Results 141 to 150 of about 221,929 (334)

Lung Cancer Segmentation With Transfer Learning: Usefulness of a Pretrained Model Constructed From an Artificial Dataset Generated Using a Generative Adversarial Network

, 2021
Mizuho Nishio, Koji Fujimoto, Hidetoshi Matsuo, Chisako Muramatsu, Ryo Sakamoto, Hiroshi Fujita   +5 more
openalex   +1 more source

Carbon Dots: Small Materials With Big Impacts on Optoelectronic Devices

Aggregate, EarlyView.
This review systematically summarizes the roles of carbon dots in optoelectronic devices, emphasizing their electro‐induced effects, and structural optimization strategies. It further analyzes performance limitations and prospects for the preparation and application of high‐quality carbon dots.
Boyang Wang, Junwei Wang, Siyu Lu
wiley   +1 more source

Transformative skeletal motion analysis: optimization of exercise training and injury prevention through graph neural networks

Frontiers in Neuroscience
IntroductionExercise is pivotal for maintaining physical health in contemporary society. However, improper postures and movements during exercise can result in sports injuries, underscoring the significance of skeletal motion analysis. This research aims
Jiaju Zhu, Zijun Ye, Meixue Ren, Guodong Ma, Guodong Ma   +4 more
doaj   +1 more source

Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution

Advanced Intelligent Discovery, EarlyView.
Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren, Qian Jiang, Xin Jin, Puming Wang, Xing Chu, Shaowen Yao, Shengfa Miao   +6 more
wiley   +1 more source

AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing

Advanced Intelligent Discovery, EarlyView.
AI‐SERS advances spectral interpretation with greater precision and speed, enhancing molecular detection, biomedical analysis, and imaging. This review explores its essential contributions to biofluid analysis, disease identification, therapeutic agent evaluation, and high‐resolution biomedical imaging, aiding diagnostic decision‐making.
Seungki Lee, Rowoon Park, Ho Sang Jung
wiley   +1 more source

Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

, 2020
Liu Yang, Dongkun Zhang, George Em Karniadakis   +2 more
openalex   +2 more sources

Wind Power Prediction considering Ramping Events Based on Generative Adversarial Network [PDF]

, 2021
Qiyue Huang
openalex   +1 more source

Generating Synthesized Computed Tomography (CT) from Magnetic Resonance Imaging Using Cycle-Consistent Generative Adversarial Network for Brain Tumor Radiation Therapy [PDF]

, 2021
Wu Juan
openalex   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Image Denoising Using Quantum Deep Convolutional Generative Adversarial Network for Medical Images

International Journal of Computational Intelligence Systems
A significant role is played by medical images in diagnosing diseases and planning the course of treatment. Noise can potentially degrade the quality of images which can lead to misdiagnosis.
Priyanka Nandal, Sudesh Pahal, Govind Murari Upadhyay   +2 more
doaj   +1 more source