Results 101 to 110 of about 30,189 (295)

Smart Flexible Tactile Sensors: Recent Progress in Device Designs, Intelligent Algorithms, and Multidisciplinary Applications

Advanced Intelligent Discovery, EarlyView.
Flexible tactile sensors have considerable potential for broad application in healthcare monitoring, human–machine interfaces, and bioinspired robotics. This review explores recent progress in device design, performance optimization, and intelligent applications. It highlights how AI algorithms enhance environmental adaptability and perception accuracy
Siyuan Wang, Ming Dong, Rongrong Bao, Caofeng Pan   +3 more
wiley   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Modeling Belt-Servomechanism by Chebyshev Functional Recurrent Neuro-Fuzzy Network

Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2008
A novel Chebyshev functional recurrent neuro-fuzzy (CFRNF) network is developed from a combination of the Takagi-Sugeno-Kang (TSK) fuzzy model and the Chebyshev recurrent neural network (CRNN).
Yuan-Ruey HUANG, Yuan KANG, Ming-Hui CHU, Yeon-Pun CHANG   +3 more
doaj   +1 more source

Online At-Risk Student Identification using RNN-GRU Joint Neural Networks

Information, 2020
Although online learning platforms are gradually becoming commonplace in modern society, learners’ high dropout rates and serious academic performance require more attention within the virtual learning environment (VLE).
Yanbai He, Rui Chen, Xinya Li, Chuanyan Hao, Sijiang Liu, Gangyao Zhang, Bo Jiang   +6 more
doaj   +1 more source

DR-RNN: A deep residual recurrent neural network for model reduction

CoRR, 2017
We introduce a deep residual recurrent neural network (DR-RNN) as an efficient model reduction technique for nonlinear dynamical systems. The developed DR-RNN is inspired by the iterative steps of line search methods in finding the residual minimiser of numerically discretized differential equations.
J. Nagoor Kani, Ahmed H. Elsheikh
openaire   +2 more sources

Automating AI Discovery for Biomedicine Through Knowledge Graphs and Large Language Models Agents

Advanced Intelligent Discovery, EarlyView.
This work proposes a novel framework that automates biomedical discovery by integrating knowledge graphs with multiagent large language models. A biologically aligned graph exploration strategy identifies hidden pathways between biomedical entities, and specialized agents use this pathway to iteratively design AI predictors and wet‐lab validation ...
Naafey Aamer, Muhammad Nabeel Asim, Shan Munir, Andreas Dengel   +3 more
wiley   +1 more source

Spatio-Temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks

Frontiers in Neuroscience, 2018
We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI).
R. Devon Hjelm, R. Devon Hjelm, Eswar Damaraju, Eswar Damaraju, Kyunghyun Cho, Helmut Laufs, Sergey M. Plis, Vince D. Calhoun, Vince D. Calhoun   +8 more
doaj   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Recurrent Neural Network (RNN) Inference Cloud Computing

International Journal For Multidisciplinary Research
The purpose of this technical article is to investigate the convergence of Recurrent Neural Networks (RNNs) with Cloud Computing considering the potential benefits and challenges that may be encountered in bringing these two systems together. Recurrent Neural Networks (RNNs) constitute a particular type of artificial neural network that is capable of ...
Deepshikha Saikia -, Nihar Pratim Deka -, Parusitom Brahma -   +2 more
openaire   +1 more source

Prediction of energy consumption using recurrent neural networks (RNN) and nonlinear autoregressive neural network with external input (NARX)

, 2020
Recurrent Neural Networks (RNN) and Nonlinear Autoregressive Neural Network with External Input (NARX) are recently applied in predicting energy consumption. Energy consumption prediction for depth analysis of how electrical energy consumption is managed
Zafri Wan Yahaya, Wan Muhammad, Kamaru Zaman, Fadhlan Hafizhelmi, Mohd Fuad Abdul Latip, Abdul Latip, Mohd Fuad, Wan Muhammad Zafri Wan Yahaya, Fadhlan Hafizhelmi Kamaru Zaman   +5 more
core   +2 more sources