Results 81 to 90 of about 53,216 (300)

Adaptive Conductive Systems: Enabling Deformable Electronics Through Engineered Materials, Circuits, and Intelligent Systems

Advanced Science, EarlyView.
This review presents the research advances of adaptive conductive systems from the perspective of materials, circuits, systems, and applications. Deformable conductors consist of substrates/matrices and conductive materials, structural design of circuit interconnections, fabrication of circuit interconnections, as well as intelligent systems ...
Yufei Lu, Siyu Liang, Jingyi Niu, Jie Chen, Shuo Zhang, Hongjian Zhang, Dongsheng Jia, Wei Huang   +7 more
wiley   +1 more source

Neural network analysis in time series forecasting

Российский технологический журнал
Objectives. To build neural network models of time series (LSTM, GRU, RNN) and compare the results of forecasting with their mutual help and the results of standard models (ARIMA, ETS), in order to ascertain in which cases a certain group of models ...
B. Pashshoev, D. A. Petrusevich
doaj   +1 more source

Hourly concentration prediction of PM2.5 based on RNN-CNN ensemble deep learning model(基于RNN-CNN 集成深度学习模型的PM2.5小时浓度预测)

Zhejiang Daxue xuebao. Lixue ban, 2019
针对目前大部分PM2.5 预测模型预测效果不稳定、泛化能力不强的现状，以记忆能力较强的循环神经网络（RNN） 和特征表达能力较强的卷积神经网络（CNN） 为基础，采取Stacking 集成策略对两者进行融合，提出了RNN-CNN 集成深度学习预测模型。该模型不仅充分利用时间轴上的前后关联信息去预测未来的浓度，而且在不同层次上将自动提取的高维时序数据通用特征用于预测，以保证预测结果的稳定性。最后，对集成之前的 RNN、CNN 和集成之后的RNN-CNN 模型，以2016 年中国大陆地区1 466 ...
HUANGJie(黄婕), ZHANGFeng(张丰), DUZhenhong(杜震洪), LIURenyi(刘仁义), CAOXiaopei(曹晓裴)   +4 more
doaj   +1 more source

LSTM based Ensemble Network to enhance the learning of long-term dependencies in chatbot

International Journal for Simulation and Multidisciplinary Design Optimization, 2020
A chatbot is a software that can reproduce a discussion portraying a specific dimension of articulation among people and machines utilizing Natural Human Language.
Patil Shruti, Mudaliar Venkatesh M., Kamat Pooja, Gite Shilpa   +3 more
doaj   +1 more source

Comparison of RNN Architectures and Non-RNN Architectures in Sentiment Analysis

sinkron, 2023
This study compares the sentiment analysis performance of multiple Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks. THE METHODS EVALUATED ARE simple Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Recurrent Neural Network, and 1D ConvNets.
openaire   +2 more sources

Inhibition of MEKK3 Prevents Cerebral Cavernous Malformation Progression Via Restoring Endothelial Integrity and Restricting Microglial Activation

Advanced Science, EarlyView.
In this study, we demonstrate that aberrant MEKK3 signaling in endothelial cells and microglia drives the progression of cerebral cavernous malformations (CCMs). Furthermore, we identify DPDH as a potent small‐molecule MEKK3 inhibitor with substantial therapeutic potential for CCM treatment.
Weiwei Zheng, Suyun Yu, Liwenyu Chen, Jing Ma, Peiliang Shen, Chang Yu, Xiaoqing Xin, Qiuhong Shen, Jing Chen, Song Ke, Hao Liu, Xiaobing Jiang, Ruogu Qi, Juming Yan, Jia Li, Jaesung P. Choi, Yin Lu, Yang Zhao   +17 more
wiley   +1 more source

Classification accuracy of RNN module with raw-signal, CNN module hybrid CNN-RNN and attention-based hybrid CNN-RNN architectures with raw-image1 on five benchmark databases.

, 2018
Classification accuracy of RNN module with raw-signal, CNN module hybrid CNN-RNN and attention-based hybrid CNN-RNN architectures with raw-image1 on five benchmark databases.
Wentao Wei (2135263), Weidong Geng (5858237), Yu Hu (126677), Yongkang Wong (5384011), Yu Du (618167), Mohan Kankanhalli (5524991)   +5 more
core   +1 more source

Enhanced High Dimensionality and the Information Processing Capacity in Interfered Spin Wave‐Based Reservoir Computing, Achieved With Eight Detectors

Advanced Electronic Materials, EarlyView.
Physical reservoir computing (PRC) based on spin wave interference has demonstrated high computational performance, yet room for improvement remains. In this study, we fabricated this concept PRC with eight detectors and evaluated the impact of the number of detectors using a chaotic time series prediction task.
Sota Hikasa, Wataru Namiki, Daiki Nishioka, Maki Nishimura, Ryo Iguchi, Kazuya Terabe, Takashi Tsuchiya   +6 more
wiley   +1 more source

Comparative Analysis of Neural Network Architectures for Digital Predistortion in Power Amplifier Linearization [PDF]

EPJ Web of Conferences
In this study, we examine and contrast the effectiveness of different artificial neural network (ANN) topologies for power amplifier (PA) digital pre-distortion (DPD).
Laakouri Hafsa, Ouadefli Mostapha, Tribak Abdelwahed, Et-tolba Mohamed, Terhzaz Jaouad, Ibanez Tomas Fernandez   +5 more
doaj   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source