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1059 articles for “Model optimization”
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Process Capability Optimization of Thermoplastic Polyurethane (TPU) Printed Parts in Fused Deposition Modeling Process
Abstract: Fused Deposition Modelling (FDM) 3D printing process requires precise printed dimensions with a special assessment of process parameters using Taguchi optimization methodology. The proposed research aims to determine the optimal parametric setting to maximize the process capability index of diametral deviation and roundness of product in 3D printing process of TPU (thermoplastic polyurethane). The input factors for printing TPU in this research include the extrusion temperature, cooling fan speed, infill …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1846–1864 Read article
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Application of Genetic Algorithm in Resource Leveling and Allocation
Abstract: Resource leveling and allocation is one of the most important aspects in construction management. Critical path method and program evaluation review technique is frequently used for scheduling of construction projects. However, it is not capable of minimizing unwanted fluctuations in resource utilization profile. The basic idea of resource leveling is to minimize resource fluctuation. In addition to this, the paper uses the resource utilization period. This research paper presents an …
Published in Recent Trends in Civil Engineering & Technology · Vol. 5, Issue 3, 2015 · pp. 1–8 Read article
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Parametric Design and Multi Objective Optimization of a Two-Axle Tipper Trailer Chassis for Enhanced Strength
Abstract: The primary objective of this project is to perform a detailed analysis of the chassis frame by considering variations in cross-sectional geometry as well as material composition. The frame structure includes main longitudinal members supported by a series of cross members, and the study focuses on optimizing both these elements. Material optimization is carried out by introducing composite materials in place of conventional ones, aiming to achieve improved strength-to-weight ratio. …
Published in Trends in Machine design · Vol. 13, Issue 2, 2026 · pp. 16–25 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 · pp. 41–49 Read article
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Performance of Artificial Neural Network for Tree Species Identification using Sentinel-2 Data
Abstract: Accurate land cover mapping, especially concerning vegetation, is crucial for effective land use policy planning and sustainable forest management. Hence, achieving accuracy in mapping requires a deep understanding of composition changes, vegetation conditions, and the spatial distribution of tree species. In the spatial context of tree species, it holds significant potential for applications including invasive species monitoring, delineating contaminated areas, and biodiversity conservation. However, traditional methods for tree species identification …
Published in Journal of Remote Sensing & GIS · Vol. 15, Issue 2, 2024 · pp. 12–21 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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Speed Control of PMSM using Optimization Methods
Abstract: AbstractThis paper represents a modelling and optimized design of speed control for a permanent magnet synchronous motor (PMSM). The PI controller of inner current loop is optimized using evolutionary algorithm like Particle Swarm Optimization (PSO) method. To illustrate effect of proposed method, the performance of evaluationary algorithm is compared with traditional optimization method i.e., Ziegler-Nichols method. The main objective of the proposed work are, develop mathematical modelling of PMSM motor …
Published in Journal of Microcontroller Engineering and Applications · Vol. 6, Issue 2, 2019 · pp. 8–16 Read article
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Detection and Classification of Diabetic Retinopathy Using Deep Learning Techniques
Abstract: This project delves into the evaluation of three prominent deep learning architectures Basic CNN, ResNet, and DenseNet for their efficacy in detecting diabetic retinopathy from retinal images. Utilizing a diverse dataset, the study employs standard deep learning frameworks to train and validate each model. The focus extends to exploring the potential benefits of transfer learning on a limited dataset. Evaluation metrics like specificity, sensitivity, and accuracy are employed for a …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 2, 2024 · pp. 64–69 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 Material Design and Performance Improvement: Constructing Sustainable Polymer Nanocomposites Using Deep Learning
Abstract: In the formation of sustainable polymer nanocomposites, the effective material techniques are required to balance the mechanical qualities, environmental compatibility and processing efficiency. The optimization of polymer matrix, nanofiller loading, processing conditions and material properties is typically time consuming, resource intensive and highly dependent on trial-error methodology using standard experimental techniques. The present work provides a data-driven approach that combines deep learning with sustainable polymer nanocomposite design for predicting and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Artificial Intelligence in Healthcare for Implants and Tissue Regeneration: Advances, Challenges, and Future Directions
Abstract: Artificial intelligence (AI) has been a revolutionary influence in contemporary healthcare, especially in implant design, biomaterials research, and tissue regeneration. In regenerative medicine, AI facilitates predictive modeling, optimization, and decision-making via the analysis of intricate biological, material, and clinical information. This study analyzes current research on AI applications in implant technologies and tissue regeneration, specifically addressing scaffold engineering, biomaterial characterisation, stem cell and gene treatments, smart biomaterials, and implant planning. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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AI for Cybersecurity: Deploying Machine Learning for Network Traffic Anomaly Detection
Abstract: The growing sophistication of cyberattacks and the growth of network traffic necessitate sophisticated anomaly detection methods. This study overviews the use of artificial intelligence (AI) and machine learning (ML) to counter these challenges, as noted in current studies. It analyses supervised learning (SVM, Decision Trees), unsupervised learning (K-means, DBSCAN), and deep learning (CNNs, RNNs, Auto-encoders) approaches, considering their strengths and weaknesses. The research integrates current developments in AI/ML-based network anomaly …
Published in International Journal of Computer Science Languages · Vol. 3, Issue 2, 2025 · pp. 1–10 Read article
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Comparative Performance Study: Deterministic vs. Probabilistic Models in Retail Chains
Abstract: The finished goods, raw materials, and product stock that a business has on hand for sale are referred to as inventory. They enable the companies to achieve their sales levels and are a chance to cost control and decision making. It is a huge asset to a manufacturing firm. Inventory model permits forecasting of quantities of raw material, inventory and spare parts of the equipment to a very high level …
Published in Recent Trends in Mathematics · Vol. 2, Issue 1, 2025 · pp. 1–6 Read article
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Emerging Trends in Membrane-Based Gas Separation Technologies
Abstract: Membrane technology has emerged as a groundbreaking solution in various fields, revolutionizing industries such as water treatment, energy production, biomedicine, and environmental protection. Over the past few decades, significant advancements have been made in membrane materials, fabrication techniques, and performance optimization. With the growing global demand for efficient and sustainable separation processes, research has increasingly focused on enhancing membrane permeability, selectivity, and durability to improve performance across various industries, including …
Published in International Journal of Membranes · Vol. 2, Issue 1, 2025 · pp. 16–22 Read article
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Sustainable Supply Chain Models for Polymer and Composite Manufacturing: A Data-Driven Assessment of Circular Material Flows
Abstract: Polymer and composite manufacturing is faced with growing demands in waste reduction, resource management, and making a shift towards circular economy principles. Although urgent, the adoption of data-driven tools in each step of a supply chain to facilitate efficient cyclic material flows is low. This paper designs and empirically analyzes sustainable supply chain design in polymer and composite production with a focus on digital traceability, closed-loop and material recovery, and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 54–71 Read article
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Modelling Simulation And Analysis Of A Hybrid Wind And Pvsolar Based Street Lighting System
Abstract: Solar and wind-generated electricity is significantly less harmful to the environment than fossil-fuel- generated energy. This study describes a solar-wind hybrid power system that generates electricity by utilizing the sun's and wind's renewable energy. India has a great deal of solar energy potential. Every year, India's land area receives around 5,000 trillion kWh of energy, with most areas receiving 4–7 kWh per sq.m. Gujarat, Maharashtra, and Tamil Nadu have the …
Published in Journal of Thermal Engineering and Applications · Vol. 9, Issue 1, 2022 · pp. 27–35 Read article
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Predicting Multiple Diseases Using Machine Learning: A Data-Driven Approach
Abstract: The increasing prevalence of chronic and life-threatening diseases highlights the need for innovative healthcare solutions that enable early detection and proactive management. The Multiple Disease Prediction Platform is a web-based system utilizing machine learning (ML) and deep learning (DL) algorithms to analyze user-inputted health data, generating real-time predictions of potential health risks. By leveraging Python’s Streamlit library, the platform provides an interactive and accessible diagnostic experience, eliminating the need for …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 16–35 Read article
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TensorFlow: Architecture, Applications, and Future Challenges
Abstract: TensorFlow, an open-source machine learning platform created by Google, has revolutionized how artificial intelligence (AI) systems are built and implemented. Designed to support scalable and flexible model training across CPUs, GPUs, and TPUs, TensorFlow enables researchers and developers to construct advanced deep learning models with efficiency and precision. This study provides an in-depth examination of TensorFlow's architecture, including its use of dataflow graphs and tensor-based computation. We explore its adaptability …
Published in Journal of Open Source Developments · Vol. 12, Issue 2, 2025 · pp. 41–50 Read article
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Polymer-Based Acousto-Optic Ultrasonic Sensor for Non-Destructive Evaluation of Dielectric Insulation
Abstract: A dual-polymer fiber-optic sensor for monitoring partial discharge (PD) activity in high-voltage polymeric insulation is presented in this work for non-destructive evaluation and dielectric testing applications. Dielectric weakness within polymeric insulation leads to partial discharge activity, generating ultrasonic acoustic waves that propagate through the medium. The developed sensing assembly comprises a conical polymer-based horn that gathers and concentrates the ultrasonic acoustic emission energy generated by dielectric weakness, and a single-mode …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 11–24 Read article
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Diabetes Risk Prediction from Survey Data Using Machine Learning Algorithms
Abstract: Diabetes mellitus represents one of the most significant global health challenges, affecting millions worldwide and leading to severe complications if left undiagnosed or poorly managed. Early detection and risk assessment are crucial for preventing the progression of this chronic condition. This research presents a comprehensive machine learning approach for predicting diabetes risk using survey-based health parameters. The study implements and compares four prominent classification algorithms: Logistic Regression, K-Nearest Neighbors (KNN), …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article