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120 articles for “machine learning prediction”
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ML-Driven Optimization Framework for the Analysis, Design, and Development of Efficient Wireless Power Transfer Systems for EV Charging
Abstract: The fast uptake of electric vehicles (EVs) has heightened the necessity of effective, dependable and convenient charging systems. The Wireless Power Transfer (WPT) systems can be taken as a potential solution as they allow charging cells without contact, without any risks, and without any overcrowding; the efficiency of the system is strongly influenced by the alignment of coils, the fluctuations of air-gaps, the conditions of the loads, and geometrical arrangements …
Published in International Journal of Manufacturing and Production Engineering · Vol. 4, Issue 1, 2026 · pp. 1–9 Read article
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Development of a Generative AI Model for Early Detection and Prevention of Electrical Faults in Thermal Power Plants
Abstract: Electrical faults in thermal power plants can lead to severe equipment damage, production downtime, and safety hazards if not detected in advance. This study presents the development of a Generative Artificial Intelligence (GenAI) model for the early detection and prevention of electrical faults using predictive analytics. The proposed framework integrates Generative Adversarial Networks (GANs) with deep learning (CNN) and machine learning algorithms (Random Forest, Logistic Regression) to enhance data diversity, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 1–10 Read article
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AI-Powered IoT System for Early Detection and Monitoring of Livestock Health
Abstract: Protection of food production exists through livestock farming operations that advance economic global power. Continuous challenges to agricultural industry practices result in harmed animal health and enable disease spread as well as environmental threats to their welfare. Implementing current innovative solutions right away is necessary to solve these problems. The AI and IoT-based smart livestock health monitoring system functions as the fundamental development approach across this industry. The present integrated …
Published in International Journal of Electrical and Communication Engineering Technology · Vol. 3, Issue 1, 2025 · pp. 1–8 Read article
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Magnetic and Radioactive Nanoparticles for Improved Theranostics and AI Assisted Radiation Therapy
Abstract: Nanoparticles based therapeutic and theranostics technique is becoming an active area of research in nanomedicine. The sensitivity, biocompatibility, and stability of magnetic and radioactive nanoparticles determine their functionality. This research highlights the impacts of magnetic and radioactive nanoparticles on therapeutic techniques namely, cell therapy, gene therapy, and tissue regeneration. Then, it is intended to brief a principal role of these therapeutic techniques to envisage theranostics medicine and radiation therapy using …
Published in International Journal of Advance in Molecular Engineering · Vol. 3, Issue 2, 2025 · pp. 10–21 Read article
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Microstructural Design and Functional Properties of Polycrystalline Materials
Abstract: Polycrystalline materials, composed of an aggregate of crystallites or grains, are foundational to modern engineering applications due to their versatile functional properties. The microstructural design—encompassing grain size, shape, orientation, phase distribution, and grain boundary characteristics—plays a pivotal role in determining mechanical, thermal, electrical, and magnetic behavior. This abstract explores the intricate relationship between microstructure and functionality, emphasizing how tailored processing techniques such as thermomechanical treatments, sintering, and additive manufacturing can …
Published in International Journal of Crystalline Materials · Vol. 2, Issue 2, 2025 · pp. 16–20 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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Python's Applications in the Profession of Data Science
Abstract: Because of its ease of use, adaptability, and huge ecosystem of libraries, Python has become one of the most influential programming languages in the field of data science. Python is highly valued for its straightforward and versatile nature. This study delves into its various uses in data science, including tasks like data preprocessing, exploratory data analysis (EDA), statistical modeling, machine learning, and creating visualizations. Libraries like Pandas and NumPy make …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 1, 2025 · pp. 23–30 Read article
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AI and Access to Justice: An Indian Perspective
Abstract: Artificial intelligence is set to transform the legal profession, influencing everything from legal research and document review to legal advice and decision-making. This study investigates the possible uses of AI in the legal area, such as predictive analytics and AI-powered legal research tools. This study explains the role of AI in Judicial Systems. The study covers the related literature, challenges and opportunities in Indian judicial system. The usage of AI …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 2, 2025 · pp. 30–37 Read article
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AI-Powered Defense: Advancing Cybersecurity Through Artificial Intelligence Innovations
Abstract: In the current landscape of escalating cyber threats and increasingly sophisticated attack vectors, integrating artificial intelligence (AI) within cybersecurity strategies has become a critical step forward. This study explores the role of AI in strengthening cybersecurity by utilizing its strengths in data analysis, pattern recognition, and predictive modeling. Through AI, organizations can greatly enhance their ability to detect and respond to threats. Machine learning algorithms enable ongoing adaptation to new …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 1, 2026 · pp. 35–41 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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A Conceptual Framework for AI-Integrated Metabolomics in Predictive Health Systems for Resource-Constrained Environments
Abstract: The increasing prevalence of non-communicable diseases (NCDs) continues to place a significant strain on healthcare systems, particularly in low- and middle-income regions where access to early diagnostic infrastructure is limited. Conventional healthcare approaches remain largely reactive, often detecting diseases at advanced stages when treatment effectiveness is reduced. This challenge underscores the need for predictive, cost-effective, and data-driven healthcare solutions. This study presents a conceptual framework that integrates metabolomics with artificial …
Published in Emerging Trends in Metabolites · Vol. 3, Issue 2, 2026 Read article
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Semantics Analysis of Expected Goals in Soccer Data Using Machine Learning
Abstract: In recent years, the increasing availability of soccer data has greatly enhanced the accuracy and depth of player performance evaluation. Soccer, being one of the most popular sports worldwide, attracts millions of fans due to its simple rules, minimal equipment requirements, and high entertainment value. However, analyzing an entire match manually can be time-consuming, leading to a growing demand for automated methods that can summarize and interpret game data efficiently. …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 2, 2025 · pp. 31–47 Read article
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Multi-Layered AI-Driven Security in Wireless Ecosystems
Abstract: The proliferation of next-generation wireless technologies, from 5G/6G networks to the pervasive Internet of Things (IoT), has birthed a hyperconnected digital ecosystem of unprecedented scale and dynamism. This interconnectedness, however, introduces a vast and volatile attack surface, rendering conventional, signature-based security paradigms fundamentally obsolete. This paper posits that the only viable defense is an offensive, self-adaptive one, predicated on the integration of artificial intelligence (AI) directly into the wireless security …
Published in International Journal of Wireless Security and Networks · Vol. 4, Issue 1, 2026 · pp. 21–28 Read article
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Assessing the Robustness of Machine Learning Models for Wireless Intrusion Detection Under Adversarial Traffic Perturbations
Abstract: As the Internet of Things (IoT) devices and wireless communication networks continue to grow rapidly, protecting systems from cyber threats has become increasingly important. Machine learning–based intrusion detection systems (IDS) have shown strong potential in detecting abnormal and malicious network activities, yet their effectiveness and resilience when facing adversarial attacks are still not sufficiently explored. This research evaluates Machine Learning (ML) models–XGBoost, random forest, and multi-layer perceptron (MLP)—in detecting attacks …
Published in International Journal of Wireless Security and Networks · Vol. 4, Issue 1, 2026 · pp. 29–34 Read article
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Detection of Pneumonia in COVID-19 Patients Using X-ray Images
Abstract: This study explores the use of chest X-ray image analysis and deep learning methods to identify pneumonia in COVID-19 patients. Due to the pandemic, Proper as well as immediate examination of COVID-19 is now essential for patient care and disease control. This study proposes a novel approach that uses convolutional neural networks (CNNs) to automatically predict pneumonia in COVID-19 patients using chest X-ray images. In this study, an X-ray of …
Published in International Journal of Radio Frequency Innovations · Vol. 1, Issue 1, 2023 · pp. 13–23 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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The Scientific Foundations of Programming Languages: Bridging Theory and Practical Application
Abstract: The study of programming languages within computer science is fundamental to the development of efficient, reliable, and scalable software systems. However, the degree to which these languages adhere to scientific principles remains a topic of debate. This paper explores the scientific nature of computer science languages by examining their theoretical foundations, design principles, and practical applications. It evaluates how programming languages are grounded in mathematical logic, formal semantics, and computational …
Published in International Journal of Computer Science Languages · Vol. 3, Issue 1, 2025 · pp. 32–41 Read article
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Recent Applications and Influences of Artificial Intelligence (AI) In Chemical and Allied Sciences
Abstract: Artificial Intelligence (AI), the future tool of mankind that can revolutionise scientific research by making it faster, add more efficiently and accurately. During the pandemic situation, the scientific community was parted into two distinct groups, the computation-dependent community could easily continue their research work from their resident, while the works of the other group of researchers with lab-oriented research stopped entirely. In this connection use of AI becomes important and …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 22–48 Read article
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Remote Sensing and Atmospheric Modelling: Data, Processes, Integration and Future Directions
Abstract: Atmospheric modelling plays a central role in weather forecasting, climate projection, and air quality assessment; however, the availability, accuracy, and representativeness of atmospheric observations fundamentally constrain its reliability. Over the past two decades, rapid advances in remote sensing (RS) have transformed atmospheric observation by providing spatially continuous, multiscale measurements of key atmospheric variables, including aerosols, trace gases, clouds, precipitation, and atmospheric thermodynamic profiles. This review synthesises recent progress in integrating …
Published in International Journal of Atmosphere · Vol. 3, Issue 1, 2026 · pp. 54–67 Read article
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Intelligent Earth: AI As A Catalyst For Climate Action
Abstract: Artificial Intelligence (AI) is assuming an increasingly influential role in climate science, providing advanced tools capable of interpreting vast, complex, and multi-dimensional environmental datasets. Traditional climate modeling approaches, while grounded in physical principles, frequently struggle to deliver high-resolution, real-time, and region-specific forecasts because of heavy computational demands, incomplete observations, and uncertainties in representing small -- scale processes. Artificial intelligence (AI) techniques, especially machine learning and deep learning, provide strong substitutes …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 4, Issue 1, 2026 · pp. 48–52 Read article