Vai al contenuto principale della pagina

Computational Intelligence and Intelligent Systems : 10th International Symposium, ISICA 2018, Jiujiang, China, October 13–14, 2018, Revised Selected Papers / / edited by Hu Peng, Changshou Deng, Zhijian Wu, Yong Liu



(Visualizza in formato marc)    (Visualizza in BIBFRAME)

Titolo: Computational Intelligence and Intelligent Systems : 10th International Symposium, ISICA 2018, Jiujiang, China, October 13–14, 2018, Revised Selected Papers / / edited by Hu Peng, Changshou Deng, Zhijian Wu, Yong Liu Visualizza cluster
Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2019
Edizione: 1st ed. 2019.
Descrizione fisica: 1 online resource (XI, 378 p. 152 illus., 83 illus. in color.)
Disciplina: 016.403
Soggetto topico: Artificial intelligence
Algorithms
Computer vision
Artificial Intelligence
Computer Vision
Persona (resp. second.): PengHu
DengChangshou
WuZhijian
LiuYong
Nota di bibliografia: Includes bibliographical references and index.
Nota di contenuto: Nature-Inspired Computing -- Bio-Inspired Computing -- Novel Operators in Evolutionary Algorithms -- Automatic Object Segmentation and Detection, and Image Colorization -- Multilingual Automatic Document Classication and Translation -- Knowledge-Based Articial Intelligence -- Predictive Data Mining.
Sommario/riassunto: This book constitutes the thoroughly refereed proceedings of the 10th International Symposium, ISICA 2018, held in Jiujiang, China, in October 2018. The 32 full papers presented were carefully reviewed and selected from 83 submissions. The papers are organized in topical sections on nature-inspired computing; bio-inspired computing; novel operators in evolutionary algorithms; automatic object segmentation and detection; and image colorization; multilingual automatic document classication and translation; knowledge-based articial intelligence; predictive data mining.
Titolo autorizzato: Computational Intelligence and Intelligent Systems  Visualizza cluster
ISBN: 981-13-6473-7
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910350233903321
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Serie: Communications in Computer and Information Science, . 1865-0937 ; ; 986
Artificial Intelligence Applications and Innovations [[electronic resource] ] : 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings, Part II / / edited by Ilias Maglogiannis, Lazaros Iliadis, Elias Pimenidis
Machine Learning and Metaheuristics Algorithms, and Applications [[electronic resource] ] : First Symposium, SoMMA 2019, Trivandrum, India, December 18–21, 2019, Revised Selected Papers / / edited by Sabu M. Thampi, Ljiljana Trajkovic, Kuan-Ching Li, Swagatam Das, Michal Wozniak, Stefano Berretti
The Development of Deep Learning Technologies [[electronic resource] ] : Research on the Development of Electronic Information Engineering Technology in China
Computing Science, Communication and Security [[electronic resource] ] : First International Conference, COMS2 2020, Gujarat, India, March 26–27, 2020, Revised Selected Papers / / edited by Nirbhay Chaubey, Satyen Parikh, Kiran Amin
Artificial intelligence and security : 6th International Conference, ICAIS 2020, Hohhot, China, July 17-20, 2020, Proceedings. Part II / / Xingming Sun; Jinwei Wang; Elisa Bertino
Artificial intelligence and security : 6th International Conference, ICAIS 2020, Hohhot, China, July 17-20, 2020 : proceedings. Part I / / Xingming Sun; Jinwei Wang; Elisa Bertino
Information, communication and computing technology : 5th International Conference, ICICCT 2020, New Delhi, India, May 9, 2020, revised selected papers / / edited by Costin Badica [and three others]
AI - the new intelligence in sales : tools, applications and potentials of Artificial Intelligence / / Livia Rainsberger
Rainsberger Livia
Hybrid intelligent approaches for smart energy : practical applications / / edited by Senthil Kumar Mohan [and three others]
Transparency and Interpretability for Learned Representations of Artificial Neural Networks / / Richard Meyes
Meyes Richard