Results 181 to 190 of about 3,120,690 (293)

Bayesian networks as prognostic models in oncology: a systematic review and recommendations for clinical practice. [PDF]

BMJ Oncol
Reijnen C, Pijnenborg JMA, Hoskin P, Mcwilliam A, Lucas PJF, Hommersom A, Kwisthout J, Choudhury A.   +7 more
europepmc   +1 more source

Enhancing Specific Stiffness of Nano‐Architected Materials Through Generative AI and Active Learning

Advanced Science, EarlyView.
A structure‐centric generative framework searches voxelated design space directly, coupling a variational autoencoder with multi‐objective optimization and active learning to progressively expand the validated design domain. The discovered nano‐architectures, realized as pyrolytic carbon by two‐photon lithography, deliver 18–23% higher stiffness and 26–
Jinwook Yeo, Peter Serles, Donggeun Park, Julia R. Greer, Seunghwa Ryu   +4 more
wiley   +1 more source

Unraveling Spatiotemporal Heterogeneity and Driving Mechanism of Vegetation Recovery in Co-Seismic Landslide Areas via Nonlinear Trajectories and Bayesian Networks. [PDF]

Ecol Evol
Wan M, Zhao W, Wu J, Yang Y, Zhao J.
europepmc   +1 more source

Terahertz Channel Modeling, Estimation and Localization in RIS‐Assisted Systems

Advanced Electronic Materials, EarlyView.
Reconfigurable intelligent surfaces have become a recent intensive research focus. Based on practical applications, channel strategies for RIS‐assisted terahertz wireless communication systems are categorized into three different types: channel modeling, channel estimation, and channel localization.
Hongjing Wang, Weipeng Wang, Xuan Cong, Shiyu Wang, Gao Li, Sen Gong, Lin Huang, Hongxin Zeng, Yaxin Zhang, Ziqiang Yang   +9 more
wiley   +1 more source

A Treatment Decision Model for Cutaneous Squamous Cell Carcinoma Based on Bayesian Networks. [PDF]

Cancers (Basel)
Ghura E, Gaebel J, Neumuth T, Dietz A, Wichmann G, Stoehr M.   +5 more
europepmc   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

physically interpretable residual strength prediction of corroded pipelines via symbolic Bayesian networks. [PDF]

Sci Rep
Chen M, Zhang Y, Ye Y, Lu Y.
europepmc   +1 more source

Stochastic‐MTJ Sampler Arrays for In‐Array Monte–Carlo Estimation

Advanced Electronic Materials, EarlyView.
A low‐energy‐barrier magnetic tunnel junction array is operated as a probability‐domain sampler: each cell's random switching, programmed through a shared digital‐to‐analog converter, makes the per‐column multiply–accumulate an unbiased Monte–Carlo expectation estimator that returns both a mean and a calibrated uncertainty.
Ran Zhang, Nellie Laleni, Sina Ranjbar, Andreas Tsiougkos, Nandakishor Yadav, Vasilis Pavlidis, Thomas Kämpfe   +6 more
wiley   +1 more source

Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization

Advanced Electronic Materials, EarlyView.
Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali, David Lehninger, Florencio Sánchez, Ignasi Fina   +3 more
wiley   +1 more source

Bayesian networks for predicting clinical outcomes in COVID-19 patients: A retrospective study in a resource-limited setting. [PDF]

PLoS One
Filamant TC, Raherinirina AF, Totohasina A.   +2 more
europepmc   +1 more source