Artificial Intelligence Applications to Smart City and Smart Enterprise
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 an...
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| Format: | Book Chapter |
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Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2020
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| 001 | doab_20_500_12854_69323 | ||
| 005 | 20210501 | ||
| 020 | |a books978-3-03936-438-1 | ||
| 020 | |a 9783039364374 | ||
| 020 | |a 9783039364381 | ||
| 024 | 7 | |a 10.3390/books978-3-03936-438-1 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 072 | 7 | |a KNTX |2 bicssc | |
| 100 | 1 | |a Impedovo, Donato |4 edt | |
| 700 | 1 | |a PIRLO, Giuseppe |4 edt | |
| 700 | 1 | |a Impedovo, Donato |4 oth | |
| 700 | 1 | |a PIRLO, Giuseppe |4 oth | |
| 245 | 1 | 0 | |a Artificial Intelligence Applications to Smart City and Smart Enterprise |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2020 | ||
| 300 | |a 1 electronic resource (374 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a 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. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
| 546 | |a English | ||
| 650 | 7 | |a Information technology industries |2 bicssc | |
| 653 | |a spatio-temporal | ||
| 653 | |a residual networks | ||
| 653 | |a bus traffic flow prediction | ||
| 653 | |a advance rate | ||
| 653 | |a shield performance | ||
| 653 | |a principal component analysis | ||
| 653 | |a ANFIS-GA | ||
| 653 | |a tunnel | ||
| 653 | |a online learning | ||
| 653 | |a extreme learning machine | ||
| 653 | |a cyclic dynamics | ||
| 653 | |a transfer learning | ||
| 653 | |a knowledge preservation | ||
| 653 | |a Feature Adaptive | ||
| 653 | |a optimization | ||
| 653 | |a Bacterial Foraging algorithm | ||
| 653 | |a Swarm Intelligence algorithm | ||
| 653 | |a Isolated Microgrid | ||
| 653 | |a traffic surveillance video | ||
| 653 | |a state analysis | ||
| 653 | |a Grassmann manifold | ||
| 653 | |a neural network | ||
| 653 | |a machine-learning | ||
| 653 | |a quality of life | ||
| 653 | |a Better Life Index | ||
| 653 | |a bagging | ||
| 653 | |a ensemble learning | ||
| 653 | |a pedestrian attributes | ||
| 653 | |a surveillance image | ||
| 653 | |a semantic attributes recognition | ||
| 653 | |a multi-label learning | ||
| 653 | |a large-scale database | ||
| 653 | |a traffic congestion detection | ||
| 653 | |a minimizing traffic congestion | ||
| 653 | |a traffic prediction | ||
| 653 | |a deep learning | ||
| 653 | |a urban mobility | ||
| 653 | |a ITS | ||
| 653 | |a Vehicle-to-Infrastructure | ||
| 653 | |a neural networks | ||
| 653 | |a LSTM | ||
| 653 | |a embeddings | ||
| 653 | |a trajectories | ||
| 653 | |a motion behavior | ||
| 653 | |a smart tourism | ||
| 653 | |a driver's behavior detection | ||
| 653 | |a texting and driving | ||
| 653 | |a convolutional neural network | ||
| 653 | |a smart car | ||
| 653 | |a smart cities | ||
| 653 | |a smart infotainment | ||
| 653 | |a driver distraction | ||
| 653 | |a cameras | ||
| 653 | |a convolution | ||
| 653 | |a detection | ||
| 653 | |a image recognition | ||
| 653 | |a DSS | ||
| 653 | |a diabetes prediction | ||
| 653 | |a homecare assistance information system | ||
| 653 | |a muti-attribute analysis | ||
| 653 | |a artificial training dataset | ||
| 653 | |a machine learning | ||
| 653 | |a big data | ||
| 653 | |a data analysis | ||
| 653 | |a sensors | ||
| 653 | |a Internet of Things | ||
| 653 | |a vehicular networks | ||
| 653 | |a VDTN | ||
| 653 | |a routing | ||
| 653 | |a message scheduling | ||
| 653 | |a traffic flow prediction | ||
| 653 | |a wavenet | ||
| 653 | |a TrafficWave | ||
| 653 | |a RNN | ||
| 653 | |a GRU | ||
| 653 | |a SAEs | ||
| 653 | |a risk assessment | ||
| 653 | |a neural architecture search | ||
| 653 | |a recurrent neural network | ||
| 653 | |a automated driving vehicle | ||
| 653 | |a decision support system | ||
| 653 | |a artificial intelligence | ||
| 653 | |a disaster management | ||
| 653 | |a Smart city | ||
| 653 | |a program management | ||
| 653 | |a integrated model | ||
| 653 | |a smart city | ||
| 653 | |a intelligence transportation system | ||
| 653 | |a computer vision | ||
| 653 | |a potential pedestrian safety | ||
| 653 | |a data mining | ||
| 653 | |a healthcare | ||
| 653 | |a Apache Spark | ||
| 653 | |a disease detection | ||
| 653 | |a symptoms detection | ||
| 653 | |a Arabic language | ||
| 653 | |a Saudi dialect | ||
| 653 | |a Twitter | ||
| 653 | |a high performance computing (HPC) | ||
| 653 | |a spatial-temporal dependencies | ||
| 653 | |a traffic periodicity | ||
| 653 | |a graph convolutional network | ||
| 653 | |a traffic speed prediction | ||
| 653 | |a vehicular traffic | ||
| 653 | |a surveillance video | ||
| 653 | |a big data analysis | ||
| 653 | |a autonomous driving | ||
| 653 | |a life quality | ||
| 653 | |a pattern recognition | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/3115 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/69323 |7 0 |z DOAB: description of the publication |