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776 articles for “decisions”
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The Green Cost of Generative Ai: Environmental Sustainability Implications of Large-Scale Ai Systems
Abstract: Generative Artificial Intelligence (GenAI) has advanced rapidly in scale and complexity, enabling powerful capabilities in automated content creation, multimodal reasoning and real-time decision support across sectors. While these systems offer significant technological and economic benefits, their environmental implications are not fully examined. Large-scale GenAI models rely on high-performance computing infrastructure that consumes substantial energy and resources throughout their lifecycle, raising critical sustainability concerns. This paper offers a sustainability-oriented assessment of …
Published in International Journal of Sustainability · Vol. 3, Issue 1, 2026 · pp. 12–20 Read article
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Automating time based Household electricity Bill Calculations for Efficient Budgeting
Abstract: Recurrent household expenses must be precisely, in time, and openly tracked to make efficient household budgeting. Conventional monthly billing performance tends to delay the financial information, which results in the inefficient cash flow management and inability to modify short-term expenditure patterns. The proposed research is a system to automatize the calculation of household bills per week in order to deliver more frequent and practical insights into the household spending habits. …
Published in International Journal of Electrical Power and Machine Systems · Vol. 4, Issue 1, 2026 · pp. 1–16 Read article
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Explainable Machine Learning Integrated with Polymer-Based Diagnostic Technologies for Liver Health Classification
Abstract: Early and reliable assessment of liver health is essential for timely treatment, yet most machine-learning approaches face limitations such as class imbalance and low clinical interpretability. This study proposes a polymer-integrated, explainable machine-learning framework that combines SMOTE-based data balancing, Logistic Regression, and XAI techniques (SHAP and LIME) for transparent liver-health classification. In addition to ML modelling, the study emphasizes the emerging role of polymer-based biosensors, microfluidic polymer chips, polymer nanomaterials, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 631–643 Read article
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AWPRATOR: Autonomous Waste Picking Robot and Tracking of Routes
Abstract: Traditional waste management systems, especially in urban and semi-urban areas, relies heavily on manual methods like manual collection of waste, and fossil-fuel based garbage trucks. These methods often face many challenges such as inefficient high dependency on human labor, inefficient planning of routes, health risk for sanitation workers, lack of adaptability, real-time decision making. With the increase in the number of smart cities, the need for intelligent, autonomous, and eco-friendly …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 4, Issue 1, 2026 · pp. 13–21 Read article
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Polymers and Composites in the Design and Construction of Unmanned Aerial Vehicles: A Comprehensive Technical Review
Abstract: The emergence of Unmanned Aerial Vehicles (UAVs) over the last few years has mostly been made possible by breakthroughs in material science. This review is concerned with an overview of some of the polymers and composites that are being commonly used in drone manufacturing in recent times, especially in relation to the influence of material choice on drone performance and capability. It sheds light on the drastic change in the …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 874–882 Read article
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Structure–Property Correlation of Infill Topology and Density on Tensile and Flexural Performance of FDM-Printed PLA and ABS
Abstract: Additive Manufacturing (AM), particularly Fused Deposition Modeling (FDM), has become a widely adopted manufacturing technology due to its design flexibility, low cost, and capability for rapid prototyping. However, the mechanical performance of FDM-printed components is strongly influenced by internal structural parameters such as infill pattern and infill density, in addition to the intrinsic material behaviour. This study investigates the structure–property relationship between infill topology, density, and mechanical performance of PLA …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 4, Issue 1, 2026 · pp. 26–37 Read article
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Machine Learning-Based Quantification of Polymer Structure Property Relationships for Predictive Material Design
Abstract: Polymer structures exhibit complex, hierarchical arrangements that strongly influence macroscopic properties, yet consistent quantification remains challenging due to nonlinear interactions and limited unified modeling strategies. Existing approaches inadequately capture generalized structure–property mappings across diverse polymer systems. This research aims to establish a machine learning-based quantification model for polymer structure–property relationships to support predictive material design. A Polymer Structure Property Dataset of 5,000 polymer samples includes structural descriptors and experimentally measured …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 737–754 Read article
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Diabetes Risk Prediction from Survey Data Using Machine Learning Algorithms
Abstract: Diabetes mellitus represents one of the most significant global health challenges, affecting millions worldwide and leading to severe complications if left undiagnosed or poorly managed. Early detection and risk assessment are crucial for preventing the progression of this chronic condition. This research presents a comprehensive machine learning approach for predicting diabetes risk using survey-based health parameters. The study implements and compares four prominent classification algorithms: Logistic Regression, K-Nearest Neighbors (KNN), …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
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A Lightweight Cost-Sensitive Explainable Ensemble Framework for Early Heart Disease Risk Prediction
Abstract: Cardiovascular disease is still one of the leading causes of death, and hence, the early prediction of risk is a very important task in preventive medicine. Although recent studies have shown encouraging results in the application of machine learning algorithms to the prediction of heart disease, it has been noticed that most of the algorithms are more concerned with accuracy-driven optimization than the concerns of safety and false negatives. In …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
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Balancing Growth and Sustainability: Developing a Socio-ecological Urban Design Framework for Edge Cities
Abstract: In the rapidly urbanizing world, edge cities, urban areas situated on the outskirts of larger metropolitan regions have emerged as dynamic centres of economic activity. However, their development often prioritizes growth over sustainability, leading to ecological degradation and social inequities. This dissertation presents a socio-ecological urban design framework aimed at balancing growth with sustainability in edge cities. By integrating principles from ecology, sociology, and urban planning, this framework addresses the …
Published in International Journal of Urban Design and Development · Vol. 4, Issue 2, 2026 · pp. 1–24 Read article
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AI and ML-Driven Immersive Technologies: A New Era in Education
Abstract: The very fast adoption of Artificial Intelligence (AI) and Machine Learning (ML) in education has transformed contemporary teaching and learning ecosystems driven by advances in immersive technologies and the growing engagement of global technology leaders with virtual environments. AI-powered educational platforms enable adaptive and personalized learning pathways by dynamically adjusting content, pace and instructional strategies to learners’ preferences, abilities and learning styles by improving engagement, retention and academic outcomes. Deep …
Published in International Journal of Education Sciences · Vol. 3, Issue 1, 2026 · pp. 116–123 Read article
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AI Adoption in Medical Libraries: A Study on SDMH’s Use of Ovid Discovery AI and Its Impact on Evidence-Based Medicine
Abstract: The role of Artificial Intelligence (AI) in transforming medical libraries is increasingly significant as these libraries evolve to become intelligent, responsive hubs for knowledge dissemination. This paper explores the adoption of AI tools at Santokba Durlabhji Memorial Hospital (SDMH) Medical Library, focusing on the integration of Ovid Discovery AI, a cutting-edge tool designed to enhance literature search accuracy, summarize content, and provide context-aware recommendations. Using a mixed-methods approach, the study …
Published in Journal of Advancements in Library Sciences · Vol. 13, Issue 2, 2026 · pp. 1–9 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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A Comprehensive Review of Machine Learning and Explainable AI Techniques for Disease Prediction Systems
Abstract: Large amounts of diverse medical data have been produced because of the quick development of digital healthcare systems, offering substantial chances to use machine learning methods for clinical decision support and illness prediction. By identifying intricate patterns in clinical data, machine learning-based models have shown great promise in early disease detection, risk assessment, and personalised healthcare. However, issues with transparency, interpretability, and reliability have been brought up by the growing …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 4, Issue 1, 2026 · pp. 20–28 Read article
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Lemon Sign: The Diagnostic Indicator for Spina Bifida
Abstract: The “lemon sign” is a distinctive ultrasonographic finding that serves as a diagnostic indicator for spina bifida, a congenital neural tube defect characterized by incomplete closure of the spinal cord. This sign is observed in fetal imaging and is considered an early and reliable marker for detecting spina bifida, particularly when combined with other prenatal diagnostic tools such as the “banana sign.” The lemon sign is characterized by a flattened, …
Published in International Journal of Midwifery Nursing And Practices · Vol. 4, Issue 1, 2026 · pp. 1–6 Read article
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Automatic Car Controller Based on Sign Board using Deep Learning and IOT
Abstract: The rapid growth of intelligent transportation systems has increased the demand for safer and more efficient driving solutions. Conventional vehicles often rely heavily on human intervention, which can lead to accidents due to negligence, fatigue, or poor visibility of traffic signs. This project proposes an automated car control system that utilizes deep learning and Internet of Things (IoT) technologies to recognize traffic signboards and respond accordingly. The primary objective is …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 2, 2026 Read article
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ML Analysis of Factors Affecting Vaccination in Rural Children: A Machine Learning Approach
Abstract: Vaccination remains one of the most effective public health interventions for preventing childhood diseases, yet rural regions in India continue to experience uneven immunization coverage due to multiple socioeconomic and geographic barriers. This research applies machine learning techniques to identify and analyze the major determinants influencing childhood vaccination uptake in rural communities. The study utilizes survey-based demographic, socioeconomic, and healthcare-related parameters to build predictive models that classify children as vaccinated …
Published in International Journal of Vaccines · Vol. 3, Issue 2, 2026 Read article
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Virtual Machine Cost & Computing comparison between cloud service
Abstract: Cloud computing has revolutionized enterprise IT infrastructure with virtual machines forming the cornerstone of Infrastructure as a Service deployment. This study provides a detailed comparative evaluation of virtual machine pricing and computational performance among three leading cloud computing platforms: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Through quantitative analysis of current pricing data from September 2025, independent performance benchmarks from Cockroach Labs 2021 Report, and market …
Published in International Journal of Mobile Computing Technology · Vol. 4, Issue 2, 2026 Read article
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Analyzing and Predicting Academic Behavior from Peer Pressure Indicators Using Machine Learning
Abstract: The academic achievement of a student is determined by their capability, but also by the companions with whom they associate. Friends can have a positive impact on students' motivation for school, and at times friends are distractions leading to a lack of attention on their school assignments. This particular study focuses on the number and quality of companions students associate with and to what extent that could be used as …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 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