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43 articles for “machine learning (ML)”
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Unlocking Insights, Optimizing Processes: A Review Of Machine Learning In Action
Abstract: Machine learning (ML) has become a disruptive technology with applications in many different fields and sectors. Machine learning techniques are transforming the way data is examined, patterns are found, and decisions are made in a variety of industries, including healthcare, finance, manufacturing, and transportation. ML is improving drug discovery, treatment strategies, and diagnostics in the healthcare industry. In the financial sector, this means maximizing risk mitigation, credit scoring, and fraud …
Published in International Journal of Power Electronics Controllers and Converters · Vol. 10, Issue 1, 2024 · pp. 14–22 Read article
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Predictive Models for Drinking Water Quality: Leveraging Machine Learning for Improved Safety and Efficiency
Abstract: Quality of drinking water is one of the critical determinants of public health, leading to severe environmental and health-related issues when poor. The paper examines the integration of machine learning techniques that could improve accuracy and efficiency in assessing drinking water quality. The traditional techniques of monitoring drinking water are resource-intensive yet lack timely insight into contamination events. In this scenario, an evaluation of the performance of ML models such …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 11, Issue 1, 2025 Read article
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The Life-changing Impact of Mental Health Trackers
Abstract: This study explores the revolutionary potential of mental health trackers, which are cutting-edge digital technologies that have become essential in tackling the many issues surrounding psychological well-being in modern times. This study article delves into the various typologies and technological breakthroughs of mental health trackers, emphasizing their ability to empower people from all walks of life to actively monitor and nurture their mental health. The research results emphasize how mental …
Published in International Journal of Software Computing and Testing · Vol. 10, Issue 1, 2024 · pp. 29–33 Read article
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AI-Driven Predictive Maintenance in Smart Homes: Enhancing Efficiency and Reliability
Abstract: The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has transformed smart homes into intelligent, self-regulating environments. Despite these advancements, ensuring the long-term reliability and efficiency of interconnected devices remains a complex challenge due to device heterogeneity, dynamic usage patterns, and continuous data streams. This study investigates AI-driven predictive maintenance (PdM) as a proactive strategy for detecting early signs of equipment degradation in residential settings. The proposed …
Published in International Journal of Distributed Computing and Technology · Vol. 12, Issue 1, 2026 · pp. 1–6 Read article
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Advances and Emerging Trends in Classical and Modern Thermodynamics.
Abstract: Thermodynamics, as a foundational discipline of physical science, has continually evolved from its classical roots to modern, interdisciplinary applications. Classical thermodynamics established the laws governing energy, entropy, and equilibrium, providing the framework for analyzing heat engines, refrigeration cycles, and chemical processes. Recent advances have expanded these principles into new domains, integrating statistical mechanics, quantum theory, and non‑equilibrium systems. Emerging trends highlight the role of thermodynamics in nanoscale and quantum technologies, …
Published in International journal of Thermodynamics and Chemical Kinetics · Vol. 12, Issue 1, 2026 · pp. 1–8 Read article
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Use of Natural Language Processing for Hate Speech Detection on SocialMedia:
Abstract: The exponential growth of social media platforms has led to an increase in user- generated content, enhancing global connectivity but also facilitating the spread of harmful language, including hate speech. Addressing this issue requires robust, Naïve Bayes, Decision Tree, K-Nearest Neighbours (KNN), Linear Regression, and Random Forest—were tested, with Linear Regression achieving the highest accuracy automated systems for detecting and mitigating offensive content. This paper presents a comprehensive analysis of …
Published in International Journal of Digital Communication and Analog Signals · Vol. 11, Issue 2, 2025 · pp. 9–16 Read article
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Review of Anomaly Detection Techniques in MANETs: AMachine Learning Approach to Intrusion Detection, Attack Prediction, and Routing Security
Abstract: Mobile Ad hoc Networks (MANETs) are decentralized, self-organizing networks that offer flexibility and scalability for a variety of applications. However, their dynamic topology and limited resources make them highly susceptible to security threats. Traditional security measures often struggle to address the complexities of evolving and sophisticated attacks within such networks. This paper reviews the role of machine learning (ML) techniques in enhancing anomaly detection capabilities in MANETs, focusing on their …
Published in International Journal of Broadband Cellular Communication · Vol. 11, Issue 2, 2026 · pp. 1–5 Read article
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Evaluation of Machine Learning and Deep Learning Models for Classification Tasks on Crime Data: A Comparative Study
Abstract: The classification of crime data has grown more and more important for public safety and law enforcement due to the increasing demand for precise and effective predictive models. To classify crime data, this study assesses and contrasts the effectiveness of several machine learning (ML) and deep learning (DL) models, such as decision trees, support vector machines, neural networks, and convolutional neural networks. The study evaluates the models' accuracy, efficiency, and …
Published in International Journal of Digital Communication and Analog Signals · Vol. 11, Issue 1, 2025 · pp. 1–15 Read article
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Deep Learning for Smarter Farming: Innovations in Plant Disease Identification
Abstract: Agriculture is the backbone of global food security, yet plant diseases significantly impact crop yields and farmers' livelihoods. Traditional disease detection methods are often time-consuming and require expert intervention, making them less feasible for large-scale farming. Recent advancements in deep learning have revolutionized plant disease identification by offering automated, accurate, and efficient solutions. This article explores innovative deep learning techniques, including convolutional neural networks (CNNs) and transformer-based models, that enhance …
Published in International Journal of Microwave Engineering and Technology · Vol. 11, Issue 1, 2025 · pp. 27–32 Read article
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Hybrid Machine Learning and Deep Learning approach for Adaptive congestion control in 5G/6G Network
Abstract: The rapid growth of data traffic, fueled by emerging applications such as autonomous systems, augmented reality (AR), virtual reality (VR), and the Internet of Things (IoT), poses major challenges for modern communication networks. With the rollout of 5G and the continuous transition to 6G, sophisticated congestion control techniques are needed to satisfy the demanding requirements of massive machine-type communication (mMTC), ultra-reliable low-latency communication (URLLC), and improved mobile broadband (eMBB). Conventional …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 11, Issue 2, 2025 · pp. 1–8 Read article
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Smart Agriculture Using IoT: A Paradigm Shift in Modern Farming
Abstract: The rapid advancements on the Internet of Things (IoT) have revolutionized agriculture by integrating sensors, automation, and data analytics to enhance productivity, resource efficiency, and sustainability. With growing challenges such as climate change, population growth, and the need for efficient resource management, IoT-driven smart agriculture presents a transformative approach to modern farming. By leveraging IoT-enabled devices, farmers can gain real-time insights into soil conditions, weather patterns, crop health, and resource …
Published in International Journal of Digital Communication and Analog Signals · Vol. 11, Issue 1, 2025 · pp. 33–37 Read article
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Health Monitoring System for Fault Identification on Centrifugal Pump Using ML
Abstract: Centrifugal pumps are crucial equipment widely used in various industries, and their reliable operation is essential for maintaining production and preventing costly downtimes. This paper proposes a comprehensive health monitoring system for centrifugal pumps that combines model-driven and data- driven approaches, leveraging artificial intelligence (AI) and machine learning (ML) techniques. The system incorporates demand prediction using artificial neural networks and fault diagnosis employing different ML classifiers, such as decision trees …
Published in International Journal of Mechanics and Design · Vol. 10, Issue 2, 2024 · pp. 1–12 Read article
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An Overview on Metal Processing using Machine Learning
Abstract: Metalworking, which is an important part of modern industry, includes a wide range of steps, such as shaping and casting the metal, machining it, and riveting it. The industry has always relied on trial and error and other empirical methods, but machine learning (ML) is now causing a big change. It is expected that this change will lead to higher production, better product quality, and better use of resources. The …
Published in International Journal of Manufacturing and Materials Processing · Vol. 12, Issue 1, 2026 · pp. 43–53 Read article
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Enhancing Facial Expression Analysis Using CNNs: Insights from ML and Deep Learning Comparisons
Abstract: Emotion recognition from facial manifestations increases the interaction between humans and computers by enabling the interaction between humans and computers to effectively explain human feelings. This study presents a comparative analysis of three approaches to the emotional recognition of faces: a custom Convolutional Neural Network (CNN) model, Traditional Machine Learning (ML) methods, and other deep learning architectures such as VGG, ResNet, and Hybrid CNN–RNN models. The proposed CNN-based system, developed …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 23–31 Read article
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Predicting User Engagement on Social Media: A Comparative Study of Machine Learning-based Modeling Approaches
Abstract: Abstract Even while mobile social apps are becoming more and more important in people's daily lives, little is known about the elements that motivate users to interact with these apps. The goal of this work is to analyze and define the transition patterns of individual users' in-app actions as a temporally changing action graph. According to our investigation, action graphs are a useful tool for characterizing user behaviour patterns and …
Published in International Journal of Computer Aided Manufacturing · Vol. 11, Issue 2, 2025 · pp. 41–62 Read article
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The Transformative Power of AI and Machine Learningin Education
Abstract: A revolutionary factor that evolves how learning is experienced and how educational systems function is the incorporation of artificial intelligence (AI) and machine learning (ML) in the classroom. Data- driven decision-making, administrative work automated processes, real-time feedback, and education personalization are all made possible by AI and ML technologies, which are no longer just found in research and industry. This study addresses the various ways that AI and ML can …
Published in International Journal of Computer Aided Manufacturing · Vol. 10, Issue 2, 2024 · pp. 1–5 Read article
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Estimation of Crop Yield Prediction Using Machine Learning Technique.
Abstract: Agriculture serves as the primary source of livelihood for a significant portion of India's population, but it faces a myriad of challenges, including climate variability, soil fertility issues, and reliance on traditional farming practices. To address these challenges, researchers are increasingly turning to machine learning (ML) methodologies to predict crop yields more accurately. Studies have delved into the effectiveness of various ML algorithms such as Random Forest, Gaussian Process Regression, …
Published in International Journal of Agrochemistry · Vol. 10, Issue 1, 2024 · pp. 7–12 Read article
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Advancements in Predictive Maintenance: A Review of Data-Driven Approaches, Machine Learning, and AI in Diverse Industrial Landscapes
Abstract: The Internet of Things (IoT), the use of machine learning (ML), and artificial intelligence (AI)-driven advances in predictive maintenance are the key topics of this study. With the goal of offering a thorough grasp of existing approaches and potential future developments, it analyzes applications in predictive maintenance in many sectors. Predictive maintenance approaches are the major topic of discussion, with an emphasis on their advantages, disadvantages, difficulties, and prospects. Additionally, …
Published in International Journal of Computer Aided Manufacturing · Vol. 10, Issue 2, 2024 · pp. 19–32 Read article
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IoT-Based Fault Detection in Distribution Lines
Abstract: Quickly detecting and pinpointing failures in electrical distribution lines are important for shortening the duration of service interruption, reducing maintenance costs and increasing safety. In this work, we propose a realistic architecture for an Internet-of-Things (IoT)-enabled fault detection system designed for distribution networks. In order to continually assess electrical characteristics including voltage, current, frequency, temperature, and line status, the suggested architecture combines inexpensive, dispersed sensor nodes placed along feeders and …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 1, 2026 · pp. 1–6 Read article
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Cloud Security Using Machine Learning: A Study
Abstract: The rapid adoption of cloud computing has revolutionized IT infrastructure, offering unprecedented scalability, flexibility, and cost efficiency. However, this paradigm shift introduces a new spectrum of sophisticated security challenges that traditional, signature-based security mechanisms often struggle to address effectively given the dynamic, multi-tenant, and distributed nature of cloud environments. This paper explores the critical role of Machine Learning (ML) as a potent paradigm for strengthening cloud infrastructure. ML emerges as …
Published in International Journal of Distributed Computing and Technology · Vol. 11, Issue 2, 2025 · pp. 21–30 Read article