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158 articles for “Decision Support Model”
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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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House Price Estimation Using Linear Regression: A Machine Learning Perspective
Abstract: House price prediction plays a crucial role in the real estate industry, helping buyers, sellers, and investors make well-informed decisions. Accurate estimation of property values enables stakeholders to assess market trends, plan investments, and minimize financial risks. This study focuses on the application of linear regression, a fundamental and widely used machine learning algorithm, to predict house prices based on multiple influencing factors. These factors include location, property size, number …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 Read article
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The Role of Mathematical Reasoning in Legal Analysis: Bridging Two Disciplines
Abstract: This research paper explores the integration of mathematical reasoning into legal analysis, emphasizing the inherent similarities between the two disciplines. Both fields rely on logical structures, such as deductive reasoning, inductive reasoning, and other formal methods of thought. By examining these parallels, the paper reveals the nascent but growing interaction between mathematics and law, uncovering how the organizational schemes that underpin mathematical principles can enhance legal argumentation, decision-making, and analysis. …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 11, Issue 3, 2024 · pp. 1–5 Read article
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ArcGIS Applications in COVID-19 Spatial Epidemiology: A Comprehensive Systematic Review
Abstract: Addressing complicated community health concerns frequently necessitates the establishment of health practices. Professionals who study community health using information technology require a thorough framework. Health care professionals and authorities have had the ability to comprehend health-related geographical data and make timely judgments in different situations. In the field of epidemic disease prevention, including a viral spread model into a GIS is a popular issue. As a result, a GIS as …
Published in Journal of Remote Sensing & GIS · Vol. 16, Issue 2, 2025 · pp. 17–25 Read article
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Collaborative Robotics and Smart Automation: Enhancing Human–Robot Synergy in Industry 5.0
Abstract: Industry 5.0 marks a paradigm shift from efficiency-centric automation to a human-centred, sustainable, and collaborative production environment . In this context, collaborative robots, commonly referred to as cobots, play a central role by enabling direct and safe interaction between humans and machines within shared workspaces. These systems are designed to support human operators by undertaking repetitive, precision-intensive, and physically demanding tasks, thereby allowing humans to focus on supervisory control, problem-solving, …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 4, Issue 1, 2026 · pp. 22–29 Read article
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Transforming Human Resources Leveraging AI Across the Associate Lifecycle for Strategic Success
Abstract: AI is taking the lead in changing the game in human resources by mitigating challenges and optimizing processes throughout the entire associate lifecycle. From pre-hire, AI helps with interview bias, enhances hire projections, and supports talent acquisition with predictive analytics. Once onboarded, AI helps with compensation benchmarking, automates performance feedback with the mitigation of bias, and analyzes associate sentiment through NLP and LLMs. In the middle of the life cycle, …
Published in Recent Trends in Programming languages · Vol. 12, Issue 1, 2025 · pp. 30–36 Read article
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Integrated Surface Water–Groundwater Dynamics: Implications for Pollution Pathways, Prevention, and Environmental Control
Abstract: Water resources worldwide are increasingly threatened by pollution pressures amplified by climate change and intensified human activities. The vulnerability of surface water and groundwater systems to contamination is strongly governed by their dynamic hydrologic connectivity, which is often overlooked in pollution prevention and control frameworks. Rising global temperatures, altered precipitation regimes, land-use change, and intensified abstraction patterns modify recharge processes, flow paths, and contaminant transport mechanisms across environmental landscapes. This …
Published in International Journal of Pollution: Prevention & Control · Vol. 4, Issue 1, 2026 · pp. 34–40 Read article
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A Systematic Study of AI-Powered Robotics for Ocean Cleanup of Plastics
Abstract: The escalating crisis of plastic pollution in marine ecosystems demands innovative solutions beyond conventional cleanup methods. This paper presents a systematic study of artificial intelligence (AI)-powered robotics for ocean plastic cleanup, evaluating their efficiency, technological advancements, and challenges. Autonomous systems, such as AI-driven surface drones (ASVs), underwater robots (autonomous underwater vehicles/remotely operated vehicles [AUVs/ROVs]), and swarm robotics, leverage machine learning (ML) and computer vision to detect, classify, and collect plastic …
Published in Journal of Advancements in Robotics · Vol. 12, Issue 2, 2025 · pp. 1–10 Read article
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AI Voice Detection Tool
Abstract: In today’s digital era, distinguishing between AI-generated and human voices is more important than ever. This project introduces an AI-based voice detection system designed to accurately identify synthetic voices, ensuring security and authenticity across various applications like cybersecurity, media verification, and fraud prevention.Our system works by analyzing incoming audio samples and comparing them against a diverse database of both AI-generated and real human voices. Using advanced machine learning and signal …
Published in Journal of Instrumentation Technology & Innovations · Vol. 16, Issue 1, 2026 · pp. 1–8 Read article
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Jowar Millet Crop Monitoring and Analysis Robot (JMAR): A Smart Solution for Plant and Soil Health in Jowar Millet Farming
Abstract: Farmers cultivating jowar millet (Sorghum) face significant challenges in maintaining crop health and optimizing yield due to the limitations of traditional plant disease detection and soil health assessment methods. Visual inspection and indigenous knowledge are labour-intensive, time-consuming, and often inaccurate, while soil monitoring requires specialized equipment that is not always affordable or accessible. These issues hinder timely intervention and can lead to crop losses and soil degradation. To address these …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 14, Issue 2, 2025 · pp. 8–18 Read article
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Skin Disease prediction and classification from dermoscopy images using Neural Network
Abstract: Skin diseases are among the most common health-related problems affecting people of all age groups, and their occurrence often varies with seasonal and environmental conditions. Delayed or incorrect diagnosis of skin disorders can lead to severe complications, making early and accurate detection extremely important for effective treatment and prevention. In recent years, rapid advancements in deep learning and neural network technologies have significantly contributed to the development of automated medical …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article
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Early Alzheimer's Disease Detection Using Deep Ensemble Learning and MRI Image Analysis
Abstract: Early detection of Alzheimer's disease (AD) is crucial to slowing cognitive decline and enabling timely clinical interventions. Traditional diagnostic methods, including cognitive tests and single-model classifiers, have limited sensitivity during early stages of the disease. This paper presents a deep ensemble learning approach that integrates multiple convolutional neural networks (CNNs) for accurate Alzheimer's disease detection using structural Magnetic Resonance Imaging (MRI) data. The proposed framework utilizes ResNet50, VGG16, and DenseNet121 …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 Read article
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Early Alzheimer’s Disease Prediction Using Vision Transformers and Attention-Guided MRI Analysis
Abstract: Alzheimer’s Disease (AD) continues to be a major global health concern, with early detection being crucial for effective intervention. While conventional machine learning and convolutional neural network (CNN) approaches have made notable progress in automated AD diagnosis using MRI data, they often struggle with capturing long-range dependencies and maintaining spatial contextual awareness. In this research, we propose a novel framework using Vision Transformers (ViTs) for early Alzheimer’s prediction from 3D …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 1, 2026 · pp. 30–40 Read article
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Role of Generative AI in Redefining Data Analytics
Abstract: The rapid evolution of data-driven technologies has introduced both significant challenges and promising opportunities within the field of data analytics. Among the most impactful advancements is Generative Artificial Intelligence (Generative AI), a groundbreaking subset of AI that is reshaping how data is interpreted, generated, and utilized. Unlike traditional analytical tools that rely solely on existing data patterns, generative AI possesses the capability to create synthetic data, simulate complex scenarios, and …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 2, 2025 · pp. 01–07 Read article
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The Contribution of A. I. in Pharmaceutical Software
Abstract: The integration of Artificial Intelligence (AI) in pharmaceutical software has significantly transformed drug development, regulatory processes, and clinical management. AI-powered tools are revolutionizing data analysis, predictive modeling, and decision-making, enhancing the efficiency and accuracy of drug discovery and development. This article explores the multifaceted contributions of AI to pharmaceutical software, including its applications in drug screening, personalized medicine, clinical trial optimization, and regulatory compliance. Additionally, we examine how AI is …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 3, Issue 1, 2025 · pp. 1–10 Read article
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Adaptive Task Scheduling And Resource Optimization Using Ai Middleware
Abstract: Modern distributed and heterogeneous computing systems face significant challenges in dealing with dynamically changing workloads, resource fragmentation, and changing latencies; existing traditional, or rule-based, schedulers are no longer useful in achieving the best system performance. Such limitations highlight the importance of the adaptive scheduling paradigms that are able to learn, to forecast and reaction to the real red conditions in the system. The middleware of artificial-intelligence is also an attractive …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 Read article
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Integrating Genetic Algorithms with Lean Manufacturing for Enhanced Production Efficiency
Abstract: Lean manufacturing is a well-established philosophy focusing on the systematic reduction of waste and the ongoing development of value supplied to the customer. It emphasizes efficiency, quality, and adaptability through ideas such as just-in-time production, continuous improvement (Kaizen), and value stream optimization. However, the increased complexity of modern production systems, driven by global rivalry, product variety, and rapid technology innovation, has shown the limitations of classic lean tools in achieving …
Published in Journal of Production Research & Management · Vol. 15, Issue 3, 2025 · pp. 38–43 Read article
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Ethical Challenges in Natural Language Processing: A Comparative Study of Solutions Across Multiple Domains
Abstract: This comparative analysis investigates the ethical challenges associated with natural language processing (NLP) by reviewing and synthesizing insights from ten influential and widely cited publications in the field. As NLP technologies are increasingly integrated into domains such as healthcare, finance, education, and governance, ethical concerns related to algorithmic bias, data privacy, fairness, accountability, and system transparency have become more prominent. This paper systematically examines how different researchers conceptualize and address …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 13, Issue 1, 2026 · pp. 01–07 Read article
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A Value-Belief-Attitude Model of Local Food Consumption
Abstract: The paper explores the Value-Belief-Attitude (VBA) model of local food consumption, investigating how cultural, social, and economic factors influence attitudes toward local food. Key considerations include the importance of regional cuisines, support for local farmers, and the health benefits of fresh, locally sourced produce. The paper also delves into the concept of “locavorism” as a consumer ideology centered around the trustworthiness and benefits of local foods. Additionally, it examines long-term …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 15, Issue 1, 2025 · pp. 11–18 Read article
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Advancements in Agricultural Forecasting: A Review of Machine Learning Based Crop Yield Prediction
Abstract: Agricultural productivity plays a critical role in global food security, and accurate crop yield prediction is essential for optimizing resource allocation and decision-making in farming. The rapid advancements in Machine Learning (ML) and Deep Learning(DL)have transformed agricultural forecasting, enabling data-driven approaches for crop prediction. This review paper provides a comprehensive analysis of various ML and DL techniques applied in crop yield forecast, highlighting the ineffectiveness, challenges, and future directions. The …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 3, 2025 · pp. 32–38 Read article