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446 articles for “FEA analysis”
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A Comparative Study Between GSM and CNN to Develop Gesture Detection Based Alert System for Women Safety
Abstract: Women’s safety is a pressing issue in today’s world, and technology can play a crucial role in addressing it. This project introduces a facial expression recognition device that uses Convolution Neural Network (CNN) technology and develop it’s comparison with an expression system with use of GSM is done. Unlike traditional methods relying on manual activation or dedicated devices, this system reacts instantly to threatening situations by recognizing predefined gestures, ensuring …
Published in International Journal of Electrical Power and Machine Systems · Vol. 2, Issue 1, 2024 · pp. 24–30 Read article
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Predictive Modeling of Polymer Composites for Medical Implants Using Artificial Intelligence Techniques
Abstract: The use of polymers in biomaterials was now key to designing the next generation of medical implants, which need to be strong and also compatible with living tissue. Tests for biocompatibility, such as those done in the laboratory and by doing experiments on animals, require much time and many resources, so the need for computer-based approaches becomes clear. An artificial intelligence approach was provided in this study to determine how …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 665–692 Read article
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Machine Learning Framework for Optimizing Polymer–Metal Oxide Composites as Charge Selective Layers in Perovskite Solar Cells
Abstract: To achieve high-performance and stability of perovskite solar cells (PSCs), it was important to incorporate innovative interfacial materials to tune the balanced charge extraction, low recombination, and enhanced operational lifespan. On this note, polymer composites with metal oxides have been proposed as promising candidates as charge selective layers (CSLs), whereby they present a rare combination of tunable energy levels, improved film forming abilities, and better interface engineering capabilities. In this …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 1073–1098 Read article
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Development of a Generative AI Model for Early Detection and Prevention of Electrical Faults in Thermal Power Plants
Abstract: Electrical faults in thermal power plants can lead to severe equipment damage, production downtime, and safety hazards if not detected in advance. This study presents the development of a Generative Artificial Intelligence (GenAI) model for the early detection and prevention of electrical faults using predictive analytics. The proposed framework integrates Generative Adversarial Networks (GANs) with deep learning (CNN) and machine learning algorithms (Random Forest, Logistic Regression) to enhance data diversity, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 1–10 Read article
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Experimental Assessment and Statistical Argument of Al-Si/CSA/MoS2 Hybrid Composites for Mechanical and Tribological Characteristics
Abstract: To augment the mechanical and tribological properties of Al-Si matrix composites complement with molybdenum disulphide (MoS₂) and coconut shell ash (CSA), a mixed experimental and Face-Centered Composite (FCC)strategy was employed. A liquid metallurgical method called stir casting was used to create hybrid composites with 5–15 wt.% CSA and 1–3 wt.% MoS₂. A FCC experimental design with thirty runs was used to thoroughly evaluate the materials. This design allowed for the …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 792–802 Read article
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AI-Driven Prediction of Mechanical and Thermal Properties in Polymer-Based Functionally Graded Composites
Abstract: The proposed architecture of the current paper is an artificial intelligence (AI)-driven model of forecasting mechanical and thermal aspects of polymer-based functionally-graded composites (FGCs). Traditional micromechanical and finite element models, which are practical in homogeneous composites, might not be able to account in nonlinear interaction that is caused by compositional gradient. To overcome the challenge, machine learning (ML) models like artificial neural network (ANN), support vectors regression (SVR), and gradient-boosted …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 70–89 Read article
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AI-Designed Functionally Graded Polymer Composites for Multifunctional Thin Films
Abstract: The design of multifunctional polymer composite thin films requires simultaneous optimization of mechanical, optical, barrier, and thermal properties—objectives often in conflict when using conventional homogeneous materials. This study presents an artificial intelligence-driven framework for designing functionally graded material (FGM) architectures in polymer nanocomposite thin films. We integrated machine learning with physics-based modeling to optimize compositional gradients across film thickness, achieving superior performance compared to homogeneous and discrete multilayer alternatives. A …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1026–1041 Read article
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Optimized Sentiment Analysis Through TextBlob and Hybrid RNN Models
Abstract: In today’s world, analyzing people’s feelings from what they write online has become very important. This is because there is a large amount of content created by users. To make this analysis accurate and fast, we present a method. This method uses a mix of two approaches: one that looks up words in a dictionary and another that uses computer learning. TextBlob is an affordable tool for getting an initial …
Published in International Journal of Computer Science Languages · Vol. 4, Issue 1, 2026 · pp. 29–28 Read article
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Applying Text Analysis Methods for Emotion Recognition
Abstract: This article presents a comprehensive study of sentiment analysis, a vital task in the realms of natural language processing (NLP) and artificial intelligence (AI). Sentiment analysis involves the extraction and classification of subjective information from textual data, determining whether the sentiment expressed is positive or negative. This paper investigates different approaches and methodologies used in sentiment analysis, encompassing machine learning models as well. Additionally, it discusses the challenges faced in …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 11, Issue 2, 2024 · pp. 12–22 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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Examining Research Patterns and Concepts in Relation to Frameworks for Sustainable Supply Chain Management (SSCM)
Abstract: Structures abound in writing about Sustainable Supply Chain Management (SSCM). Nevertheless, no effort has been made to date to identify the anomalies in the systems that are in place now and the associated research trends. This research is motivated primarily by a survey of SSCM structures and feature abnormalities. The analysis also promotes new applications and aids in the analysis of SSCM structure constructions and exploration patterns. Using the Scopus …
Published in International Journal of Industrial and Product Design Engineering · Vol. 1, Issue 2, 2023 · pp. 26–41 Read article
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Energy-efficient Image Classification on Edge Devices: Implementation and Evaluation
Abstract: Image classification is a computer vision problem where an algorithm determines a class or label for a given image. Various real-time applications like object recognition, medical diagnosis, person recognition, etc. Image classification property on edge devices is useful for autonomous vehicles, surveillance, and healthcare and internet of things deployments. The advancement of deep learning based methods and graphics processing units (GPU) devices allows efficient processing locally. The study utilizes a …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 3, 2024 · pp. 10–18 Read article
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Terraform: Accelerating Infrastructure Deployment Through Infrastructsure as Code
Abstract: This paper provides an in-depth analysis of Terraform; an open-source Infrastructure as Code (IaC) tool developed by HashiCorp. It explores how Terraform transforms the way infrastructure is built, managed, and maintained across different cloud platforms and on-premises systems. Through a detailed analysis of its features, workflow, and real-world applications, we demonstrate how Terraform significantly enhances operational efficiency, reduces deployment times, and ensures consistency in infrastructure management. The paper includes code …
Published in Journal of Open Source Developments · Vol. 11, Issue 3, 2024 · pp. 7–15 Read article
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Design, Synthesis, and Spectroscopic Elucidation of 3-Methyl-1-Phenylpyrazol-5-One: A Cheminformatics-Aided Approach to Heterocyclic Drug Scaffolds
Abstract: Pyrazolone, a notable heterocyclic compound, has drawn increasing interest due to its broad pharmacological profile and structural versatility. The synthesis, characterisation, and biological assessment of new pyrazolone derivatives are examined in this work. A comprehensive review of existing synthetic strategies is presented, emphasizing green and efficient methodologies. Novel derivatives were synthesized using varied starting materials and catalytic systems, followed by structural confirmation through spectroscopic analyses, including NMR, IR, and mass …
Published in International Journal of Cheminformatics · Vol. 3, Issue 1, 2025 · pp. 29–40 Read article
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Recycling and Reinforcement of Retired EV Battery Materials in Polymer Composites for Sustainable Engineering Applications
Abstract: The rapid proliferation of electric vehicles (EVs) has led to a substantial increase in lithium-ion battery waste, necessitating sustainable strategies for material recovery and reuse. This review explores the valorization of retired Electrical Vehicle batteries within polymer and composite systems, highlighting second-life applications as a promising pathway toward circular material utilization. Batteries retaining 70–80% of their original capacity remain suitable for extended use; however, beyond conventional energy storage, their constituent …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 362–371 Read article
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Identification of Papaya Fruit Ripening Process Using AI
Abstract: Identifying the ripening process of papaya fruit using artificial intelligence involves employing machine learning algorithms to analyze various features such as color changes, texture alterations and chemical compositions. This model is capable of analyzing visual cues to determine the stage of ripeness. The dataset compares images of papaya at various ripening stages, and our AI model demonstrated high accuracy in classifying these stages. Employing machine learning algorithms and image processing …
Published in Research & Reviews : Journal of Food Science & Technology · Vol. 13, Issue 2, 2024 · pp. 23–30 Read article
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Fractal-Entropy Guided Adaptive Signal Reconstruction for Non-Stationary Biomedical and Communication Systems
Abstract: This paper presents a novel Fractal-Entropy Guided Adaptive Signal Reconstruction (FEG- ASR) framework designed for accurate processing of non-stationary signals in biomedical and communication systems. The proposed approach integrates fractal dimension analysis with entropy- based feature evaluation to capture the intrinsic complexity and irregularity of time-varying signals. By dynamically adapting reconstruction parameters based on fractal-entropy measures, the method effectively separates noise from meaningful signal components while preserving critical information. The …
Published in Current Trends in Signal Processing · Vol. 16, Issue 1, 2026 Read article
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Synthesis and Characterization of Sustainable, Low-Cost, Biomass-Derived Carbon Films from Hevea Brasiliensis (Rubber) Wood for Environmentally Friendly Gas Sensors with Enhanced Selectivity and Sensitivity to Polar Vapors and Hazardous Gases
Abstract: Hevea brasiliensis (rubber) wood sawdust was utilized to fabricate carbon gas sensors for detecting 1000 ppm of ethanol, methanol, acetone, carbon dioxide, and ammonia gases. The carbon particles were prepared by phosphoric acid impregnation followed by thermal activation and then fabricated into films on conductive and non-conductive substrates using the doctor blade method. X-ray diffraction (XRD) analysis revealed distinct graphitic features with peaks at the (002) and (100) planes, indicating …
Published in Journal of Thin Films, Coating Science Technology & Application · Vol. 12, Issue 1, 2025 · pp. 22–36 Read article
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Machine Learning-Based Quantification of Polymer Structure Property Relationships for Predictive Material Design
Abstract: Polymer structures exhibit complex, hierarchical arrangements that strongly influence macroscopic properties, yet consistent quantification remains challenging due to nonlinear interactions and limited unified modeling strategies. Existing approaches inadequately capture generalized structure–property mappings across diverse polymer systems. This research aims to establish a machine learning-based quantification model for polymer structure–property relationships to support predictive material design. A Polymer Structure Property Dataset of 5,000 polymer samples includes structural descriptors and experimentally measured …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 737–754 Read article
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Real-time DDoS Attack Prediction in SDN Environments Using Machine Learning
Abstract: The ever-growing reliance on sdn-based services necessitates robust security measures against Distributed Denial-of-Service (DDoS) attacks that threaten service availability. This project investigates the development of a real-time prediction system for DDoS attacks in sdn environments, leveraging the power of machine learning. The proposed system employs a Decision Tree classification algorithm implemented in Python. To ensure accurate attack identification, the system meticulously addresses data preprocessing challenges inherent in network traffic datasets. …
Published in Journal Of Network security · Vol. 13, Issue 1, 2025 · pp. 16–27 Read article