GENERATIVE AI FOR MULTICULTURAL MUSIC EDUCATION Vaibhav Kaushik, Ananta Narayana, Dayal Singh Satha, Joshila Grace, R. Elankavi, et al. 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, et al. 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.
Forecasting Network Intrusions To Prevent Secrecy Leakage Using Machine Learning R. G. Kumar, R. Elankavi, Y. Suchitra, S. Sofiya, Himanshu Kumar Thakur, et al. 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, et al. 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, et al. 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.
Sign Language Recognition Using Neural Network R.Elankavi, A.Bhavya, G.Bharath, K.S.Amrutha, T.R.Geethika, et al. 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, et al. 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, et al. 2024 Asia Pacific Conference on Innovation in Technology Apcit 2024, 2024
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