20 found
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  1. AI-Enabled Human Capital Management: Tools for Strategic Workforce Adaptation.M. Arulselvan - 2025 - Journal of Science Technology and Research (JSTAR) 5 (1):530-538.
    This paper explores the application of AI-driven HR analytics in shaping workforce agility, focusing on how real-time data collection, analysis, and modeling foster an adaptable workforce. It highlights the role of predictive analytics in forecasting workforce needs, identifying skill gaps, and optimizing talent deployment. Additionally, the paper discusses how AI enhances strategic decision-making by providing precise metrics and insights into employee behavior, productivity, and satisfaction. The integration of AI into HR systems ultimately shifts HR from a traditionally reactive to a (...)
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  2.  57
    Harnessing Machine Learning to Predict Chronic Kidney Disease Risk.M. Arulselvan - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-16.
    Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD.
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  3. Parallel Processing Techniques for Optimizing Data-Intensive Applications on Accelerated Computing Platforms.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):540-550.
    Our findings reveal that implementing accelerated computing can achieve substantial improvements, often reducing computation times by more than 60% compared to traditional sequential methods. This paper details the experimental setup, including algorithm selection and parallelization techniques, and discusses the role of memory bandwidth and latency in achieving optimal performance. Based on the analysis, we propose a streamlined methodology to guide the deployment of accelerated computing frameworks in various industries. Concluding with a discussion on future directions, we highlight potential advancements in (...)
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  4.  88
    A Blueprint for Success: Transforming Legacy Databases with Snowflake Migration Strategies.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):575-585.
    The study outlines the technical challenges encountered during migration, including data compatibility issues, schema conversion, and security compliance, as well as strategies to mitigate these obstacles. Testing and validation techniques are applied throughout the migration, highlighting essential checkpoints to confirm data accuracy and optimal performance in the Snowflake environment. Postmigration performance metrics are evaluated to illustrate improvements in query execution, scalability, and overall system efficiency compared to the legacy system. The results underscore the advantages of Snowflake’s architecture in handling high-volume, (...)
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  5. 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 techniques (...)
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  6. 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 classify cyberbullying (...)
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  7. Women and Two-Wheelers: A Study on Preferences, Satisfaction, and Buying Behavior.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (4):504-514.
    The producers of Automobile products innovated a new thought of designing the two- wheelers in such a way to attract the women. Today most of the women prefer to travel through two-wheelers. A wide variety of two-wheelers of all category light- weighted, medium – weighted and heavy weighted vehicles have been introduced in the market. The objective of the study is to know the preference and satisfaction of women consumers over two-wheelers and the various aspects, which determines the purchase or (...)
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  8.  68
    Smart Deduplication Framework for Optimized Data Management in Hybrid Cloud.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):587-597.
    The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This (...)
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  9.  88
    Innovative EdTech Models to Address Systemic Inequities in Indian Schooling.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):520-526.
    This study explores the role of government policies, public-private partnerships, and emerging technologies in bridging the digital divide, ensuring inclusive and equitable access to quality education for all students. By analyzing case studies and statistical data, this research identifies key areas where the EdTech revolution can be leveraged to close gaps in the education system while fostering innovation in pedagogical practices. Finally, the paper presents recommendations for ensuring that the EdTech revolution contributes to systemic equity, rather than exacerbating existing disparities.
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  10. 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. Future work (...)
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  11. 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 generated by (...)
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  12. Smart Homes with Augmented Reality and IoT Integration for Enhanced User Experience and Automation.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):389-394.
    The proposed system allows users to monitor and control various home appliances, security systems, and environmental controls through AR interfaces, which overlay digital information on the physical environment. IoT devices communicate with each other and with the AR system, providing real-time data and enabling automated responses based on user preferences or environmental conditions. This synergy between AR and IoT facilitates a more responsive and intelligent home environment that adapts to the needs of its occupants.
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  13.  54
    Intelligent Malware Detection Empowered by Deep Learning for Cybersecurity Enhancement.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):625-635.
    With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed by (...)
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  14.  50
    Optimizing Robotic Systems for Stock Management in Pick and Place Operations.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):670-680.
    The design also addresses potential challenges such as robot mobility, collision avoidance, and space optimization. Performance metrics, including accuracy, time efficiency, and system scalability, are measured using simulation-based experiments in a controlled environment. The results show significant improvements in operational efficiency compared to traditional stock management approaches. This integration paves the way for future advancements in fully automated warehouses, reducing the need for human labor and increasing reliability. Finally, we discuss potential enhancements, including AI-based decision-making algorithms, multi-robot collaboration, and integration (...)
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  15.  96
    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 an additional (...)
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  16.  52
    A Comparative Study of Advanced Techniques for Predicting Air Quality with Deep Learning.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):575-586.
    In recent years, the prediction of air quality has become a critical task due to its significant impact on human health and the environment. With urbanization and industrial growth, the need for accurate air quality forecasting has become more urgent. Traditional methods for air quality prediction are often based on statistical models or physical simulations, which, while valuable, can struggle to capture the complexity of air pollution dynamics. This study explores the use of deep learning techniques to predict air quality, (...)
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  17.  94
    Adaptive SVM Techniques for Optimized Detection of Known and Novel Cyber Intrusions.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):398-405.
    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.
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  18.  46
    Data-Driven Health Monitoring: Visual and Analytical Solutions for Improved Care.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):640-655.
    This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote patient care.
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  19.  91
    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 as Genetic (...)
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  20.  87
    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 confidentiality of (...)
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