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Artificial intelligence applications to smart city and smart enterprise / Donato Impedovo (2020)  

Public Titre : Artificial intelligence applications to smart city and smart enterprise Type de document : Monographie Auteurs : Donato Impedovo, Éditeur scientifique ; Giuseppe Pirlo, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 374 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03936-438-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] algorithme génétique [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] gestion urbaine [Termes IGN] Inférence floue [Termes IGN] métadonnées [Termes IGN] navigation autonome [Termes IGN] planification urbaine [Termes IGN] système de transport intelligent [Termes IGN] trafic routier [Termes IGN] ville intelligente [Termes IGN] vision par ordinateur Résumé : (éditeur) Smart cities operate under more resource-efficient management and economy than ordinary cities. As such, advanced business models have emerged around smart cities, which led to the creation of smart enterprises and organizations that depend on advanced technologies. This book includes 21 selected and peer-reviewed articles contributed in the wide spectrum of artificial intelligence applications to smart cities. Chapters refer to the following areas of interest: vehicular traffic prediction, social big data analysis, smart city management, driving and routing, localization, safety, health, and life quality. Note de contenu : 1- Artificial intelligence applications to smart city and smart enterprise 2- Global spatial-temporal graph convolutional network for urban traffic speed prediction 3- TrafficWave: Generative deep learning architecture for vehicular traffic flow prediction 4- Grassmann manifold based state analysis method of traffic surveillance video 5- Improved spatio-temporal residual networks for bus traffic flow prediction 6- Sehaa: A big data analytics tool for healthcare symptoms and diseases detection using Twitter, Apache Spark, and machine learning 7- Smart cities big data algorithms for sensors location 8- Managing a smart city integrated model through smart program management 9- Conceptual framework of an intelligent decision support system for smart city disaster management 10- Vision-based potential pedestrian risk analysis on unsignalized crosswalk using data mining techniques 11- Development of deep learning based human-centered threat assessment for application to automated driving vehicle 12- Modeling and solution of the routing problem in vehicular Delay-Tolerant networks: A dual, deep learning perspective 13- “Texting & Driving” detection using deep convolutional neural networks 14- Deep learning system for vehicular re-routing and congestion avoidance 15- Identifying foreign tourists’ nationality from mobility traces via LSTM neural network and location embeddings 16- Feature adaptive and cyclic dynamic learning based on infinite term memory extreme learning machine 17- LSTM DSS automatism and dataset optimization for diabetes prediction 18- Convolutional models for the detection of firearms in surveillance videos 19- PARNet: A joint loss function and dynamic weights network for pedestrian semantic attributes recognition of smart surveillance image 20- Supervised machine-learning predictive analytics for national quality of life scoring 21- Bacterial foraging-based algorithm for optimizing the powerGeneration of an isolated microgrid 22- Optimizgtion of EPB shield performance with adaptive neuro-fuzzy inference system and Genetic algorithm Numéro de notice : 28448 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/URBANISME Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03936-438-1 En ligne : https://doi.org/10.3390/books978-3-03936-438-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98929