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246 articles for “hybrid modeling”
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Harnessing NLP for Automation and Intelligence Across Sectors
Abstract: Natural Language Processing or NLP is a vital subset of Artificial Intelligence or AI which enables machines to interpret, understand, and communicate using human language in a remarkable way. From the traditional rule-based approaches to the modern advanced deep learning techniques such as transformers, neural networks, and hybrid models, NLP has been evolving year by year. This study reflects on various applications of NLP, including sentiment analysis, machine translation, analysis …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 23–32 Read article
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A Carbon Assessment Framework for the Estimation of Carbon Footprint by the Usage of Liquified Petroleum Gas
Abstract: Various higher educational universities around the globe have tried to compensate for their carbon footprints, and awareness of ecology and environmental responsibility have increased. The primary motive of this work is to provide a recent study about the carbon emission accounting of carbon emission in higher education and enhance the mitigation techniques. Carbon footprint can be measured by using various methodologies like input-output analysis, hybrid models, and life cycle assessments …
Published in Journal of Energy, Environment & Carbon Credits · Vol. 15, Issue 3, 2025 · pp. 1–5 Read article
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Comprehensive Comparative Analysis of Intrusion Detection Systems: Evaluating Signature Based, Anomaly Based, and Hybrid Approaches
Abstract: In the fast-changing world of cybersecurity, Intrusion Detection Systems (IDS) play a vital role in protecting digital resources. This study offers an in-depth comparative analysis to evaluate the efficiency and performance of different IDS solutions. It examines a variety of both commercial and opensource platforms, encompassing signature based, anomaly based, and hybrid models, to assess their effectiveness in identifying and responding to a wide range of cyber threats. Methodologies for …
Published in International Journal of Information Security Engineering · Vol. 3, Issue 2, 2025 · pp. 39–44 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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A Detailed Review on Traditional v/s Modern Education: Navigating the Path to Learning
Abstract: This abstract compares traditional and modern education methodologies, analyzing their respective approaches, strengths, and weaknesses. Traditional education, characterized by face-to-face instruction, textbooks, and standardized testing, emphasizes rote learning and adherence to established curriculum. In contrast, modern education integrates technology, interactive learning tools, and personalized approaches to foster critical thinking, creativity, and adaptability. While traditional education provides a structured framework and cultural continuity, modern education addresses diverse learning styles and prepares …
Published in International Journal of Education Sciences · Vol. 2, Issue 2, 2025 · pp. 67–74 Read article
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Parallel Greedy Approach for Phylogenetic Tree Construction in the Context of Marine Species
Abstract: The rebuilding of phylogenetic trees for marine species shows major computing problems because of the massive genomic data and the huge biodiversity inherent in ocean ecosystems. Traditional phylogenetic methods are accurate but become more expensive when they are processing with thousands of marine taxa parallelly. This article shows a critical analysis of parallel greedy algorithms as an adaptable solution for large-scale marine phylogenetics. It examines the main principles of greedy …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 33–45 Read article
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Effect of hybrid plant-based cutting fluid in turning operation of EN19 steel and modelling of heat transfer equation for multiple phase colloids
Abstract: This article describes a project where an alternative to conventional Mineral Oil (MO) based cutting fluids is tried on the turning operation of steel samples. The deposit of petroleum is getting depleted day by day, the pollution due to fossil fuel is increasing exponentially and petroleum is getting more and more expensive. Due to these reasons, nonpetroleum (MO) based alternative cutting fluids have been in business for quite a while. …
Published in Journal of Polymer & Composites · Vol. 11, Issue 12, 2023 · pp. 175–185 Read article
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Dosing Control of Urea in Selective Catalytic Reduction (SCR) to enhance the reduction of Nitrogen oxides
Abstract: Selective Catalytic Reduction (SCR) is an effective aftertreatment technique designed to comply with rigorous emission criteria established by global regulatory authorities for the elimination of nitrogen oxides from exhaust streams. Since NOx and ammonia reagents are poisonous and an excess of either is therefore very undesired, it poses an intriguing control problem, particularly at high conversion. SCR systems must reduce NOx emissions as much as possible and reduce the possibility …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 110–120 Read article
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Microvita: A New Hybrid Particle Bridging the Fermionic and Bosonic Domains in Particle Physics
Abstract: In particle physics, the established classification of particles into fermions and bosons—obeying Fermi-Dirac and Bose-Einstein statistics, respectively—has shaped theoretical and experimental frameworks for nearly a century. This study introduces 'Microvita', a novel theoretical particle that exhibits hybrid characteristics modulated by a continuous parameter α ∈ [0,1]. Through this parameter, Microvita interpolates between bosonic and fermionic behavior in terms of spin, statistical distributions, and operator algebra. We explore the foundational algebra …
Published in Research & Reviews : Journal of Physics · Vol. 15, Issue 1, 2026 · pp. 14–27 Read article
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Effect of Welding Factors on Nugget Size of Polymer-Metal Composite Sheets Using Computational Methods
Abstract: Resistance spot welding (RSW) is a vital technique for joining materials in industries like automotive and aerospace. This study extends the application of RSW to polymer-metal composite sheets by developing 2D axisymmetric, thermo-electro-mechanical coupled model in ANSYS. The focus is on analyzing the temperature distribution, nugget formation, and parameter optimization in hybrid composite sheets, emphasizing the unique challenges posed by polymers' thermal and electrical properties. These properties differ significantly from …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 612–623 Read article
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Modelling and Interpretation of A Novel Battery-Motor amalgamated Thermal Management System using rGO/CO3O4 based Hybrid Nano-composite Coolant for Electric Vehicle Applications
Abstract: Battery and motor have to be given equivalent importance to maintain the lifetime, thermal characteristics, efficiency and safety of Electric Vehicles (EVs). Thermal management of EVs need to be considered for battery and motor because of dynamic loading conditions. This research proposes a novel Battery-Motor Integrated Thermal Management System (BMITMS) for EV applications. An EV assembled with LiFePO4 battery pack and a three-phase induction motor has been considered on this …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 225–243 Read article
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Data-Driven Life Prediction of Fiber-Reinforced Polymer Composites Using IoT Sensing and Machine Learning Algorithms
Abstract: The accurate prediction of fatigue life in fiber-reinforced polymer (FRP) composites remains a major challenge due to their nonlinear, multi-mechanism degradation behavior under variable loading conditions. This study presents a data-driven framework, H-LiProNet, which combines real-time IoT sensing with hybrid machine learning to estimate remaining useful life (RUL) in FRP composites. The proposed system integrates embedded Fiber Bragg Grating (FBG) and acoustic emission (AE) sensors to capture strain and damage …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 116–130 Read article
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Enhancing Production Line Efficiency: Simulating and Optimizing Single and Parallel Line Processes
Abstract: During a time of fast-paced industrial growth, increasing production line effectiveness is a core issue for manufacturers looking to maximize output, reduce waste, and stay competitive. This study explores the use of simulation-based optimization methods to enhance single and parallel production line designs. Stepping beyond traditional trial-and-error methods, the research utilizes Siemens Tecnomatix Plant Simulation to simulate actual manufacturing scenarios, considering intricacies like buffer capacities, machine sequencing, and event-driven scheduling. …
Published in Journal of Production Research & Management · Vol. 15, Issue 1, 2025 · pp. 22–32 Read article
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Enhanced Sustainable Concrete Mix Design Using LLMs and Advanced Machine Learning Techniques
Abstract: Large Language Models (LLMs) are emerging as transformative tools in materials science, offering human-like reasoning, zero-shot problem solving, and the ability to integrate fuzzy laboratory knowledge with structured data. This study extends and reinterprets the original systematic benchmark for using LLMs in sustainable concrete design, particularly for Alkali-Activated Concrete (AAC). We introduce an enhanced, multi-model framework combining LLM-based inverse design, Random Forest regression, Gaussian Process Regression (GPR), and a lightweight …
Published in Recent Trends in Civil Engineering & Technology · Vol. 16, Issue 2, 2026 Read article
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A Review on Artificial Intelligence Techniques for Analyzing Deforestation and Illegal Logging Using Satellite Imagery
Abstract: Deforestation and illegal logging remain critical environmental threats, driving biodiversity loss, climate change, and socio-economic disruption. Conventional monitoring techniques frequently do not yield real-time, large-scale insights. Recent developments in Artificial Intelligence (AI), especially in deep learning and computer vision, have revolutionized the ability to analyze high-resolution satellite images for detecting deforestation and monitoring illegal logging. This review synthesizes recent developments in AI-driven approaches, highlighting convolutional neural networks (CNNs), anomaly detection …
Published in International Journal of Satellite Remote Sensing · Vol. 4, Issue 1, 2026 · pp. 1–9 Read article
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Automated Crop Disease Detection Using Convolutional Neural Networks
Abstract: Crop diseases contribute to major losses in agricultural production worldwide generating enormous economic costs. This study investigates the possibility of Convolutional Neural Networks (CNN) imaging techniques to auto-detect diseases associated with plants through image processing. A model was developed and trained on a publicly available plant disease dataset containing labeled images of several diseases. The CNN could classify various plant diseases with accuracy of 95%, precision of 92%, and recall …
Published in International Journal of Cheminformatics · Vol. 3, Issue 2, 2025 · pp. 7–15 Read article
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Entropy, Symmetry, and Data Fusion: Emerging Methods in Multi-Objective Decision- Making and Smart Systems
Abstract: In the era of intelligent technologies and data-driven systems, multi-objective decision-making (MODM) has become an essential aspect of managing complex environments such as smart cities, autonomous systems, and cyber-physical networks. As decision-making scenarios become increasingly dynamic and uncertain, there is a growing need for advanced methodologies that can handle diverse objectives, conflicting constraints, and incomplete information. This review highlights the emerging role of entropy, symmetry, and data fusion as foundational …
Published in Emerging Trends in Symmetry · Vol. 1, Issue 2, 2025 · pp. 44–49 Read article
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Performance Analysis of Dual Junction Solar Cell Devices utilizing Subcells of In0.51Ga0.49P and GaAs to Study Key Solar Cell Parameters via TCAD based Simulation
Abstract: In the present work, a multi-junction solar cell was designed to obtain better performance over single-junction solar cells. The proposed structure is composed of different layers of diverse semiconductor materials stacked on each other. Here, an In0.51Ga0.49P/GaAs double-junction solar cell was outlined as having a distinctive composite material (GaAs) as the tunneling junction. To optimize the solar cell's effectiveness, In0.47Ga0.15Al0.37P was used in the window layer and the back-surface field …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 829–837 Read article
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Recent Advances in Content-based Image Retrieval: Techniques and Applications
Abstract: Content-based image retrieval (CBIR) plays a vital role in computer vision, driven by the increasing need for fast and accurate image retrieval across fields like healthcare, e-commerce, and digital libraries. This study offers a detailed review of CBIR methodologies, charting their progression from traditional feature extraction techniques, such as Local Binary Patterns (LBP), to contemporary deep learning-driven methods. The transformative impact of convolution neural networks (CNNs) is highlighted, emphasizing their …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 67–71 Read article
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Phisherman: A Phishing Email Detection Browser Extension
Abstract: Phishing attacks continue to pose significant security risks, exploiting email as a primary vector to deceive users and compromise sensitive information. To counter these threats, Phisherman presents a sophisticated, real-time phishing detection system that integrates both rule-based methods and deep learning for heightened accuracy. Built as a cross-browser extension, compatible with Chrome, Firefox, and Edge through the WebExtension API, Phisherman combines traditional verification checks, such as DNS blacklisting, SPF, DKIM, …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 99–105 Read article