Results for 'Attribute-Based Keyword Search (ABKS)'

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  1. Advanced Attribute-Based Keyword Search for Secure Cloud Data Storage Solutions.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):350-360.
    This paper delves into the integration of optimization techniques within ABKS to enhance search efficiency and data security in cloud storage environments. We explore various optimization strategies, such as index compression, query processing enhancement, and encryption optimization, which aim to reduce computational overhead while maintaining robust security measures. Through a comprehensive analysis, the paper illustrates how these techniques can significantly improve the performance of cloud storage systems, ensuring both security and usability. Experimental results demonstrate that optimized ABKS (...)
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  2. OPTIMIZED SECURE CLOUD STORAGE USING ATTRIBUTE-BASED KEYWORD SEARCH.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):338-349.
    In the modern digital era, cloud storage has become an indispensable service due to its scalability, accessibility, and cost-effectiveness. However, with the vast amount of sensitive information stored on cloud platforms, ensuring data security and privacy remains a critical challenge. Traditional encryption techniques, while secure, often hinder efficient data retrieval, especially when using keyword searches. To address this, attribute-based keyword search (ABKS) offers a promising solution by allowing secure, fine-grained access control and efficient (...) searches over encrypted data. This paper delves into the integration of optimization techniques within ABKS to enhance search efficiency and data security in cloud storage environments. We explore various optimization strategies, such as index compression, query processing enhancement, and encryption optimization, which aim to reduce computational overhead while maintaining robust security measures. Through a comprehensive analysis, the paper illustrates how these techniques can significantly improve the performance of cloud storage systems, ensuring both security and usability. Experimental results demonstrate that optimized ABKS not only accelerates search queries but also reduces storage costs, making it a viable solution for modern cloud storage challenges. Future research directions include exploring advanced machine learning algorithms for predictive search optimizations and further improving the resilience of ABKS against emerging security threats. (shrink)
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  3.  68
    Optimized Attribute-Based Search and Secure Storage for Cloud Computing Environments.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):361-370.
    In the modern digital era, cloud storage has become an indispensable service due to its scalability, accessibility, and cost-effectiveness. However, with the vast amount of sensitive information stored on cloud platforms, ensuring data security and privacy remains a critical challenge. Traditional encryption techniques, while secure, often hinder efficient data retrieval, especially when using keyword searches. To address this, attribute-based keyword search (ABKS) offers a promising solution by allowing secure, fine-grained access control and efficient (...) searches over encrypted data. (shrink)
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  4. Intelligent Encryption and Attribute-Based Data Retrieval for Secure Cloud Storage Using Machine Learning.A. Manoj Prabaharan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):415-425.
    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 (...)
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  5.  56
    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 (...)
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  6.  77
    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 (...)
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  7. OPTIMIZED CARDIOVASCULAR DISEASE PREDICTION USING MACHINE LEARNING ALGORITHMS.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):350-359.
    Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, optimized through (...)
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  8.  53
    Machine Learning-Driven Optimization for Accurate Cardiovascular Disease Prediction.Yoheswari S. - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):350-359.
    The research methodology involves data preprocessing, feature engineering, model training, and performance evaluation. We employ optimization methods such as Genetic Algorithms and Grid Search to fine-tune model parameters, ensuring robust and generalizable models. The dataset used includes patient medical records, with features like age, blood pressure, cholesterol levels, and lifestyle habits serving as inputs for the ML models. Evaluation metrics, including accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC), assess the model's predictive power.
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  9.  84
    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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  10.  61
    Cloud-Enabled Risk Management of Cardiovascular Diseases Using Optimized Predictive Machine Learning Models.Kannan K. S. - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):460-475.
    Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, (...)
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  11.  64
    Scalable Cloud Solutions for Cardiovascular Disease Risk Management with Optimized Machine Learning Techniques.A. Manoj Prabaharan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):454-470.
    The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might be (...)
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  12.  61
    Optimized Cloud Computing Solutions for Cardiovascular Disease Prediction Using Advanced Machine Learning.Kannan K. S. - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):465-480.
    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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