Secure File Sharing using Blockchain-Based Frameworks Vasanth N S, Selva Jothi M, Pavithra S 2025 International Conference on Data Science Agents and Artificial Intelligence Icdsaai 2025, 2025 With growing concerns of data security and privacy related to digital file sharing, this research paper proposes a framework that strengthens secure file sharing across heterogeneous platforms. The solution exploits blockchain technology in combination with attribute-based encryption to protect anonymity of users and ensure data integrity. Blockchain, being a decentralized ledger, manages access without any centralized system, and the smart contracts define rules regarding file access and usage. The framework minimizes the risks associated with traditional methods by storing access permissions securely on the blockchain. Compared to the existing solutions like cloud-based encryption, End-To-End Encryption (E2EE), Attribute-Based Encryption (ABE), and Peer-To-Peer (P2P) file sharing, this approach provides better security, privacy, and reliability. This work addresses modern challenges in digital file sharing, providing a trustworthy and robust solution for secure communication.
Advancements, Methods, and Obstacles in Emotion Recognition with Multimodal Approaches Arunkumar M, Athiban R, Selva Jothi M, S Pavithra 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things Idciot 2025, 2025 Emotion recognition has gained an avid curiosity in the research area due to its broad range of applications in human-computer interaction particularly in real time emotion recognition, sentiment analysis, and affective computing. Deep learning techniques are advancing exponentially, automating feature engineering and transcending traditional machine learning techniques. Appeal of the above models can be highly beneficial but also raise certain technical and ethical concerns that are not often discussed enough in the existing studies. While highlighting the recent advancements, the methods involved and obstacles in emotion recognition, this review also proposes future directions in developing a standard unified framework for multimodal approaches. Categorizing and analysing the recent studies depends upon the model types employed, data modalities used and the type of fusion techniques used, this review was able to gain insights into the effect of recent multimodal approaches addressing technical and ethical concerns. Utilization of fusion techniques in multimodal models is still not reliable but they better convey the connection between different modalities and have a huge potential for development in the future. This review provides insights into the state of art multimodal methods using modalities like facial expressions, body gestures, vocal cues, text, and physiological signals for researchers to understand the trends and future directions in this field.
Deep Learning Solutions for Knee Osteoarthritis Prediction with Optimized Convolutional Neural Networks M. Selvajothi, Dinesh Kumar Budagam, K. Vijaya Lakshmi, T. Geetha, A. Lizy, B. Vanmathi 2025 Global Conference in Emerging Technology Ginotech 2025, 2025 A generative in nature joint disorder that is predominantly affects middle-aged and older adults is knee osteoarthritis (KOA). However, there are significant obstacles to an objective and effective early diagnosis due to technical bottlenecks including noise, artifacts, and modality. In this study, a comprehensive Deep Learning (DL) approach is presented that Adaptive Wiener Filter (AWF) for preprocessing, Segmentation using K Means Clustering, feature selection using Gray Level Co-Occurrence Matrix (GLCM), and Sea Gull Optimization Convolutional Neural Network (SGO-CNN) for Knee KOA prediction. As a result, it will support KOA research as well as draw attention to shortcomings and possible issues with use in clinical practice. The first phase involves Adaptive Wiener Filter. The proposed approach is validated using Python Software with KOA prediction dataset. Performance image, including accuracy, precision, recall and f-score, demonstrates superior’s results in predicting KOA prediction utilizing the proposed approach.
Machine Learning for Diabetic Retinopathy: Gaussian SVM Model for Accurate Analysis in Retinal Fundus Images S Sri Abirami, K Pradeep, K Mridhulla Shree, M.Selva Jothi 2023 International Conference on Data Science Agents and Artificial Intelligence Icdsaai 2023, 2023 The most common ocular ailment in people with diabetes that causes vision loss in this population is diabetic retinopathy. Patients are protected from vision loss by diabetic retinopathy. The findings of this study point to the need for an early diagnosis system that can help the physician in analyzing retinal images. The main goal is to classify severity level from any given retinal images automatically. To accomplish this, an initial image processing phase is utilized to distinguish and isolate blood vessels, micro-aneurysms, and hard exudates. This process enables the retrieval of particular attributes that can be applied by a support vector machine in order to ascertain the severity of retinopathy for every retinal image. The efficacy of this idea has been evaluated using a database consisting of 300 retinal images that have been categorized based on the severity of diabetic retinopathy. The outcome yielded a maximum sensitivity of 95.95% and an accuracy of 92.66%. An assessment of the algorithm’s resilience to variations in its parameters has also been conducted. The suggested framework will enhance the efficiency and accuracy of disease detection compared to existing frameworks
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MOST CITED SCHOLAR PUBLICATIONS
Secure File Sharing using Blockchain-Based Frameworks NS Vasanth, S Pavithra 2025 International Conference on Data Science, Agents & Artificial … , 2025 2025 Citations: 1
Operational Analysis of a 10 kW Bifacial Grid-Connected Photovoltaic system A Karthick, MS Jothi, AJS Ali, D Kushwah, R Kushwah, G Pandiyarajan 2026 International Conference on Electronics and Renewable Systems (ICEARS … , 2026 2026
Deep Learning Solutions for Knee Osteoarthritis Prediction with Optimized Convolutional Neural Networks M Selvajothi, DK Budagam, KV Lakshmi, T Geetha, A Lizy, B Vanmathi 2025 Global Conference in Emerging Technology (GINOTECH), 1-6 , 2025 2025
Advancements, Methods, and Obstacles in Emotion Recognition with Multimodal Approaches M Arunkumar, R Athiban, M Selva Jothi 2025 3rd International Conference on Intelligent Data Communication … , 2025 2025
Machine Learning for Diabetic Retinopathy: Gaussian SVM Model for Accurate Analysis in Retinal Fundus Images SS Abirami, K Pradeep, KM Shree, MS Jothi 2023 International Conference on Data Science, Agents & Artificial … , 2023 2023
