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The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved state-of-the-art applications in many fields, such as computer vision, speech and text processing, medical applications, and IoT (Internet of Things). The probability of a successful outcome from a neural network is linked to selection of an appropriate network architecture and training algorithm. Accordingly, much of the recent research on neural networks has been devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. This book gives significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications.

Information technology industries --- facial image analysis --- facial nerve paralysis --- deep convolutional neural networks --- image classification --- Chinese text classification --- long short-term memory --- convolutional neural network --- Arabic named entity recognition --- bidirectional recurrent neural network --- GRU --- LSTM --- natural language processing --- word embedding --- CNN --- object detection network --- attention mechanism --- feature fusion --- LSTM-CRF model --- elements recognition --- linguistic features --- POS syntactic rules --- action recognition --- fused features --- 3D convolution neural network --- motion map --- long short-term-memory --- tooth-marked tongue --- gradient-weighted class activation maps --- ship identification --- fully convolutional network --- embedded deep learning --- scalability --- gesture recognition --- human computer interaction --- alternative fusion neural network --- deep learning --- sentiment attention mechanism --- bidirectional gated recurrent unit --- Internet of Things --- convolutional neural networks --- graph partitioning --- distributed systems --- resource-efficient inference --- pedestrian attribute recognition --- graph convolutional network --- multi-label learning --- autoencoders --- long-short-term memory networks --- convolution neural Networks --- object recognition --- sentiment analysis --- text recognition --- IoT (Internet of Thing) systems --- medical applications

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The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved state-of-the-art applications in many fields, such as computer vision, speech and text processing, medical applications, and IoT (Internet of Things). The probability of a successful outcome from a neural network is linked to selection of an appropriate network architecture and training algorithm. Accordingly, much of the recent research on neural networks has been devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. This book gives significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications.

Information technology industries --- facial image analysis --- facial nerve paralysis --- deep convolutional neural networks --- image classification --- Chinese text classification --- long short-term memory --- convolutional neural network --- Arabic named entity recognition --- bidirectional recurrent neural network --- GRU --- LSTM --- natural language processing --- word embedding --- CNN --- object detection network --- attention mechanism --- feature fusion --- LSTM-CRF model --- elements recognition --- linguistic features --- POS syntactic rules --- action recognition --- fused features --- 3D convolution neural network --- motion map --- long short-term-memory --- tooth-marked tongue --- gradient-weighted class activation maps --- ship identification --- fully convolutional network --- embedded deep learning --- scalability --- gesture recognition --- human computer interaction --- alternative fusion neural network --- deep learning --- sentiment attention mechanism --- bidirectional gated recurrent unit --- Internet of Things --- convolutional neural networks --- graph partitioning --- distributed systems --- resource-efficient inference --- pedestrian attribute recognition --- graph convolutional network --- multi-label learning --- autoencoders --- long-short-term memory networks --- convolution neural Networks --- object recognition --- sentiment analysis --- text recognition --- IoT (Internet of Thing) systems --- medical applications

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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.

Information technology industries --- 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 --- DSS --- diabetes prediction --- homecare assistance information system --- muti-attribute analysis --- artificial training dataset --- machine learning --- big data --- data analysis --- sensors --- Internet of Things --- vehicular networks --- VDTN --- routing --- message scheduling --- traffic flow prediction --- wavenet --- TrafficWave --- RNN --- GRU --- SAEs --- risk assessment --- neural architecture search --- recurrent neural network --- automated driving vehicle --- decision support system --- artificial intelligence --- disaster management --- Smart city --- program management --- integrated model --- smart city --- intelligence transportation system --- computer vision --- potential pedestrian safety --- data mining --- healthcare --- Apache Spark --- disease detection --- symptoms detection --- Arabic language --- Saudi dialect --- Twitter --- high performance computing (HPC) --- spatial-temporal dependencies --- traffic periodicity --- graph convolutional network --- traffic speed prediction --- vehicular traffic --- surveillance video --- big data analysis --- autonomous driving --- life quality --- pattern recognition

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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.

Information technology industries --- 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 --- DSS --- diabetes prediction --- homecare assistance information system --- muti-attribute analysis --- artificial training dataset --- machine learning --- big data --- data analysis --- sensors --- Internet of Things --- vehicular networks --- VDTN --- routing --- message scheduling --- traffic flow prediction --- wavenet --- TrafficWave --- RNN --- GRU --- SAEs --- risk assessment --- neural architecture search --- recurrent neural network --- automated driving vehicle --- decision support system --- artificial intelligence --- disaster management --- Smart city --- program management --- integrated model --- smart city --- intelligence transportation system --- computer vision --- potential pedestrian safety --- data mining --- healthcare --- Apache Spark --- disease detection --- symptoms detection --- Arabic language --- Saudi dialect --- Twitter --- high performance computing (HPC) --- spatial-temporal dependencies --- traffic periodicity --- graph convolutional network --- traffic speed prediction --- vehicular traffic --- surveillance video --- big data analysis --- autonomous driving --- life quality --- pattern recognition