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224 articles for “neural network prediction”
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Artificial Neural Network Based Defect Prediction in Casting
Abstract: The main problems which are facing by most of the casting industries is loss of productivity which is due to casting defects occurred during the production time. The main casting defects are cracks, misruns, blowholes scabs and airlocks. Most of the investigations made in this area is only discussing the defects occurred after a cast is made and no method has yet been developed to prevent the defects before casting. …
Published in Journal of Mechatronics and Automation · Vol. 2, Issue 2, 2015 · pp. 33–38 Read article
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Artificial Neural Network Based Prediction of Impact Loads and Thickness in CFRP and GFRP Composite Laminates
Abstract: Recent technological advancements, particularly the integration of neural networks, have facilitated a predictive approach to complex engineering problems, especially those involving composite materials with directional properties. The scarcity of literature on predicting impact damage using experimental and ultrasonic flaw detection data motivated this study. Experimental assessment of impact damage on carbon fiber/epoxy (CFRP) and glass fiber/epoxy (GFRP) composites was conducted using low-velocity drop weight impact testing. Damage assessment employed an …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 2, Issue 1, 2024 · pp. 34–45 Read article
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Prediction of Excitation Current of Synchronous Machines Based on Neural Network Model
Abstract: There are several difficulties found to estimate the excitation current & and optimum input parameters of synchronous motors. Heuristic methods are frequently used to weightt the problem's parameters or optimum coefficients. As a result, a neural network model is modified in this study to explore the best parameters and estimate the excitation current of a synchronous motor with minimal prediction errors for both the testing dataset and cross validation. Excitation …
Published in Recent Trends in Electronics Communication Systems · Vol. 10, Issue 1, 2023 · pp. 28–33 Read article
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Graph Neural Networks for Molecular Scale Property Prediction and Inverse Design of Thermoset Polymer Nanocomposites: A Computational Framework
Abstract: Thermoset polymer nanocomposites exhibit properties that are highly sensitive to molecular scale formulation decisions, yet the vast design space remains largely unexplored because of the high cost of experimental characterisation and fully atomistic simulation. This paper presents TNC GNN, a dual mode graph neural network framework developed for the computational design of thermoset nanocomposite formulations. The forward module employs an attention augmented Message Passing Neural Network with 3D geometric encoding …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Skin Disease prediction and classification from dermoscopy images using Neural Network
Abstract: Skin diseases are among the most common health-related problems affecting people of all age groups, and their occurrence often varies with seasonal and environmental conditions. Delayed or incorrect diagnosis of skin disorders can lead to severe complications, making early and accurate detection extremely important for effective treatment and prevention. In recent years, rapid advancements in deep learning and neural network technologies have significantly contributed to the development of automated medical …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article
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Leakage Rate Prediction through Composite Liner due to Geomembrane Defect using Neural Network
Abstract: The paper presents the leakage rate prediction using artificial neural network from the liner made of soil and geomembrane. The defect in the geomembrane considered was having different shapes. Three different shapes such as square, rectangular and circular of the defect have been considered. The input variables considered for the artificial neural network (ANN) were (i) head on the top of the soil (ii) area of the defect (iii) hydraulic …
Published in Journal of Geotechnical Engineering · Vol. 6, Issue 3, 2019 · pp. 8–17 Read article
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Using Convolutional Neural Networks (CNN) for Age and Gender Prediction
Abstract: The network, security, and care have all become more dependent on age and gender identification. It's commonly used for children's access to age-appropriate content. To expand its reach, social media uses it to provide layered adverts and marketing. Face recognition has progressed to the point where we need to map it out further in order to achieve more usable results using various methodologies. In this study, we suggest using deep …
Published in Journal of Instrumentation Technology & Innovations · Vol. 12, Issue 1, 2022 · pp. 27–32 Read article
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Predicting And Forecasting Stocks
Abstract: Stock value estimation may be a well-liked and vital topic in money and tutorial studies. Share Market is associate untidy place for predicting since there aren't any vital rules to estimate or predict the value of a share within the share market. Several ways like technical analysis, basic analysis, statistical analysis, and applied mathematics analysis, etc. area unit all want to conceive to predict the value within the share market …
Published in Journal of Electronic Design Technology · Vol. 13, Issue 1, 2022 · pp. 1–5 Read article
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Enhancing Power Conversion Efficiency in Tandem Solar Cells with Temporal Dynamic Graph Neural Network
Abstract: In modern homes, people want good comfort and also less electricity bill, so managing heating load and cooling load become very important. Heating Load (HL) and Cooling Load (CL) depend on many things like wall material, window size, sunlight, ventilation, and weather. Because of this many factors, calculation and optimization of HL and CL is little difficult and many time normal formulas give wrong or not perfect results. So in …
Published in Journal of Semiconductor Devices and Circuits · Vol. 13, Issue 2, 2026 · pp. 12–19 Read article
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A Physics-Informed Graph Neural Network Framework for Real- Time Thermal-Aware Fault Prediction and Adaptive Power Optimization in Heterogeneous System-on-Chip Architectures
Abstract: Heterogeneous System-on-Chip (SoC) architectures are increasingly adopted in edge computing, artificial intelligence, autonomous systems, and high-performance embedded platforms due to their superior computational efficiency and flexibility. However, increasing integration density and workload diversity introduce severe thermal hotspots, accelerated device degradation, and unexpected hardware faults that adversely affect system reliability and energy efficiency. This study proposes a Physics-Informed Graph Neural Network (PI-GNN) framework for real-time thermal- aware fault prediction and adaptive …
Published in Journal of VLSI Design Tools and Technology · Vol. 16, Issue 2, 2026 · pp. 19–28 Read article
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Predictive Modeling System for Automated Skin Lesion Classification Using Deep Neural Networks and Voting Ensembles
Abstract: Skin cancer is one of the most prevalent cancers globally. Early and accurate diagnosis is critical for timely treatment and improved prognosis. This study presents a predictive modeling system for automated classification of skin lesions from dermoscopic images using deep neural networks and voting ensemble techniques. A customized 16-layer convolutional neural network architecture is developed for feature learning from lesion images. The concept of horizontal voting ensemble is implemented by …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 2, 2023 · pp. 29–35 Read article
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Real-Time Air Quality Prediction Using IoT-Integrated Polymer Sensors and Recurrent Neural Networks
Abstract: Real-time air quality monitoring remains a critical challenge in urban environments, where traditional sensor infrastructures often suffer from limited responsiveness, poor scalability, and high deployment costs. The increasing prevalence of NO₂ pollution, a key contributor to respiratory and cardiovascular ailments, demands advanced sensing platforms capable of both accurate detection and predictive inference. Existing methods either rely on rigid electronic sensors lacking adaptability or on statistical forecasting models that fail to …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 332–347 Read article
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Prediction of Process Parameters of Friction Stir Welding Using Artificial Neural Network
Abstract: In this paper an artificial neural network (ANN) approach is used to predict the process parameters of friction stir welding (FSW). Initially, the experiments are conducted using the design of experiment (DoE) approach on FSW using L27 orthogonal array. The experiments are conducted using speed, feed, and tool tilt angle as input parameters for DoE and tensile strength, hardness, and ductility as output. ANN is created having 25 neurons and …
Published in Journal of Polymer & Composites · Vol. 11, Issue 3, 2023 · pp. 13–25 Read article
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Thermal Performance of Flat Tube Louver Fin Heat Exchanger using Artificial Neural Network
Abstract: In this paper, an application of artificial neural networks (ANNs) is presented to predict the outlet temperatures on both fluid side of flat tube louver fin type heat exchanger used in flat tube louver fin heat exchanger. A validated numerical code is developed using MATLAB to generate huge data sets which gives outlet temperature of both the fluids. Three-layer feed-forward back propagation neural network is developed to model the thermal …
Published in Journal of Thermal Engineering and Applications · Vol. 4, Issue 3, 2017 · pp. 6–12 Read article
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Integrated, Geospatial Risk Assessment of Air, Water, and Soil Pollution Impacts on Agricultural Sustainability using Advanced Digital Technologies
Abstract: The systemic threat posed by the convergence of air, water, and soil contaminants represents a critical challenge to global agricultural resilience and food security. Traditional, site-specific pollutant monitoring methods are insufficient for capturing the dynamic, diffuse, and often nonlinear nature of environmental risk pathways that permeate agrarian landscapes. This study presents a robust framework for comprehensive risk assessment utilizing a synergistic suite of modern tools designed for spatial, temporal, and …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 3, Issue 2, 2025 · pp. 28–37 Read article
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Digital Twin Assisted Intelligent Prediction of Polymer Composite Degradation Under Environmental Exposure
Abstract: Polymer matrix composites (PMCs) deployed in aerospace, marine, automotive, and renewable-energy structures are continuously subjected to coupled environmental stressors — ultraviolet (UV) radiation, moisture ingress, thermal cycling, and mechanical loading — that progressively degrade their mechanical performance. Conventional accelerated ageing tests and empirical lifetime models are time-consuming, destructive, and poorly suited to in-service, asset-specific degradation forecasting. This paper proposes a Digital Twin (DT) assisted intelligent prediction framework that fuses a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Prediction of shear strength of reinforced concrete beams using Artificial Neural Network and evaluated by Finite Element Software
Abstract: ABSTRACTIn this paper, the Artificial Neural Network (ANN) and the Adaptive Neuro-Fuzzy Inference Framework (ANFIS) are utilized to foresee the shear quality of Reinforced Concrete (RC) shafts, and the models are contrasted and American Concrete Institute (ACI) and Iranian Concrete Institute (ICI) observational codes. The ANN display, with Multi-Layer Perceptron (MLP), utilizing a Back-Propagation (BP) algorithm, is utilizedto foresee the shear quality of RC pillars. Six vital parameters are chosen …
Published in Journal of Construction Engineering, Technology & Management · Vol. 8, Issue 1, 2018 · pp. 34–42 Read article
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Stock Market Forecasting Using Artificial Neural Networks (ANNs): A Review
Abstract: AbstractThis paper reviews all recent work done for stock market prediction using machine learning and artificial intelligence (AI). Artificial neural networks (ANNs), a field of artificial intelligence (AI), is relatively latest, dynamic and promising technique in stock market forecasting, an area that has been of much research. From this literature review, it is concluded that ANNs is very valuable for predicting world stock markets.Keywords: artificial neural network (ANNs), stock market, …
Published in Journal of Computer Technology & Applications · Vol. 4, Issue 2, 2013 · pp. 18–29 Read article
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Artificial Neural Network Model for Stock Market Forecasting
Abstract: AbstractIn recent years, many attempts have been made to predict the behavior of bonds, currencies, stocks or stock markets. Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. The aim of this paper is to predict stock market using artificial neural networks (ANNs). The authors used feed forward neural network trained by back-propagation algorithm to make …
Published in Journal of Computer Technology & Applications · Vol. 5, Issue 1, 2014 · pp. 7–12 Read article
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Study and Prediction of Radiation Effects in Solar Power Plants using Neuro Fuzzy and Neural Network
Abstract: Neural and neuro-fuzzy frameworks are utilized, to figure temperature and sun powered radiation. The principle benefit of these frameworks is that they don't need any earlier information on the qualities of the information time-series to foresee their future qualities. These frameworks with various models have been prepared utilizing as information estimations of the above meteorological boundaries acquired from the National Observatory of Athens. In the wake of having reproduced a …
Published in Trends in Electrical Engineering · Vol. 12, Issue 1, 2022 · pp. 8–19 Read article