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316 articles for “Strategy Prediction”
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Antimicrobial Resistance and AI-Based Strategies for Rapid Pathogen Detection
Abstract: Antimicrobial resistance (AMR) has become a major global health threat, significantly reducing the effectiveness of antimicrobial therapies and increasing the burden of infectious diseases worldwide. The rapid emergence of multidrug-resistant pathogens has created an urgent need for faster, more accurate, and scalable diagnostic approaches to support timely treatment and effective infection control. Artificial intelligence (AI) has emerged as a promising technology capable of transforming pathogen detection and AMR surveillance through …
Published in Recent Trends in Infectious Diseases · Vol. 3, Issue 2, 2026 · pp. 26–36 Read article
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Leveraging AI and Machine Learning for Early Prediction and Prevention of Non- Communicable Diseases in Resource-Limited Settings
Abstract: Populations in these regions face persistent structural barriers, such as underdeveloped healthcare infrastructure, shortages of trained health professionals, and fragmented or incomplete health information systems. These limitations delay timely diagnosis, restrict access to preventive care, and compromise effective disease management. In recent years, rapid progress in artificial intelligence (AI) and machine learning (ML) has opened promising avenues to mitigate these challenges. Practical applications already emerging include mobile health platforms for …
Published in Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy · Vol. 15, Issue 1, 2026 · pp. 9–15 Read article
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Statistical and AI Approaches to Measure Sustainability Performance of Enterprises
Abstract: Measuring sustainability performance has become a critical priority for enterprises facing increasing regulatory pressure, stakeholder expectations, and global sustainability challenges. Traditional assessment methods, largely based on static indicators and manual reporting, often struggle to capture the multidimensional, dynamic, and data-intensive nature of sustainability. This study explores the integration of statistical and artificial intelligence (AI) approaches to evaluate and enhance the sustainability performance of enterprises in a more robust, accurate, and …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 · pp. 30–36 Read article
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A Hybrid Multi-Physics Ensemble Deep Learning Framework for Simultaneous Prediction of Thermal and Electrical Conductivity in Functional Polymer-Based Nanocomposites
Abstract: Polymer-based nanocomposites have become promising materials for applications in energy storage, flexible electronics, biomedical devices, aerospace components, and advanced engineering because of their tunable thermal and electrical properties. Reliable prediction of these properties is essential for accelerating material design; however, existing analytical models and conventional machine learning techniques often fail to represent the complex interactions among filler characteristics, polymer matrices, processing conditions, and interfacial transport phenomena. This work presents HMEP-Net, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1062–1082 Read article
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Artificial Intelligence for Improved Healthcare: A Case Study and Applications
Abstract: Artificial intelligence (AI) in healthcare ushers in a revolutionary period of innovation, but it also brings with it significant ethical dilemmas. This paper explores the complex relationship between AI and healthcare, emphasizing both its useful applications and the moral conundrums that arise. Ethical issues span a wide range, including patient privacy, transparency, accountability, and the unintentional reinforcement of biases in AI algorithms. Privacy concerns take center stage as healthcare providers …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 1, 2025 · pp. 1–12 Read article
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Big Data, Big Impact: The Role of Analytics in Modern Business
Abstract: In modern business, “Big Data” signifies the vast amount of data collected from various sources, and “Big Data Analytics” refers to the process of analyzing this data to extract valuable insights, enabling companies to make data-driven decisions, optimize operations, better understand customers, and ultimately gain a competitive edge by identifying trends, patterns, and opportunities that might otherwise be missed. This study analyzes large datasets, by which businesses can gain deeper …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 2, 2025 · pp. 01–11 Read article
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Trend analysis of Normalized Difference Vegetation Index using Landsat Satellite data: Study from in and Gondar town, North West Ethiopia
Abstract: Abstract This current research study presents the change detection method for the study of Satellite image supported on Normalized Difference Vegetation Index (NDVI). NDVI utilizes the Multi-Spectral remote sensing data method to determine Vegetation Index, land cover classification, vegetation, water bodies, bush land, agricultural area, thick forest and thin forest with few bands grouping of the remote sensed data. Land resources are easily understood by calculating their Normalized Difference Vegetation …
Published in Journal of Control & Instrumentation · Vol. 10, Issue 3, 2019 · pp. 25–33 Read article
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Integrating Sensor Technologies and Machine Learning for Detection and Mitigation of Structural Deformity and Slope Failure in Opencast Mines
Abstract: With furtherance in the mining industry, accidents due to slope failure are frequent in mining sites. Slope instability, a complex process, seriously threatens the miner’s life and properties. The damage inflicted by slope failures in the recent past has pulled the attention of authorities toward implementing disaster risk reduction measures. This research aims to develop an innovative approach that combines sensor technologies and machine learning techniques to detect and mitigate …
Published in Journal of Communication Engineering & Systems · Vol. 13, Issue 3, 2023 · pp. 38–45 Read article
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Enhancing Safety Protocols in Industrial Operations
Abstract: Industrial safety is a crucial aspect of maintaining both operational efficiency and protecting the workforce in hazardous environments. Despite significant advancements in safety technologies and regulatory frameworks, industrial accidents remain a persistent problem, often leading to severe injuries, fatalities, and financial losses. Such incidents typically arise from a combination of factors, including human error, equipment malfunctions, and insufficient safety protocols. In response to these challenges, this paper examines key strategies …
Published in Journal of Industrial Safety Engineering · Vol. 11, Issue 3, 2024 · pp. 1–5 Read article
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AI-Optimized Nano-Silica Reinforced PCM Composites for Predictive Solar-Thermal Energy Storage Networks
Abstract: This study presents an AI-optimized nano-silica reinforced polymer composite phase change material (PCM) for predictive solar-thermal energy storage networks. The proposed composite combines paraffin wax, high-density polyethylene (HDPE), and uniformly dispersed nano-silica particles to improve thermal conductivity, structural stability, leakage resistance, and long-term cycling performance. The composite was fabricated through melt blending and ultrasonication-assisted nanoparticle dispersion, followed by comprehensive morphological, chemical, thermal, and thermophysical characterization using scanning electron microscopy (SEM), …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
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Influence of SlS Parameters on Strength and Fatigue Durability of Polyamide-12
Abstract: Selective Laser Sintering (SLS), a laser-based polymer powder bed fusion process, has gained prominence in the fabrication of high-performance thermoplastics such as Polyamide-12 (PA12). This paper provides an extensive review of how key SLS process parameters—such as laser power, scan speed, hatch spacing, and energy density—affect the mechanical properties of PA12 components, with a specific focus on tensile strength and fatigue resistance. Studies demonstrate that increasing energy input enhances particle …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 178–191 Read article
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A Hybrid Machine Learning Approach for Enhanced Patient Diagnosis and Health Outcome Prediction
Abstract: Rapid and accurate diagnosis is essential to present day practitioners of medicine, yet can be complicated by the enormous volume and complexity inherent in clinical data. To this end, we here propose a hybrid machine learning model in combination with Recursive Feature Elimination (RFE) and ensemble voting to enhance the diagnostic accuracy by integrating multiple models. Trained on real-world electronic health data, including lab results, demographics and medical history, the …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 2, 2026 Read article
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An Investigation of Model Predictive Control in Self-driving Vehicles
Abstract: Autonomous vehicles, which are often known as self-driving automobiles or driverless cars, are vehicles that can navigate and operate without human intervention. They require efficient controllers capable of handling complexities, with reduced computational costs, and should handle multiple inputs and outputs simultaneously. Model predictive control (MPC) possesses all these characteristics which means it can be utilized effectively for the same purpose. MPC for autonomous vehicles proposes various ways of achieving …
Published in Trends in Electrical Engineering · Vol. 14, Issue 1, 2024 · pp. 40–50 Read article
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Integrating Atmospheric Science: Understanding Greenhouse Gases, Aerosols, and Air Quality Dynamics
Abstract: Atmospheric science investigates the Earth’s atmospheric systems to understand their composition, dynamics, and the implications for climate, weather, and air quality. This review explores five primary areas within the field: atmospheric composition, atmospheric modeling, remote sensing, air pollution, and boundary layer dynamics, highlighting critical challenges and advancements. Rising levels of greenhouse gases (GHGs), including carbon dioxide and methane, continue to drive global warming, while feedback mechanisms—like cloud interactions and surface …
Published in International Journal of Atmosphere · Vol. 1, Issue 1, 2024 · pp. 32–35 Read article
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Implement Explainable Machine Learning to Improve Conductivity in Polymer-CNT Nanocomposites: Supporting Adaptive, Flexible, and Long-Lasting IoT Wrap-Around Electronics Applications
Abstract: The rapid growth of Internet of Things (IoT) technologies requires electronic components that are adaptable, lightweight, and durable, and that can continue to function well in diverse contexts and circumstances. Polymer–carbon nanotube (CNT) nanocomposites have become interesting choices for these kinds of uses because they are more flexible, conduct electricity better, and can be made to fit specific needs. However, improving conductivity in these heterogeneous systems remains a major challenge …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 238–254 Read article
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A Comprehensive survey of robust image quality metrics for satellite imagery
Abstract: Satellite imagery is essential for applications like environmental monitoring, urban development, precision agriculture, defence surveillance, and disaster response. The reliability of these applications is closely tied to the quality of the captured images, which may be compromised by atmospheric effects, sensor imperfections, compression artifacts, and transmission noise. As a result, accurate image quality assessment (IQA) is essential to ensure trustworthy analysis and informed decision-making in satellite-based systems. The distinctive properties …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 15, Issue 1, 2026 · pp. 7–20 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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Artificial Intelligence and Machine Learning Approaches for Corrosion Prediction and Management of Steel Reinforcement in Concrete: A Systematic Review
Abstract: Load bearing concrete structures need steel reinforcement bar (rebar), which are prone to attack by the corrosive environment inside the concrete due to constant ingress of moisture, pollutant gases and anions (mainly Cl−, SO42−). In recent models for the potential life span, the causes of failure of concrete structures have been established principally due to the chloride (Cl−) ion, because of the ease in transportation of Cl− in concrete and …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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Enzyme Stability Prediction using BERT and CNN-A Deep Learning Approach for Enhanced Biocatalysis
Abstract: An important factor in determining the efficacy of industrial enzymes used in various biotechnological applications is their stability. The goal of this study is to develop a predictive model for industrial enzyme stability, which is essential to the efficiency of these enzymes in biotechnological applications. The research takes a comprehensive strategy to comprehend the parameters affecting enzyme stability by combining statistical analysis, deep learning algorithms (BERT and CNN), and molecular …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 14, Issue 2, 2024 · pp. 19–35 Read article
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Forecasting Commodity Prices Using Deep Learning Techniques: An Empirical Evidence from India
Abstract: Commodity price forecasting is instrumental in financial markets, providing framework for investment choices and risk management practices. Traditional models, including statistical and machine learning approaches, have limitations in capturing the nonlinear and volatile nature of commodity prices. Deep learning (DL) techniques have emerged as promising alternatives, leveraging advanced neural networks to enhance predictive accuracy. This study presents a thorough and comprehensive examination of deep learning applications in commodity price prediction, …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 08–12 Read article