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| Autore: |
Zheng Huiru
|
| Titolo: |
Advances in Computational Intelligence Systems : Contributions Presented at the 23rd UK Workshop on Computational Intelligence (UKCI 2024), September 2-4, 2024, Ulster University, Belfast, UK / / edited by Huiru Zheng, David Glass, Maurice Mulvenna, Jun Liu, Hui Wang
|
| Pubblicazione: | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Edizione: | 1st ed. 2024. |
| Descrizione fisica: | 1 online resource (550 pages) |
| Disciplina: | 006.3 |
| Soggetto topico: | Computational intelligence |
| Artificial intelligence | |
| Computational Intelligence | |
| Artificial Intelligence | |
| Altri autori: |
GlassDavid
MulvennaMaurice
LiuJun
WangHui
|
| Nota di contenuto: | Cross-scale Heterogeneous Convolution Change Detection Based on Spatial-Spectral Information Fusion for Remote Sensing Imagery -- Developing Variational Generative Models using Predictive Coding -- Retrieval Augmented Large Language Model Chatbots in Higher Education: A Study on University Open Days -- Contrastive Learning for Limited Medical Data - A Bipartite Strategy for Detection Tasks. |
| Sommario/riassunto: | This book comprises the papers presented at the 23rd UK Workshop on Computational Intelligence (UKCI 2024) held at Ulster University, Belfast, UK from 2 to 4 September 2024. UKCI is the premier UK event for presenting leading research on all aspects of Computational Intelligence. The book is divided into five sections: machine learning, intelligent robotics/navigation, biomedical applications, image processing and applications of computational intelligence. It highlights recent research developments in the field and so will be of interest to those in the academic community and industry seeking a greater understanding of advances in both techniques and applications. |
| Titolo autorizzato: | Advances in Computational Intelligence Systems ![]() |
| ISBN: | 3-031-78857-5 |
| Formato: | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910921013303321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |