Results 51 to 60 of about 236,656 (279)
Frontiers in Earth ScienceIdentifying lithology is crucial for geological exploration, and the adoption of artificial intelligence is progressively becoming a refined approach to automate this process.
Saâd Soulaimani +6 more
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Time-varying probability model of the reduction in bending capacity of RC beams due to corrosion of steel bars [PDF]
Archives of Civil EngineeringDue to the reduction in bending capacity of RC beams being affected by multiple stochastic uncertainties, employing a deterministic function model to study the bending capacity of RC beams often leads to analysis errors that are difficult to accept. This
Peng Tan, Shibin Kang, Zhanqiang Feng
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Advanced Materials, EarlyView.The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj +8 more
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International Journal of Distributed Sensor Networks, 2016 This article offers a hybrid computational approach that combines an artificial neural network with Bayesian probability to improve on the conventional artificial neural network model.
Pao-Kuan Wu, Tsung-Chih Hsiao, Ming Xiao
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Magnetic Textiles: A Review of Materials, Fabrication, Properties, and Applications
Advanced Materials Technologies, EarlyView.Magnetic textiles (M‐textiles) are emerging as a programmable materials platform that merges magnetic matter with hierarchical textile structures. This article consolidates magnetic material classes, textile architectures, and fabrication and magnetization strategies, revealing structure–property–function relationships that govern magneto‐mechanical ...
Li Ke +3 more
wiley +1 more source
IEEE AccessAiming at the limitations of existing agricultural pest image recognition technology, a novel agricultural pest recognition algorithm based on convolutional neural network and Bayesian method is proposed. During the process, convolutional neural networks
Ling Zhang, Fahui Wu, Wensen Yu
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Investigating Performance of Bayesian and Levenberg-Marquardt Neural Network in Comparison Classical Models in Stock Price Forecasting [PDF]
تحقیقات مالی, 2017 Accurate forecasting of stock prices according to high volatility and inherent risk of stock market is a major concern of investors and financial analysts, hence applying novel approaches to predict the stock priceisan inevitable necessity.
Hossein Fakhari +2 more
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Bayesian Recurrent Neural Networks
, 2017 In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by
Fortunato, Meire +2 more
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Advanced Materials Technologies, EarlyView.This study shows that a lightweight blackbox neural network provides a practical, cost‐effective solution for bidirectional process prediction in laser‐induced graphene (LIG) fabrication. Achieving high predictive performance with minimal overhead, the approach democratizes machine learning (ML) for resource‐limited environments.
Maxim Polomoshnov +3 more
wiley +1 more source
Unsupervised Spiking Neural Network with Dynamic Learning of Inhibitory Neurons
Sensors, 2023 A spiking neural network (SNN) is a type of artificial neural network that operates based on discrete spikes to process timing information, similar to the manner in which the human brain processes real-world problems.
Geunbo Yang +7 more
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