana Priya, Aramadaka Sathwik, Matta Raveendar Babu
    Proceeding of 2024 International Conference on Communication Computing and Energy Efficient Technologies I3ceet 2024, 2024
    Eye diseases represent a significant public health challenge, particularly in regions with limited access to specialized medical care. In India, where millions suffer from curable blindness, the need for early detection and intervention is critical. This paper proposes a novel approach leveraging deep learning techniques, including Convolutional Neural Networks (CNNs) such as ResNet, VGG16, and VGG19, to automate the identification of eye diseases based on visually observable symptoms. By analyzing digital images of eyes, our model categorizes and detects conditions. The integration of CNNs enables accurate and efficient analysis, facilitating early disease detection and prompting patients to seek timely medical attention. This automated system holds promise for improving healthcare outcomes and reducing the burden of preventable blindness worldwide.
  • Small Object Detection & Classification In Cardiac Images For the Improvised Early Diagnosis
    B. Geethavani, R. Elankavi, M. Swarna Latha, J. Yugandhar, S. Vinay Varma, K. Pavithra
    2024 4th Asian Conference on Innovation in Technology Asiancon 2024, 2024
  • Sign Language Recognition Using Neural Network
    R.Elankavi, A.Bhavya, G.Bharath, K.S.Amrutha, T.R.Geethika, M.Anand
    Proceeding of 2024 International Conference on Communication Computing and Energy Efficient Technologies I3ceet 2024, 2024
  • Harnessing Ai For Robust Deep Fake Detection In Image
    B. Pavan Kumar, R. Elankavi, K. Kesava, M. Krishna Dinesh, K. Susmitha, N Pallavi
    2024 International Conference on Smart Technologies for Sustainable Development Goals Icstsdg 2024, 2024
  • Adaptive DDoS Detection with DNN and FUZZY
    K. Maheswari, R. Elankavi, Koneti Lahari, Jootoori Navitha, Peta Manoj, Bala Obanna Gari Maheshwar Reddy
    2024 Asia Pacific Conference on Innovation in Technology Apcit 2024, 2024
  • Anomaly Detection for Internet of Things Security Attacks Based on Recent Optimal Federated Deep Learning Model
    Udayakumar Dr.R., Anuradha Dr.M., Dr. Yogesh Manohar Gajmal, Elankavi Dr.R.
    Journal of Internet Services and Information Security, 2023
  • IMPROVED PARTICLE SWARM OPTIMIZATION WITH DEEP LEARNING-BASED MUNICIPAL SOLID WASTE MANAGEMENT IN SMART CITIES
    R. Udayakumar, R. Elankavi, V. R. Vimal, R. Sugumar
    Revista De Gestao Social E Ambiental, 2023
  • Assessing Learning Behaviors Using Gaussian Hybrid Fuzzy Clustering (GHFC) in Special Education Classrooms
    Dr.R. Udayakumar
    Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications, 2023
  • Smart IoT based Human Well-being Monitoring in Health Care System
    R Elankavi, P Krishnamoorthy, J. Jerin Jose, R. Surekha
    3rd International Conference on Electronics and Sustainable Communication Systems Icesc 2022 Proceedings, 2022
  • Cloud services for device utilization and optimization of hybrid network
    A. Arunachalam, G Michael, R Elankavi, A Kumaravel, K Rangarajan, et al.
    International Journal of Recent Technology and Engineering, 2019
  • The influence of stable methodologies on cyberinformatics
    S Sri, R Elankavi, S. Srividhya, R. Kavitha, R Kavitha, et al.
    International Journal of Recent Technology and Engineering, 2019
  • An exploration of fiber-optic cables
    C Geetha, Mary Linda, Mr Elankavi, A Kumaravel, K Rangarajan, et al.
    International Journal of Recent Technology and Engineering, 2019
  • Synthesizing multicast solutions using heterogeneous information
    B Sundarraj, R Elankavi, S. Sangeetha, A Kumaravel, K Rangarajan, et al.
    International Journal of Recent Technology and Engineering, 2019
  • Merkle hash tree with hash based digital signature for cloud data confidentiality and security
    A. Arunachalam, G Michael, R Elankavi, A Kumaravel, K Rangarajan, et al.
    International Journal of Recent Technology and Engineering, 2019
  • Towards the synthesis of DHCP
    A. Arunachalam, G Michael, R Elankavi, A Kumaravel, K Rangarajan, et al.
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • System failure recognition and identification by analyzing syslog and SNS data: Applying big data analysis to network operations
    D.Vimala, P.Nandhini, R. Elankavi
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Biometric authentication in cloud computing
    R. Velvizhi, P. Nandhini, R. Elankavi
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • A case for context-free grammar
    A. Arunachalam, G Michael, R Elankavi, A Kumaravel, K Rangarajan, et al.
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Safeguard clothing and dive decrease
    D Jeyapriya, G Michael, R Elankavi, Stephen Jijo, A Kumarave, et al.
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Implementing aggregate-key for sharing data in cloud environment using cryptographic encryption
    K. P. Kaliyamurthie, G Michael, R Elankavi, Stephen Jijo, S Sherman, et al.
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Evaluating architecture using compact modalities
    Allin Geo, A. Arunachalam, G Michael, Mr Elankavi, N Ch Om Sky, et al.
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Dynamic web page security using fingerprint and card system
    K.P.Kaliyamurthie, G.Michael, R.Elankavi, Stephen Anto Jijo
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Automation proving OTP with cloud computing
    Eurasian Journal of Analytical Chemistry, 2018
  • Networks in cloud for peerless pursuance
    Eurasian Journal of Analytical Chemistry, 2018
  • Security provocation: Converse level in cloud computing
    Eurasian Journal of Analytical Chemistry, 2018
  • Biometrics at coherent to deflect surveillance
    Eurasian Journal of Analytical Chemistry, 2018
  • A new approach for cloud data security: From single to Cloud-of-Clouds
    R. Kalaiprasath, R. Elankavi, R. Udayakumar
    International Journal on Smart Sensing and Intelligent Systems, 2017
  • Cloud security and compliance - A semantic approach in end to end security
    R. Kalaiprasath, R. Elankavi, R. Udayakumar
    International Journal on Smart Sensing and Intelligent Systems, 2017
  • Data mining with big data revolution hybrid
    R. Elankavi, R. Kalaiprasath, R. Udayakumar
    International Journal on Smart Sensing and Intelligent Systems, 2017
  • Wireless Zigbee Network Cluster - Capacity calculation and secure data conveyance using indegree
    R. Elankavi, R. Kalaiprasath, R. Udayakumar
    International Journal on Smart Sensing and Intelligent Systems, 2017
  • Efficient cloud platform providing an omnipresent healthcare services
    Eurasian Journal of Analytical Chemistry, 2017
  • Voice command execution with speech recognition and synthesize
    Eurasian Journal of Analytical Chemistry, 2017
  • Secure and efficient way of handling medical records in cloud
    Eurasian Journal of Analytical Chemistry, 2017
  • Precip soothsaying of analyzing data networks
    Eurasian Journal of Analytical Chemistry, 2017
  • Dynamic web page security using fingerprint and card system
    Eurasian Journal of Analytical Chemistry, 2017
  • Refining service delay using temporal task scheduling for profit maximization in hybrid cloud
    Eurasian Journal of Analytical Chemistry, 2017
  • Captcha as a graphical passwords - A new security primitive based on hard AI problems
    Eurasian Journal of Analytical Chemistry, 2017
  • Multiple relay orthogonal frequency division multiplexing for total throughput maximization
    Eurasian Journal of Analytical Chemistry, 2017
  • Comparison of randomized algorithms and web browsers
    Eurasian Journal of Analytical Chemistry, 2017
  • Decoupling wide-area networks from advanced search in multi-processors
    Eurasian Journal of Analytical Chemistry, 2017
  • Use of RAID and neural networks in simulation of thin clients
    Eurasian Journal of Analytical Chemistry, 2017
  • Evaluation of 802.11 mesh networks using a new methodology
    Eurasian Journal of Analytical Chemistry, 2017
  • Cloud security and compliance - A semantic approach in end to end security
    International Journal of Mechanical Engineering and Technology, 2017
  • A fast clustering algorithm for high-dimensional data
    International Journal of Civil Engineering and Technology, 2017
  • Potential exploitation of broadcasting system using multiple smart directional antennas -help of sensor network
    International Journal of Mechanical Engineering and Technology, 2017
  • Cloud Information Accountability (CIA) framework ensuring accountability of data in cloud and security in end to end process in cloud terminology
    International Journal of Civil Engineering and Technology, 2017

RECENT SCHOLAR PUBLICATIONS

  • Anomaly detection for internet of things security attacks based on recent optimal federated deep learning model
    R Udayakumar, M Anuradha, YM Gajmal, R Elankavi
    Journal of Internet Services and Information Security 13 (3), 104-121 , 2023
    2023
    Citations: 18
  • IMPROVED PARTICLE SWARM OPTIMIZATION WITH DEEP LEARNING-BASED MUNICIPAL SOLID WASTE MANAGEMENT IN SMART CITIES.
    R Udayakumar, R Elankavi, VR Vimal, R Sugumar
    Environmental & Social Management Journal/Revista de Gestão Social e … , 2023
    2023
    Citations: 95
  • Assessing learning behaviors using gaussian hybrid fuzzy clustering (ghfc) in special education classrooms
    MAKDR Udayakumar
    2023
    Citations: 26
  • Smart IoT based Human Well-being Monitoring in Health Care System
    R Elankavi, P Krishnamoorthy, JJ Jose, R Surekha
    2022 3rd International Conference on Electronics and Sustainable … , 2022
    2022
    Citations: 5
  • Dynamic web page security using fingerprint and card system
    KP Kaliyamurthie, G Michael, R Elankavi, SA Jijo
    International Journal of Innovative Technology and Exploring Engineering 8 … , 2019
    2019
    Citations: 2
  • Implementing aggregate-key for sharing data in cloud environment using cryptographic encryption
    KP Kaliyamurthie, G Michael, R Elankavi, SA Jijo
    International Journal of Innovative Technology and Exploring Engineering 8 … , 2019
    2019
    Citations: 4
  • Networks in Cloud for Peerless Pursuance
    D Jeyapriya, R Elankavi
    Eurasian Journal of Analytical Chemistry 13 (4), 928-932 , 2018
    2018
  • Security Provocation: Converse Level in Cloud Computing
    D Jeyapriya, R Elankavi
    Eurasian Journal of Analytical Chemistry 13 (4), 933-937 , 2018
    2018
  • Biometrics at Coherent to Deflect Surveillance
    D Jeyapriya, R Elankavi
    Eurasian Journal of Analytical Chemistry 13 (4), 918-922 , 2018
    2018
  • Automation Proving OTP with Cloud Computing
    D Jeyapriya, R Elankavi
    Eurasian Journal of Analytical Chemistry 13 (4), 923-927 , 2018
    2018
  • Merkle hash tree with hash based digital signature for cloud data confidentiality and security
    AR Arunachalam, G Michael, R Elankavi
    Journal of Pure and Applied Mathematics 119 (12), 12233-12242 , 2018
    2018
    Citations: 2
  • Data mining with big data revolution hybrid
    R Elankavi, R Kalaiprasath, R Udayakumar
    International Journal on Smart Sensing and Intelligent Systems 10 (5), 560-573 , 2017
    2017
    Citations: 10
  • A New Approach for Cloud Data Security: From Single to Cloud-of-Clouds
    R Kalaiprasath, R Elankavi, R Udayakumar
    International Journal on Smart Sensing and Intelligent Systems 10 (5), 604-613 , 2017
    2017
  • Wireless ZigBee Network Cluster-Capacity Calculation and Secure Data Conveyance Using Indegree
    R Elankavi, R Kalaiprasath, R Udayakumar
    International Journal on Smart Sensing and Intelligent Systems 10 (5), 174-185 , 2017
    2017
    Citations: 2
  • Cloud security and compliance-a semantic approach in end to end security
    R Kalaiprasath, R Elankavi, R Udayakumar
    International Journal on Smart Sensing and Intelligent Systems 10 (5), 482-494 , 2017
    2017
    Citations: 132
  • Potential Exploitation of Broadcasting System Using Multiple Smart Directional Antennas-Help of Sensor Network
    R Elankavi, R Kalaiprasath, R Udayakumar
    International Journal of Mechanical Engineering and Technology (IJMET) 8 (6 … , 2017
    2017
    Citations: 3
  • A fast clustering algorithm for high-dimensional data
    R Elankavi, R Kalaiprasath, DR Udayakumar
    International Journal Of Civil Engineering And Technology (Ijciet) 8 (5 … , 2017
    2017
    Citations: 66
  • Efficient Cloud Platform Providing an Omnipresent Healthcare Services
    R Elankavi, R Udayakumar
    Eurasian Journal of Analytical Chemistry 12 (4), 26-34 , 2017
    2017
  • Women’s Safety System Using IBEACON Technology
    R Elankavi, R Udayakumar
    Eurasian Journal of Analytical Chemistry 12 (4), 73-92 , 2017
    2017
  • Secure and Efficient Way of Handling Medical Records in Cloud
    R Elankavi, R Udayakumar
    Eurasian Journal of Analytical Chemistry 12 (4), 35-43 , 2017
    2017

MOST CITED SCHOLAR PUBLICATIONS

  • Cloud security and compliance-a semantic approach in end to end security
    R Kalaiprasath, R Elankavi, R Udayakumar
    International Journal on Smart Sensing and Intelligent Systems 10 (5), 482-494 , 2017
    2017
    Citations: 132
  • IMPROVED PARTICLE SWARM OPTIMIZATION WITH DEEP LEARNING-BASED MUNICIPAL SOLID WASTE MANAGEMENT IN SMART CITIES.
    R Udayakumar, R Elankavi, VR Vimal, R Sugumar
    Environmental & Social Management Journal/Revista de Gestão Social e … , 2023
    2023
    Citations: 95
  • A fast clustering algorithm for high-dimensional data
    R Elankavi, R Kalaiprasath, DR Udayakumar
    International Journal Of Civil Engineering And Technology (Ijciet) 8 (5 … , 2017
    2017
    Citations: 66
  • Assessing learning behaviors using gaussian hybrid fuzzy clustering (ghfc) in special education classrooms
    MAKDR Udayakumar
    2023
    Citations: 26
  • Cloud information accountability (cia) framework ensuring accountability of data in cloud and security in end to end process in cloud terminology
    R Kalaiprasath, R Elankavi, DR Udayakumar
    International Journal Of Civil Engineering And Technology (Ijciet) Volume 8 … , 2017
    2017
    Citations: 26
  • Anomaly detection for internet of things security attacks based on recent optimal federated deep learning model
    R Udayakumar, M Anuradha, YM Gajmal, R Elankavi
    Journal of Internet Services and Information Security 13 (3), 104-121 , 2023
    2023
    Citations: 18
  • Data mining with big data revolution hybrid
    R Elankavi, R Kalaiprasath, R Udayakumar
    International Journal on Smart Sensing and Intelligent Systems 10 (5), 560-573 , 2017
    2017
    Citations: 10
  • Data collection in wireless sensor networks-a literature survey
    R Elankavi, R Udayakumar, R Kalaiprasath
    International Journal Of Innovative Research In Computer And Communication … , 2013
    2013
    Citations: 7
  • Smart IoT based Human Well-being Monitoring in Health Care System
    R Elankavi, P Krishnamoorthy, JJ Jose, R Surekha
    2022 3rd International Conference on Electronics and Sustainable … , 2022
    2022
    Citations: 5
  • Implementing aggregate-key for sharing data in cloud environment using cryptographic encryption
    KP Kaliyamurthie, G Michael, R Elankavi, SA Jijo
    International Journal of Innovative Technology and Exploring Engineering 8 … , 2019
    2019
    Citations: 4
  • Potential Exploitation of Broadcasting System Using Multiple Smart Directional Antennas-Help of Sensor Network
    R Elankavi, R Kalaiprasath, R Udayakumar
    International Journal of Mechanical Engineering and Technology (IJMET) 8 (6 … , 2017
    2017
    Citations: 3
  • Dynamic web page security using fingerprint and card system
    KP Kaliyamurthie, G Michael, R Elankavi, SA Jijo
    International Journal of Innovative Technology and Exploring Engineering 8 … , 2019
    2019
    Citations: 2
  • Merkle hash tree with hash based digital signature for cloud data confidentiality and security
    AR Arunachalam, G Michael, R Elankavi
    Journal of Pure and Applied Mathematics 119 (12), 12233-12242 , 2018
    2018
    Citations: 2
  • Wireless ZigBee Network Cluster-Capacity Calculation and Secure Data Conveyance Using Indegree
    R Elankavi, R Kalaiprasath, R Udayakumar
    International Journal on Smart Sensing and Intelligent Systems 10 (5), 174-185 , 2017
    2017
    Citations: 2
  • Captcha as a graphical passwords-A new security primitive based on hard AI problems
    R Elankavi, R Udayakumar
    Eurasian J. Anal. Chem 12 (4), 93-100 , 2017
    2017
    Citations: 2
  • Cloud computing security towards end to end terminology
    R Kalaiprasath, R Elankavi, U Kumar
    International Journal of Pure and Applied Mathematics 115 (8), 637-641 , 2017
    2017
    Citations: 1
  • Networks in Cloud for Peerless Pursuance
    D Jeyapriya, R Elankavi
    Eurasian Journal of Analytical Chemistry 13 (4), 928-932 , 2018
    2018
  • Security Provocation: Converse Level in Cloud Computing
    D Jeyapriya, R Elankavi
    Eurasian Journal of Analytical Chemistry 13 (4), 933-937 , 2018
    2018
  • Biometrics at Coherent to Deflect Surveillance
    D Jeyapriya, R Elankavi
    Eurasian Journal of Analytical Chemistry 13 (4), 918-922 , 2018
    2018
  • Automation Proving OTP with Cloud Computing
    D Jeyapriya, R Elankavi
    Eurasian Journal of Analytical Chemistry 13 (4), 923-927 , 2018
    2018