Results 131 to 140 of about 96,348 (272)

Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy. [PDF]

World J Gastroenterol, 2022
Dalal S, Onyema EM, Malik A.
europepmc   +1 more source

Recent Advances in Sustainable Single‐Atom Catalysts from Biomass and Solid Waste: Design, Synthesis and Applications

Advanced Science, EarlyView.
Biomass‐ and solid waste‐derived sustainable single‐atom catalysts (Sus‐SACs) provide a cost‐effective and renewable approach to catalyst design. This review summarizes precursor selection, including AI‐assisted screening, synthesis strategies with emphasis on ultrafast methods, and advanced characterization techniques.
Hongzhe He, Yuhang Qiu, Ruoqun Zhang, Sasha Yang, Jingwei Wang, Jianglong Yu, Lian Zhang, Ning Chen, Baiqian Dai   +8 more
wiley   +1 more source

Bioinspired Cold‐Laminated Ultrathin Hydrogels as a Broadly Adaptive Platform for Physiological Monitoring

Advanced Science, EarlyView.
A cold‐lamination strategy is introduced to fabricate ultrathin, nanomesh‐reinforced hydrogel bioelectronics with controlled thickness, tunable mechanics, and reversible adhesion. By mechanically interlocking a TPU nanomesh within a temperature‐responsive hydrogel, the platform enables robust epidermal and implantable cardiac interfaces, supporting ...
Hui Chen, Jian Zhou, Jianfei Xie, Lin Shi, Lu Wang, Yuanfan Yang, Yihao Guo, Jianhui Cao, Qingyun Fu, Yongqing Fu, Huigao Duan   +10 more
wiley   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison. [PDF]

BMC Med Res Methodol, 2022
Pfob A, Lu SC, Sidey-Gibbons C.
europepmc   +1 more source

Machine Learning for Designing Perovskites and Perovskite‐Inspired Solar Materials: Emerging Opportunities and Challenges

Advanced Science, EarlyView.
This review offers a comprehensive comparison between perovskites and perovskite‐inspired materials (PIMs), focusing on their crystal structures, electronic properties, and chemical compositions. It evaluates the applicability of machine learning (ML) descriptors and models across both material classes.
Yangfan Zhang, Yiming Xia, Ali Shakiba, Hongrui Zhang, Xiaojing Hao, Priyank V. Kumar, Mahesh P. Suryawanshi   +6 more
wiley   +1 more source

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

Advanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

Hyperparameter Tuning and Automatic Image Augmentation for Deep Learning-Based Angle Classification on Intraoral Photographs-A Retrospective Study. [PDF]

Diagnostics (Basel), 2022
Cejudo Grano de Oro JE, Koch PJ, Krois J, Garcia Cantu Ros A, Patel J, Meyer-Lueckel H, Schwendicke F.   +6 more
europepmc   +1 more source

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning. [PDF]

Sensors (Basel), 2022
Isabona J, Imoize AL, Kim Y.
europepmc   +1 more source