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49 articles for “Back-Propagation Neural Network”
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Effect of Various Process Parameters on MRR in Manual Air Plasma Arc Cutting of AISI 1017 Mild Steel using ANN
Abstract: As per recent industrial surveys it is investigated that manufacturing companies define the quality of thermal cutting process by the dimension of work material and cutting surface appearance. Therefore, the surface roughness and material removal rate (MRR) are primarily considered. In this work the effect of three input parameters air pressure (P), cutting current (I) and cutting velocity (v) on material removal rate (MRR) is obtained experimentally. An artificial neural …
Published in Journal of Mechatronics and Automation · Vol. 2, Issue 2, 2015 · pp. 8–14 Read article
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Neuromorphic Spiking Neural Networks and Event-Driven Hardware: Architectures, Learning Paradigms, and Experimental Realizations
Abstract: With the current computational boom the research community is seeking for more sustainable energy efficient i.e. biologically inspired models of conventional Artificial Neural Networks (ANNs). Spiking Neural Networks (SNNs) known as the third generation of neural network models, provide a revolutionary approach by mimicking the asynchronized event-driven and temporally accurate signaling of the mammalian brain. Whereas conventional deep learning models operate with real-valued activations and dense matrix multiplications, SNNs use …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 · pp. 39–46 Read article
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Modeling the Solar Radiation Parameter over Abuja using Neural Networks
Abstract: Many computer simulation models which predict growth, development and yield of agronomic and horticultural crops require daily weather data as input. One of these inputs is daily total solar radiation, which in many cases is not available owing to the high cost and complexity of the instrumentation needed to collect the data. In this work, a neural network model of the solar radiation over Abuja, Nigeria is developed. The model …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 6, Issue 3, 2017 · pp. 40–48 Read article
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A Novel Strategy for Weight Initialization in Sigmoidal Feed-forward Artificial Neural Networks
Abstract: In this paper, a novel method of weight initialization is proposed. The proposed method of weight initialization distributes the initial weights and thresholds in such a manner that they lie in different regions of the activation function used at the hidden layer. The proposed method is compared with six other popular weight initialization methods on ten function approximation problems using the RPROP (Resilient Back-propagation) and Levenberg-Marquardt algorithms for training. Two …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 5, Issue 1, 2018 · pp. 62–75 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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BREAKDOWN VOLTAGE TEST OF DIFFERENT SOLID INSULATING MATERIALS USING ARTIFICIAL NEURAL NETWORK MODEL
Abstract: In this paper we are presenting Artificial Neural Network (MFNN) model which has different possible inputs affecting the breakdown voltage that are working temperature, the insulating material thickness, dielectric strength of insulating material, volume resistivity of materials, dissipation factor, conductivity of materials, and the materials relative permittivity that also predicts the breakdown voltage as a function of all these inputs parameters. It is important to train the Artificial Neural Network …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 6, Issue 2, 2018 · pp. 22–29 Read article
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Review on Evaluation Techniques Such as DOE and ANN Used for Evaluate Heat Flow Parameters for Resistance Spot Welding
Abstract: Resistance spot welding is a widely used process of metal joining in industries. In resistance spot welding process, the time of welding is very little so, during welding process maintenance of high accuracy and precision is very difficult. Resistance spot welding has a wide application in automobile industry, household and domestic appliances manufacturing industry and fabrication industry like air craft manufacturing industry. For better output quality of product, save time …
Published in Trends in Mechanical Engineering & Technology · Vol. 5, Issue 3, 2015 · pp. 26–32 Read article
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Artificial Neural Network Implementation in FPGA using Multiplexer-based Weight Updating for Efficient Resource Utilization
Abstract: This paper presents a novel scheme for field-programmable gate array (FPGA) implementation of an artificial neural network (ANN). The proposed implementation is aimed at reducing resource requirement, without compromising on the speed so that a complex ANN architecture could be realized on a single chip at a lower cost. The weight updating process in different layers of the ANN has been carried using a simple MUX-based architecture. Backpropagation algorithm which …
Published in Journal of Semiconductor Devices and Circuits · Vol. 2, Issue 1, 2015 · pp. 1–5 Read article
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An Analysis of Multimodal Fusion in Deepfake Detection for Video Samples
Abstract: In today’s rapidly evolving digital landscape, deepfake technology stands as both a marvel and a threat to privacy and security. Deepfakes, hyper-realistic synthetic media created using artificial intelligence (AI), can deceive and manipulate on an unprecedented scale, from political propaganda to compromising videos of public figures. This research navigates deepfake detection, focusing on two advanced methodologies: the vision transformers (ViT) image classifier and the Meso4 method. The ViT model utilizes …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 3, 2024 · pp. 19–27 Read article