Jabbar, M. A.
Smart Urban Computing Applications. - 1st ed. - 1 online resource (258 pages) - Computing and Information Science and Technology Series . - Computing and Information Science and Technology Series .
Cover -- Half-Title -- RIVER PUBLISHERS SERIES IN COMPUTING AND INFORMATION SCIENCE AND TECHNOLOGY -- Title -- Copyrights -- Contents -- Preface -- List of Contributors -- List of Figures -- List of Tables -- List of Abbreviations -- 1 Requirement Analysis of Data Analytics Software Within the Scope of a Smart University -- 1.1 Introduction -- 1.2 Literature Review -- 1.2.1 Smart campus studies -- 1.2.2 Requirements analysis studies -- 1.3 Proposed Methodology -- 1.3.1 Software development process -- 1.3.2 Software development models and its steps -- 1.4 V-Model -- 1.4.1 Previous steps of the project -- 1.4.2 V-model of software requirements specifications -- 1.4.3 Planning with the core team -- 1.5 Adaptation of the Proposed Methodology -- 1.6 Conclusion -- 1.7 Acknowledgment -- References -- 2 Performance Analysis of Deep Learning Models for Re-identification of a Person in a Public Surveillance System -- 2.1 Introduction -- 2.2 Literature Survey -- 2.2.1 Existing video surveillance commercial products -- 2.2.2 A general automated visual surveillancesystem framework -- 2.2.3 Multi-camera tracking and person re-identification -- 2.2.4 ReID system framework -- 2.2.5 Overview of previous Work in ReID -- 2.2.6 Multi-camera tracking andperson-re-identification datasets -- 2.2.7 Challenges faced by ReID system -- 2.3 Proposed System -- 2.4 Experimental Results and Discussion -- 2.5 Conclusion -- References -- 3 Exploiting Trajectory Data to Improve Smart City Services -- 3.1 Introduction -- 3.2 General Framework of Urban Computing -- 3.3 Trajectory Data and Trajectory Data Mining -- 3.3.1 Trajectory data -- 3.3.2 Trajectory data mining -- 3.3.2.1 Primary mining methods -- 3.3.2.2 Secondary mining methods -- 3.4 Applications of Trajectories -- 3.5 Issues for Trajectory Data Mining -- 3.6 Publicly Available Trajectory Datasets. 3.7 Conclusion and Future Work -- References -- 4 An End-End Framework for Autonomous Driving Cars in a CARLA Simulator -- 4.1 Introduction -- 4.2 Related Work -- 4.2.1 Autonomous driving simulators -- 4.2.2 Object detection -- 4.2.3 Literature review -- 4.3 Proposed Work -- 4.3.1 Methodology -- 4.3.2 Work flow -- 4.3.3 Traffic signal detection -- 4.3.3.1 Dataset selection -- 4.3.3.2 Dataset pre-processing and loading -- 4.3.3.3 Model selection -- 4.3.4 End-End framework -- 4.3.4.1 Dataset selection -- 4.3.4.2 Dataset pre-processing and loading -- 4.3.4.3 Model -- 4.4 Evaluation Metrics -- 4.5 Result of Prediction -- 4.5.1 Traffic signal detection -- 4.5.2 End-End framework -- 4.6 Conclusion -- References -- 5 IoT and Artificial Intelligence Techniques for Public Safety and Security -- 5.1 Introduction -- 5.2 Proposed Method -- 5.3 Smart City Technology Framework -- 5.4 Other Technologies that are Connected -- 5.5 Conclusion and Future Work -- References -- 6 Deep Learning Approaches for the Classification of IoT-based Hyperspectral Images -- 6.1 Introduction -- 6.2 IoT in Remote Sensing and HSI Analysis -- 6.2.1 Applications of IoT-based remote sensing and HSI -- 6.2.2 Challenges in IoT-based remote sensing and HSI -- 6.3 Preliminaries -- 6.3.1 Dimensionality reduction -- 6.3.1.1 Principal component analysis (PCA) -- 6.3.1.2 Kernel PCA -- 6.3.2 Deep learning models -- 6.3.2.1 Gated recurrent unit -- 6.3.2.2 Long short-term memory -- 6.3.2.3 3D CNN -- 6.3.2.4 Auto-encoders -- 6.3.2.5 Generative adversarial network -- 6.3.3 Activation function -- 6.4 Methodology -- 6.4.1 Gated recurrent unit -- 6.4.2 Long short-term memory -- 6.4.3 3D CNN -- 6.4.4 Auto-encoder -- 6.4.5 Generative adversarial network -- 6.5 Experimental Setup -- 6.5.1 Hyperspectral datasets -- 6.5.2 Experimental setup and parameters -- 6.6 Results and Discussion -- 6.7 Conclusion. References -- 7 Artificial Intelligence and IoT for Smart Cities -- 7.1 Introduction -- 7.2 Artificial Intelligence -- 7.3 Artificial Intelligence History -- 7.4 Benefits of AI -- 7.5 Limitations or Challenges of AI -- 7.6 Applications of Artificial Intelligence [14, 21] -- 7.7 IoT -- 7.8 History of IoT -- 7.9 Advantages of the Internet of Things (IoT) -- 7.9 Disadvantages of Internet of Things (IoT) -- 7.10 Application of IoT -- 7.11 Smart Cities -- 7.12 How Do Smart Cities Work? -- 7.13 Advantages of Smart Cities [3] -- 7.14 Disadvantages of Smart Cities -- 7.15 Need for Smart City -- 7.16 Smart City Security -- 7.17 Smart Cities in the Various Parts of the World -- 7.18 Conclusion -- References -- 8 Intelligent Facility Management System forSelf-sustainable Homes in Smart Cities:An Integrated Approach -- 8.1 Introduction -- 8.2 BIM and Energy Efficient Buildings -- 8.3 BIM in MEP Application -- 8.4 Integration of BIM and Wireless Sensor Networks (WSNs) -- 8.5 AI in Smart Homes -- 8.5.1 Data exchange levels of powered smart homes (AI and IoT) -- 8.6 Genetic Algorithm Based Strategy for Efficient Energy Management -- 8.7 Intelligent Model Algorithms for IoT Application -- 8.8 Intelligence Awareness Target (IAT) -- 8.9 Intelligence Energy Efficiency (IE2S) Algorithm -- 8.9.1 Intelligence service (IST) algorithm -- 8.10 BIM and Big Data Analytics -- 8.10.1 Data acquisition -- 8.10.2 Communication technologies -- 8.10.3 Hadoop ecosystem -- 8.10.4 Decision making -- 8.11 Conclusion -- References -- 9 Artificial Intelligence and IoT for Smart Cities -- 9.1 Introduction -- 9.1.1 Impact of AI in smart city -- 9.1.2 Advantages of smart city -- 9.2 Implementation of Smart City -- 9.2.1 Aspects of smart city -- 9.2.2 Smart city components -- 9.3 Artificial Intelligence (AI) in Smart City -- 9.3.1 AI-enabled smart city applications -- 9.3.1.1 Machine learning. 9.3.1.2 Deep learning -- 9.3.2 Applications of AI in smart city applications -- 9.4 Challenges in IoT-based Smart City Implementation -- 9.5 Conclusion -- References -- Index -- About the Editors -- BackCover.
9788770227483
Smart cities.
Internet of things.
Artificial intelligence.
Electronic books.
TD159.4
Smart Urban Computing Applications. - 1st ed. - 1 online resource (258 pages) - Computing and Information Science and Technology Series . - Computing and Information Science and Technology Series .
Cover -- Half-Title -- RIVER PUBLISHERS SERIES IN COMPUTING AND INFORMATION SCIENCE AND TECHNOLOGY -- Title -- Copyrights -- Contents -- Preface -- List of Contributors -- List of Figures -- List of Tables -- List of Abbreviations -- 1 Requirement Analysis of Data Analytics Software Within the Scope of a Smart University -- 1.1 Introduction -- 1.2 Literature Review -- 1.2.1 Smart campus studies -- 1.2.2 Requirements analysis studies -- 1.3 Proposed Methodology -- 1.3.1 Software development process -- 1.3.2 Software development models and its steps -- 1.4 V-Model -- 1.4.1 Previous steps of the project -- 1.4.2 V-model of software requirements specifications -- 1.4.3 Planning with the core team -- 1.5 Adaptation of the Proposed Methodology -- 1.6 Conclusion -- 1.7 Acknowledgment -- References -- 2 Performance Analysis of Deep Learning Models for Re-identification of a Person in a Public Surveillance System -- 2.1 Introduction -- 2.2 Literature Survey -- 2.2.1 Existing video surveillance commercial products -- 2.2.2 A general automated visual surveillancesystem framework -- 2.2.3 Multi-camera tracking and person re-identification -- 2.2.4 ReID system framework -- 2.2.5 Overview of previous Work in ReID -- 2.2.6 Multi-camera tracking andperson-re-identification datasets -- 2.2.7 Challenges faced by ReID system -- 2.3 Proposed System -- 2.4 Experimental Results and Discussion -- 2.5 Conclusion -- References -- 3 Exploiting Trajectory Data to Improve Smart City Services -- 3.1 Introduction -- 3.2 General Framework of Urban Computing -- 3.3 Trajectory Data and Trajectory Data Mining -- 3.3.1 Trajectory data -- 3.3.2 Trajectory data mining -- 3.3.2.1 Primary mining methods -- 3.3.2.2 Secondary mining methods -- 3.4 Applications of Trajectories -- 3.5 Issues for Trajectory Data Mining -- 3.6 Publicly Available Trajectory Datasets. 3.7 Conclusion and Future Work -- References -- 4 An End-End Framework for Autonomous Driving Cars in a CARLA Simulator -- 4.1 Introduction -- 4.2 Related Work -- 4.2.1 Autonomous driving simulators -- 4.2.2 Object detection -- 4.2.3 Literature review -- 4.3 Proposed Work -- 4.3.1 Methodology -- 4.3.2 Work flow -- 4.3.3 Traffic signal detection -- 4.3.3.1 Dataset selection -- 4.3.3.2 Dataset pre-processing and loading -- 4.3.3.3 Model selection -- 4.3.4 End-End framework -- 4.3.4.1 Dataset selection -- 4.3.4.2 Dataset pre-processing and loading -- 4.3.4.3 Model -- 4.4 Evaluation Metrics -- 4.5 Result of Prediction -- 4.5.1 Traffic signal detection -- 4.5.2 End-End framework -- 4.6 Conclusion -- References -- 5 IoT and Artificial Intelligence Techniques for Public Safety and Security -- 5.1 Introduction -- 5.2 Proposed Method -- 5.3 Smart City Technology Framework -- 5.4 Other Technologies that are Connected -- 5.5 Conclusion and Future Work -- References -- 6 Deep Learning Approaches for the Classification of IoT-based Hyperspectral Images -- 6.1 Introduction -- 6.2 IoT in Remote Sensing and HSI Analysis -- 6.2.1 Applications of IoT-based remote sensing and HSI -- 6.2.2 Challenges in IoT-based remote sensing and HSI -- 6.3 Preliminaries -- 6.3.1 Dimensionality reduction -- 6.3.1.1 Principal component analysis (PCA) -- 6.3.1.2 Kernel PCA -- 6.3.2 Deep learning models -- 6.3.2.1 Gated recurrent unit -- 6.3.2.2 Long short-term memory -- 6.3.2.3 3D CNN -- 6.3.2.4 Auto-encoders -- 6.3.2.5 Generative adversarial network -- 6.3.3 Activation function -- 6.4 Methodology -- 6.4.1 Gated recurrent unit -- 6.4.2 Long short-term memory -- 6.4.3 3D CNN -- 6.4.4 Auto-encoder -- 6.4.5 Generative adversarial network -- 6.5 Experimental Setup -- 6.5.1 Hyperspectral datasets -- 6.5.2 Experimental setup and parameters -- 6.6 Results and Discussion -- 6.7 Conclusion. References -- 7 Artificial Intelligence and IoT for Smart Cities -- 7.1 Introduction -- 7.2 Artificial Intelligence -- 7.3 Artificial Intelligence History -- 7.4 Benefits of AI -- 7.5 Limitations or Challenges of AI -- 7.6 Applications of Artificial Intelligence [14, 21] -- 7.7 IoT -- 7.8 History of IoT -- 7.9 Advantages of the Internet of Things (IoT) -- 7.9 Disadvantages of Internet of Things (IoT) -- 7.10 Application of IoT -- 7.11 Smart Cities -- 7.12 How Do Smart Cities Work? -- 7.13 Advantages of Smart Cities [3] -- 7.14 Disadvantages of Smart Cities -- 7.15 Need for Smart City -- 7.16 Smart City Security -- 7.17 Smart Cities in the Various Parts of the World -- 7.18 Conclusion -- References -- 8 Intelligent Facility Management System forSelf-sustainable Homes in Smart Cities:An Integrated Approach -- 8.1 Introduction -- 8.2 BIM and Energy Efficient Buildings -- 8.3 BIM in MEP Application -- 8.4 Integration of BIM and Wireless Sensor Networks (WSNs) -- 8.5 AI in Smart Homes -- 8.5.1 Data exchange levels of powered smart homes (AI and IoT) -- 8.6 Genetic Algorithm Based Strategy for Efficient Energy Management -- 8.7 Intelligent Model Algorithms for IoT Application -- 8.8 Intelligence Awareness Target (IAT) -- 8.9 Intelligence Energy Efficiency (IE2S) Algorithm -- 8.9.1 Intelligence service (IST) algorithm -- 8.10 BIM and Big Data Analytics -- 8.10.1 Data acquisition -- 8.10.2 Communication technologies -- 8.10.3 Hadoop ecosystem -- 8.10.4 Decision making -- 8.11 Conclusion -- References -- 9 Artificial Intelligence and IoT for Smart Cities -- 9.1 Introduction -- 9.1.1 Impact of AI in smart city -- 9.1.2 Advantages of smart city -- 9.2 Implementation of Smart City -- 9.2.1 Aspects of smart city -- 9.2.2 Smart city components -- 9.3 Artificial Intelligence (AI) in Smart City -- 9.3.1 AI-enabled smart city applications -- 9.3.1.1 Machine learning. 9.3.1.2 Deep learning -- 9.3.2 Applications of AI in smart city applications -- 9.4 Challenges in IoT-based Smart City Implementation -- 9.5 Conclusion -- References -- Index -- About the Editors -- BackCover.
9788770227483
Smart cities.
Internet of things.
Artificial intelligence.
Electronic books.
TD159.4