Results 181 to 190 of about 288,690 (232)

Improvement of Exciton Hamiltonians for Photosynthetic Reaction Centers by Bayesian Optimization. [PDF]

ACS Omega
Shimooka W, Kitoh-Nishioka H, Itoh S, Kimura A.   +3 more
europepmc   +1 more source

Retinal Vessel Segmentation: A Comprehensive Review From Classical Methods to Deep Learning Advances (1982–2025)

Advanced Intelligent Systems, EarlyView.
Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal, Jan Kubíček, Jana Bahrová, Alice Varyšová, Martin Augustýnek, Marek Penhaker, Juraj Timkovič   +6 more
wiley   +1 more source

Capsule Neural Networks with Bayesian Optimization for Pediatric Pneumonia Detection from Chest X-Ray Images. [PDF]

J Clin Med
Salamon S, Książek W.
europepmc   +1 more source

Towards Advanced Intelligent and Perceptive Soft Grippers

Advanced Intelligent Systems, EarlyView.
Implementing soft yet strong and intelligent soft grippers request innovative and creative solutions in designing soft bodies and seamlessly integrating actuated systems with hierarchical sensing. This review systematically analyses soft grippers with a deep understanding of core components, from fundamental design principles to actuation and sensing ...
Haneul Kim, Hayeon Shin, Seik Park, Changbeom Shim, Seonggun Joe   +4 more
wiley   +1 more source

Credit risk prediction model for listed companies based on improved reinforcement learning and Bayesian optimization hyperband. [PDF]

PLoS One
Yuanqing C, Gao Z, Jian Z, Alizadehsani R, Pławiak P.   +4 more
europepmc   +1 more source

Disentangling Aleatoric and Epistemic Uncertainty in Physics‐Informed Neural Networks: Application to Insulation Material Degradation Prognostics

Advanced Intelligent Systems, EarlyView.
Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez, Jokin Alcibar, Joel Pino, Mikel Sanz, Jose I. Aizpurua   +4 more
wiley   +1 more source

Bayesian Optimization-Enhanced Machine Learning for Osteosarcoma Risk Stratification Based on Sphingolipid Metabolism. [PDF]

Hum Mutat
Zhong Y, He R, Jiang Z, Lin Q, Peng F, Jin W.   +5 more
europepmc   +1 more source

ParamNet: A Physics‐Guided Deep Learning Framework for Intelligent Self‐Inversion of Vacuum Optical Levitation Systems

Advanced Intelligent Systems, EarlyView.
A physics‐guided deep learning framework, ParamNet, is introduced for the intelligent self‐inversion of vacuum optical tweezers. By fuzing dual‐branch time–frequency features with physical dynamical constraints, it achieves high‐accuracy calibration of trap parameters from short‐window, low‐frequency trajectories, outperforming traditional methods ...
Qi Zheng, Wenfeng Fan, Feng Li, Jian Wu, Wei Quan   +4 more
wiley   +1 more source

Robotic optimization of powdered beverages leveraging computer vision and Bayesian optimization. [PDF]

Front Robot AI
Szymańska E, Hughes J.
europepmc   +1 more source