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1966 articles for “Model” (ranking capped at the first 2,000 matches — narrow the search to see the rest)
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Fracture Analysis of FRP Composites under Thermo-Mechanical Loads for Different Geometry Cutouts
Abstract: Fiber-reinforced composites (FRPs) are used extensively in structural and non-structural components of the aerospace and automotive industries. To utilize these materials for structural applications, it is necessary to understand the fracture behavior of the material. In the present investigation of carbon fiber laminates, studies were carried out to understand the fracture toughness characteristics of the carbon fiber laminates with mechanical, thermal, and thermo-mechanical loadings of modes I, II, and III. …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 3, Issue 2, 2025 · pp. 1–10 Read article
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SpecForesight: A Predictive Analytics Pipeline for Laptop Price Forecasting
Abstract: This paper frames laptop pricing as a supervised predictive analytics problem, transforming product specifications into feature-rich signals to forecast price with calibrated regression models and operational guardrails against drift. A structured pipeline ingests tabular listings, performs data cleaning, and engineers domain-informed features (e.g., central processing unit (CPU) family and clocks, graphics processing unit (GPU) tiering, memory/storage density, display, and touch capabilities), followed by encoding and normalization to optimize model learnability. …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 61–71 Read article
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Data-Driven Energy Forecasting for Smart Homes: Ensemble Learning from IoT Meters and Relevance for Polymer-Composite Based Smart Infrastructure
Abstract: Reliable estimation of household electricity demand is relevant in creating efficiency in energy usage, optimization of the loads, and intelligent demand-side management in intelligent grid systems. This paper introduces a varied machine learning model that approaches residential electric consumption prediction using an assortment of ensemble regression boosts, including Linear Regression, Lasso Regression, Decision Tree Regressor, Random Forest, and Gradient Boosting, to predict residential electricity consumption environments on a time-series arrested …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 29–64 Read article
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Creating a “Sustainable Future” Through Secure AI
Abstract: It is imperative in the present world that we figure out a way to move toward a sustainable future. However, a sustainable future from an energy standpoint can only be built by a sustainably intelligent society. Yet, individuals who come together to form a society tend to neglect discussions around sustainability, considering it something that should be driven in a top-down manner. In reality, with the rapid emergence of artificial …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 13, Issue 1, 2026 · pp. 1–7 Read article
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Optimizing Machinability in Wire EDM of AISI P20 Steel Employing Composite Material Wires with Hybrid Neural Network Approach
Abstract: AISI P20+Ni steel is extensively used for forging dies, plastic moulds, and automotive die components due to its excellent polishability, hardness, and homogeneity. This research utilizes Wire Electrical Discharge Machining (WEDM) to process pre-hardened AISI P20+Ni steel, focusing on minimizing both recast layer thickness (RLT) and kerf width (KW). The performance of wires made from composite materials, including zinc-coated brass wire (ZBW), cryogenically treated ZBW (CZBW), and ultrasonic vibration-assisted brass …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1120–1133 Read article
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Harnessing Deep Learning to Explore Microbial Community Structure and Carbon Storage Capacity in Mangrove Ecosystems: A Framework for Computationally
Abstract: Mangrove ecosystems represent one of the most efficient natural carbon sinks on Earth, functioning as critical blue carbon habitats that sustain diverse microbial communities responsible for biogeochemical cycling and long-term carbon storage. Despite their global ecological significance, accurately quantifying and predicting carbon sequestration in mangrove systems remains challenging due to the complex interactions between microbial diversity, sediment chemistry, and environmental drivers. This study presents a comprehensive and sustainable artificial intelligence …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 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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Bridging the Gap Between Product Development and Entrepreneurship in Engineering Curriculum: A Framework-Based Approach Aligned with NEP 2020
Abstract: Despite advancements in technical education, Indian engineering graduates frequently enter the workforce lacking critical competencies in product development (PD) and entrepreneurship. This imbalance has drawn scrutiny from industry and policymakers alike, with national directives such as the National Education Policy (NEP 2020) emphasizing a shift toward “experiential, holistic, integrated and inquiry-driven” learning models. Similarly, the All-India Council for Technical Education (AICTE) has embedded innovation labs, internship mandates and entrepreneurship courses …
Published in International Journal of Education Sciences · Vol. 3, Issue 1, 2026 · pp. 87–100 Read article
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Very Short-Term Load Forecasting Using Gaussian Process Regression
Abstract: Very Short-Term Load Forecasting (VSTLF) is critical for real-time grid stability, frequency control, and economic dispatch. This study proposes a Gaussian Process Regression (GPR)-based framework for one-hour-ahead load forecasting using hourly data from January 2020 to April 2024 for Delhi, India. The model incorporates meteorological data such as temperature, humidity, and dew point with lagged load values. The research takes into account time-related dependencies and seasonal changes in order to …
Published in Trends in Electrical Engineering · Vol. 16, Issue 1, 2025 · pp. 91–104 Read article
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Explainable Machine Learning for Process Parameter Optimization in Gradient 3D-Printed Polymer Composites
Abstract: The explainable machine learning-based structure may be employed to achieve a favorable process parameter of the graduate 3D-printed polymer composite structures to improve the mechanical and thermal properties without compromising the transparency of the decisions made during the fabrication process. Gradient composite specimens were made by systematically varied process parameters like nozzle temperature, raster orientation, deposition speed, gradient transition rate and fused filament fabrication. A predictive model of tensile strength …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 847–866 Read article
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Nitrosamine Accumulation, Processing Variables, and Indigenous Plant Inhibitors in Nigerian Traditionally Processed Meats
Abstract: N-nitrosamines are classified as probable or possible human carcinogens by the International Agency for Research on Cancer. Carcinogenic N-nitrosamines — principally N-nitrosodimethylamine (NDMA) and N-nitrosodiethylamine (NDEA) — are formed in abundance during the preparation of widely consumed Nigerian traditional processed meats including suya, kilishi, and balangu. This original investigation combined a six geopolitical zone of Nigerian market survey with laboratory-controlled model system experiments, effects of processing parameters on N-Nitrosamine formation …
Published in Journal of Modern Chemistry & Chemical Technology · Vol. 17, Issue 1, 2026 · pp. 61–72 Read article
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A Review on AI and Machine Learning for Predictive Maintenance and FDD in RAC Systems
Abstract: The paper reviews the existing AI/ML methods first in the general context of predictive maintenance and FDD of RAC systems, then specifically focusing on granular cooling appliances. Perspectives and insights are provided on the reasons why potentially valuable models do not make it into practice more often, and where future research and development should be headed. New emerging topics for decision support systems to include domain knowledge and physics-based modeling …
Published in Journal of Refrigeration, Air conditioning, Heating and ventilation · Vol. 13, Issue 1, 2026 · pp. 15–25 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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Study on Single-Slope Solar Still for Experimental and Data-Driven Analysis for Improving Productivity with Different Basin Materials.
Abstract: This study investigates the single-slope solar still under the diurnal variation of water temperature and distillate yield under identical operating conditions. Experimental analysis was conducted to evaluate the performance enhancement through the incorporation of natural basin materials, namely hemp and sand. The water distillation process is focused on improving potable water productivity and thermal behaviour. The inclusion of hemp and sand in the basin leads to noticeable differences in productivity …
Published in Emerging Trends in Chemical Engineering · Vol. 13, Issue 2, 2026 · pp. 31–46 Read article
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Experimental and Process Optimization Study on Thermal Stress Reduction in TiC–Steel Brazed Joints Using Polymer-Derived Composite Interlayers
Abstract: In this, an experimental investigation aimed at reducing crack formation due to thermal stress in TiC-steel brazed joints through optimization of key process parameters is presented. The primary objective was to develop an integrated and reliable brazing strategy by examining the effects of filler material selection, brazing gap, cooling conditions and type of flux. In this study, polymer-derived composite interlayers were developed through controlled synthesis and nanocomposite engineering to mitigate …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 524–540 Read article
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Structural Optimization of FDM-Processed ASA Polymer Frames through Acetone Solvent Welding, Variable Infill Strategy, and Layer Orientation: Experimental Validation via Quadcopter Flight Testing
Abstract: Acrylonitrile styrene acrylate (ASA) is an amorphous terpolymer with superior UV resistance compared to acrylonitrile butadiene styrene (ABS), as its acrylate rubber phase lacks photodegradation-prone carbon–carbon double bonds. This study proposes acetone solvent welding as a polymer joining method to produce monolithic structures from FDM-processed ASA components, addressing three processing challenges: achieving structural continuity through polymer chain interdiffusion at solvent-wetted interfaces, correcting thermal warping via post-print geometric correction during welding, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 689–703 Read article
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Dynamic Response Analysis of Isotropic and Orthotropic Rectangular Plates under Clamped-Free Conditions
Abstract: This study explores Theoretical and numerical tools of determining the free vibration properties of isotropic and fiber-reinforced composite rectangular plates. The effect of anisotropy of materials on the dynamic response of the plates is compared between the Aluminium plates and the glass-epoxy laminates. The model used in the study is a three-dimensional finite element model that is designed using a combination of SolidWorks and ANSYS workflow. Clamped-free boundary conditions are …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 258–274 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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Fracture Analysis of Laminated composite plates using Extended Finite Element Method: A Review
Abstract: Laminated composite plates are used in aerospace, automotive, and marine industries. They feature great durability against fatigue, a high strength-to-weight ratio, and mechanical attributes that may be altered. However, they are prone to fracture and delamination under complex loading, requiring accurate fracture analysis for structural integrity. Traditional finite element methods (FEM) need extensive mesh refinement for modelling crack propagation which increases the computational costs. The Extended Finite Element Method (XFEM) …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 4, Issue 1, 2026 · pp. 16–25 Read article