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349 articles for “Machine Learning Optimization”
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AI-Powered Solutions for Sustainable Waste Management in Construction Projects
Abstract: The construction industry is a significant contributor to global waste, posing challenges to sustainability and environmental health. This research explores AI-powered solutions for sustainable waste management in construction projects, focusing on optimizing waste reduction, recycling, and resource efficiency. By integrating machine learning algorithms and IoT-enabled sensors, real-time monitoring of waste generation and segregation can be achieved. Predictive analytics and AI-driven decision-making tools are employed to enhance material reuse and minimize …
Published in Recent Trends in Civil Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 1–5 Read article
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Develop a Data Science Approach for Optimizing Energy Consumption
Abstract: Optimizing energy consumption has become a critical challenge in the era of sustainability and increasing energy demand. Efficient energy management is essential to address environmental concerns, reduce costs, and ensure resource availability for future generations. This project leverages data science techniques to evaluate and improve energy consumption across diverse sectors, including residential, industrial, and commercial domains. By integrating advanced analytics, machine learning models, and real-time data processing, the project aims …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 31–44 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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AI-Driven Multi-Objective Optimization of Conductive Polymer Composites for High-Performance Flexible Electronics
Abstract: The development of conductive polymer composites (CPCs) is critical for advancing flexible and wearable electronic technologies. However, the conventional trial-and-error approach to material formulation is time-consuming and often inefficient due to the high-dimensional nature of the design space. This study introduces a novel AI-driven framework that integrates machine learning (ML) with multi-objective optimization to accelerate the discovery of high-performance CPCs. A dataset of 1,000 experimentally reported formulations was compiled, capturing …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 734–745 Read article
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DR. REVIVE: An AI-Powered Medical Recommendation System for Optimised Resources and Improved Patient Care
Abstract: Dr. Revive is an AI-powered medical recommendation system designed to enhance virtual healthcare interactions by connecting patients, doctors, and healthcare stakeholders. Leveraging advanced machine learning algorithms, it analyses user-reported symptoms to provide initial medical recommendations, serving as a reliable first point of guidance. With access to a comprehensive medical database, the platform delivers accurate and timely advice, empowering patients while supporting healthcare professionals with data-driven decision-making. By offering a complete …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 3, 2024 · pp. 1–10 Read article
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AI-Designed Functionally Graded Polymer Composites for Multifunctional Thin Films
Abstract: The design of multifunctional polymer composite thin films requires simultaneous optimization of mechanical, optical, barrier, and thermal properties—objectives often in conflict when using conventional homogeneous materials. This study presents an artificial intelligence-driven framework for designing functionally graded material (FGM) architectures in polymer nanocomposite thin films. We integrated machine learning with physics-based modeling to optimize compositional gradients across film thickness, achieving superior performance compared to homogeneous and discrete multilayer alternatives. A …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1026–1041 Read article
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Harnessing Sustainable Technologies: Advancing Renewable Energy for Climate Change Mitigation
Abstract: Climate change poses a significant threat to global ecosystems, economies, and human livelihoods, necessitating urgent action to transition toward sustainable technologies. Renewable energy systems have emerged as a cornerstone of this effort, offering clean, efficient, and scalable alternatives to fossil fuels. This paper explores the critical role of sustainable technologies in mitigating climate change, focusing on advancements in solar, wind, hydroelectric, and biomass energy systems. Key innovations, such as high-efficiency …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 16, Issue 3, 2025 · pp. 11–18 Read article
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3D Printing of Polymer-Based Functionally Graded Materials: Recent Developments and Challenges
Abstract: Additive manufacturing (AM), specifically 3D printing, has become a useful technique for fabricating functionally graded materials (FGMs) because it can facilitate the spatial distribution of materials. Polymer FGMs (P-FGMs) have gained a great deal of interest due to their lightweight, customizable, multifunctional properties. In comparison to conventional fabrication, 3D printing allows better control of composition and microstructure, which results in materials with controlled mechanical, thermal, and biological properties. This review …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 338–347 Read article
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Carbon Capture Using Polymer Based Membranes - A Green Solution to Climate Change
Abstract: Growing concerns over greenhouse gas emissions have intensified the search for efficient and eco-friendly carbon capture technologies. This study explores the potential of innovative polymer-based membranes as a sustainable approach to controlling CO2 emissions. The paper outlines the fundamental mechanisms of gas transport in membranes, reviews recent advancements in polymer material design, and examines various membrane configurations suited for industrial applications. Special attention is given to newly developed high-performance polymers …
Published in International Journal of Membranes · Vol. 2, Issue 2, 2025 · pp. 13–18 Read article
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Next-Generation Biodegradable Polymer Composites: Enhancing Mechanical and Thermal Performance through Green Reinforcements
Abstract: Next-generation biodegradable polymer composites, combining compostable matrices such as polylactic acid (PLA), polyhydroxyalkanoates (PHAs) and starch-based polymers with green reinforcements (e.g., nanocellulose, lignin, agricultural residues and other bio-fillers), offer a pragmatic route to reconcile high performance with end-of-life sustainability. This paper examines recent advances in the design, processing and interfacial engineering of such composites to enhance mechanical stiffness, strength, toughness and thermal stability while preserving—or intentionally controlling—biodegradation pathways. Emphasis is …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1780–1794 Read article
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Experimental Assessment and Statistical Argument of Al-Si/CSA/MoS2 Hybrid Composites for Mechanical and Tribological Characteristics
Abstract: To augment the mechanical and tribological properties of Al-Si matrix composites complement with molybdenum disulphide (MoS₂) and coconut shell ash (CSA), a mixed experimental and Face-Centered Composite (FCC)strategy was employed. A liquid metallurgical method called stir casting was used to create hybrid composites with 5–15 wt.% CSA and 1–3 wt.% MoS₂. A FCC experimental design with thirty runs was used to thoroughly evaluate the materials. This design allowed for the …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 792–802 Read article
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Leveraging Digital Marketing Techniques for Newly Developed Software Using AI
Abstract: In today’s digital era, most software developers prefer using paid advertising campaigns for their newly developed software to strengthen their online visibility and attract new users; this research explores some strategies to increase and maximize the reach of newly developed software through digital marketing campaigns, particularly focusing on the use of artificial intelligence (AI) tools to optimize campaign performance and increase the reach of newly developed software. To fully assess …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 1, 2026 · pp. 12–22 Read article
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Radio Frequency Next-Generation Advances
Abstract: Radio frequency (RF) technology is a key enabler for modern wireless communications, driving the evolution of telecommunications, healthcare, aerospace, defence and the Internet of Things (IoT). Faster, more reliable and energy-efficient communication systems have been developed at a rapid pace due to recent discoveries in RF engineering. This article discusses novel advancements in RF technologies including enhanced antenna design, millimeter-wave communication, software defined radio, smart spectrum management, and RF-based sensor …
Published in International Journal of Radio Frequency Innovations · Vol. 4, Issue 1, 2026 Read article
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An Innovative Approach to Find the Optimum Lubricant for Diverse Applications Based on Scikit-Learn Library Using Python
Abstract: This paper presents an innovative approach for finding the optimum lubricant using the Scikit-learn library in Python. The proposed approach uses a linear regression model to analyze a dataset of lubricant properties and performance, specifically the viscosity, wear, and friction. The model is trained on the dataset to predict the wear and friction for a given viscosity, which can be used to identify the optimum lubricant. By analyzing a dataset …
Published in Journal of Polymer & Composites · Vol. 13, Issue 3, 2025 · pp. 25–35 Read article
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Customer Churn Prediction Using ML Algorithms
Abstract: Comprehending customer churn is essential for businesses aiming to enhance and sustain customer relationships. This study introduces a machine learning approach aimed at forecasting customer churn by leveraging demographic and behavioral data. Our research involved developing predictive models using support vector machines (SVM), random forests, and decision trees, evaluating their efficacy using real-world data from the telecom industry. Our findings underscore that random forests consistently outperform SVM and decision trees …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 2, 2024 · pp. 70–75 Read article
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Machine Learning Assisted Timing Violation Prediction in Sub-7nm VLSI Physical Design
Abstract: The continuous scaling of semiconductor technology into the sub-7nm regime has introduced significant challenges in timing closure due to process variability, interconnect delay, power density, and manufacturing uncertainties. Conventional static timing analysis techniques often require extensive computational resources and iterative optimization cycles, resulting in increased design complexity and longer turnaround time. This research proposes a Machine Learning Assisted Timing Violation Prediction framework for sub-7nm VLSI physical design to improve early-stage …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 4, Issue 1, 2026 Read article
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Role of Artificial Intelligence in Simulation and Therapeutics in Neurodegenerative Diseases
Abstract: Neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, etc., are a cause of significant mortality rates due to a lack of curative treatments and their complex nature. Traditional therapeutic methodologies have several disadvantages such as slow diagnosis and a lack of effective treatments. They mainly focused on the management of the disease rather than curing it. The integration of artificial intelligence in the simulation and therapeutics of neurodegenerative …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 1, 2026 · pp. 19–29 Read article
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Experimental Validation and Implementation Framework for Optimized Methane Yield Prediction in Anaerobic Digestion
Abstract: The correct validation and realistic application of optimized anaerobic digestion (AD) models are essential steps in transferring biogas production systems to real-life. This paper outlines an experimental validation and deployment pipeline of an AI-optimized model of the methane yield prediction model based on the application of more advanced machine learning and Bayesian optimization methods. Others The validated surrogate-assisted optimization model was tested with controlled laboratory-scale AD experiments at optimized operating …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 4, Issue 1, 2026 · pp. 25–32 Read article
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Renewable Solar Energy Prediction in India : 2025-2030
Abstract: As India endeavors to realize its objective of achieving 500 GW of renewable energy capacity by the year 2030, the precise forecasting of solar power generation is rendered increasingly essential. This scholarly article conducts a comprehensive review of the utilization of machine learning (ML) methodologies in the prediction of solar energy across diverse Indian states, underscoring their potential to address the complexities associated with the variability of solar irradiance. Conventional …
Published in Journal of Power Electronics and Power Systems · Vol. 14, Issue 3, 2024 · pp. 41–46 Read article
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Harnessing Shell Scripting for Autonomous System Management: A Vision for the Future
Abstract: As IT systems become increasingly complex, the demand for efficient and automated management solutions is more critical than ever. This paper investigates the pivotal role of shell scripting in the development of autonomous systems that can self-manage and optimize their operations. Shell scripting, with its powerful automation capabilities, serves as a foundational tool for orchestrating various tasks, including system monitoring, data analysis, and deployment processes. We begin by examining current …
Published in Journal of Advances in Shell Programming · Vol. 11, Issue 3, 2024 · pp. 17–31 Read article