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  1.  32
    Efficient Cloud-Enabled Cardiovascular Disease Risk Prediction and Management through Optimized Machine Learning.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):454-475.
    The world's leading cause of morbidity and death is cardiovascular diseases (CVD), which makes early detection essential for successful treatments. This study investigates how optimization techniques can be used with machine learning (ML) algorithms to forecast cardiovascular illnesses more accurately. ML models can evaluate enormous datasets by utilizing data-driven techniques, finding trends and risk factors that conventional methods can miss. In order to increase prediction accuracy, this study focuses on adopting different machine learning algorithms, including Decision Trees, Random Forest, Support (...)
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  2.  28
    Intelligent Cloud Storage System with Machine Learning-Driven Attribute-Based Access Control.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):435-445.
    Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and (...)
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  3.  24
    Machine Learning for Optimized Attribute-Based Data Management in Secure Cloud Storage.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):434-450.
    Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We (...)
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  4.  24
    Satisfaction Analysis of Women Consumers Regarding Performance and Safety in Two-Wheelers.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (Jstar 5 (4):450-460.
    The sample size of the study was conducted in Tiruchirappalli city with 75 respondents through Non-Probability Random Sampling Method. The tools and techniques used were simple percentage, chi-square and ANOVA. The obtained result of the study that majority of the women prefer TVS Scooty and most of the respondents prefer two wheelers due to convenient while driving and majority of the respondents have great impact on colour and model prefer the vehicle. New inventions and designs were introduced to meet the (...)
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  5.  23
    Secure Cloud Storage with Machine Learning-Optimized Attribute-Based Access Control Protocols.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):420-435.
    This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. Exploring sophisticated machine learning algorithms for predictive search improvements (...)
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  6. Intelligent Phishing Content Detection System Using Genetic Ranking and Dynamic Weighting Techniques.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):480-490.
    The Genetic Ranking Optimization Algorithm (GROA) is used to rank phishing content based on multiple features by optimizing the ranking system through iterative selection and weighting. Dynamic weighting further enhances the process by adjusting the weights of features based on their importance in real-time. This hybrid approach enables the model to learn from the data, improving classification over time.
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