Reprint
Artificial Intelligence Applications to Smart City and Smart Enterprise
Edited by
November 2020
374 pages
- ISBN978-3-03936-437-4 (Hardback)
- ISBN978-3-03936-438-1 (PDF)
This book is a reprint of the Special Issue Artificial Intelligence Applications to Smart City and Smart Enterprise that was published in
Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary
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.
Format
- Hardback
License
© 2021 by the authors; CC BY license
Keywords
spatio-temporal; residual networks; bus traffic flow prediction; advance rate; shield performance; principal component analysis; ANFIS-GA; tunnel; online learning; extreme learning machine; cyclic dynamics; transfer learning; knowledge preservation; Feature Adaptive; optimization; Bacterial Foraging algorithm; Swarm Intelligence algorithm; Isolated Microgrid; traffic surveillance video; state analysis; Grassmann manifold; neural network; machine-learning; quality of life; Better Life Index; bagging; ensemble learning; pedestrian attributes; surveillance image; semantic attributes recognition; multi-label learning; large-scale database; traffic congestion detection; minimizing traffic congestion; traffic prediction; deep learning; urban mobility; ITS; Vehicle-to-Infrastructure; neural networks; LSTM; embeddings; trajectories; motion behavior; smart tourism; driver’s behavior detection; texting and driving; convolutional neural network; smart car; smart cities; smart infotainment; driver distraction; cameras; convolution; detection; image recognition; LSTM; DSS; diabetes prediction; homecare assistance information system; muti-attribute analysis; artificial training dataset; smart cities; machine learning; big data; data analysis; sensors; Internet of Things; vehicular networks; VDTN; routing; message scheduling; deep learning; traffic flow prediction; wavenet; TrafficWave; deep learning; RNN; LSTM; GRU; SAEs; risk assessment; deep learning; neural architecture search; recurrent neural network; automated driving vehicle; decision support system; big data; artificial intelligence; Internet of Things; disaster management; Smart city; program management; integrated model; smart city; intelligence transportation system; computer vision; potential pedestrian safety; data mining; smart cities; healthcare; Apache Spark; disease detection; symptoms detection; Arabic language; Saudi dialect; Twitter; machine learning; big data; high performance computing (HPC); spatial-temporal dependencies; traffic periodicity; graph convolutional network; traffic speed prediction; smart city; artificial intelligence; vehicular traffic; surveillance video; big data analysis; computer vision; autonomous driving; life quality; healthcare; sensors; machine learning; pattern recognition