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77 articles for “Optimized Machine Learning”
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Robotics and Automation in Mechanical Engineering: Transforming Modern Manufacturing Systems
Abstract: Robotics and automation have significantly transformed mechanical engineering, particularly in manufacturing, precision assembly, and intelligent systems integration. With the advancement of sensors, control systems, artificial intelligence, and mechatronics, robotic systems are now capable of performing complex tasks with high accuracy, repeatability, and efficiency. This article explores the role of robotics in modern mechanical applications, including industrial automation, collaborative robots, predictive maintenance, and smart manufacturing. It also discusses design considerations, challenges, …
Published in International Journal of Manufacturing and Production Engineering · Vol. 4, Issue 1, 2026 · pp. 27–33 Read article
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Atmospheric Remote Sensing: Bridging Technology and Environmental Challenges
Abstract: The rapid advancements in remote sensing technologies have revolutionized the field of atmospheric studies, offering unprecedented capabilities for detailed observation, analysis, and understanding of the Earth's atmosphere. These technological innovations have proven to be instrumental in tackling critical environmental challenges, providing scientists and researchers with the tools needed to monitor and analyze atmospheric phenomena with greater precision and depth.This article delves into the historical development, current state, and diverse applications …
Published in International Journal of Atmosphere · Vol. 2, Issue 1, 2025 · pp. 14–19 Read article
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Innovations in Mineral Science and Engineering for Sustainable Resource Development
Abstract: The growing global demand for mineral resources, coupled with increasing environmental and social concerns, has intensified the need for sustainable approaches in mineral science and engineering. Traditional mining and mineral processing practices, while essential for industrial development, are often associated with high energy consumption, resource depletion, and environmental degradation. In response, recent innovations in mineral science and engineering have focused on improving resource efficiency, minimizing environmental impact, and ensuring long-term …
Published in International Journal of Minerals · Vol. 3, Issue 1, 2026 · pp. 31–36 Read article
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Comprehensive Review of the Fundamental and Functional Properties of Crystalline Materials
Abstract: Crystalline materials, characterized by their highly ordered atomic arrangements, serve as the backbone of modern engineering and technology. This review provides a detailed examination of their diverse properties, categorized into mechanical, thermal, electrical, and optical domains. We analyze fundamental mechanical parameters such as the elastic modulus, yield strength, and fracture toughness, alongside functional behaviors like fatigue and creep. The discussion extends to thermal transport and expansion, electrical conductivity and resistivity, …
Published in International Journal of Crystalline Materials · Vol. 3, Issue 1, 2026 · pp. 20–24 Read article
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Epidemiology and transmission of infectious diseases study using Machine learning
Abstract: Infectious diseases remain a formidable global health challenge, characterized by rapid evolution and complex transmission dynamics that often outpace traditional epidemiological surveillance and response mechanisms. This study investigates the transformative potential of machine learning (ML) methodologies to enhance our understanding and prediction of infectious disease epidemiology and transmission. Leveraging diverse datasets—including clinical records, genomic sequences, environmental factors, social mobility data, and real-time digital footprints—we studies and presented various ML models …
Published in International Journal of Pathogens · Vol. 2, Issue 2, 2025 Read article
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Enhancing User Engagement and Content Relevance: A Novel Approach to Social Media Post Recommendation System
Abstract: The social media post recommendation system is an innovative solution aimed at optimizing content delivery for users in today's digital age. Its primary motive is to tailor online experiences, ensuring users receive posts most relevant to their preferences. Various machine learning algorithms are employed to suggest related posts. This system leverages advanced algorithms and analytics, producing key results that highlight user engagement metrics and content relevance. Preliminary findings of this …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 2, 2024 · pp. 1–7 Read article
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The Influence of Data Analytics on Sports Performance
Abstract: Data analytics has drastically changed how we evaluate, improve, and maintain athletic performance. Coaches used to use subjective observations as well as only limited numbers of statistics to consider player performance; however, tracking technology is now advancing at a fast pace. There are now very large amounts of real-time data available on athletes in regards to speed, movement patterns, fatigue, efficiency, etc. This enables all teams to more accurately make …
Published in Recent Trends in Sports · Vol. 3, Issue 1, 2026 · pp. 15–21 Read article
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Enhancing Mutual Fund Investment Decision-making Using Machine Learning: A Survey
Abstract: In India, a significant portion of individuals save a part of their income for a secure future. Government and various public sector financial companies also provide some saving schemes through banks, post offices, and Life Insurance Corporation (LICs) such as Recurring Deposit (RD), Public Provident Fund (PPF), Sukanya Samridhhi Account (SSA) fixed deposits, etc. Over the past decade, many individuals have shifted their saving schemes to vigorously searching for investment …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 1, 2024 · pp. 43–51 Read article
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The Role of Artificial Intelligence in Enhancing Athlete Performance and Training Strategies
Abstract: Artificial Intelligence (AI) is transforming modern sports by improving athlete performance, training methods, and decision-making processes. The integration of AI technologies such as machine learning, data analytics, wearable sensors, and computer vision has enabled coaches and sports scientists to analyze large amounts of performance data with greater accuracy and efficiency. Athletes' physiological indicators, movement patterns, injury risks, and recuperation processes are all monitored by these technology. Training regimens can therefore …
Published in Recent Trends in Sports · Vol. 3, Issue 2, 2026 · pp. 1–7 Read article
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An Investigative Study on Cache-Oblivious Data Structures
Abstract: Cache-oblivious data structures and data management systems have emerged as critical components in modern computing environments, aiming to optimize memory access patterns across different levels of the memory hierarchy without explicit knowledge of cache sizes or configurations. This study presents an overview of cache-oblivious techniques, including adaptive data structures, compression, parallel processing, and security considerations. The workexplores future directions in cache-oblivious systems, such as non-volatile memory support, graph processing, edge …
Published in International Journal of Data Structure Studies · Vol. 1, Issue 2, 2023 · pp. 33–37 Read article
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AI and ML in the Chemical Industry: A Review of Transformative Applications and Future Prospects
Abstract: The chemical industry, a key growth indicator of the global manufacturing ecosystem, is experiencing a digital transformation driven mainly by advancements in Artificial Intelligence (AI) and Machine Learning (ML) in this sector. These technologies are totally revolutionizing current and traditional methodologies by significantly improving process efficiency, reducing costs of manufacturing, accelerating R&D, and improving safety and sustainability standards. Proper utilization of Artificial intelligence (AI) and machine learning (ML) in chemical …
Published in International Journal of Cheminformatics · Vol. 3, Issue 2, 2025 · pp. 1–6 Read article
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The Future of Robotics: A Review of AI-Enabled Robotics Research, Development, and Applications
Abstract: Robotics powered by artificial intelligence (AI) is transforming contemporary industries by empowering machines to learn, adapt, and operate on their own in intricate, changing contexts. The breadth and capabilities of automation have been greatly expanded by the convergence of AI technologies with robots, including machine learning, deep learning, computer vision, and natural language processing (NLP). With an emphasis on technological advancements, application areas, and research advances, this study examines current …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 3, Issue 2, 2025 · pp. 33–38 Read article
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An Overview on AI-Driven IoT Based Decision Making in Climate change Study: KSK approach in Climate Change Study
Abstract: As the Earth’s climate enters a state of unprecedented volatility, the traditional methods of ecological observation—characterized by delayed reporting and fragmented data—are no longer sufficient. This study investigates the paradigm shift toward AI-driven IoT (KSK Approach)-based decision-making frameworks as the primary frontier in climate science. By deploying a "planetary nervous system" of interconnected sensors—measuring everything from soil moisture in the Sahel to glacial melt rates in the Arctic—we generate a …
Published in International Journal of Climate Conditions · Vol. 3, Issue 1, 2026 · pp. 1–10 Read article
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Diabetes Risk Prediction from Survey Data Using Machine Learning Algorithms
Abstract: Diabetes mellitus represents one of the most significant global health challenges, affecting millions worldwide and leading to severe complications if left undiagnosed or poorly managed. Early detection and risk assessment are crucial for preventing the progression of this chronic condition. This research presents a comprehensive machine learning approach for predicting diabetes risk using survey-based health parameters. The study implements and compares four prominent classification algorithms: Logistic Regression, K-Nearest Neighbors (KNN), …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
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Harvestify: ML Based Tool for Home Gardening and Farming
Abstract: This study presents a cutting-edge application that will transform home gardening and agriculture practices using machine learning (ML) approaches. The main goal is to provide data-driven insights to home gardeners and farmers, enabling them to implement efficient and sustainable farming practices. Crop disease detection, fertiliser recommendation, and a community section for user engagement comprise the three main elements that make up the system's architecture. The Crop Disease Detection module analyses …
Published in International Journal of Electrical and Communication Engineering Technology · Vol. 2, Issue 2, 2024 · pp. 18–28 Read article
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Time Series Sales Forecasting Using ARIMA Model
Abstract: Sales forecasting is a critical application in various industries and presents one of the most challenging problems worldwide. One method of prediction involves identifying patterns in historical data, where the outcome is known in advance and can be validated using more recent data. If a pattern consistently leads to the same outcome, it can be considered a genuine relationship. This method is highly flexible and can be utilized with diverse …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 1, 2024 · pp. 17–27 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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AI-Driven Robotics for Sustainable Solutions in Disaster Management
Abstract: Disasters, whether natural or man-made, present significant challenges to societies worldwide. Efficient response, recovery, and mitigation strategies are crucial to minimizing human suffering, loss of life, and economic damage. Traditional disaster management strategies, while effective to some degree, often face limitations related to human resources, response time, accessibility, and safety. The integration of artificial intelligence (AI) and robotics into disaster management offers transformative potential for overcoming these challenges. This paper …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 3, Issue 1, 2025 · pp. 24–30 Read article
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Iron Oxide/Chitosan Nanocomposite: Properties and Design for AI Enhanced Immunotherapy and Regenerative Medicine
Abstract: Biopolymers are valuable complex materials. They attract attentions of many scientists, engineers, and medical professionals’ due to their distinguished properties for various applications. In this research, emphasis is given to Fe3O4/Chitosan nanocomposite which has desirable biophysical properties compared to pure Chitosan nanoparticles. Following the green synthesis procedures and characterization methods including thermo gravimetric analysis, AI assisted biomedical application of; Fe3O4/Chitosan nanocomposite is presented. The effect of alkali typically, KOH, in …
Published in International Journal of Cheminformatics · Vol. 3, Issue 2, 2025 · pp. 22–32 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