Results 231 to 240 of about 145,055 (276)

Advances in causal discovery methods for ecological time series

Biological Reviews, EarlyView.
ABSTRACT Recent advances in data collection technologies (e.g. automated sensor networks, satellite remote sensing, and high‐throughput sequencing) have greatly expanded the availability of ecological time series, enabling new opportunities for causal analyses in dynamic ecosystems.
Kenta Suzuki, Masato Yamamichi, Yutaka Osada, Masayuki Ushio, Kohei Nakajima, Hiroshi Masuya, Shohei Shimizu   +6 more
wiley   +1 more source

Shape-based disease grading via functional maps and graph convolutional networks with application to Alzheimer's disease. [PDF]

BMC Med Imaging
Mayer J, Baum D, Ambellan F, von Tycowicz C, Alzheimer’s Disease Neuroimaging Initiative.   +4 more
europepmc   +1 more source

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

SignFormer-GCN: Continuous sign language translation using spatio-temporal graph convolutional networks. [PDF]

PLoS One
Arib SH, Akter R, Rahman S, Rahman S.
europepmc   +1 more source

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

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

Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study. [PDF]

Sci Rep
Agyekum EA, Kong W, Agyekum DN, Issaka E, Wang X, Ren YZ, Tan G, Jiang X, Shen X, Qian X.   +9 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

[Image classification of osteoarthritis based on improved shifted windows transformer and graph convolutional networks]. [PDF]

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
Jiang L, Cao H, Ma Z.
europepmc   +1 more source

Predicting Organ-Specific Toxicity of Selective Androgen Receptor Modulators, using Transfer Learning on Graph Convolutional Networks

Kalian AD, Silva AC, Lee J, Dorne JC, Potter C, Benfenati E, Osborne OJ, Guo M, Hogstrand C.   +8 more
europepmc   +1 more source