machine learning (ML)
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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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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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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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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