Results 261 to 270 of about 203,239 (310)

Quantifying sensor contribution in vibration-based structural health monitoring using explainable multichannel convolutional neural networks. [PDF]

Sci Rep
Dadoulis GI, Manolis GD.
europepmc   +1 more source

Chronic Disease Monitoring Using Advanced Compliant Materials for Bioelectronics

Advanced Electronic Materials, EarlyView.
Compliant bioelectronic systems enable continuous monitoring of chronic disease through soft, stretchable materials and tissue‐conformal designs that support stable electrophysiological, mechanical, and biochemical sensing. Integration of diverse sensing modalities with thoughtful material selection, device architectures, and advanced fabrication ...
Han Kim, Mabel Bartlett, Liyang Wang, Karl Bates, Mengdi He, Myungbo Kim, Carmel Majidi, Tzahi Cohen‐Karni   +7 more
wiley   +1 more source

Predicting Difficult Tracheal Intubation Using Multi-Angle Photographic Analysis with Convolutional Neural Networks and EfficientNet. [PDF]

Diagnostics (Basel)
Koca E, Kutlusoy S, Er MB, Koca T.
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Using convolutional neural networks with late fusion to predict heart disease. [PDF]

Sci Rep
AlSekait DM, Zakariah M, Amin SU, Dubey P, Khan ZI.   +4 more
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Hybrid quantum-classical-quantum convolutional neural networks. [PDF]

Sci Rep
Long C, Huang M, Ye X, Futamura Y, Sakurai T.   +4 more
europepmc   +1 more source

SigmaFormer: Augmenting transformer encoders with COSMO sigma profiles for pure component property prediction

AIChE Journal, EarlyView.
Abstract Transformer‐based molecular models pretrained on SMILES strings demonstrate strong performance in property prediction. However, these model often lack explicit integration of molecular surface charge distributions that govern intermolecular interactions such as hydrogen bonding and polarity.
Tae Hyun Kim, Silabrata Pahari, Joseph Sang‐Il Kwon   +2 more
wiley   +1 more source

Convolutional neural networks for prostate cancer detection, classification, and segmentation: A systematic review and bibliometric analysis. [PDF]

Eur J Radiol Open
Gülmez B.
europepmc   +1 more source