Results 141 to 150 of about 199,039 (272)

A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm

Scientific Reports
We constructed an early prediction model for postoperative pulmonary complications after thoracoscopic surgery using machine learning and deep learning algorithms.
Cheng-Mao Zhou, Qiong Xue, HuiJuan Li, Jian-Jun Yang, Yu Zhu   +4 more
doaj   +1 more source

Anomaly detection in chest ¹⁸F-FDG PET/CT by Bayesian deep learning. [PDF]

Jpn J Radiol, 2022
Nakao T, Hanaoka S, Nomura Y, Hayashi N, Abe O.   +4 more
europepmc   +1 more source

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Advanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li, Lado Filipovic   +8 more
wiley   +1 more source

Bayesian deep learning for reliable oral cancer image classification. [PDF]

Biomed Opt Express, 2021
Song B, Sunny S, Li S, Gurushanth K, Mendonca P, Mukhia N, Patrick S, Gurudath S, Raghavan S, Tsusennaro I, Leivon ST, Kolur T, Shetty V, Bushan VR, Ramesh R, Peterson T, Pillai V, Wilder-Smith P, Sigamani A, Suresh A, Kuriakose MA, Birur P, Liang R.   +22 more
europepmc   +1 more source

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source

The origin and evolution of open habitats in North America inferred by Bayesian deep learning models. [PDF]

Nat Commun, 2022
Andermann T, Strömberg CAE, Antonelli A, Silvestro D.   +3 more
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Patient-specific uncertainty and bias quantification of non-transparent convolutional neural network model through knowledge distillation and Bayesian deep learning. [PDF]

Proc SPIE Int Soc Opt Eng, 2023
Gong H, Yu L, Leng S, Hsieh SS, Fletcher JG, McCollough CH.   +5 more
europepmc   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning. [PDF]

Nat Commun, 2021
Leitherer A, Ziletti A, Ghiringhelli LM.
europepmc   +1 more source