Results for ' Optimization Techniques '

974 found
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  1.  53
    OPTIMIZATION TECHNIQUES FOR LOAD BALANCING IN DATA-INTENSIVE APPLICATIONS USING MULTIPATH ROUTING NETWORKS.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):377-382.
    In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of data generated (...)
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  2.  53
    ENHANCED SLA-DRIVEN LOAD BALANCING ALGORITHMS FOR DATA CENTER OPTIMIZATION USING ADVANCED OPTIMIZATION TECHNIQUES.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):369-376.
    In modern data centers, managing the distribution of workloads efficiently is crucial for ensuring optimal performance and meeting Service Level Agreements (SLAs). Load balancing algorithms play a vital role in this process by distributing workloads across computing resources to avoid overloading any single resource. However, the effectiveness of these algorithms can be significantly enhanced through the integration of advanced optimization techniques. This paper proposes an SLA-driven load balancing algorithm optimized using methods such as genetic algorithms, particle swarm (...), and simulated annealing. By focusing on both resource utilization and SLA compliance, the proposed approach aims to reduce latency, improve throughput, and maximize overall system efficiency. (shrink)
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  3. A Review on Resource Provisioning Algorithms Optimization Techniques in Cloud Computing.M. R. Sumalatha & M. Anbarasi - 2019 - International Journal of Electrical and Computer Engineering 9 (1).
    Cloud computing is the provision of IT resources (IaaS) on-demand using a pay as you go model over the internet. It is a broad and deep platform that helps customers builds sophisticated, scalable applications. To get the full benefits, research on a wide range of topics is needed. While resource over provisioning can cost users more than necessary, resource under provisioning hurts the application performance. The cost effectiveness of cloud computing highly depends on how well the customer can optimize the (...)
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  4.  23
    Latency-Aware Packet Transmission Optimization in Duty-Cycled WSNs.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):444-459.
    Wireless Sensor Networks (WSNs) have become increasingly prevalent in various applications, ranging from environmental monitoring to smart cities. However, the limited energy resources of sensor nodes pose significant challenges in maintaining network longevity and data transmission efficiency. Duty-cycled WSNs, where sensor nodes alternate between active and sleep states to conserve energy, offer a solution to these challenges but introduce new complexities in data transmission. This paper presents an optimized approach to aggregated packet transmission in duty-cycled WSNs, utilizing advanced optimization (...)
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  5.  41
    Optimization Algorithms for Load Balancing in Data-Intensive Systems with Multipath Routing.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):377-382.
    : In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of data (...)
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  6.  75
    Multipath Routing Optimization for Enhanced Load Balancing in Data-Heavy Networks.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):377-382.
    In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of data generated (...)
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  7. Global Optimization Studies on the 1-D Phase Problem.Jim Marsh, Martin Zwick & Byrne Lovell - 1996 - Int. J. Of General Systems 25 (1):47-59.
    The Genetic Algorithm (GA) and Simulated Annealing (SA), two techniques for global optimization, were applied to a reduced (simplified) form of the phase problem (RPP) in computational crystallography. Results were compared with those of "enhanced pair flipping" (EPF), a more elaborate problem-specific algorithm incorporating local and global searches. Not surprisingly, EPF did better than the GA or SA approaches, but the existence of GA and SA techniques more advanced than those used in this study suggest that these (...)
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  8.  47
    OPTIMIZED CYBERBULLYING DETECTION IN SOCIAL MEDIA USING SUPERVISED MACHINE LEARNING AND NLP TECHNIQUES.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):421-435.
    The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and (...)
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  9.  43
    Innovative Approaches in Cardiovascular Disease Prediction Through Machine Learning Optimization.M. Arul Selvan - 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, (...)
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  10.  41
    OPTIMIZED INTRUSION DETECTION MODEL FOR IDENTIFYING KNOWN AND INNOVATIVE CYBER ATTACKS USING SUPPORT VECTOR MACHINE (SVM) ALGORITHMS.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):398-404.
    The ever-evolving landscape of cyber threats necessitates robust and adaptable intrusion detection systems (IDS) capable of identifying both known and emerging attacks. Traditional IDS models often struggle with detecting novel threats, leading to significant security vulnerabilities. This paper proposes an optimized intrusion detection model using Support Vector Machine (SVM) algorithms tailored to detect known and innovative cyberattacks with high accuracy and efficiency. The model integrates feature selection and dimensionality reduction techniques to enhance detection performance while reducing computational overhead. By (...)
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  11. Intentional Sampling by Goal Optimization with Decoupling by Stochastic Perturbation.Julio Michael Stern, Marcelo de Souza Lauretto, Fabio Nakano & Carlos Alberto de Braganca Pereira - 2012 - AIP Conference Proceedings 1490:189-201.
    Intentional sampling methods are non-probabilistic procedures that select a group of individuals for a sample with the purpose of meeting specific prescribed criteria. Intentional sampling methods are intended for exploratory research or pilot studies where tight budget constraints preclude the use of traditional randomized representative sampling. The possibility of subsequently generalize statistically from such deterministic samples to the general population has been the issue of long standing arguments and debates. Nevertheless, the intentional sampling techniques developed in this paper explore (...)
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  12.  53
    OPTIMIZED DRIVER DROWSINESS DETECTION USING MACHINE LEARNING TECHNIQUES.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):395-400.
    Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as (...)
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  13. Attack Prevention in IoT through Hybrid Optimization Mechanism and Deep Learning Framework.Regonda Nagaraju, Jupeth Pentang, Shokhjakhon Abdufattokhov, Ricardo Fernando CosioBorda, N. Mageswari & G. Uganya - 2022 - Measurement: Sensors 24:100431.
    The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge data (...)
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  14. SVM-Enhanced Intrusion Detection System for Effective Cyber Attack Identification and Mitigation.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):397-403.
    The ever-evolving landscape of cyber threats necessitates robust and adaptable intrusion detection systems (IDS) capable of identifying both known and emerging attacks. Traditional IDS models often struggle with detecting novel threats, leading to significant security vulnerabilities. This paper proposes an optimized intrusion detection model using Support Vector Machine (SVM) algorithms tailored to detect known and innovative cyberattacks with high accuracy and efficiency. The model integrates feature selection and dimensionality reduction techniques to enhance detection performance while reducing computational overhead. By (...)
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  15.  32
    OPTIMIZING CONSUMER BEHAVIOUR ANALYTICS THROUGH ADVANCED MACHINE LEARNING ALGORITHMS.Yoheswari S. - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):362-370.
    Consumer behavior analytics has become a pivotal aspect for businesses to understand and predict customer preferences and actions. The advent of machine learning (ML) algorithms has revolutionized this field by providing sophisticated tools for data analysis, enabling businesses to make data-driven decisions. However, the effectiveness of these ML algorithms significantly hinges on the optimization techniques employed, which can enhance model accuracy and efficiency. This paper explores the application of various optimization techniques in consumer behaviour analytics using (...)
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  16. PHISHING CONTENT CLASSIFICATION USING DYNAMIC WEIGHTING AND GENETIC RANKING OPTIMIZATION ALGORITHM.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):471-485.
    Phishing attacks remain one of the most prevalent cybersecurity threats, affecting individuals and organizations globally. The rapid evolution of phishing techniques necessitates more sophisticated detection and classification methods. In this paper, we propose a novel approach to phishing content classification using a Genetic Ranking Optimization Algorithm (GROA), combined with dynamic weighting, to improve the accuracy and ranking of phishing versus legitimate content. Our method leverages features such as URL structure, email content analysis, and user behavior patterns to enhance (...)
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  17.  57
    Automated Cyberbullying Detection Framework Using NLP and Supervised Machine Learning Models.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):421-432.
    The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and (...)
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  18.  49
    Cloud-Based IoT System for Outdoor Pollution Detection and Data Analysis.Prathap Jeyapandi - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):424-430.
    Air pollution is a significant environmental concern that affects human health, ecosystems, and climate change. Effective monitoring and management of outdoor air quality are crucial for mitigating its adverse effects. This paper presents an advanced approach to outdoor pollution measurement utilizing Internet of Things (IoT) technology, combined with optimization techniques to enhance system efficiency and data accuracy. The proposed framework integrates a network of IoT sensors that continuously monitor various air pollutants, such as particulate matter (PM), carbon monoxide (...)
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  19. Multitask Music-Based Therapy Optimization in Aging Neurorehability by Activation of the Informational Cognitive Centers of Consciousness.Florin Gaiseanu - 2020 - Gerontology and Geriatric Studies 6 (3):1-5.
    The rapid increase of the old age people imposes the reconsideration of the rehabilitation techniques and procedures and/or the development of the existing ones, at least from two points of view: the limitation use of the pharmaceutical drugs because of their secondary effects in the debilitated organisms and their avoidance; the high risk of the induced anxiety states, depression or other symptoms as a consequence of the main disease, i.e. the neuro-degenerative or mobility dysfunctions, limiting again the use of (...)
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  20.  73
    Transforming Consumer Behavior Analysis with Cutting-Edge Machine Learning.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):360-368.
    The research outlines a workflow that incorporates data collection, preprocessing, model training, and optimization. Real-world datasets from retail and e-commerce sectors are utilized to validate the proposed methodology, showcasing substantial improvements in model performance. The results indicate that optimized models not only provide better predictions of consumer behaviour but also enhance customer segmentation and targeting strategies. The study concludes with recommendations for future research, including the exploration of hybrid optimization techniques and the application of these methods in (...)
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  21.  53
    OPTIMIZING CONSUMER BEHAVIOUR ANALYTICS THROUGH ADVANCED MACHINE LEARNING ALGORITHMS.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):360-368.
    Consumer behavior analytics has become a pivotal aspect for businesses to understand and predict customer preferences and actions. The advent of machine learning (ML) algorithms has revolutionized this field by providing sophisticated tools for data analysis, enabling businesses to make data-driven decisions. However, the effectiveness of these ML algorithms significantly hinges on the optimization techniques employed, which can enhance model accuracy and efficiency. This paper explores the application of various optimization techniques in consumer behaviour analytics using (...)
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  22. Real-Time Phishing Detection Using Genetic Algorithm-Based Ranking and Dynamic Weighting Optimization.A. Manoj Prabaharan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):491-500.
    The rapid evolution of phishing techniques necessitates more sophisticated detection and classification methods. In this paper, we propose a novel approach to phishing content classification using a Genetic Ranking Optimization Algorithm (GROA), combined with dynamic weighting, to improve the accuracy and ranking of phishing versus legitimate content. Our method leverages features such as URL structure, email content analysis, and user behavior patterns to enhance the detection system's decision-making process. The Genetic Ranking Optimization Algorithm (GROA) is used to (...)
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  23.  42
    Machine Learning-Based Cyberbullying Detection System with Enhanced Accuracy and Speed.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):421-429.
    The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and (...)
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  24.  35
    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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  25.  47
    Advanced Driver Drowsiness Detection Model Using Optimized Machine Learning Algorithms.S. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):396-402.
    Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as (...)
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  26.  71
    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 keyword searches over encrypted data. This (...)
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  27.  37
    Wireless IoT Sensors for Environmental Pollution Monitoring in Urban Areas.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):434-441.
    The data collected by these sensors are transmitted to a centralized system where optimization algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), are applied to optimize sensor placement, data transmission, and processing efficiency. This ensures accurate, real-time pollution monitoring and data analysis, providing actionable insights for policymakers, environmental agencies, and the general public. The system's performance is evaluated through simulations and real-world experiments, demonstrating its capability to deliver reliable and timely pollution data. (...)
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  28. 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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  29.  51
    Intelligent Driver Drowsiness Detection System Using Optimized Machine Learning Models.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):397-405.
    : Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such (...)
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  30.  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, (...)
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  31.  28
    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 (...)
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  32.  36
    Low-Power IoT Sensors for Real-Time Outdoor Environmental Pollution Measurement.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):430-440.
    The data collected by these sensors are transmitted to a centralized system where optimization algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), are applied to optimize sensor placement, data transmission, and processing efficiency. This ensures accurate, real-time pollution monitoring and data analysis, providing actionable insights for policymakers, environmental agencies, and the general public. The system's performance is evaluated through simulations and real-world experiments, demonstrating its capability to deliver reliable and timely pollution data. (...)
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  33.  35
    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, (...)
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  34.  50
    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, (...)
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  35.  47
    Efficient Data Center Management: Advanced SLA-Driven Load Balancing Solutions.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):368-376.
    In modern data centers, managing the distribution of workloads efficiently is crucial for ensuring optimal performance and meeting Service Level Agreements (SLAs). Load balancing algorithms play a vital role in this process by distributing workloads across computing resources to avoid overloading any single resource. However, the effectiveness of these algorithms can be significantly enhanced through the integration of advanced optimization techniques. This paper proposes an SLA-driven load balancing algorithm optimized using methods such as genetic algorithms, particle swarm (...), and simulated annealing. By focusing on both resource utilization and SLA compliance, the proposed approach aims to reduce latency, improve throughput, and maximize overall system efficiency. The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. (shrink)
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  36.  36
    Optimizing Data Center Operations with Enhanced SLA-Driven Load Balancing".S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):368-376.
    The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. Extensive simulations are conducted using synthetic and real-world datasets to evaluate the performance of the proposed algorithm. The results demonstrate that the optimized load balancing approach outperforms traditional algorithms in terms of SLA compliance, resource utilization, and energy efficiency. The findings suggest that the integration of optimization techniques into load balancing algorithms can (...)
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  37.  28
    Hybrid Cloud-Machine Learning Framework for Efficient Cardiovascular Disease Risk Prediction and Treatment Planning.Kannan K. S. - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):460-480.
    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 (...)
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  38.  37
    Cloud-Based Secure Storage: A Framework for Efficient Encryption, Decryption, and Data Dispersion.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):427-434.
    The exponential growth of cloud storage has necessitated advanced security measures to protect sensitive data from unauthorized access. Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple cloud servers, ensuring that no (...)
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  39.  36
    Enhanced Secure Cloud Storage: An Integrated Framework for Data Encryption and Distribution.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):416-427.
    Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple cloud servers, ensuring that no single server holds the complete dataset. Decryption occurs only when the dispersed data fragments are reassembled, which adds (...)
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  40.  37
    Efficient Aggregated Data Transmission Scheme for Energy-Constrained Wireless Sensor Networks.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):445-460.
    Optimization algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are employed to determine the optimal aggregation and transmission schedules, taking into account factors such as network topology, node energy levels, and data urgency. The proposed approach is validated through extensive simulations, demonstrating significant improvements in energy consumption, packet delivery ratio, and overall network performance. The results suggest that the optimized aggregated packet transmission method can effectively extend the lifespan of duty-cycled WSNs while ensuring reliable data (...)
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  41.  32
    OPTIMIZED CLOUD SECURE STORAGE: A FRAMEWORK FOR DATA ENCRYPTION, DECRYPTION, AND DISPERSION.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):415-426.
    The exponential growth of cloud storage has necessitated advanced security measures to protect sensitive data from unauthorized access. Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple cloud servers, ensuring that no (...)
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  42.  31
    Efficient Cryptographic Methods for Secure Searchable Data in IoT Frameworks.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):409-415.
    As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising the integrity and (...)
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  43.  58
    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 (...)
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  44.  34
    OPTIMIZED ENCRYPTION PROTOCOL FOR LIGHTWEIGHT AND SEARCHABLE DATA IN IOT ENVIRONMENTS.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):408-414.
    As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising the integrity and (...)
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  45.  34
    Scalable Encryption Protocol for Searchable Data Management in IoT Systems.S. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):409-415.
    As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising the integrity and (...)
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  46. Content marketing model for leading web content management.Igor Britchenko, Iryna Diachuk & Maksym Bezpartochnyi - 2019 - Atlantis Press 318:119-126.
    This paper is envisaged to provide the Ukrainian businesses with suggestions for a content marketing model for the effective management of website content in order to ensure its leading position on the European and world markets. Our study employed qualitative data collection with semi-structured interviews, survey, observation methods, quantitative and qualitative methods of content analysis of regional B2B companies, as well as the comparative analysis. The following essential stages of the content marketing process as preliminary search and analysis, website content (...)
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  47. OPTIMIZING DATA SCIENCE WORKFLOWS IN CLOUD COMPUTING.Tummalachervu Chaitanya Kanth - 2024 - Journal of Science Technology and Research (JSTAR) 4 (1):71-76.
    This paper explores the challenges and innovations in optimizing data science workflows within cloud computing environments. It begins by highlighting the critical role of data science in modern industries and the pivotal contribution of cloud computing in enabling scalable and efficient data processing. The primary focus lies in identifying and analyzing the key challenges encountered in current data science workflows deployed in cloud infrastructures. These challenges include scalability issues related to handling large volumes of data, resource management complexities in optimizing (...)
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  48. Predicting Player Power In Fortnite Using Just Nueral Network.Al Fleet Muhannad Jamal Farhan & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (9):29-37.
    Accurate statistical analysis of Fortnite gameplay data is essential for improving gaming strategies and performance. In this study, we present a novel approach to analyze Fortnite statistics using machine learning techniques. Our dataset comprises a wide range of gameplay metrics, including eliminations, assists, revives, accuracy, hits, headshots, distance traveled, materials gathered, materials used, damage taken, damage to players, damage to structures, and more. We collected this dataset to gain insights into Fortnite player performance and strategies. The proposed model employs (...)
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  49. AI-Completeness: Using Deep Learning to Eliminate the Human Factor.Kristina Šekrst - 2020 - In Sandro Skansi (ed.), Guide to Deep Learning Basics. Springer. pp. 117-130.
    Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...)
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  50. Synthetic Biology and Biofuels.Catherine Kendig - 2012 - In Paul B. Thompson & David M. Kaplan (eds.), Encyclopedia of Food and Agricultural Ethics. New York: Springer Verlag.
    Synthetic biology is a field of research that concentrates on the design, construction, and modification of new biomolecular parts and metabolic pathways using engineering techniques and computational models. By employing knowledge of operational pathways from engineering and mathematics such as circuits, oscillators, and digital logic gates, it uses these to understand, model, rewire, and reprogram biological networks and modules. Standard biological parts with known functions are catalogued in a number of registries (e.g. Massachusetts Institute of Technology Registry of Standard (...)
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