Engineering Applications of Neural Networks (e-bog) af -
Pimenidis, Elias (redaktør)

Engineering Applications of Neural Networks e-bog

802,25 DKK
This book constitutes the refereed proceedings of the 24th International Conference on Engineering Applications of Neural Networks, EANN 2023, held in Leon, Spain, in June 2023.The 41 revised full papers and 8 revised short papers presented were carefully reviewed and selected from 125 submissions. The papers are organized in topical sections on artificial intelligence - computational method…
This book constitutes the refereed proceedings of the 24th International Conference on Engineering Applications of Neural Networks, EANN 2023, held in Leon, Spain, in June 2023.The 41 revised full papers and 8 revised short papers presented were carefully reviewed and selected from 125 submissions. The papers are organized in topical sections on artificial intelligence - computational methods - ethology; classification - filtering  - genetic algorithms; complex dynamic networks' optimization/ graph neural networks; convolutional neural networks/spiking neural networks; deep learning modeling; deep/machine learning  in engineering; LEARNING (reinforcemet - federated - adversarial - transfer); natural language  - recommendation systems.
E-bog 802,25 DKK
Forfattere Pimenidis, Elias (redaktør)
Forlag Springer
Udgivet 06.06.2023
Genrer Educational equipment and technology, computer-aided learning (CAL)
Sprog English
Format pdf
Beskyttelse LCP
ISBN 9783031342042

This book constitutes the refereed proceedings of the 24th International Conference on Engineering Applications of Neural Networks, EANN 2023, held in Leon, Spain, in June 2023.The 41 revised full papers and 8 revised short papers presented were carefully reviewed and selected from 125 submissions. The papers are organized in topical sections on artificial intelligence - computational methods - ethology; classification - filtering  - genetic algorithms; complex dynamic networks' optimization/ graph neural networks; convolutional neural networks/spiking neural networks; deep learning modeling; deep/machine learning  in engineering; LEARNING (reinforcemet - federated - adversarial - transfer); natural language  - recommendation systems.