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260 articles for “Data availability”
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Efficient Data Backup and Recovery Automation Using Shell Scripts in UNIX Systems
Abstract: Data backup and recovery constitute essential components of system administration in UNIX-based environments, playing a critical role in maintaining data integrity, ensuring continuous availability, and providing resilience against various forms of data loss. In modern computing systems, data is a vital asset, and its loss—whether due to hardware failures, software corruption, human error, or cyber threats—can lead to significant operational disruptions and financial consequences. Therefore, implementing reliable and efficient backup …
Published in Journal of Advances in Shell Programming · Vol. 13, Issue 1, 2026 · pp. 48–54 Read article
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Use of Modern Techniques in Library and Information Centers
Abstract: The integration of modern techniques in libraries and information centers has significantly revolutionized the way information is accessed, managed, and disseminated. Advancements in technology have moved libraries beyond their traditional roles as book repositories, enabling them to serve as dynamic information hubs. Automated Library Systems (ALS) have streamlined processes such as cataloging, circulation, and acquisitions, making it easier for patrons to search and access materials Radio Frequency Identification (RFID) technology …
Published in Journal of Advancements in Library Sciences · Vol. 12, Issue 1, 2025 · pp. 57–61 Read article
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Shortest Route Identification Model for Urban Solid Waste Disposal Management Using GIS: A Case of Gulu City, Northern Uganda
Abstract: GIS has been employed for integration of both spatial and non-spatial data. It can be applied to any service that is dependent on network like water and power supplies, sewage and transportation. This study was done in Gulu municipality to address the problem of poor solid waste disposal management. It proposes a shortest route identification model to minimize travel distance and reduce collection time by the trucks. The constraints that …
Published in Journal of Remote Sensing & GIS · Vol. 15, Issue 1, 2024 · pp. 1–19 Read article
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Gradient Boosted Regression Tree Approach to Predicting Toxic Interactions on X and YouTube
Abstract: In the digital age, social media platforms play a vital role in facilitating user engagement, encompassing both positive interactions and avenues for negative, often harmful behaviors. Recognizing and addressing toxic exchanges is paramount to nurturing healthy online communities and preserving users’ well-being. This study introduces a novel method for identifying toxic interactions by utilizing Gradient Boosting Regression Trees (GBRT) algorithm, a machine learning approach renowned for its exceptional accuracy and …
Published in Trends in Opto-electro & Optical Communication · Vol. 15, Issue 3, 2025 · pp. 7–14 Read article
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Monitoring of Ship Deployment Through Emerging Technologies
Abstract: The mission for naval vessels encompasses defining combat tasks, deployment statuses, and timing requirements to optimize combat patrol effectiveness and daily ship management. This involves inheriting, developing, and optimizing ship deployment strategies while establishing new deployment categories with distinct names, connotations, personnel, and equipment needs to ensure organic integration and synergy. Emphasis is placed on maintaining continuity, stability, and forward-thinking to meet the demands of warship combat operations, facilitate management …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 59–66 Read article
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AI Powered Invention in Pharmaceuticals Boosting Innovation
Abstract: Artificial intelligence has the potential to transform the drug discovery process, making the process more efficient, accurate and faster. But the success of artificial intelligence depends on the availability of good data, resolution of ethical issues, and awareness of the limitations of artificial intelligencebased methods. The present article examined the benefits, challenges, and shortcomings of skills in the workplace and suggested strategies and practical actions to overcome current challenges. Data …
Published in Research and Reviews: A Journal of Pharmaceutical Science · Vol. 15, Issue 2, 2024 · pp. 102–107 Read article
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Identifying and Mitigating Chemical Hazards Through Hazard Identification and Risk Assessment Using Failure Mode and Effects Analysis (FMEA)
Abstract: Chemical exposure represents a significant occupational health and safety concern in the chemical industry. Effectively addressing these hazards requires teamwork among various occupational health and safety experts, such as general OHS professionals, occupational hygienists, and occupational health practitioners. This research focuses on analyzing hazards and developing control measures for industrial chemicals, evaluating their toxicity, and using these assessments in regulatory decision making. It starts by addressing the availability of toxicological …
Published in Journal of Industrial Safety Engineering · Vol. 11, Issue 1, 2024 · pp. 52–62 Read article
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Cardiovascular Illness Detection and Categorization with Innovative Neural Networks
Abstract: Health-related problems are increasingly prevalent in modern-day societies and are significantly shaped by a multitude of factors encountered in everyday life. Among these, cardiovascular diseases have emerged as one of the primary causes of death on a global scale, posing serious challenges to public health systems. In response to this growing concern, the present study proposes a machine learning-based framework that is not only highly effective but also reliable and …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 21–30 Read article
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Role of Machine Learning Principles for Efficient Nuclear Fuel Management and Design
Abstract: The introduction of machine learning (ML) and evolutionary computation methods in addressing complex nuclear fuel management challenges has brought a significant positive change in the domain of nuclear fuel management. Key applications include fuel assembly design optimization, core loading pattern determination, burnup calculation acceleration, fuel performance prediction, and spent fuel characterization. The analysis reveals significant improvements in computational efficiency, prediction accuracy, and optimization capabilities when ML techniques are properly integrated …
Published in Journal of Nuclear Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 22–33 Read article
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Enhancing Glaucoma Diagnosis with Deep Learning: A Study Using ResNet-50 and DenseNet-121
Abstract: Glaucoma is a leading cause of irreversible blindness worldwide, mainly resulting from progressive optic nerve damage, often related to elevated intraocular pressure. Early detection is essential to prevent vision loss, but traditional diagnostic methods rely on specialized equipment and trained professionals, making large-scale screening difficult. This study uses a publicly available fundus imaging dataset to explore the effectiveness of deep learning models for glaucoma detection. These datasets provide medical images, …
Published in International Journal of Brain Sciences · Vol. 2, Issue 2, 2025 · pp. 9–18 Read article
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ANN-Based Adaptive Rotor Current Control for DFIG Wind Systems: A Comparative Dynamic Analysis
Abstract: The variability of rotor current management in Doubly Fed Induction Generator (DFIG)-based wind energy conversion systems is crucial for maintaining stability in power extraction under fluctuating wind and grid circumstances. Traditional proportional-integral (PI) controllers, despite their ease of use, frequently exhibit diminished performance when faced with parameter uncertainty, nonlinear behaviors, and rapid wind fluctuations.This paper presents an adaptive rotor current control strategy, which is an Artificial Neural Network (ANN)-based approach …
Published in Journal of Power Electronics and Power Systems · Vol. 16, Issue 1, 2026 · pp. 41–53 Read article
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A Study of Virtual Machines Environment in Cloud Computing
Abstract: The Cloud computing is most widely used technological concept which gives the efficient result in concern to availability and utilizing of resources, processing and management of the data on servers. These capabilities make cloud computing to decorate the programs of it. The concept of virtualization of cloud computing is main feature of this technology which provide a platform to many business application data, academic IT tools and industry to explore …
Published in Recent Trends in Parallel Computing Read article
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A Novel Secure Cloud Storage Solution: Combining AES-OTP, RSA, and Time-Limited Access Control with Adaptive Key Management
Abstract: The reliance of cloud computing on the data processing and storage structure creates serious security risks. Ensuring availability, security, and integrity of data in cloud settings becomes a challenge for both the individual and the enterprise. This paper will, therefore, introduce a novel Hybrid Cryptographic Framework that combines RSA, One-Time Pad (OTP), and AES as a means of enhancing data security in cloud storage. It employs RSA for secure key …
Published in Current Trends in Signal Processing · Vol. 14, Issue 3, 2024 · pp. 11–20 Read article
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Robust Classification of Traffic Signs Using Relief Feature Reduction Technique
Abstract: Ensuring driver safety amidst the rapid growth of global population and vehicular density continues to be a paramount challenge for transportation authorities and governments worldwide. With the rise of smart mobility solutions and autonomous driving technologies, the ability to detect, classify, and respond to traffic signs accurately has become critically important, especially under diverse and adverse environmental conditions such as rain, fog, or poor lighting. Reliable traffic sign recognition not …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 30–37 Read article
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Brain Tumor Detection Through CNN: Techniques, Dataset Insights, and Methodology
Abstract: Computer technologies are playing huge roles in some areas of the medical domain like surgery and therapy of different diseases. Researchers are doing studies and trying to experiment to detect different diseases like cancer, virus infections, and leprosy. There are many different medical imaging datasets that are publicly available for medical research purposes of diseases like cancer, virus infections, and leprosy, etc. where we can be able to access large …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 1, 2025 · pp. 30–40 Read article
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Advanced Private Cloud Security and Privacy Preservation Through the Integration of Machine Learning and Cryptography
Abstract: In modern technological landscapes, private cloud security is of paramount concern due to the ever-increasing volume and complexity of cyber threats. This research work explores the integration of machine learning and cryptography to enhance security within private cloud environments. This study aims to mitigate vulnerabilities that may compromise data integrity, confidentiality, and availability in private cloud infrastructures by using machine learning algorithms and strong cryptography. By detecting anomalous cloud patterns …
Published in International Journal of Advanced Control and System Engineering · Vol. 2, Issue 1, 2024 · pp. 1–10 Read article
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Signal Drift Compensation in Polymer-Based Wearable Biosensors Using Data Processing Techniques
Abstract: Polymer-based wearable biosensors have emerged as promising platforms for continuous physiological monitoring due to their mechanical flexibility, low operating voltage, and compatibility with soft biological interfaces. However, their long-term deployment remains challenging because of signal drift caused by polymer ageing, hydration–dehydration cycles, ionic trapping, and environmental variations. These effects introduce baseline fluctuations and sensitivity degradation, which compromise the reliability and interpretability of physiological measurements. This study proposes a data-processing–driven framework …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 197–207 Read article
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Predicting Dielectric Constants of Polymers Using Molecular Structural Descriptors and Explainable Machine Learning: A Data-Driven Approach
Abstract: Accurate prediction of dielectric constants in polymeric materials is fundamental to the rational design of advanced electronic components, energy storage capacitors, flexible substrates, and high-frequency communication circuits. Conventional approaches to identifying suitable polymer dielectrics rely on extensive experimental synthesis and characterisation, which are both time-consuming and resource-intensive. In this work, an explainable machine learning framework is developed to predict the dielectric constant of polymers directly from molecular structural descriptors derived …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 297–304 Read article
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Genomic Selection for Grain Yield in Wheat Using Machine Learning on DArT Molecular Markers: A Comparative Evaluation Across Multi-Environment Trials
Abstract: Genomic selection (GS) predicts complex quantitative traits directly from genome-wide molecular markers, bypassing the need for extensive phenotypic trials and accelerating plant breeding cycles. We conducted a comparative evaluation of seven regression approaches — ridge regression (the machine-learning equivalent of RR-BLUP), Lasso, Elastic Net, Partial Least Squares, linear Support Vector Regression, Random Forest, and Gradient Boosting — for predicting grain yield from 1,279 Diversity Array Technology (DArT) molecular markers genotyped …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 Read article
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An Investigative Study on Secure Coding Practices with Shell Scripting
Abstract: This investigative research delves into secure coding practices within shell scripting, aiming to reduce prevalent security vulnerabilities and improve the overall security stance of shell scripts. It emphasizes three key areas: static analysis, dynamic analysis, and manual code review. Through static analysis, the code structure, usage of unsafe functions, and potential vulnerabilities are examined without executing the script. Dynamic analysis entails running the script in controlled settings to detect runtime …
Published in Journal of Advances in Shell Programming · Vol. 11, Issue 1, 2024 · pp. 16–23 Read article