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95 articles for “auto ML”
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Automated Intelligence, Machine Learning, and Big Data in Education: A Practical Framework, Synthetic Demonstration, and Deployment Guidance
Abstract: Artificial intelligence (AI), machine learning (ML), and big-data methods are increasingly used to improve educational decision making through personalization, early-warning systems, scalable feedback, and operational analytics. This manuscript proposes a practical end-to-end framework for educational AI/ML projects, covering problem definition, data engineering, modeling, evaluation, intervention design, and responsible governance. To provide a complete and reproducible template without exposing sensitive student data, we present a synthetic demonstration study that mirrors typical …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article
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A Systematic Study of AI-Powered Robotics for Ocean Cleanup of Plastics
Abstract: The escalating crisis of plastic pollution in marine ecosystems demands innovative solutions beyond conventional cleanup methods. This paper presents a systematic study of artificial intelligence (AI)-powered robotics for ocean plastic cleanup, evaluating their efficiency, technological advancements, and challenges. Autonomous systems, such as AI-driven surface drones (ASVs), underwater robots (autonomous underwater vehicles/remotely operated vehicles [AUVs/ROVs]), and swarm robotics, leverage machine learning (ML) and computer vision to detect, classify, and collect plastic …
Published in Journal of Advancements in Robotics · Vol. 12, Issue 2, 2025 · pp. 1–10 Read article
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Automation and Robotics for Quality Control in Manufacturing: A Review of Technologies and Applications
Abstract: Automation and robotics technologies have rapidly evolved, transforming modern manufacturing processes by improving productivity, quality, and operational efficiency. This review examines key advancements such as cloud robotics, machine vision, Industry 4.0 robotics, Building Information Modeling (BIM) combined with Computer Numerical Control (CNC), joystick-controlled automation, and intelligent manufacturing systems. These technologies utilize artificial intelligence (AI), machine learning (ML), digital twins, collaborative robots, programmable logic controllers (PLCs), and cyber-physical systems (CPS) to …
Published in Journal of Mechatronics and Automation · Vol. 12, Issue 3, 2025 · pp. 36–48 Read article
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Aerodynamic Optimization of UAV Wings Using Machine Learning
Abstract: Unmanned Aerial Vehicles (UAVs) are increasingly deployed across defense, transportation, agriculture, and environmental monitoring, demanding improved aerodynamic efficiency to enhance endurance, stability, and payload capacity. Traditional aerodynamic optimization approaches, relying on computational fluid dynamics (CFD) simulations and wind tunnel experiments, are often time-consuming and computationally expensive. This study proposes a machine learning (ML)-driven framework for the aerodynamic optimization of UAV wing geometries, aiming to significantly reduce design cycles while improving …
Published in International Journal on Drones · Vol. 2, Issue 1, 2026 · pp. 1–7 Read article
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AI Approaches in Gait and Posture Analysis: A Review
Abstract: This review synthesizes current research on the application of artificial intelligence (AI) in gait and posture analysis, focusing on methodologies, algorithms, and clinical applications. It examines the use of machine learning (ML) and deep learning (DL) techniques to extract relevant features from sensorderived data, offering objective, and automated assessments that surpass traditional methods. A systematic literature review was conducted, analyzing studies that utilized AI for gait and posture analysis with …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 1–3 Read article
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Machine Learning Revolutionizing Server Management and Performance
Abstract: The modern data center is a complex and dynamic environment, grappling with ever-increasing workloads, stringent performance demands, and the constant pressure for cost optimization. As such, applying machine learning (ML) directly to the server infrastructure offers a powerful avenue for achieving advanced automation, resource optimization, and proactive problem resolution. This article explores the transformative potential of integrating machine learning into server systems, leveraging insights gleaned from the abstract and conclusion …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 36–44 Read article
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Integrated Frameworks for Artifical Intelligence in Radioactive Waste Characterization and Nuclear Lifecycle Safety
Abstract: The management and characterization of radioactive waste represent a pivotal challenge for the global energy sector, requiring the convergence of advanced physics, material science, and computational intelligence. As the nuclear industry undergoes a paradigm shift toward decommissioning legacy facilities and establishing deep geological repositories, the limitations of traditional, manually-intensive waste management processes have become increasingly apparent. Rigid separation from the biosphere is required for radioactive waste, which is defined by …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 2, 2026 Read article
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A Survey On Leveraging Machine Learning for Phishing Attack Prediction and Detection
Abstract: Phishing is one of the biggest cybersecurity threats that exploits user trust by masquerading as a legitimate site or email to steal personal and sensitive information. A state- of-the-art-phishing detection systems survey, this review showcases the evolution from traditional list-based techniques, including blacklisting and whitelisting to machine learning and deep learning models. While list-based systems cannot evolve to detect new and zero-day attacks, the ML algorithms of Decision Tree, Random …
Published in Journal of Microelectronics and Solid State Devices · Vol. 12, Issue 3, 2025 · pp. 1–10 Read article
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Exploring Artificial Intelligence in the Finance Sector
Abstract: Artificial intelligence (AI), machine learning (ML), and progressive algorithms illustrate a substantial technological leap with wide applications across sectors like automobiles, healthcare, gaming, finance, entertainment, and more. The foremost objective of AI is to produce intelligent, independent systems capable of self-sustaining decision-making. This study delivers a concise summary of AI, concentrating on its transformative influence on finance, especially within banking, asset firms, derivatives markets, and insurance enterprises. It summarizes the …
Published in E-Commerce for Future & Trends · Vol. 11, Issue 3, 2024 · pp. 16–23 Read article
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Revolutionizing Wireless Communication: AI & ; ML in the Era of 6G
Abstract: With rapid technological advancement, sophisticated techniques are significantly enhancing the performance of wireless networks. In parallel, the growth of artificial intelligence (AI) has empowered systems to perform intelligent decision-making, automate processes, analyze data, generate insights, and predict future outcomes. AI systems are now capable of learning and adapting to dynamic environments. Particularly, machine learning and deep learning techniques have achieved remarkable success across a wide range of applications in recent …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 12, Issue 3, 2025 · pp. 29–36 Read article
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Revolutionizing Petrology and Mineralogy: The Study of AI and Advanced Sensor Technologies
Abstract: Petrology and mineralogy are fundamental to understanding Earth's intricate processes, from crustal evolution to economic resource formation. However, traditional methods, while precise, are often laborious, time-consuming, and occasionally subject to interpretive bias. This abstract explores the transformative potential of integrating cutting-edge Artificial Intelligence (AI) and advanced sensor technologies to revolutionize data acquisition, analysis, and interpretation in these critical geosciences. Advanced sensor technologies, including high-resolution spectral imaging (hyperspectral, Raman), automated X-ray …
Published in International Journal of Minerals · Vol. 2, Issue 2, 2025 · pp. 1–11 Read article
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EMERGING TECHNOLOGIES RESHAPING INDUSTRIES
Abstract: Emerging technologies such as Artificial Intelligence (AI), machine learning and 5 G technology are reshaping industries rapidly. This paper contains knowledge about some of the key emerging technologies and their real-world applications in various sectors like health care, education, agriculture, transportation, finance. This includes some of the challenges and considerations arising due to the widespread use of these emerging technologies. Also exploring about the future directions due to adopting these …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 12, Issue 3, 2025 · pp. 1–10 Read article
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AI for Cybersecurity: Deploying Machine Learning for Network Traffic Anomaly Detection
Abstract: The growing sophistication of cyberattacks and the growth of network traffic necessitate sophisticated anomaly detection methods. This study overviews the use of artificial intelligence (AI) and machine learning (ML) to counter these challenges, as noted in current studies. It analyses supervised learning (SVM, Decision Trees), unsupervised learning (K-means, DBSCAN), and deep learning (CNNs, RNNs, Auto-encoders) approaches, considering their strengths and weaknesses. The research integrates current developments in AI/ML-based network anomaly …
Published in International Journal of Computer Science Languages · Vol. 3, Issue 2, 2025 · pp. 1–10 Read article
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Deep Learning Applications in Bone Fracture Detection for Improved Radiographic Diagnostics
Abstract: Bone fracture detection is a critical aspect of medical diagnostics, traditionally relying on manual interpretation of radiographic images by experienced radiologists. This discipline has undergone a revolution with the introduction of machine learning (ML), which can improve accuracy, shorten diagnosis times, and lessen human error. This study investigates the use of different machine learning methods to enhance and automate the identification of bone fractures in radiography pictures. We utilized a …
Published in International Journal of Optical Innovations & Research · Vol. 2, Issue 2, 2024 · pp. 17–22 Read article
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Intelligent Decision Support System for Production Planning and Control in an Automotive Assembly Line
Abstract: The automobile industry has realized the importance of Intelligent Decision Support Systems (IDSS) to improve production planning and control on assembly lines. In the context of automobile manufacturing, this review paper examines the most recent developments, methodology, and applications of IDSS. Key trends, obstacles, and possibilities are discovered through an in-depth examination of the literature. Examining real-world case studies and success stories, the integration of artificial intelligence (AI) and machine …
Published in International Journal of Industrial and Product Design Engineering · Vol. 1, Issue 1, 2023 · pp. 16–21 Read article
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Machine Learning in Nuclear Medical Applications: A Review of Research Frontiers
Abstract: Nuclear medicine, encompassing PET, SPECT, and targeted radionuclide therapy, generates high-dimensional, quantitative data uniquely suited for machine learning (ML) analysis. This review synthesizes current research applications of ML across six key domains. Positron emission tomography (PET), single-photon emission computed tomography (SPECT), and targeted radionuclide therapy are examples of nuclear medicine modalities that generate high- dimensional, quantitative datasets that are particularly well-suited for machine learning (ML)-driven analysis. These imaging methods provide …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 1, 2026 · pp. 19–24 Read article
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Using AIML to Enhance Demand Forecasting in Business
Abstract: Artificial intelligence machine learning (AIML) can play a significant role in enhancing demand forecasting in business. AIML is a programming language designed for creating chatbots and conversational agents, but its application extends beyond simple interactions. In the context of demand forecasting, AIML can be utilized to analyze historical data, customer interactions, and market trends. By implementing AIML algorithms, businesses can create intelligent models that learn from past demand patterns, customer …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 1, 2024 · pp. 35–40 Read article
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The Role of Artificial Intelligence and Machine Learning in Redefining Global Healthcare Systems and Advancing Medical Innovation
Abstract: Health Services are being revolutionized with AI and ML through improved accuracy, efficiency and accessibility in the delivery of health care. With AI and ML, it is now possible for health care professionals to assess varying amounts of complex clinical data in a relatively short amount of time, therefore, creating opportunities for early detection of disease, increasing the odds of accurate diagnosis, and improving the ability to make informed clinical …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 4, Issue 1, 2026 · pp. 14–19 Read article
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Griscelli Syndrome Type 2: A Case Report on Its Diagnosis and Management
Abstract: Griscelli syndrome (GS) is an autosomal recessive disorder which is very rare and is characterized by dilution of pigments and variable immunodeficiency. Researchers have identified three types of this disorder which are distinguished by their genetic cause and pattern of signs and symptoms: Type 1, Type 2 and Type 3, caused by the mutation of MYO5A, RAB27A and MLPH genes respectively. Subtype 2 being the most common, is characterized by …
Published in Research and Reviews : A Journal of Immunology · Vol. 8, Issue 2, 2018 · pp. 5–7 Read article
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Enhancing Irrigation Efficiency through Machine Learning and IoT Integration
Abstract: This study provides an integrated approach to enhance irrigation systems by fusing machine learning (ML) with Internet of Things (IoT) technologies. Agriculture is a major contributor to India’s economy, which is referred to as the backbone of the country. However, its production is highly dependent on several environmental and agronomic factors, with water being a critical resource. Irrigation consumes about 84% of the total available water in India; however, a …
Published in Journal of Electronic Design Technology · Vol. 15, Issue 3, 2024 · pp. 17–22 Read article