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Artificial Intelligence Applications to Smart City and Smart Enterprise

Artificial Intelligence Applications to Smart City and Smart Enterprise

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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.

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Keywords

  • advance rate
  • ANFIS-GA
  • Apache Spark
  • Arabic language
  • artificial intelligence
  • artificial training dataset
  • automated driving vehicle
  • Autonomous driving
  • Bacterial Foraging algorithm
  • bagging
  • Better Life Index
  • big data
  • Big Data Analysis
  • bus traffic flow prediction
  • Cameras
  • Computer vision
  • convolution
  • convolutional neural network
  • cyclic dynamics
  • data analysis
  • Data mining
  • decision support system
  • deep learning
  • Detection
  • diabetes prediction
  • Disaster Management
  • disease detection
  • driver distraction
  • driver’s behavior detection
  • DSS
  • Economics, finance, business & management
  • embeddings
  • ensemble learning
  • extreme learning machine
  • Feature Adaptive
  • graph convolutional network
  • Grassmann manifold
  • GRU
  • Healthcare
  • High Performance Computing (HPC)
  • homecare assistance information system
  • image recognition
  • Industry & industrial studies
  • Information technology industries
  • integrated model
  • intelligence transportation system
  • Internet of Things
  • Isolated Microgrid
  • ITS
  • knowledge preservation
  • large-scale database
  • life quality
  • LSTM
  • Machine learning
  • machine-learning
  • Media, information & communication industries
  • message scheduling
  • minimizing traffic congestion
  • motion behavior
  • multi-label learning
  • muti-attribute analysis
  • neural architecture search
  • Neural Network
  • neural networks
  • online learning
  • optimization
  • pattern recognition
  • pedestrian attributes
  • potential pedestrian safety
  • principal component analysis
  • program management
  • Quality of life
  • recurrent neural network
  • residual networks
  • Risk assessment
  • RNN
  • Routing
  • SAEs
  • Saudi dialect
  • semantic attributes recognition
  • sensors
  • shield performance
  • Smart car
  • Smart cities
  • Smart city
  • smart infotainment
  • Smart tourism
  • spatial-temporal dependencies
  • spatio-temporal
  • state analysis
  • surveillance image
  • surveillance video
  • Swarm intelligence algorithm
  • symptoms detection
  • texting and driving
  • traffic congestion detection
  • traffic flow prediction
  • traffic periodicity
  • traffic prediction
  • traffic speed prediction
  • traffic surveillance video
  • TrafficWave
  • trajectories
  • transfer learning
  • tunnel
  • twitter
  • urban mobility
  • VDTN
  • vehicle-to-infrastructure
  • Vehicular networks
  • vehicular traffic
  • wavenet

Links

DOI: 10.3390/books978-3-03936-438-1

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