Results 91 to 100 of about 3,905,952 (289)

Neural networks using Bayesian training [PDF]

, 2003
summary:Bayesian probability theory provides a framework for data modeling. In this framework it is possible to find models that are well-matched to the data, and to use these models to make nearly optimal predictions.
Levický, Miroslav, Andrejková, Gabriela   +1 more
core   +1 more source

Artificial Intelligence Meets Micro/Nanorobotics

Advanced Materials, EarlyView.
Artificial intelligence is transforming micro‐ and nanorobots from externally controlled, task‐specific machines into adaptive, autonomous systems. Machine learning, multimodal perception, digital twins, AI‐guided materials and geometry design enhance propulsion, localization, decision‐making, whichaccelerates clinical and environmental applications ...
Fatma M. Yurtsever, Minitha Cherukutty Ramakrishnan, Ian Kuula Ross, Ivan Zelinka, Hamed Shahsavan, Michal Otyepka, Martin Pumera   +6 more
wiley   +1 more source

Bayesian Sheaf Neural Networks

CoRR
32 pages, 4 ...
Gillespie, Patrick, Hamdan, Layal Bou, Schizas, Ioannis, Boothe, David L., Maroulas, Vasileios   +4 more
openaire   +3 more sources

Bayesian Learning of Neural Network Architectures

CoRR, 2019
The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019)
Georgi Dikov, Patrick van der Smagt, Justin Bayer   +2 more
openaire   +2 more sources

Electrolyte Engineering Challenges and Opportunities for Next‐Generation Aqueous Ammonium‐Ion Batteries

Advanced Materials, EarlyView.
Aqueous ammonium‐ion batteries (AAIBs) face a hydrogen‐bond paradox: the HB network enables fast NH4+ transport but triggers water decomposition. This review dissects this dilemma, evaluates multiple electrolyte engineering strategies, and outlines four future directions for next‐generation AAIB design ABSTRACT Aqueous ammonium‐ion batteries (AAIBs ...
Zi‐Hang Huang, Xiaoyu Liu, Shuai Mao, Lingfeng Zhu, Zhijun Wu, Hongge Pan, Tianyi Ma   +6 more
wiley   +1 more source

Bioengineered Interfaces for Peripheral Nerve Sensory Restoration

Advanced Materials, EarlyView.
Half of amputees abandon their prosthetics for lack of feeling. This review charts the full path from peripheral nerve injury to restored sensation, through surgical, regenerative, noninvasive, and implanted approaches, and shows how injury type and interface material properties determine which strategy can deliver naturalistic feedback, and why ...
Sydney Swedick, Salim El Hadwe, Kesi Liang, George G. Malliaras, Damiano G. Barone   +4 more
wiley   +1 more source

Bayesian neural network learning for repeat purchase modelling in direct marketing. [PDF]

We focus on purchase incidence modelling for a European direct mail company. Response models based on statistical and neural network techniques are contrasted.
Van den Poel, D, Vanthienen, Jan, Baesens, Bart, Dedene, Guido, Viaene, Stijn   +4 more
core  

Advanced MXene‐Based Multifunctional Nanoarchitecture Materials Engineered for Adsorptive Cleanup of Hazardous Radioactive Pollutants: A Comprehensive Critical Review

Advanced Materials Interfaces, EarlyView.
This work critically reviews MXenes as highly effective multifunctional nanomaterials for the adsorption of radio‐contaminants, demonstrating a remarkable adsorption capacity of up to 1376.75 mg/g and cyclic stability of 2–8 cycles, with complexation, electrostatic interactions, and the numerical strength of MXene active sites playing a key operational
Stephen Sunday Emmanuel, Shepherd M. Tichapondwa   +1 more
wiley   +1 more source

The use of Bayesian networks to facilitate implementation of water demand management strategies [PDF]

, 2008
Bayesian networks have received increasing recognition in recent years as a potentially effective tool in supporting water management decisions. Despite a number of reports of their use, no formal evaluation of the effectiveness of Bayesian networks ...
Inman, David
core   +4 more sources

Deep learning solutions for smart city challenges in urban development

Scientific Reports
In the realm of urban planning, the integration of deep learning technologies has emerged as a transformative force, promising to revolutionize the way cities are designed, managed, and optimized.
Pengjun Wu, Zhanzhi Zhang, Xueyi Peng, Ran Wang   +3 more
doaj   +1 more source