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Titlebook: Artificial Intelligence and Machine Learning; 32nd Benelux Confere Mitra Baratchi,Lu Cao,Frank W. Takes Conference proceedings 2021 Springe

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發(fā)表于 2025-3-28 17:31:54 | 只看該作者
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發(fā)表于 2025-3-29 00:20:50 | 只看該作者
Conference proceedings 2021 Netherlands, in November 2020. Due to the COVID-19 pandemic the conference was held online.?.The 12 papers presented in this volume were carefully reviewed and selected from 41 regular submissions. They address various aspects of artificial intelligence such as natural language processing, agent te
44#
發(fā)表于 2025-3-29 07:06:28 | 只看該作者
1865-0929 eiden, The Netherlands, in November 2020. Due to the COVID-19 pandemic the conference was held online.?.The 12 papers presented in this volume were carefully reviewed and selected from 41 regular submissions. They address various aspects of artificial intelligence such as natural language processing
45#
發(fā)表于 2025-3-29 11:14:47 | 只看該作者
Paggie Kim,Jennifer Burns-Benggon,Haley Reisal space and time, with both designs having linear energy complexity. The designs were implemented in a simulator to successfully solve the one-max optimization problem, serving as a proof of concept for running genetic algorithms as spiking neural networks.
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發(fā)表于 2025-3-29 15:15:37 | 只看該作者
Paggie Kim,Jennifer Burns-Benggon,Haley Reisused to predict phenomena of perceptual organization—with some metric-based processing. Results are discussed against two empirical experiments, one of which conducted along this work, together with the development of a Python version of the SIT encoding algorithm PISA.
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發(fā)表于 2025-3-29 18:49:21 | 只看該作者
A Spiking Neuron Implementation of Genetic Algorithms for Optimization,al space and time, with both designs having linear energy complexity. The designs were implemented in a simulator to successfully solve the one-max optimization problem, serving as a proof of concept for running genetic algorithms as spiking neural networks.
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發(fā)表于 2025-3-29 23:24:24 | 只看該作者
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發(fā)表于 2025-3-30 02:20:52 | 只看該作者
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發(fā)表于 2025-3-30 07:32:12 | 只看該作者
https://doi.org/10.1007/978-3-662-69586-9al activity behaviour change and how the intervention affects physical activity behaviour and its determinants. We also evaluate the contributions of Bayesian network analysis compared to traditional statistical analyses in this field. Finally, possible extensions on the performed analyses are proposed.
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