Results 131 to 140 of about 112,373 (246)

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

A disease-agnostic approach to ensemble learning for infectious disease forecasting. [PDF]

Nat Commun
Murph AC, Beesley LJ, Gibson GC, Castro LA, Del Valle SY, Osthus D.   +5 more
europepmc   +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

Subway door fault prediction employing stacking ensemble learning. [PDF]

Sci Rep
Song H, Tang S, Xia J, Zhang L, Kang H, Li P.   +5 more
europepmc   +1 more source

Non‐Noble Metal Nanocatalysts for Hydrogen Evolution

Advanced Science, EarlyView.
Recent overviews and latest developments of diverse classes of non‐noble catalysts for application toward high‐performance photocatalysis, thermocatalytic steam reforming, and electrocatalysis. Toward sustainability objectives, several advanced characterization tools in combination with the latest breakthroughs in machine learning for material ...
Lina Jaya Diguna, Gede Herry Arum Wijaya, Md Abdul Kuddus Sheikh, Tobias Haposan, Fidelis Stefanus Hubertson Simanjuntak, Arramel Arramel, Muhammad Danang Birowosuto, Konstantin Sergeevich Novoselov   +7 more
wiley   +1 more source

Dynamic Ensemble Learning with Transfer Learning for Fatigue Performance Prediction in Ni-Based Superalloys. [PDF]

Materials (Basel)
Yang J, Du F, Lv H, Li W, Wu D.
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

Automated bone marrow cell classification using ensemble learning: performance, generalization, and clinical interpretability. [PDF]

Front Med (Lausanne)
Mehmood S, Zubair M, Abbas S, Alharbi RM, Alduailij M, Khan MA, Ghazal TM.   +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

MELGene: knowledge-enhanced multimodel ensemble learning for disease-gene association prediction. [PDF]

Brief Bioinform
Tian H, Yang K, Liu Z, Gao H, Yu J, Zhang L, Zhou X.   +6 more
europepmc   +1 more source