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43 articles for “machine learning (ML)”
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A Study of 3D Printing Technology and M.L. Algorithms for Enhanced Production
Abstract: Abstract The realm of 3D printing technology, also referred to as additive manufacturing, has garnered increasing interest in recent times due to its capacity to construct intricate geometric structures. Fused deposition modeling (FDM) stands out among the various techniques and has gained widespread adoption. However, achieving optimal outcomes with FDM poses a challenge, necessitating meticulous selection of process parameters. Presently, many methodologies rely on Machine Learning (ML) algorithms akin to …
Published in International Journal of Computer Aided Manufacturing · Vol. 11, Issue 1, 2025 · pp. 6–15 Read article
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A Study on Machine-Learning-Driven Insight into theThermal Behavior of Polymers and Composite Materials
Abstract: The thermal performance of polymeric and composite systems underpins their suitability for aerospace, electronics, and energy‑storage applications, yet reliable prediction of temperature‑dependent properties remains a formidable challenge. In this work we present a data‑driven framework that harnesses modern machine‑learning (ML) techniques to forecast key thermal metrics – glass‑transition temperature (Tg), melting point (Tm), thermal conductivity (k), and decomposition onset (Td) – from a chemically informed descriptor space. A curated database …
Published in International Journal of Polymer Science and Engineering · Vol. 12, Issue 1, 2026 · pp. 1–11 Read article
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A Comprehensive Review of Smart Parking Systems: Technologies, Challenges, and Future Directions
Abstract: Abstract Smart Parking Systems (SPS) are gaining significant attention as a critical solution to address the growing concerns of urban congestion, inefficient use of parking spaces, and environmental sustainability. This paper presents a comprehensive review of SPS, focusing on the key technologies, challenges, and future directions. We explore the integration of advanced technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and sensor networks in …
Published in International Journal of Manufacturing and Materials Processing · Vol. 11, Issue 1, 2025 · pp. 1–13 Read article
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An Overview on AI in Thermal Behaviour of Polymers
Abstract: The integration of Artificial Intelligence (AI) and Machine Learning (ML) into the thermalanalysis of polymers and composite materials has transitioned from experimental curiosity to acornerstone of material science. By replacing costly and time-consuming trial-and-errorlaboratory testing with predictive modeling, AI is accelerating the development of advancedthermal interface materials, fire-retardant polymers, and aerospace-grade composites. Theprediction and optimization of the thermal behavior of polymers and composite materialsrepresent a cornerstone of modern materials science, …
Published in International Journal of Composite and Constituent Materials · Vol. 12, Issue 1, 2026 · pp. 12–16 Read article
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Deep Learning Techniques for Optimizing Resource Allocation and Sustainability in the Next Generation of 6G Wireless Networks
Abstract: The upcoming sixth generation (6G) of wireless communication networks aims to meet challenging requirements such as ultra-reliable low-latency services, massive connectivity for billions of smart devices, and intelligent automation across highly diverse environments. Along with these goals, the issues of energy efficiency and environmental sustainability are becoming equally important, as the rising number of users and traffic volumes will directly increase power demand and carbon emissions. To lower total energy …
Published in International Journal of Broadband Cellular Communication · Vol. 11, Issue 2, 2026 Read article
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An Integrated Machine Learning and IoT-Based Framework for Predictive Urban Water Demand Management and Distribution Optimization
Abstract: Abstract Urban water resource management has become increasingly complex due to rapid urbanization, population growth, climate variability, and aging infrastructure. Conventional water management approaches are often reactive, inefficient, and incapable of handling real-time fluctuations in demand and supply. This study proposes an integrated framework that combines machine learning (ML) techniques with Internet of Things (IoT)-based real-time monitoring to enhance water demand prediction and optimize distribution systems in urban environments. The …
Published in International Journal of Water Resources Engineering · Vol. 12, Issue 1, 2026 Read article
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Predicting Traffic Congestion in Mumbai Using Artificial Intelligence
Abstract: Abstract — Mumbai's ongoing traffic problems are brought on by the city's fast urbanization, population expansion, and growing reliance on private automobiles. The majority of traffic management technologies in use today are reactive, frequently reacting only after congestion has already arisen. Using information from a variety of sources, such as commuter surveys, weather reports, and traffic incident logs, this study explores the application of artificial intelligence (AI) and machine learning …
Published in International Journal of Transportation Engineering and Traffic System · Vol. 12, Issue 2, 2026 Read article
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Future-Ready Library Buildings: AI and IoT for Equity and Compliance
Abstract: Libraries are buildings presenting unique challenges when integrating different types of spaces under a single roof, like quiet reading spaces, central lobbies, book staging areas, archival rooms, discussion zones, seminar rooms, e-resources labs, etc. These spaces require specialized norms related to construction and involve varied daylighting requirements, anthropometric needs, indoor air environment requirements, and other specific functional needs. Moreover, libraries must follow various requirements mentioned in relevant IS codes, fire …
Published in International Journal of Sustainable Building Technology · Vol. 9, Issue 1, 2026 · pp. 19–24 Read article
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Data Science for Environmental Modelling
Abstract: Abstract: Soil moisture is a critical Earth system variable linking atmospheric, hydrological, and ecological processes. However, observations are sparse and fragmented: in situ gauges provide point data, satellite missions (SMAP, SMOS, Sentinel-1) offer broad coverage at coarse resolutionQ, and land‐surface models (GLDAS, ERA5, etc.) yield estimates with their own biase. Here we propose a conceptual framework for integrated environmental data modelling, combining rainfall, temperature, and soil moisture information with machine …
Published in International Journal of Water Resources Engineering · Vol. 12, Issue 1, 2026 · pp. 33–40 Read article
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Data Science for Environmental Modelling
Abstract: Abstract: Soil moisture is a critical Earth system variable linking atmospheric, hydrological, and ecological processes. However, observations are sparse and fragmented: in situ gauges provide point data, satellite missions (SMAP, SMOS, Sentinel-1) offer broad coverage at coarse resolutionQ, and land‐surface models (GLDAS, ERA5, etc.) yield estimates with their own biase. Here we propose a conceptual framework for integrated environmental data modelling, combining rainfall, temperature, and soil moisture information with machine …
Published in International Journal of Water Resources Engineering · Vol. 12, Issue 1, 2026 Read article
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A Review on Design and Analysis Model for Detectionand Classification of Lung Diseases Using Medical Images
Abstract: A key component of the human respiratory system, the lung regulates blood oxygen levels through gasexchange. Diseases linked to the lungs are becoming more common as pollution levels rise. In order topreserve human life, it is crucial to identify lung disorders early. In order to forecast lung disease, clinicalprofessionals analyze medical imaging, such as CT or X-rays. Clinicians identify lung disease by applyingtheir medical expertise and knowledge. Because expert knowledge …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 11, Issue 2, 2025 Read article
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Revolutionizing Design and Manufacturing: The Role ofComputer Languages in CAD and CAM
Abstract: The evolution of computer languages has played a crucial role in the advancement of modern computational technologies, with significant contributions to fields, such as Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM). Computer languages provide the essential tools to communicate between human users and machine systems, enabling the automation of design and manufacturing processes. This review explores the development and classification of computer languages, highlighting their impact on CAD and CAM …
Published in International Journal of Computer Aided Manufacturing · Vol. 11, Issue 1, 2025 · pp. 30–34 Read article
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Need of Modern Technologies in Agriculture
Abstract: The surge in population growth coupled with the diminishing availability of cultivable land has significantly heightened the strain on arable areas. While the provision of sustenance for the underprivileged remains a pressing issue, the focus has expanded beyond mere food provision to ensuring a nutritionally balanced diet. Consequently, there arises an urgent imperative to delve into contemporary methodologies aimed at not only feeding the burgeoning population but also preserving essential …
Published in International Journal of Distributed Computing and Technology · Vol. 10, Issue 1, 2024 · pp. 1–6 Read article
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Decision Trees in Intrusion Detection: A Comparative Analysis of Machine Learning Techniques
Abstract: Recent malware sophistication poses immense challenges in intrusion detection systems, making it imperative to develop highly sophisticated methodologies for detection of fast-evolving cyber threats. Traditional IDS typically employs a signature-based detection approach and has limitations in identifying unknown and obfuscated attacks, particularly zero-day vulnerabilities. This paper discusses the existing limitations of IDS based on ML techniques to further improve the detection capability of such systems. Machine learning algorithms, particularly decision …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 11, Issue 1, 2025 Read article
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Decision Trees in Intrusion Detection: A Comparative Analysis of Machine Learning Techniques
Abstract: Recent malware sophistication poses immense challenges in intrusion detection systems, making it imperative to develop highly sophisticated methodologies for detection of fast-evolving cyber threats. Traditional IDS typically employs a signature-based detection approach and has limitations in identifying unknown and obfuscated attacks, particularly zero-day vulnerabilities. This paper discusses the existing limitations of IDS based on ML techniques to further improve the detection capability of such systems. Machine learning algorithms, particularly decision …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 11, Issue 1, 2025 Read article
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Climate Change Modeling Using Machine Learning and Big Data
Abstract: Climate change is now one of the biggest issues that affect the world today and studying its impacts for it is very significant in enabling the formulation of social policies. This paper also discusses how climate sciences, machine learning, and big data analytics can greatly help in the improvement of the current capacities in climate modeling. Explaining the methods of climate modeling, we look at how the big data offers …
Published in International Journal of Software Computing and Testing · Vol. 11, Issue 1, 2025 · pp. 6–11 Read article
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Integrating Machine Learning with Edge Computing for Real Time Data Analytics
Abstract: Smart devices such as mobile phones, sensors, cameras, etc., are producing a huge amount of data every second. This data is normally sent to cloud servers for processing. This approach is normally slow and expensive, and it also raises a lot of privacy issues as data is sent over the internet, which takes a lot of time to return with results. This study aims to address the issue through the …
Published in International Journal of Distributed Computing and Technology · Vol. 12, Issue 2, 2026 Read article
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Optimizing Internal Combustion Engine Performance and Fleet Fuel Economy through Machine Learning and AI using Big Data Analytics
Abstract: Abstract This paper presents a software-defined operational intelligence approach for optimizing internal combustion engine performance and fleet fuel economy using Big Data Analytics combined with Machine Learning and Artificial Intelligence. Real-world vehicle data is transformed into engineered features and virtual sensing outputs to estimate fuel consumption, driver behaviour, and component health without additional hardware. The proposed system integrates fuel optimization, predictive maintenance, driver assistance, and sustainability assessment into a unified …
Published in International Journal of I.C. Engines and Gas Turbines · Vol. 12, Issue 1, 2026 · pp. 1–7 Read article
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Optimizing Internal Combustion Engine Performance and Fleet Fuel Economy through Machine Learning and AI using Big Data Analytics
Abstract: This paper presents a software-defined operational intelligence approach for optimizing internal combustion engine performance and fleet fuel economy using Big Data Analytics combined with Machine Learning and Artificial Intelligence. Real-world vehicle data is transformed into engineered features and virtual sensing outputs to estimate fuel consumption, driver behaviour, and component health without additional hardware. The proposed system integrates fuel optimization, predictive maintenance, driver assistance, and sustainability assessment into a unified one-stop …
Published in International Journal of I.C. Engines and Gas Turbines · Vol. 12, Issue 1, 2026 · pp. 35–51 Read article
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Employing ML and DL to Optimize an ElectrochemicalBiosensor
Abstract: A major field of study in science, that have made the understanding of the world and made homo sapiensthe dominant species on the planet is our understanding of medicine. Under this vast umbrella is thedetection and classification of microorganisms. Electrochemical biosensors have emerged aspromising tools in this regard due to their high sensitivity, selectivity, and potential for miniaturization.By integrating a Machine Learning & Deep Learning model for the detection of …
Published in International Journal of Chemical Separation Technology · Vol. 10, Issue 1, 2024 · pp. 33–38 Read article