Results 151 to 160 of about 73,468 (309)

Recognizing Events in Videos Using Deep Learning Techniques

Syrian Journal for Science and Innovation
Neural network models have revolutionized action recognition in videos, enabling precise and efficient processing of complex visual data. These AI-powered tools mimic or even surpass human abilities in understanding visual information.
Waseem Safi, Hisham Muawen
doaj   +1 more source

Hardware‐Attentive Programmable Fourier Ptychography Enables Task‐Adaptive Label‐Free Virtual Staining

Advanced Science, EarlyView.
Task‐adaptive programmable optics enables label‐free virtual staining through optical‐attention‐guided acquisition and reconstruction. By optimizing wavelength, illumination angle, exposure time, and imaging depth, the framework learns task‐relevant optical measurements, generating clinically interpretable virtual stains with improved fidelity, non ...
Tianyue He, Wenyi Jing, Le Zhang, Qican Zhang, Tingdong Kou, Xin He, Yan Qiu, Yang Lu, Jian Cui, Yongfu Wen, Zhenrong Zheng, Hongying Zhang, Dayong Jin, Junfei Shen   +13 more
wiley   +1 more source

APPLICATIONS OF CONVOLUTIONAL NEURAL NETWORKS (CNNS) IN MEDICAL IMAGE SECURITY [PDF]

The rapid growth of digital healthcare systems has led to an increasing reliance on medical imaging for diagnosis and treatment, raising significant concerns regarding data security and patient privacy.

core   +2 more sources

Molecularly Engineered Wing‐Shaped Azobenzene Memristors for Logic‐in‐Memory and Edge Visual Intelligence

Advanced Science, EarlyView.
Rational engineering of terminal substituents in symmetric azobenzene‐based molecules enables precise control over conformationally coupled charge‐transfer processes. This design yields tunable nonvolatile resistive memory behaviors, ranging from write‐once‐read‐many‐times (WORM) to rewritable switching.
Yanze Liu, Tao Han, Jiahui Ding, Hong Lian, Lingling Yao, Zhaoxin Xu, Xingyu Zhang, Shuanglong Wang, Jiangnan Xia, Tianchi Zhang, Weiwei Kang, Qingchen Dong   +11 more
wiley   +1 more source

Deep learning model combination and regularization using convolutional neural networks [PDF]

, 2014
Convolutional neural networks (CNNs) were inspired by biology. They are hierarchical neural networks whose convolutional layers alternate with subsampling layers, reminiscent of simple and complex cells in the primary visual cortex [Fuk86a].
Frazão, Xavier Marques
core  

ORBIT‐AMD: Ordinal Risk, Bilateral Imaging, and Trajectory Learning for Age‐Related Macular Degeneration in Multi‐Cohorts

Advanced Science, EarlyView.
Eligibility flow and real‐world AMD burden in the UKB retinal imaging cohort and TMUEH external‐validation cohort. Overview of the ORBIT‐AMD architecture, integrating retinal representation pretraining, bilateral eye‐graph modeling and concept bottleneck learning to support ordered risk, bilateral context, interpretable lesion concepts, longitudinal ...
Xuehao Cui, Dejia Wen, Patrick Yu‐Wai‐Man, Xiaorong Li   +3 more
wiley   +1 more source

Engineering Strategies to Suppress Thermal Runaway Propagation in Lithium‐Ion Battery: Mechanisms, Metrics, Materials, and Evaluation Methods

Advanced Science, EarlyView.
Thermal runaway propagation can transform a single‐cell failure into a system‐level hazard in lithium‐ion battery packs. This review clarifies how heat transfer, gas venting, combustion, and configuration govern cell‐to‐cell failure, and links measurable metrics, pathway‐oriented suppression materials, and experimental/modeling tools to guide safer ...
Jinrong Su, Anna DiFelice, Jayani Mawela, Jianing Gan, Shuai Che, Wonhee Cho, Hanghang Yan, Yaohong Xiao, Xinxin Yao, Xiangwei Guo, Rui Wang, Dingchuan Xue, Hanlong Liu, Solomon Adera, Zhan Chen, Lei Chen   +15 more
wiley   +1 more source

Geometrical aspects of lattice gauge equivariant convolutional neural networks [PDF]

Lattice gauge equivariant convolutional neural networks (L-CNNs) are a framework for convolutional neural networks that can be applied to non-abelian lattice gauge theories without violating gauge symmetry.
Müller, David I.,, Aronsson, Jimmy,, Schuh, Daniel,   +2 more
core  

TraNCE: Transformative Nonlinear Concept Explainer for CNNs [PDF]

Convolutional neural networks (CNNs) have succeeded remarkably in various computer vision tasks. However, they are not intrinsically explainable. While feature-level understanding of CNNs reveals where the models looked, concept-based explainability ...
Akpudo, Ugochukwu Ejike, Lewis, Andrew, Zhou, Jun, Gao, Yongsheng   +3 more
core   +1 more source

A Closed‐Loop Framework for Inverse Design: Dynamic Training and Intelligent Optimization for Heterostructured Materials

Advanced Science, EarlyView.
To accelerate the inverse design of heterostructured metal matrix composites, a closed‐loop scientific machine learning framework integrates continual learning prediction with NSGA‐II‐PMCP optimization. The framework maps microstructural descriptors to strength, toughness, and modulus, expands high‐quality Pareto solutions, and guides experimentally ...
Zhiyan Zhong, Xueru Zheng, Xiao Zhou, Zhengheng Tao, Wan Han, Jingyi Pan, Dian Wu, Ning Gao, Lei Liu, Zhongyang Wang, Fanchao Meng, Tongxiang Fan   +11 more
wiley   +1 more source