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1089 articles for “data modelling”
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Role of Machine Learning Principles for Efficient Nuclear Fuel Management and Design
Abstract: The introduction of machine learning (ML) and evolutionary computation methods in addressing complex nuclear fuel management challenges has brought a significant positive change in the domain of nuclear fuel management. Key applications include fuel assembly design optimization, core loading pattern determination, burnup calculation acceleration, fuel performance prediction, and spent fuel characterization. The analysis reveals significant improvements in computational efficiency, prediction accuracy, and optimization capabilities when ML techniques are properly integrated …
Published in Journal of Nuclear Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 22–33 Read article
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Pharmacogenomics: Unlocking the Genetic Basis of Drug Response for Precision Medicine
Abstract: Pharmacogenomics, a fusion of pharmacology and genomics, explores how genetic variations influence individual responses to medications. This field is revolutionizing modern medicine by moving away from a one-size-fits-all approach toward personalized treatment strategies. By identifying specific genetic markers, pharmacogenomics aims to enhance drug efficacy, minimize adverse drug reactions, and improve overall patient outcomes. Key methodologies in this discipline include candidate gene analysis, genome-wide association studies, and haplotype analysis, all of …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 2, 2025 · pp. 52–59 Read article
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Multi-Sensor System for Underwater Pothole Detection to Enhance Road Safety During Monsoon Seasons
Abstract: Monsoon seasons across India and similar tropical regions severely compromise road safety by causing water accumulation that conceals dangerous potholes beneath stagnant pools, leading to frequent vehicle damage, tire punctures, and fatal accidents. Traditional detection methods relying on smartphone accelerometers, ultrasonic sensors, or machine vision fail under flooded conditions due to acoustic signal reflection at water surfaces and optical distortions from glare and turbidity. This research proposes an innovative multi-sensor …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 4, Issue 1, 2026 · pp. 25–32 Read article
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Depression Detection Using AI with Chatbot Support
Abstract: Depression is a major global health concern and a significant contributor to suicide rates worldwide. India reports a high number of suicide cases, making the early detection of mental distress and depression essential for timely intervention. This research presents an AI-based system for depression detection that integrates deep learning, natural language processing (NLP), and a chatbot for user support. The system analyzes facial expressions using convolutional neural networks (CNNs) and …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 14, Issue 1, 2025 · pp. 01–08 Read article
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Physicochemical Transitions and Polymerization Dynamics in Multi-Generational Dentin Adhesives: A Critical Review of the Resin-Dentin Composite Interface
Abstract: Adhesive dentistry has undergone a transformative refinement over the past three decades, transitioning from technique-sensitive, multi-step etch-and-rinse protocols to streamlined universal formulations. This narrative review critically synthesizes evidence from thirty peer-reviewed investigations to evaluate the evolution of dentin bonding agents from the fourth through the eighth generations. The analysis places particular emphasis on the physicochemical dynamics of the resin-dentin interface, including interfacial bond strength metrics, marginal integrity, and microleakage behavior. …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 209–228 Read article
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Machine Learning Innovations for Effective Spam Comment Filtering in Social Networks
Abstract: The increasing prevalence of social media platforms has revolutionized communication, fostering unparalleled levels of connectivity and data exchange. However, the widespread increase in spam comments presents a serious threat to the integrity of online discussions, potentially undermining the quality of interactions. To confront this issue, our proposed model utilizes machine learning techniques to bolster spam comment detection across various social media platforms. This endeavor involves a thorough investigation encompassing data …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 2, 2024 · pp. 19–24 Read article
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Signal Feature Extraction and Machine Learning Techniques for Human Activity Recognition
Abstract: Human Activity Recognition (HAR) has emerged as a critical field of study with diverse applications in healthcare, fitness tracking, smart homes, and human-computer interaction. The aim of this research is to create an efficient HAR system through advanced techniques characterized by signal feature extraction and machine learning algorithms. The MEMS sensors are used appropriately during data mining to extract time-domain, frequency-domain, and statistical features, which are subsequently passed to the …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 12, Issue 1, 2025 · pp. 24–41 Read article
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Computational Analysis of Leaf Spring System with Functionally Graded Materials Using ANSYS
Abstract: Leaf springs are important parts of a vehicle’s suspension system, and their main function is to absorb shocks, improve stability, and make the ride more comfortable. Traditionally, ASTM A36 steel is used because it is strong, long-lasting, affordable, and easy to get. However, this type of steel is very dense, which makes vehicles heavier. This extra weight makes the vehicle less efficient and increases emissions. Because of these issues, there …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 380–397 Read article
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AI-Powered Emotion Recognition in Dog
Abstract: Understanding animal emotions is important for improving veterinary care, human animal interaction, and overall pet well-being. Inspired by previous research that utilized a modified EfficientNetB5 model for emotion classification in cats and dogs, our study builds upon this foundation with a focus on real-time emotion recognition in dogs. While earlier approaches achieved high accuracy using Dense Residual and Squeeze-and-Excitation blocks, they often lacked real-time applicability and were not optimized for …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 4, Issue 1, 2026 · pp. 20–32 Read article
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Enhancing Facial Recognition: Assessing CNNs for Detecting Image Manipulation
Abstract: Deepfake technology, powered by highly advanced deep learning models, has raised significant concerns regarding media manipulation, identity theft, and the spread of online disinformation. Due to the increasing sophistication of deepfake content, traditional forensic methods often fail to detect such artificially generated images with high accuracy. Consequently, deep learning-based approaches have become essential in combating this challenge. This study compares six prominent deep learning architectures: VGG16, ResNet50, MobileNetV2, InceptionV3, EfficientNetB0, …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 2, 2025 · pp. 27–36 Read article
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Optimization of Structural Health Monitoring Using Artificial Neural Network and Comparison with Traditional Method: A Comprehensive Review
Abstract: Structural health monitoring (SHM) has a critical role in ensuring civil infrastructure safety, reliability, and durability through real-time, condition-based monitoring. Traditional SHM systems employ hundreds of sensors such as accelerometers, strain gauges, and displacement transducers for monitoring vast amounts of data for structural inspection, but do not effectively manage complicated nonlinear data. This research paper, “Optimization of Structural Health Monitoring Using Artificial Neural Network and Comparison with Traditional Methods,” investigates …
Published in Journal of Structural Engineering and Management · Vol. 13, Issue 1, 2026 · pp. 23–33 Read article
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Machine Learning-Driven Force Analysis for Tool Wear Prediction Systems
Abstract: A system designed to forecast tool wear by utilizing a force sensor to monitor the wear of the tool's flank and applying a Convolutional Neural Network (CNN) for forecasting purposes. The methodology is demonstrated through experiments in milling, utilizing dry machining with a ball endmill on a stainless-steel component. The flank wear of the tool is directly assessed using a digital microscope throughout the operation. The forecasts produced by the …
Published in Journal of Mechatronics and Automation · Vol. 11, Issue 3, 2024 · pp. 16–25 Read article
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A Comprehensive Review of CNN-Based Framework for Multi-Sign Detection of Diabetic Retinopathy in Fundus Images Using Public Datasets
Abstract: Diabetic retinopathy (DR) is one of the main causes of vision impairment. Blindness prevention and effective treatment depend on early detection. A thorough deep learning-based framework for the automatic segmentation and simultaneous detection of exudates, hemorrhages, and microaneurysms – three important DR indicators – from retinal fundus images is presented in this work. These three pathological signs’ corresponding annotated image patches, along with background (no-sign) areas, were used to train …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 14–23 Read article
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Advanced Digital Twin and AI Integration for Real-Time Optimization in Polymer Production
Abstract: The integration of Internet of Things (IoT) with Artificial Intelligence (AI) technologies opens up considerable avenues for reshaping polymer manufacturing by improving operational effectiveness, securing exceptional product standards, and advancing sustainability in the environment. This academic manuscript delineates an advanced framework that integrates IoT and AI with synergistic technologies, including blockchain, edge computing, and digital twin methodologies, to revolutionize polymer manufacturing processes. The proposed architecture utilizes IoT sensors for the …
Published in Journal of Polymer & Composites · Vol. 13, Issue 3, 2025 · pp. 81–89 Read article
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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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Impact of Fracture in Geometrical, Material, and Load Rate Characteristics on J-Integral
Abstract: The present study investigates the influence of load rate, geometrical parameters, and material properties on the J-integral behavior of AA2050-T84 aluminum-lithium alloy using three-dimensional elastic–plastic fracture mechanics (EPFM) analysis. Finite element simulations were carried out using the ABAQUS software to evaluate crack driving forces in compact tension (C(T)) specimens under various mechanical and thermal conditions. The study focuses on the combined effects of strain rate, temperature-dependent strain hardening, specimen thickness …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 4, Issue 1, 2026 · pp. 38–45 Read article
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Hybrid Quantum–Machine Learning Framework for Nonlinear Rheological Modeling of Polymer and Composite Materials
Abstract: In polymer and composite materials, a major challenge lies in predicting their nonlinear rheological response, owing to complex multiscale interactions that are not captured by traditional constitutive laws or conventional machine learning approaches. In this study, a hybrid Quantum Machine Learning (QML) model comprising Quantum Support Vector Machine (QSVM) and Quantum Neural Network (QNN) architectures is proposed for viscosity prediction without requiring any specific rheological equation. To train and test …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
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Secure Framework for Government Tender Allocation
Abstract: Governments and public sector entities worldwide are actively seeking innovative strategies to adapt to rapid technological progress, aiming to enhance governance effectiveness, streamline work processes, and optimize expenditure. Blockchain technology stands out as a prime example, captivating the interest of governments globally in recent years. Its ability to offer heightened security, enhanced traceability, and cost-efficient infrastructure positions blockchain as a versatile solution applicable across diverse sectors. Typically, governments engage third-party …
Published in Current Trends in Information Technology · Vol. 14, Issue 2, 2024 · pp. 23–27 Read article
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Remote Sensing in Atmospheric Studies: Enhancing Understanding of Climate Dynamics and Air Quality, Atmospheric Monitoring and Analysis; Emerging Technologies and Future Directions
Abstract: Remote sensing has emerged as a transformative tool in atmospheric sciences, providing detailed and comprehensive insights into various atmospheric phenomena. Its advanced capabilities have revolutionized our understanding of climate dynamics, air quality, and atmospheric composition, enabling more accurate monitoring and analysis. This paper reviews the critical role of remote sensing technologies in enhancing knowledge of atmospheric processes and their practical applications in areas such as climate change monitoring, air pollution …
Published in International Journal of Atmosphere · Vol. 2, Issue 1, 2025 · pp. 1–5 Read article
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Deep Learning Based Detection and Classification of Brain Tumors Using MRI Images
Abstract: Brain tumor detection using magnetic resonance imaging (MRI) is a critical task in the early detection and treatment of brain tumors. Manual analysis of brain tumor detection using MRI is a tedious task that requires expertise in the field. Therefore, this study proposes a deep learning-based approach for brain tumor detection and classification using Convolutional Neural Networks (CNN). The proposed approach preprocesses the MRI image using normalization, resizing, and noise …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 Read article