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628 articles for “neural”
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Optimization of Structural Health Monitoring Using Artificial Neural Network and Comparison with Traditional Method: A Comprehensive Review
Abstract: Structural health monitoring (SHM) has a critical role in ensuring civil infrastructure safety, reliability, and durability through real-time, condition-based monitoring. Traditional SHM systems employ hundreds of sensors such as accelerometers, strain gauges, and displacement transducers for monitoring vast amounts of data for structural inspection, but do not effectively manage complicated nonlinear data. This research paper, “Optimization of Structural Health Monitoring Using Artificial Neural Network and Comparison with Traditional Methods,” investigates …
Published in Journal of Structural Engineering and Management · Vol. 13, Issue 1, 2026 · pp. 23–33 Read article
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Power and Area - Aware Recursive Multiplier Architecture Utilizing Polymer Composites for Neural Network Acceleration
Abstract: Approximate computing is widely applied in error - tolerant systems as an effective technique to enhance circuit performance by deliberately allowing occasional inaccuracies instead of strictly ensuring precise results for every computation. Among the fundamental building blocks of digital systems, multipliers play a crucial role in signal processing, control systems, and machine learning applications; however, they demand significant power, silicon area, and timing resources. Leveraging error - tolerant approximate multipliers …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1320–1337 Read article
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AI-Driven Prediction of Square-Hole Laser Trepanning Performance in AA7075/15%SiC/15% Glass Fiber Hybrid Composites Using Taguchi–ANOVA and Deep Neural Networks
Abstract: Hybrid AA7075 composites reinforced with 15% silicon carbide (SiC) and 15% glass fiber were fabricated via the stir casting technique to improve machining and structural performance. The addition of dual reinforcements into the aluminum matrix was aimed at enhancing hardness, thermal stability, and surface quality during non-traditional drilling operations. Square-hole drilling was performed using a laser trepanning process, and the key responses—hole size accuracy, surface roughness, and taper angle—were systematically …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1932–1943 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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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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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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An empirical investigation using artificial neural networks to evaluate the manageability of object-oriented systems
Abstract: Software can be called quality software if it produces consistent outputs over multiple time of testing. There can be very much difficulties to modify and maintain the software with poor maintainability. For assessing the characteristics of object-oriented software, such as scale, inheritance, integrity, and coupling, numerous object-oriented metrics have been recommended. In this study, we explore object-oriented variables that have the potential to be significant antecedents of software maintenance. In …
Published in Journal of Mechatronics and Automation · Vol. 9, Issue 2, 2022 · pp. 50–58 Read article
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Nonlinear Semiconductor Device Modeling using Neural Networks
Abstract: This paper describes the nonlinear semiconductor (transistor) of small and large signal modeling using a single neural network. Multilayer perceptron (MLP) with back-propagation (BP) learning is adopted in this work to model the drain current (ID) and the transconductance (gm) of the transistor. MLP modeling performance in terms of mean square error (MSE) and complexity of the network are illustrated briefly. Artificial neural network (ANN) model outcome for nonlinear function …
Published in Journal of VLSI Design Tools and Technology · Vol. 4, Issue 3, 2014 · pp. 1–6 Read article
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Design of Beam using Artificial Neural Network based Approach
Abstract: Recent developments in artificial neural network (ANN) have opened up new possibilities in the field of structural engineering. This paper demonstrates the applicability of ANN for the design of beams subjected to moment and shear. An attempt has been made to capture the mapping between the design variables using ANN. There is no direct method for design of beams. A feed forward network and back propagation training algorithm has been …
Published in Recent Trends in Civil Engineering & Technology · Vol. 4, Issue 3, 2014 · pp. 1–6 Read article
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Modeling Evapotranspiration using Artificial Neural Networks
Abstract: The prior knowledge of evapotranspiration (ET0) is crucial for estimating crop-water demand, preparation of water distribution schedules and water diversion. The present study investigates the utility of artificial neural networks (ANNs) and linear regression models (LRs) for forecasting ET0 based on hydro-meteorological data. Based on different inputs, eight ANN and LR models are developed. The results are compared with those of FAO-56 Penman-Monteith expression. The published daily climatic data from …
Published in Recent Trends in Civil Engineering & Technology · Vol. 3, Issue 3, 2013 · pp. 29–35 Read article
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Study of Global Solar Radiation Estimation based on Artificial Neural Networks Techniques
Abstract: AbstractSolar Radiation data received by earth in the form of x-rays, UV-rays, infrared rays is a prominent and useful data as it gives the information about the amount of energy received from sun at the earth. Artificial Neural Network (ANN) is brain inspired technology which learns and performs in a way similar to the way our human brain performs. Sun’s energy is of utmost importance and is freely available in …
Published in Recent Trends in Electronics Communication Systems · Vol. 7, Issue 1, 2020 · pp. 26–31 Read article
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An Efficient Fault Detection and Localization System for Three-Phase Transmission Line using Arduino and Artificial Neural Networks
Abstract: This paper presents an efficient fault detection and localization system for a three-phase transmission line using Arduino and artificial neural networks. Theproposed system is designed to detect and localize faults in real-time, reducing the downtime and improving the reliability of the power system. The system consists of three main components: the fault detection unit, the fault classification unit, and the fault localization unit. The fault detection unit uses Arduino microcontroller …
Published in Recent Trends in Electronics Communication Systems · Vol. 9, Issue 3, 2022 · pp. 35–42 Read article
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Artificial Neural Network Based SVC Switching At Distribution System for Minimal Injected Harmonics
Abstract: Electrical distribution system grieves from various problems like reactive power burden, unbalanced loading, voltage regulation and harmonic distortion. However DSTATCOMS are ideal solutions for such systems, they are not popular because of the cost and complexity of control involved. Phase wise balanced reactive power compensations are essential for fast changing loads needing dynamic power factor correcting devices leading to terminal voltage stabilization. Static Var Compensators (SVCs) remain ideal choice for …
Published in Trends in Electrical Engineering · Vol. 9, Issue 1, 2019 · pp. 41–47 Read article
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Feature Fusion Based Iris and Retina Recognition System Using Kohonon Self-Organizing Mapping Neural Network Algorithm
Abstract: This paper proposes a model of biometric security with feature fusion based iris and retina recognition system. Though, a lot of research works have done for iris recognition system and it is not new for the fusion of multimodal iris recognition in the area of biometric security and authentication system. But, it is a relatively new idea of mixing iris and retina features for human authentication. Here, the features of …
Published in Trends in Electrical Engineering · Vol. 5, Issue 2, 2015 · pp. 22–26 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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Survey of Data Cleaning and Image Recognition using Neural Networks
Abstract: AbstractThis paper reviews the existing developments of data cl eaning and image recognition using neural networks At present most of the work in image recognition is done by using more than one method. Neural networks t ake the advantage a nd show good improvement in recognition of images In t his work, the authors hav e discusse d their advantages over traditional methods.Keywords: Data cleaning, image recognition, data transformation, artificial …
Published in Journal of Computer Technology & Applications · Vol. 5, Issue 1, 2014 · pp. 30–36 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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Artificial Neural Network Modelling to Optimize Micro-Drilling Parameters of ECDM of Developed Novel Zn/(Ag+Fe)-MMC
Abstract: Several engineering fields have increased their use of metal matrix composites (MMCs) in the past few years. Due to the increase in composites, the demand for accurate machining has also become important. Specifically, pertaining to biomaterial applications, accuracy factor with desired surface finish is critical. While the near-net shape manufacturing process has advanced, MMCs frequently require post-mould machining to achieve surface quality, and dimensional tolerances. In the present study, a …
Published in Journal of Polymer & Composites · Vol. 11, Issue 1, 2023 · pp. 01–13 Read article
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A Novel Approach to Fingerprint Authentication Using Histogram Oriented Gradients for Feature Extraction and Machine Learning Convolution Neural Network for Classification
Abstract: With applied biometrics, it is possible to identify a person by examining a feature vector of attributes derived from their physical and behaviour characteristics. In biometrics, fingerprints have become one of the most famous and well known techniques of identification and authentication. In light of technological advancements and safety, fingerprint recognition has been successfully used in a variety of Civil, Defence, and Commercial applications for more than a decade. The …
Published in International Journal of Wireless Security and Networks · Vol. 1, Issue 2, 2023 · pp. 1–12 Read article
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Real-Time Gesture Recognition with Convolutional Neural Networks
Abstract: Sign language detection plays a pivotal role in bridging communication barriers for the deaf and hard of hearing community. An extensive investigation on the use of convolutional neural networks (CNNs) for sign language recognition is presented in this article. Leveraging the power of deep learning, our research aims to develop an accurate and efficient system capable of recognizing and classifying sign language gestures in real-time. The report begins with an …
Published in Journal of Electronic Design Technology · Vol. 15, Issue 2, 2024 · pp. 12–18 Read article