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1059 articles for “Model optimization”
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Machine Learning Regression Based Approach for Prediction of the Ultimate Tensile Strength of Tungsten Inert Gas Welded Joints
Abstract: Many leaders in technology education have shown that the main difference between the technical design process and the process of engineering construction analysis and efficiency [1–3]. The engineering analysis phase of the construction process is where the mathematical and scientific models principles are used to help the designer predict the design results. The engineering feasibility phase process is a systematic process that uses structural elements and conditions to allow the …
Published in Trends in Machine design · Vol. 8, Issue 2, 2021 · pp. 1–10 Read article
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Maximum Power Point Tracker and its model in MATLAB
Abstract: The study says that In a (Power-Voltage or current-voltage) curve of a solar panel, there is an optimum operating point such that the PV delivers the maximum possible power to the load. This unique point is the maximum power point (MPP) of solar panel. Its mathematical models of the components of PV module that is MPPT model uses the MPPT control unit, and Buck- Boost converter to implement a simulation …
Published in Journal of VLSI Design Tools and Technology · Vol. 9, Issue 3, 2019 · pp. 26–31 Read article
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ANN Approach to Forecasting the Strength of Nano Silica Incorporated Geopolymer Composite
Abstract: Coal and steel industry by-products, such as fly ash (FA) and blast furnace slag (GGBS), have gained significant attention as precursors for geopolymer concrete (GPC) due to their high aluminosilicate content, offering a sustainable alternative to conventional cement. Nano silica (NS), recognized for its exceptional pozzolanic activity and ability to refine microstructure, has shown potential to enhance the mechanical and durability properties of GPC. This study investigates the influence of …
Published in Journal of Polymer & Composites · Vol. 13, Issue 3, 2025 · pp. 267–278 Read article
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Optimal Location of IPFC based on Installation Cost in a Power System Network using ABC Algorithm
Abstract: This paper presents a methodology for locating the optimal position of Interline Power Flow Controller (IPFC) in a power system network based on installation cost. The proposed methodology uses both conventional and non conventional optimization tools such as LR and ABC, respectively. This methodology is formulated mathematically based on installation cost of the FACTS device and generation cost of the real power. IPFC is modeled using Power Injection (PI) model …
Published in Trends in Electrical Engineering · Vol. 2, Issue 1-2-3, 2012 · pp. 11–30 Read article
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Deep Learning Architectures for Predictive Modeling in Financial Time Series
Abstract: This study investigates the application of deep learning architectures, particularly convolutional neural networks (CNNs), to the challenging task of financial time series forecasting. Financial markets are inherently complex and influenced by a range of factors, making accurate prediction of price movements a difficult problem. In this research, historical financial data including stock prices, volumes, and other relevant indicators are used to train CNN models aimed at capturing the underlying patterns …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 45–55 Read article
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Integration of 5G and Low Earth Orbit (LEO) Satellite Communication for Global Connectivity
Abstract: This integration revolutionizes global connectivity by merging 5G's urban capabilities with LEO's wilderness coverage. This research examines how LEO satellite constellations can complement terrestrial 5G networks to extend high-speed, low-latency connectivity to underserved regions including rural areas, oceans, and airspace. Recent breakthroughs in LEO satellite technology and successful demonstrations of 5G-satellite integration point to a rapidly evolving ecosystem with significant market growth potential. To fully realize the potential of advanced …
Published in Journal of Thermal Engineering and Applications · Vol. 12, Issue 2, 2025 · pp. 15–32 Read article
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Spintronic Logic Circuits for Ultrafast Processing
Abstract: Spintronic logic has emerged as one of the most promising post-CMOS paradigms capable of addressing the speed, density, and energy challenges of deeply scaled silicon technologies. By relying on the intrinsic properties of electron spin and magnetization dynamics, spintronic devices—particularly Magnetic Tunnel Junctions (MTJs), Spin-Transfer Torque (STT), and Spin–Orbit Torque (SOT) structures—enable ultrafast, non-volatile data processing with significantly reduced energy consumption. Despite remarkable device-level advancements, circuit- level realization of high-speed, …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 3, Issue 2, 2025 · pp. 35–43 Read article
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Face Recognition Attendance System Using Local Binary Pattern Histogram Algorithm
Abstract: Maintaining accurate and tamper-proof attendance records in educational and corporate environments has long been a challenge due to the limitations of manual and biometric systems. This study introduces the development and deployment of a contactless, automated attendance system that utilizes facial recognition through the local binary pattern histogram (LBPH) algorithm. The primary goal is to offer a secure and efficient substitute for conventional attendance methods by harnessing the power of …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 · pp. 29–34 Read article
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Polymer Nanocomposites and Functional Materials for Lithium-Ion Battery Supercapacitor Hybrid Energy Storage Systems: Materials, Interfaces, and Performance Perspectives
Abstract: The growing need for high-performance energy storage solutions in electric vehicles, renewable energy applications, portable electronics, and other sectors has accelerated research and development efforts in Lithium-Ion Battery–Supercapacitor Hybrid Energy Storage Systems (HESS). By combining the high energy density of lithium-ion batteries with the high power density and fast charge/discharge characteristics of supercapacitors, HESS offers a promising approach to meeting diverse energy storage requirements. Nevertheless, several critical challenges remain that …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 96–113 Read article
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Experimental Investigations on Concentric Flow Dry Ultrasonic Assisted Wire Electric Discharge Machining (CFDUAWEDM) Using Regression Analysis
Abstract: This paper presents an application of concentric flow mode of dry dielectric (compressed air) supply for ultrasonic assisted wire electric discharge machining process (CFDUAWEDM). The concentric flow mode of dielectric supply using ultrasonic assistance is expected to offers improved surface integrity, environmental and operator friendliness during wire electric discharge machining (WEDM) process. Experimental set up was developed wherein, the ultrasonic vibration was applied to the electrode wire using an ultrasonic …
Published in Trends in Machine design · Vol. 3, Issue 2, 2016 · pp. 33–43 Read article
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ML-Based Predictive Modeling of Mechanical Properties in 3D-Printed Polymer Composites for IoT Applications
Abstract: This study aims to develop an interpretable and high-accuracy machine learning framework for predicting the mechanical properties of 3D-printed fiber-reinforced polymer composites, with a focus on structure–property correlations relevant to polymer processing and functional performance. Composite specimens based on PLA and ABS matrices were fabricated using FDM with varying weight fractions (5–20 wt%) of carbon and glass fibers. Standardized mechanical testing (ASTM D638, D256, D790) was performed to evaluate tensile …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 61–78 Read article
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Machine Learning Approach to Predict the Performability and Emissions of Diesel Engine Fueled with Doped Biodiesel Blend
Abstract: Enhancing the performability and emission characteristics of diesel engines has been a difficult task in light of growing concerns about global warming and other negative effects, as diesel accounts for 70% of global energy demand. In this study, engine performance and exhaust emissions for various fuel blends were thoroughly evaluated using machine learning techniques to predict engine emission and performance behavior. We focused on biodiesel blend and nanoparticle additive concentration …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 3, Issue 1, 2025 · pp. 1–12 Read article
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Controlling of Level Process in Non-Linear System Using Variable Frequency Drive
Abstract: Abstract—The objective of this paper is to investigate the Model Predictive Control (MPC) strategy, analyze and compare the control effects with Proportional-Integral-Derivative (PID) control strategy in maintaining a level of spherical tank system using VFD. An advanced control method, MPC has been widely used and well received in a wide variety of applications in process control, it utilizes an explicit process model to predict the future response of a process …
Published in Current Trends in Information Technology · Vol. 9, Issue 2, 2019 · pp. 24–33 Read article
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STUDY ON EFFECT OF END MILLING PARAMETERS ON CUTTING FORCES USING RESPONSE SURFACE METHOD
Abstract: Now a day’s research over improvement of surface roughness on mechanical elements has become quite significant in the operational and aesthetical point of view. To enhance accuracy and precision, manufacturing firms are adopting automated systems in order to achieve manufacturing excellence. In the present work the effect of various process parameters like spindle speed, feed and cutting fluid composition on cutting forces in End milling process is investigated by using …
Published in Trends in Mechanical Engineering & Technology · Vol. 8, Issue 2, 2018 · pp. 62–72 Read article
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Analysis of Machine Learning in Metal Processing: A Novel Prospect
Abstract: Metal is processed by a wide range of procedures, from forming and casting to machining and riveting. Metal processing is a crucial part of modern manufacturing. The application of machine learning (ML) is driving a significant change in the sector, which has historically depended on empirical knowledge and trial-and-error techniques. Increased production, improved product quality, and resource optimization are expected outcomes of this action. This study aims to explore the …
Published in Journal of Materials & Metallurgical Engineering · Vol. 16, Issue 1, 2026 · pp. 41–51 Read article
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Advancements in Metal-Plastic Hybrid Structures: Experimental Analysis and Design Optimization of 3D-Printed Honeycomb Frameworks
Abstract: The exploration of metal-plastic hybrid structures has gained significant attention due to their potential for lightweight, high-strength applications across industries such as aerospace, automotive, and construction. This study investigates the experimental and design enhancements of a metal-plastic hybrid structure utilizing a honeycomb architecture produced through 3D printing. By integrating metals with plastic polymers in a honeycomb configuration, this hybrid approach aims to combine the high strength and stiffness of metals …
Published in Trends in Machine design · Vol. 11, Issue 3, 2024 · pp. 36–43 Read article
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RoseRAG-Based Clinical Decision Support System for Precision Medication Safety
Abstract: Medication-related errors remain a major challenge in healthcare, contributing to adverse drug events, increased hospitalization rates, and substantial healthcare costs. Conventional Clinical Decision Support Systems (CDSS) primarily rely on rule-based mechanisms for identifying drug-related problems (DRPs), including drug–drug interactions, contraindications, dosing errors, therapeutic duplication, and medication omissions. Although effective in structured environments, these systems frequently generate excessive context-insensitive alerts, leading to alert fatigue and reduced clinical acceptance. Recent developments in …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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Assessing Urban Surface Runoff Management Using the SWAT Model, GIS, and the Rational Method: A Case Study of Karapakkam
Abstract: Urban surface runoff management encompasses strategies aimed at regulating rainwater flow, reducing urban flooding, enhancing groundwater recharge, and contributing to disaster risk mitigation for sustainable urban development. This study seeks to optimize surface runoff management in urban settings by using the soil and water assessment tool (SWAT) model integrated with geographic information systems (GIS) to minimize flood risks. The research focuses on the peak runoff in Karapakkam, a locality within …
Published in International Journal of Environmental Planning and Development Architecture · Vol. 4, Issue 1, 2026 · pp. 37–48 Read article
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AI-Enabled Optimization of Additively Manufactured Composite Materials for Enhanced Mechanical and Thermal Performance
Abstract: This paper discusses the optimization of multi-objective optimization of enhanced coupling of heat and mechanical properties of 3D printed polymer composite materials by artificial intelligence (AI), as a component of a multi-objective optimization framework. It aims at development of nonlinear printing parameters and material properties relationships to achieve maximum tensile strength and thermal conductivity in polymer composites produced through fused deposition modeling (FDM). Short carbon fiber reinforcement was used to …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 867–891 Read article
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Particle Swarm Optimization Framework for Accurate Battery State-of-Charge and Remaining Useful Life Estimation
Abstract: Accurate estimation of the State of Charge (SOC) and State of Health (SOH) of a battery is key to safe and efficient management of batteries in electric vehicles and energy-storage systems. However, it is challenging due to high nonlinearity, varying operating conditions, measurement noise, and limited access to comprehensive electrochemical parameters. Traditional data-driven models often generalize poorly and require heavy tuning, which can produce unstable predictions. To address these problems, …
Published in Journal of Automobile Engineering and Applications · Vol. 13, Issue 1, 2026 · pp. 53–64 Read article