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260 articles for “Model Selection”
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Risk Return Analysis Of Pharmaceuticals Companies During Covid-19
Abstract: An unknown disease (COVID-19) is spreading globally, impacting people and economies very negatively. The virus, which was first discovered in a small part of China in December 2019, has since grown fast across more than 175 countries. The virus is very contagious, thus in order to stop it, certain measures have been put in place, such as a national shutdown, air traffic control, and the wearing of masks, avoidance of …
Published in Research and Reviews: A Journal of Pharmaceutical Science · Vol. 15, Issue 1, 2024 · pp. 1–6 Read article
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A Systematic Review on Leukemia Detection and Classification Techniques Using Gene Expression
Abstract: Early diagnosis of genetic diseases is crucial for effective treatment, especially in the case of Leukemia, a type of blood cancer characterized by abnormal proliferation of white blood cells. This paper presents a systematic review of recent computational techniques for the detection and classification of Leukemia using gene expression data obtained from DNA microarray analysis. The study explores diverse methodologies including machine learning (ML), deep learning (DL), and bio-inspired algorithms …
Published in International Journal of Genetic Modifications and Recombinations · Vol. 3, Issue 2, 2025 Read article
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Design of Decoder Using Domino Logic Circuit for VLSI
Abstract: Dissipation of power from a circuit is the major issue in the design of any VLSI circuit, which decreases the life span of a device/system. NMOS and PMOS circuits are very slow while switching the state from low to high. The speed of the circuit can be increased by decreasing resistance. This process in turn increases the static power dissipation. So, because of these disadvantages, CMOS circuits are used in …
Published in Journal of VLSI Design Tools and Technology Read article
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A Comprehensive Review of Machine Learning and Explainable AI Techniques for Disease Prediction Systems
Abstract: Large amounts of diverse medical data have been produced because of the quick development of digital healthcare systems, offering substantial chances to use machine learning methods for clinical decision support and illness prediction. By identifying intricate patterns in clinical data, machine learning-based models have shown great promise in early disease detection, risk assessment, and personalised healthcare. However, issues with transparency, interpretability, and reliability have been brought up by the growing …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 4, Issue 1, 2026 · pp. 20–28 Read article
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Tribological Behaviour of PTFE based Composite Materials with Different Filler Materials: A Combine Numerical and Experimental Approach
Abstract: This study investigates the tribological behaviour of Polytetrafluoroethylene (PTFE) composites reinforced with various fillers, utilizing the Archard wear model to analyse wear mechanisms and predict wear rates. PTFE is widely recognized for its excellent chemical resistance and low friction, but its inherent wear resistance is relatively poor. To enhance its tribological properties, fillers such as glass fibres, carbon fibres, bronze, and graphite were incorporated into the PTFE matrix. In the …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 547–557 Read article
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Multimodal Disease Detection Using Deep Learning
Abstract: Artificial Intelligence (AI) is playing an increasingly pivotal role in modern healthcare, particularly in improving the speed and accuracy of disease detection. With the evolution of Machine Learning (ML), Deep Learning (DL), and high-performance computing, AI-based solutions are now capable of processing extensive medical datasets, ranging from patient records to diagnostic images, with remarkable efficiency. These systems offer immense potential for early intervention, improved clinical decision-making, and alleviating pressure on …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 129–139 Read article
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Comparative Analysis of Disc Brake Using Eco-Friendly Material
Abstract: Brakes are crucial components for slowing or stopping the vehicle. Almost all vehicles use disc brakes. The working of a disc brake is simple; when the brake pedal is pressed, braking pads are forced mechanically against the rotor or disc on both surfaces. The friction generated between the rotor and brake pads slows down the vehicle. The design and materials of disc brakes play a significant role in their performance. …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 532–544 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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Advanced Functional Polymers and Their Synergistic Role in Smart Composite Materials for Sustainable Industrial Applications
Abstract: Advanced functional polymers have revolutionized composite materials, offering enhanced properties such as self-healing, electrical conductivity, environmental adaptability, and high mechanical strength. These innovations are reshaping industries by enabling smarter, more efficient, and sustainable solutions. Functional polymers play a pivotal role in producing high-performance composite materials to meet the growing demands of sustainable industrial applications. Significant improvements in material properties mechanical strength, durability, thermal stability, and recyclability are achieved through the …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 12–27 Read article
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In Silico Identification of Therapeutic Agents for Dengue Virus by Molecular Docking
Abstract: The dengue virus causes serious health issues and a loss of quality of life. Dengue poses a yearly threat to half of the world’s population. The drugs that are based on allopathy are expensive and also exhibit toxic effects on tissues and biological activities. It is also generally accepted that most pharmacologically active drugs, including those derived from medicinal plants, are isolated from natural sources. The present study is directed …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 13, Issue 2, 2026 · pp. 01–14 Read article
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Progress in Renal Tumor Surgery: The Role of 3D Surgical Planning in Partial Nephrectomy
Abstract: Renal cell carcinoma is the most common form of kidney cancer, representing 2–3% of global cases, with the highest incidence in Western Europe. For small renal tumors, partial nephrectomy is the preferred treatment, where the tumor is surgically removed. This procedure does not affect oncological outcomes, allowing part of the kidney to remain functional. During tumor removal, the surgeon minimizes excessive bleeding and improves visibility by cutting off the arterial …
Published in Research and Reviews : Journal of Surgery · Vol. 14, Issue 3, 2025 · pp. 35–40 Read article
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Designing a Pure Talent Management Model in the Growth Centers of Technological Units of Islamic Azad University with the Foundation's Data Approach
Abstract: Talent management is one of the management fields that has experienced the greatest growth in the last two decades. Due to its competitive nature, for the first time the concept of talent management was proposed in private organizations and large multinational companies and was widely welcomed. Therefore, the main goal of this research is to design a lean talent management model in Islamic Azad University technology development centers with a …
Published in International Journal of Sustainability · Vol. 1, Issue 1, 2024 · pp. 45–50 Read article
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Employee Well-Being: Deep Learning Approaches to Stress Detection
Abstract: Stress has become a major concern for employee health, productivity, and overall well-being in today's fast-paced work environment. It is a growing global issue, affecting both individual employees and the productivity of organizations. Work-related stress occurs when the demands of a job surpass an individual's ability to manage, whether because of long hours, overwhelming responsibilities, or other pressures. Factors such as conflicts with coworkers or supervisors, constant changes, and job …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 12, Issue 1, 2025 · pp. 52–58 Read article
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A Systematic Review on The Role of Artificial Intelligence in Assisted Reproductive Technology
Abstract: Artificial Intelligence (AI) has significantly transformed Assisted Reproductive Technology (ART) over the past five years, enhancing diagnostic accuracy, treatment personalization, and overall success rates. AI-driven algorithms and machine learning models have been integrated into various aspects of ART, including sperm selection, embryo grading, and predicting implantation success. Deep learning techniques have improved image-based embryo assessment, reduced human subjectivity and increased efficiency. Additionally, AI-powered predictive analytics have helped optimize ovarian stimulation …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 1, 2026 Read article
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Physicochemical Analysis of Groundwater Quality In Owerri Municipal, Imo State, Nigeria Using MATLAB and ANN Models.
Abstract: The physicochemical analysis of groundwater quality in Owerri Municipal council, Imo state, Nigeria was carried out. Fifteen water samples were randomly selected from the five villages that make up Owerri municipal council were anlayzed for physicochemical parameters using standard methods. The results from the study area showed that the temperature ranged between 26.5-28.0oC; рH ranged between 4.80-5.32. Electrical conductivity ranged between 77-166 µЅ/cm. The total dissolved solids were between 35.4-168.4mg/l; …
Published in Emerging Trends in Chemical Engineering · Vol. 11, Issue 1, 2024 Read article
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Optimization of Turning Process Parameters by Genetic Algorithm Approach
Abstract: In this research, turning parameters were optimized through a genetic algorithm for the purpose to minimize surface roughness and to maximize the material removal rate. High finish quality is guaranteed through minimum surface roughness, and efficient process planning is facilitated through maximum material removal rate optimization. For predicting surface roughness and material removal rate with respect to spindle speed, feed rate, and depth of cut, the empirical models were developed …
Published in Journal of Production Research & Management · Vol. 15, Issue 1, 2025 · pp. 33–41 Read article
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Thermal Effects in High-Power Laser Systems: Modeling and Mitigation
Abstract: High-power laser systems are increasingly employed in industrial manufacturing, defense, medical procedures, and scientific research due to their ability to deliver high energy density with excellent spatial coherence. However, the performance and reliability of these systems are significantly influenced by thermal effects arising from optical absorption, non-radiative recombination, and inefficient heat dissipation within laser gain media and optical components. These thermal phenomena lead to adverse effects such as thermal lensing, …
Published in International Journal of Manufacturing and Production Engineering · Vol. 3, Issue 2, 2025 · pp. 9–13 Read article
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A Review of Recent Advancements in Machine Learning and Deep Learning Approaches for Pet Diseases Prediction
Abstract: This systematic study assesses recent developments in Machine Learning (ML) and Deep Learning (DL) approaches to predict pet diseases. With the increasing role of Artificial Intelligence (AI) in pet healthcare, this study identifies recent research trends, limitations, and future directions. A comprehensive search was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines in selecting 20 relevant studies from over 300 articles published between 2020 and …
Published in Research and Reviews : Journal of Veterinary Science and Technology · Vol. 14, Issue 3, 2025 · pp. 1–6 Read article
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Explainable Machine Learning for Process Parameter Optimization in Gradient 3D-Printed Polymer Composites
Abstract: The explainable machine learning-based structure may be employed to achieve a favorable process parameter of the graduate 3D-printed polymer composite structures to improve the mechanical and thermal properties without compromising the transparency of the decisions made during the fabrication process. Gradient composite specimens were made by systematically varied process parameters like nozzle temperature, raster orientation, deposition speed, gradient transition rate and fused filament fabrication. A predictive model of tensile strength …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 847–866 Read article
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Data-Driven Life Prediction of Fiber-Reinforced Polymer Composites Using IoT Sensing and Machine Learning Algorithms
Abstract: The accurate prediction of fatigue life in fiber-reinforced polymer (FRP) composites remains a major challenge due to their nonlinear, multi-mechanism degradation behavior under variable loading conditions. This study presents a data-driven framework, H-LiProNet, which combines real-time IoT sensing with hybrid machine learning to estimate remaining useful life (RUL) in FRP composites. The proposed system integrates embedded Fiber Bragg Grating (FBG) and acoustic emission (AE) sensors to capture strain and damage …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 116–130 Read article