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212 articles for “accuracy parameter”
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Heart Attack Prediction Using Machine Learning
Abstract: Heart attacks have become a prevalent and serious condition in recent years due to a variety of causes. Numerous variables, including age, sex, fat, and others, can be used to predict it. In the current study, it was found that a data set with 13 parameters and 302 distinct data values, collected from a Kaggle dataset to assess patient condition, was covered. This article delves into the application of machine …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 1, Issue 1, 2023 · pp. 8–13 Read article
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Application of Grey Wolf Optimizer (GWO) strategy for Malware Analysis
Abstract: The ever-evolving landscape of cybersecurity necessitates continuous advancements in malware analysis techniques. This study explores the deployment of the Grey Wolf Optimizer (GWO) algorithm as a novel bio-inspired optimization mechanism to address the challenges posed by modern malware threats. The primary objective is to enhance various facets of malware analysis, including feature selection, parameter optimization, and the overall efficacy of malware detection models. The study begins by introducing the GWO …
Published in International Journal of Wireless Security and Networks · Vol. 1, Issue 2, 2023 · pp. 43–53 Read article
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Drip Irrigation Pipe Manufacturing Process and Defects in It- A Review
Abstract: In the development phase of drip pipe, conventional procedures for pipe extrusion line setup process were based on tryouts and then decisions to change the process parameters were made to minimize the errors occurring in pipe extrusion process of drip pipe. Every line setup needed the same methodology and hence loss of valuable time and material scrap. The variation of raw material supplies is also considered in this optimization process. …
Published in Journal of Mechatronics and Automation · Vol. 5, Issue 1, 2018 · pp. 38–48 Read article
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Minimization of distortion during Gas Metal Arc welding process used for hydraulic shearing machine pressure plate square
Abstract: The Gas Metal Arc Welding process is widely used in many appliances because of its versatility. The change of shape and dimensions that occur after welding is known as distortion that leads to undesirable results. And to overcome this, it requires reducing the distortion within the limits. A large number of resources are used recently for reworking the weld. But it causes higher cost of production and delay for completing …
Published in Trends in Mechanical Engineering & Technology · Vol. 8, Issue 3, 2018 · pp. 33–43 Read article
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Performance Analysis of Deep CNN Architectures
Abstract: A Convolutional Neural Network (CNN) is an artificial neural network renowned for its remarkable ability to handle large image datasets effectively, particularly excelling in tasks such as image recognition and classification. The fundamental structure of a CNN relies on mathematical convolution operations, comprising essential components such as convolutional layers, activation functions, pooling layers, and fully connected layers. These components work synergistically to extract and learn hierarchical features from input data, …
Published in Journal of Communication Engineering & Systems · Vol. 14, Issue 2, 2024 · pp. 1–8 Read article
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Aerodynamic Optimization of UAV Wings Using Machine Learning
Abstract: Unmanned Aerial Vehicles (UAVs) are increasingly deployed across defense, transportation, agriculture, and environmental monitoring, demanding improved aerodynamic efficiency to enhance endurance, stability, and payload capacity. Traditional aerodynamic optimization approaches, relying on computational fluid dynamics (CFD) simulations and wind tunnel experiments, are often time-consuming and computationally expensive. This study proposes a machine learning (ML)-driven framework for the aerodynamic optimization of UAV wing geometries, aiming to significantly reduce design cycles while improving …
Published in International Journal on Drones · Vol. 2, Issue 1, 2026 · pp. 1–7 Read article
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Comparison of Models of Machine Learning and Hyperparameter Optimization Methods on Various Datasets
Abstract: The most likely phase in achieving powerful and robust machine learning models is probably the hyperparameter tuning step. The traditional exhaustive methods of search (grid search and others) ensure that the search space is covered, but are computationally inexpensive; random search is less expensive and can still miss good regions; and lastly, the modern model-based and population-based methods (Bayesian optimization, tree-structured Parzen estimator (TPE), genetic algorithms) are thought to provide …
Published in Recent Trends in Programming languages · Vol. 13, Issue 1, 2026 · pp. 35–42 Read article
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Review paper on concrete mix design optimization using machine learning based algorithm
Abstract: Concrete is an essential part of most construction works in civil engineering. The mix design of concrete is usually specified in terms of prescription or performance-based approach. One of the most important procedures is proportioning the concrete mix, which requires taking several safety precautions to get the proper amounts of elements like cement, aggregate, water, and admixtures. The current study offers a thorough analysis of the Artificial Neural Networks (ANN) …
Published in Journal of Polymer & Composites · Vol. 12, Issue 3, 2024 · pp. 311–320 Read article
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Machine Learning Assisted Timing Violation Prediction in Sub-7nm VLSI Physical Design
Abstract: The continuous scaling of semiconductor technology into the sub-7nm regime has introduced significant challenges in timing closure due to process variability, interconnect delay, power density, and manufacturing uncertainties. Conventional static timing analysis techniques often require extensive computational resources and iterative optimization cycles, resulting in increased design complexity and longer turnaround time. This research proposes a Machine Learning Assisted Timing Violation Prediction framework for sub-7nm VLSI physical design to improve early-stage …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 4, Issue 1, 2026 Read article
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Study of an Improved Quantum Particle Swarm Optimization-Based Framework for Neural Network Optimization in Modelling of Polymer Data
Abstract: The accurate forecasting of polymer viscosity at various physicochemical conditions has been quite critical due to the nonlinear interactions and interrelations between the variables. This paper suggests a better hybrid modelling framework, which involves the use of Artificial Neural Networks (ANN) and more advanced versions of Quantum Particle Swarm Optimization (QPSO) to better predict polymer viscosity. The input parameters taken are, namely, log (shear rate), polymer concentration, NaCl concentration, Ca …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 282–297 Read article
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Optimizing Sampling Techniques Using Fuzzy Set Theory: A Comprehensive Approach
Abstract: Sampling is a critical process in statistics, used to estimate population parameters without needing to examine the entire population. Traditional sampling methods, such as simple random sampling, stratified sampling, and cluster sampling, face limitations when applied to complex or heterogeneous populations with imprecise boundaries. These methods often fail to accurately represent populations with overlapping characteristics or missing data, resulting in sampling bias and reduced accuracy. To address these challenges, this …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 12, Issue 1, 2025 · pp. 29–43 Read article
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Improving Polymer Composite Properties Through Reinforcement Learning Guided Prototyping A Novel Approach for Material Engineering
Abstract: Innovative approaches integrating reinforcement learning (RL) and machine learning (ML) into the fields of polymer composite prototyping and soft actuator manufacturing for applications. This new an algorithm utilizing RL optimizes polymer composite fabrication parameters to enhance material properties efficiently. By iteratively adjusting parameters based on predefined objectives, the RL agent guides the prototyping process, promising to revolutionize polymer composite engineering. A finest control method for locked loop control of Shape …
Published in Journal of Polymer & Composites · Vol. 12, Issue 4, 2024 · pp. 208–218 Read article
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Container transportation in marine terminals and marine transportation infrastructure on the increase in export market share
Abstract: In order to achieve important policy goals like increasing global competitiveness, diversifying import sources, opening up new markets, and forging strategic partnerships, maritime transportation is essential. It also has a significant impact on reducing the economic vulnerability of nations that rely on the sale of gas and oil by carefully choosing its clients and growing the export of petroleum products, petrochemicals, and gas. This study develops a two-objective mathematical planning …
Published in Journal of Petroleum Engineering & Technology · Vol. 14, Issue 1, 2024 · pp. 36–42 Read article
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INTEGRATION OF REVIT TO MICROSOFT PROJECT FOR CONSTRUCTION PROJECT MANAGEMENT: A COMPARATIVE ANALYSIS OF SCHEDULING AND RESOURCE ALLOCATION
Abstract: Building Information Modelling (BIM) has revolutionized the construction sector by having architectural, structural, and mechanical design on one 3D platform. Autodesk Revit application enables real-time updates and changes without increasing errors and omissions. Its parametric nature allows users to develop intelligent models, enhancing decision-making, resource allocation, and cost estimation. Revit's visual representation allows effective coordination and communication with stakeholders, promoting a higher degree of client satisfaction. The application and benefits …
Published in Journal of Construction Engineering, Technology & Management · Vol. 15, Issue 3, 2025 · pp. 13–46 Read article
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Predictive Maintenance Strategies for Safety-critical Mechanical Systems
Abstract: Ensuring the reliability and safety of industrial systems is essential, especially in high-risk sectors such as aerospace, manufacturing, and energy. Predictive maintenance (PdM) has become a crucial approach for minimizing operational failures and improving maintenance efficiency. This research introduces an advanced PdM framework that enhances industrial safety by integrating Internet of Things (IoT) technology, machine learning (ML), and big data analytics. By enabling real-time monitoring and predictive fault detection, this …
Published in Journal of Industrial Safety Engineering · Vol. 12, Issue 1, 2025 · pp. 12–17 Read article
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SoloRider: An Autonomous Self-Balancing Electric Bike for Sustainable Urban Mobility
Abstract: Rapid urbanization has intensified challenges such as traffic congestion, parking inefficiency, and environmental degradation. While autonomous vehicle research predominantly focuses on four-wheel platforms, lightweight two-wheelers remain comparatively underexplored. Two-wheelers are a great option for sustainable urban transportation because of their many benefits, including their small size, lower energy consumption, better manoeuvrability, and lesser infrastructure requirements. This paper presents SoloRider, a conceptual autonomous self- balancing electric two-wheeler de- signed for sustainable …
Published in International Journal of Electronics Automation · Vol. 4, Issue 1, 2026 Read article
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AI-Driven Optimization of Biopolymer Composite Formulations Using IoT Data Streams
Abstract: Biodegradable polymer composites have emerged as a sustainable alternative to petroleum-based materials in packaging, biomedical, and structural applications. However, traditional formulation techniques for reinforced polymer composites often lack precision and fail to adapt to real-time variations during processing, resulting in suboptimal material performance. This research proposes a real-time AI-IoT-enabled framework to optimize biopolymer composite formulations. The goal is to intelligently tune composite properties such as mechanical strength, moisture resistance, and …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 85–100 Read article
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Deep Learning-Based Thermal Prediction Models for Solid-State Electronic Devices
Abstract: The rapid advancement of solid-state electronic devices in high-performance computing, communication systems, automotive electronics, and renewable energy applications has significantly increased concerns related to thermal management and device reliability. Excessive heat generation in semiconductor devices adversely affects operational efficiency, switching performance, lifespan, and overall system stability. Traditional thermal prediction methods often require complex numerical computations and extensive simulation time, making them less suitable for real-time monitoring and adaptive control applications. …
Published in International Journal of Solid State Innovations & Research · Vol. 4, Issue 1, 2026 Read article
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Casting Through Simulation- A Review
Abstract: In today’s competitive environment every foundry industries requires minimum rejection of component; rejection is caused by undesired uncertainty found in component’s it can be shrinkage cavity, sand inclusion, cold shuts etc. Reduce defect probability we need better setup and proper process control parameters without shop floor trials. Therefore industries develops many software packages for simulation of casting process it shows the virtual view like mould filling, solidification, cooling meanwhile it …
Published in Trends in Mechanical Engineering & Technology · Vol. 8, Issue 3, 2018 · pp. 12–17 Read article
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Bioanalytical Method Development and Validation for the Estimation of Levothyroxine in Human K₃EDTA Plasma by Using UPLC-MS/MS
Abstract: A rapid, sensitive, and highly selective UPLC-MS/MS method was developed and validated for the quantitative estimation of Levothyroxine in human plasma using Levothyroxine-D₃ as the internal standard (IS). Chromatographic separation was achieved on a Gemini NX-C18 column (50 × 3.0 mm, 3 µm) with a mobile phase consisting of acetonitrile and water (70:30, v/v) containing 0.015% formic acid at a flow rate of 0.5 mL/min. The total run time was …
Published in Research & Reviews: A Journal of Drug Formulation, Development and Production · Vol. 12, Issue 3, 2025 · pp. 32–46 Read article