Results 131 to 140 of about 212,585 (307)
Quantization‐aware training creates resource‐efficient structured state space sequential S4(D) models for ultra‐long sequence processing in edge AI hardware. Including quantization during training leads to efficiency gains compared to pure post‐training quantization.
Sebastian Siegel +5 more
wiley +1 more source
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) +4 more
doaj +1 more source
We address multilayer PCB anomaly detection for smart manufacturing by physical reservoir computing with HfO2‐based memristors. Crystallinity‐tuned HfO2 suppresses ferroelectricity while preserving the high‐k insulating state and confers strong short‐term memory.
Yongho Lee +7 more
wiley +1 more source
This paper presents an integrated AI‐driven cardiovascular platform unifying multimodal data, predictive analytics, and real‐time monitoring. It demonstrates how artificial intelligence—from deep learning to federated learning—enables early diagnosis, precision treatment, and personalized rehabilitation across the full disease lifecycle, promoting a ...
Mowei Kong +4 more
wiley +1 more source
ABSTRACT Real‐time online detection of rare earth element component contents is a crucial link in ensuring the stable production of the rare earth extraction and separation industry and improving the quality of rare earth products. The traditional methods for predicting the content of rare earth element components based on just‐in‐time learning fail to
Zhaohui Huang +6 more
wiley +1 more source
Fake news detection: A hybrid CNN-RNN based deep learning approach
J. Nasir, O. Khan, Iraklis Varlamis
semanticscholar +1 more source
Abstract Aim Tacrolimus dosing in the early post‐kidney transplant period is challenging due to a narrow therapeutic index and substantial interindividual pharmacokinetic (PK) variability. This study aimed to develop and validate mechanism‐informed machine learning (ML) models to support individualized tacrolimus dosing during this critical period ...
Hui Yu +4 more
wiley +1 more source
Abstract Artificial intelligence and automation are no longer just buzzwords in the biopharmaceutical industry. The manufacturing of a class of biologics, comprising monoclonal antibodies, cell therapies, and gene therapies, is far more complex than that of traditional small molecule drugs.
Shyam Panjwani, Hao Wei, John Mason
wiley +1 more source
Rockburst prediction based on data preprocessing and hyperband‐RNN‐DNN
A data preprocessing workflow is proposed to address challenges in rockburst data analysis. Coupled algorithms preprocess the data set, and hyperband optimization is used to enhance RNN performance. Results show that preprocessing improves accuracy, while dense layers enhance model stability and prediction performance.
Yong Fan +4 more
wiley +1 more source
The study developed a PVDF/SiO2 humidity sensor with superhydrophobic properties and integrated the humidity sensor into the intelligent powered air‐purifying respirator (IPAPR) for breath monitoring. Coupled with the Gated Recurrent Unit (GRU) machine learning prediction algorithm, it enables feedforward control of the fan to achieve respiratory ...
Xinjian He +9 more
wiley +1 more source

