Results 281 to 290 of about 124,232 (320)

Application of Artificial Intelligence in Clinical Microbiology: From Research to Practice

iLABMED, EarlyView.
This paper reviews the application of AI in clinical microbiology practice at home and abroad, including rapid pathogen identification, accurate characterization of microbial resistance patterns, optimization of laboratory workflows, and public health interventions.
Ting Ding, Yi‐Wei Tang, Xiaoke Hao
wiley   +1 more source

Detection of cyber attacks in electric vehicle charging systems using a remaining useful life generative adversarial network. [PDF]

Sci Rep
Tanyıldız H, Şahin CB, Dinler ÖB, Migdady H, Saleem K, Smerat A, Gandomi AH, Abualigah L.   +7 more
europepmc   +1 more source

Artificial Intelligence Revolutionizes Anti‐Infective Drug Discovery: From Target Identification to Lead Optimization

iMetaMed, EarlyView.
Artificial intelligence (AI) technology is revolutionizing antimicrobial drug development. In response to increasingly severe antimicrobial resistance challenges, AI can efficiently predict pathogen evolutionary trends, identify potential drug targets, and accelerate compound design and optimization, thereby significantly shortening the development ...
Kexin Li, Quan Yuan, Chang Qi, Ying Shi, Hong Yang, Anqi Lin, Shuofeng Yuan   +6 more
wiley   +1 more source

Single Fringe Phase Retrieval for Translucent Object Measurements Using a Deep Convolutional Generative Adversarial Network. [PDF]

Sensors (Basel)
He J, Huang Y, Wu J, Tang Y, Wang W.
europepmc   +1 more source

TargetGen‐RNN model discovers novel antibiotic compound targeting drug‐resistant Staphylococcus aureus

Interdisciplinary Medicine, EarlyView.
TargetGen‐recurrent neural network (RNN), an advanced generative model, generated 28,708 unique and novel compounds, identifying SAK‐2970 as a potent antibiotic against drug‐resistant Staphylococcus aureus. Exhibiting strong biofilm inhibition, high therapeutic efficacy, and minimal systemic toxicity in vivo, SAK‐2970 highlights its safety and clinical
Shakeel Ahmad Khan, Adnan Shakoor, Sadia Kanwal   +2 more
wiley   +1 more source

Traffic data imputation via knowledge graph-enhanced generative adversarial network. [PDF]

PeerJ Comput Sci
Liu Y, Shen G, Liu N, Han X, Xu Z, Zhou J, Kong X.   +6 more
europepmc   +1 more source

Exploring a Novel Conv‐Transformer Network for Multi‐Modality Heart Segmentation

iRADIOLOGY, EarlyView.
We propose SFAM‐TransUnet for multimodality whole heart segmentation, a novel deep learning framework combining CNNs and transformers. Extensive experiments conducted on the clinical Multi‐Modality Whole Heart Segmentation datasets demonstrate that SFAM‐TransUnet outperforms various alternative methods.
Youyou Ding, Hao Dang, Jiayi Luo, Xiaoyu Zhuo, Ningyu Huang, Junsheng Xiao, Zongwang Lv   +6 more
wiley   +1 more source

The effects of the generative adversarial network and personalized virtual reality platform in improving frailty among the elderly. [PDF]

Sci Rep
Yu Z, Dang J.
europepmc   +1 more source

Clinical Application of Using Diffusion‐Based Wasserstein Generative Adversarial Network for Morphologic Analysis of Blood Cells

Journal of Clinical Laboratory Analysis, EarlyView.
This study evaluated the accuracy of blood cell classification using 78,494 images across 13 cell classes, demonstrating a significant improvement with the DWGAN‐GP augmented dataset. The use of synthetic images increased the classification accuracy to 97.74%, outperforming both unbalanced data and commercial systems.
Hyun‐Young Kim, Emmanuel Edward Ngasa, Hee‐Jin Kim, Boram Kim, Gyujin Lim, Chang‐Hun Park, Hyeon Jeong Kwon, Mi‐Ae Jang, Jiyoung Woo   +8 more
wiley   +1 more source

Specular highlight removal by federated generative adversarial network with attention mechanism. [PDF]

Sci Rep
Zheng Y, Gao Y.
europepmc   +1 more source