Results 231 to 240 of about 149,463 (272)

Artificial intelligence in enzyme catalysis: Emerging trends and applications in biocatalyst engineering

The Canadian Journal of Chemical Engineering, EarlyView.
Schematic representation of artificial intelligence approaches in enzyme catalysis, integrating bibliometric analysis, emerging research trends, and machine learning tools for enzyme design, prediction, and industrial biocatalytic applications. Abstract This study systematically explores the applications of artificial intelligence (AI) in enzyme ...
Misael Bessa Sales, Francisco Izaias da Silva Aires, Paulo Gonçalves de Sousa Junior, Calebe Da Rocha Silva, Emanuel Lino Rodrigues, Maria Cristiane Martins de Souza, José C. S. dos Santos   +6 more
wiley   +1 more source

Simultaneous multi-well production forecasting and operational strategy awareness in heterogeneous reservoirs: a spatiotemporal attention-enhanced multi-graph convolutional network. [PDF]

Sci Rep
Lu S, Cui C, Wu Z, Qian Y, Wang J, Huang Y.   +5 more
europepmc   +1 more source

EventFlow: Real‐time neuromorphic event‐driven classification of two‐phase boiling flow regimes

Droplet, EarlyView.
We present a real‐time flow regime classification framework that integrates neuromorphic event‐driven sensing with deep recurrent neural networks. Unlike traditional frame‐based approaches, our system captures sparse event streams from an event‐based camera, representing only the dynamic brightness changes at the individual pixel level.
Sanghyeon Chang, Srikar Arani, Nishant Sai Nuthalapati, Youngjoon Suh, Nicholas Choi, Siavash Khodakarami, Md Rakibul Hasan Roni, Nenad Miljkovic, Aparna Chandramowlishwaran, Yoonjin Won   +9 more
wiley   +1 more source

Multi dynamic temporal representation graph convolutional network for traffic flow prediction. [PDF]

Sci Rep
Wu Z, Liu X, Zhang X.
europepmc   +1 more source

Rockburst prediction based on data preprocessing and hyperband‐RNN‐DNN

Deep Underground Science and Engineering, EarlyView.
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, Chenxi Yin, Guangdong Yang, Shengyong Ding, Bin Tian   +4 more
wiley   +1 more source

Correction to: Coding genomes with gapped pattern graph convolutional network. [PDF]

Bioinformatics
europepmc   +1 more source

Identifying T cell antigen at the atomic level with graph convolutional network. [PDF]

Nat Commun
Que J, Xue G, Wang T, Jin X, Wang Z, Cai Y, Yang W, Luo M, Ding Q, Zhang J, Wang Y, Yang Y, Pang F, Hui Y, Wei Z, Xiong J, Xu S, Lin Y, Sun H, Wang P, Xu Z, Jiang Q.   +21 more
europepmc   +1 more source

DriverOmicsNet: an integrated graph convolutional network for multi-omics exploration of cancer driver genes. [PDF]

Brief Bioinform
Dai YH, Chang CJ, Shen PC, Jheng WL, Lee DJ, Chen YG.   +5 more
europepmc   +1 more source

PangenomeX: a graph convolutional network-based pangenome framework for unbiased population-scale genomic variation analysis. [PDF]

Brief Bioinform
Xue Z, Wang Y, Wang X, Yuan J, Wang L, Zeng J, Jin X, Zhu H, Wang J.   +8 more
europepmc   +1 more source

Lightweight graph convolutional network with multi-attention mechanisms for intelligent action recognition in online physical education. [PDF]

PeerJ Comput Sci
You Y.
europepmc   +1 more source