Results 141 to 150 of about 43,032 (264)

A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization. [PDF]

Sci Rep
Kumar Lilhore U, Simaiya S, Sharma YK, Kaswan KS, Rao KBVB, Rao VVRM, Baliyan A, Bijalwan A, Alroobaea R.   +8 more
europepmc   +1 more source

A Versatile‐Designable Framework for Active and Programmable Shape‐Morphing Soft Matter Systems: From Inverse Design to Closed‐Loop Control

Advanced Science, EarlyView.
A versatile framework integrates addressable electrothermal actuation and strain‐constraint mechanisms to construct programmable shape‐morphing soft matter systems. By combining an analytical inverse design strategy for high‐fidelity 3D surface reconstruction with deep learning‐based closed‐loop control, this approach enables zero‐energy shape locking,
Kai Liu, Peiling Xie, Ruitong Song, Banghan Liu, Rui Guo, Jiu‐an Lv   +5 more
wiley   +1 more source

Real‐Time and Non‐Invasive Detection of Respiratory Viral Infections Using an Intelligent Odor Monitoring System (IOMS)

Advanced Science, EarlyView.
An intelligent odor monitoring system integrates an IVC animal model, gas sensor array, real‐time resistance readout, and machine‐learning analysis to continuously monitor infection‐associated odor changes. The platform captures longitudinal sensor responses, distinguishes infected from healthy states, and supports early‐stage respiratory viral ...
Yajie Shen, Weifeng Yuan, Long Li, Yucheng Zheng, Kenan Liu, Hegeng Li, Hua‐Yao Li, Yongliang Zhao, Binzhou Ying, Lanpeng Guo, Wenjian Zhang, Shu‐Ming Kuo, Zirui Zhang, Yufan Deng, Bohan Yin, Zhaocheng Luo, Ke Xu, Huan Liu   +17 more
wiley   +1 more source

Advancing the Design of High‐Efficiency Printable Hole‐Conductor‐Free Mesoscopic Perovskite Solar Cells Through Machine Learning

Advanced Science, EarlyView.
Based on the largest printable mesoscopic perovskite solar cells database we established, stacking model achieved precise PCE prediction (R2 = 0.73, MAE = 2.18%). Multiple experiments verified the accuracy of the model, which guided the fabrication of high‐PCE devices with an efficiency of 19.36%.
Hao Meng, Jingzi Zhang, Xu Zhu, Yuelin Wang, Antai Yang, Kailong Hu, Chengquan Zhong, Jiakai Liu, Menghan Dun, Xi Lin   +9 more
wiley   +1 more source

Ovarian Cancer Diagnosis and Chemoresistance Prediction Model Based on cfRNA Molecular Signature

Advanced Science, EarlyView.
A deep learning model analyzes cfRNA profiles extracted from the blood of OVCA patients. This innovative approach distinguishes OVCA from healthy controls with high accuracy. Crucially, it reliably predicts patient response to chemotherapy (sensitive versus resistant subgroups).
Qinhao Guo, Yangyang Zhang, Yongcheng Jin, Siwei Deng, Yongqi Chen, Zheng Feng, Hao Wen, Liu Wang, Yilin Li, Fanghong Ou, Yong Shen, Haiming Li, Tianyao Zhou, Xingzhu Ju, Xiaohua Wu   +14 more
wiley   +1 more source

Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction. [PDF]

IEEE Trans Med Imaging
Yu H, Fessler JA, Jiang Y.
europepmc   +1 more source

Exact Discrete Stochastic Simulation With Deep‐Learning‐Scale Gradient Optimization

Advanced Science, EarlyView.
A 203,796‐parameter gene regulatory network classifies handwritten digits with 98.4% accuracy using exact stochastic dynamics. The framework decouples forward simulation from backward differentiation, making continuous‐time Markov chain models compatible with deep‐learning optimization.
Jose M. G. Vilar, Leonor Saiz
wiley   +1 more source

Scalable HMO-CNN-SVM Framework for Skin Lesion Classification: A Metaheuristic-Driven Approach With Parallelizable Optimization for Cluster Deployment. [PDF]

Biomed Eng Comput Biol
Fendzi Mbasso W, Harrison A, Mamadiyarov Z, Liu Z, Kumar R, Shaikh MS, Metwally Mahmoud M.   +6 more
europepmc   +1 more source

Machine‐Learning Framework for Designing Stable Interfaces in All‐Solid‐State Lithium‐Ion Batteries

Advanced Science, EarlyView.
A data‐driven strategy is developed to discover coating materials for all‐solid‐state lithium batteries. Using calculations of interfacial reactivity, unsupervised pattern recognition, and machine‐learning prediction, the study identifies low‐reactivity compositional patterns and screens new lithium‐based oxide and polyanion candidates, extending ...
Sehyeok Park, Myeongcho Jang, Hun‐Gi Jung, Kyung Yoon Chung, Seungho Yu   +4 more
wiley   +1 more source

Predicting Mechanical Strength of Alkali-Activated High-Performance Concrete Using Machine-Learning Methods. [PDF]

Materials (Basel)
Biswas R, Kazemi F, Sharma A, Jankowski R, Asteris PG.   +4 more
europepmc   +1 more source