Results 161 to 170 of about 21,160 (284)

Fast and accurate prediction of adsorption energy of AgPd nanoalloys by deep learning potentials and neural networks. [PDF]

Sci Rep
Zhang W, Shan S, Ma F, Liu X, Xu P, Chen F.   +5 more
europepmc   +1 more source

AI‐Driven Precision Annealing for High Performance Fe‐Based Amorphous Alloys

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
The four stages of the research process are as follows: First, data is collected and a database is constructed. This is followed by feature selection and analysis, then the establishment of machine learning models, and finally formulation design and preparation.
Yichuan Tang, Shaopeng Liu, Silong Li, Ruonan Ma, Peinan Li, Zheng Wang, Kun Wang, Kaiyan Cao, Sidan Ding, Chao Zhou, Fanghua Tian, Sen Yang, Minxia Fang, Yin Zhang   +13 more
wiley   +1 more source

Prediction of antimicrobial resistance from MALDI-TOF mass spectra using machine learning: a validation study. [PDF]

J Clin Microbiol
Wiesmann N, Enders D, Westendorf A, Koch R, Schaumburg F.   +4 more
europepmc   +1 more source

Artificial intelligence‐assisted detection of epileptic spasms using electroencephalographic–video analysis

Epilepsia, EarlyView.
Abstract Objective This study was undertaken to develop and validate an artificial intelligence (AI) diagnostic tool using hybrid electroencephalographic (EEG)–video signals for automatic epileptic spasms (ES) detection. Methods This retrospective cohort study with internal cross‐validation and multicenter external validation was conducted from July ...
Lin Wan, Nan Lin, Wen Wang, Zi Liang, Yisu Dong, Haibo He, Jian Chen, Yuying Fan, Tong Liu, Hongjie Wang, Weike Cheng, Guangshuang Lu, Juan Wang, Bo Zhang, Qiang Lu, Guang Yang   +15 more
wiley   +1 more source

RMIS-Net: a fast medical image segmentation network based on multilayer perceptron. [PDF]

PeerJ Comput Sci
Zhang B, Xu G, Xing Y, Li N, Li D.
europepmc   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source

Advanced predictive modeling of municipal solid waste management using robust machine learning. [PDF]

Sci Rep
Chau KY, Moslehpour M, Pan SH, Akhundzada A.   +3 more
europepmc   +1 more source

A Multivariate Mixed‐Effects Regression Framework for Ground Motion Modeling: Integrating Parametric and Machine Learning Approaches

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Multivariate ground motion models (GMMs) that capture the correlation between different intensity measures (IMs) are essential for seismic risk assessment. Conventional GMMs are often developed using a two‐stage approach, where separate univariate models with predefined functional forms are fitted first, and correlation is addressed in a ...
Sayed Mohammad Sajad Hussaini, Shaghayegh Karimzadeh, Paulo B. Lourenço   +2 more
wiley   +1 more source

A comparative study of various statistical and machine learning models for predicting restaurant demand in Bangladesh. [PDF]

PLoS One
Hossain MS, Parvin F.
europepmc   +1 more source