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Biblioteca

MARC Lista (tabellare)
Big data analytics for cyber-physical system in smart city : BDCPS 2020, 28-29 December 2020, Shanghai, China / / Mohammed Atiquzzaman, Neil Yen, Zheng Xu, editors
Big data analytics for cyber-physical system in smart city : BDCPS 2020, 28-29 December 2020, Shanghai, China / / Mohammed Atiquzzaman, Neil Yen, Zheng Xu, editors
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (XLII, 1830 p. 520 illus., 268 illus. in color.)
Disciplina 005.7
Collana Advances in Intelligent Systems and Computing
Soggetto topico Big data
ISBN 981-334-572-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910484946003321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Big data analytics for cyber-physical system in smart city : BDCPS 2020, 28-29 December 2020, Shanghai, China / / Mohammed Atiquzzaman, Neil Yen, Zheng Xu, editors
Big data analytics for cyber-physical system in smart city : BDCPS 2020, 28-29 December 2020, Shanghai, China / / Mohammed Atiquzzaman, Neil Yen, Zheng Xu, editors
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (XLII, 1830 p. 520 illus., 268 illus. in color.)
Disciplina 005.7
Collana Advances in Intelligent Systems and Computing
Soggetto topico Big data
ISBN 981-334-572-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996464423003316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Big Data Analytics for Cyber-Physical System in Smart City : BDCPS 2019, 28-29 December 2019, Shenyang, China / / edited by Mohammed Atiquzzaman, Neil Yen, Zheng Xu
Big Data Analytics for Cyber-Physical System in Smart City : BDCPS 2019, 28-29 December 2019, Shenyang, China / / edited by Mohammed Atiquzzaman, Neil Yen, Zheng Xu
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020
Descrizione fisica 1 online resource (xxxiii, 2,016 pages) : illustrations
Disciplina 005.7
Collana Advances in Intelligent Systems and Computing
Soggetto topico Computational intelligence
Big data
Computational Intelligence
Big Data
Big Data/Analytics
ISBN 981-15-2568-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910484200203321
Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Big data analytics for Internet of things / / edited by Tausifa Jan Saleem, Mohammad Ahsan Chishti
Big data analytics for Internet of things / / edited by Tausifa Jan Saleem, Mohammad Ahsan Chishti
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , [2021]
Descrizione fisica 1 online resource (xx, 376 pages) : illustrations
Disciplina 005.7
Soggetto topico Big data
ISBN 1-119-74077-0
1-119-74078-9
1-119-74076-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- List of Contributors -- List of Abbreviations -- Chapter 1 Big Data Analytics for the Internet of Things: An Overview -- Chapter 2 Data, Analytics and Interoperability Between Systems (IoT) is Incongruous with the Economics of Technology: Evolution of Porous Pareto Partition (P3) -- 2.1 Context -- 2.2 Models in the Background -- 2.3 Problem Space: Are We Asking the Correct Questions? -- 2.4 Solutions Approach: The Elusive Quest to Build Bridges Between Data and Decisions -- 2.5 Avoid This Space: The Deception Space -- 2.6 Explore the Solution Space: Necessary to Ask Questions That May Not Have Answers, Yet -- 2.7 Solution Economy: Will We Ever Get There? -- 2.8 Is This Faux Naïveté in Its Purest Distillate? -- 2.9 Reality Check: Data Fusion -- 2.10 "Double A" Perspective of Data and Tools vs. The Hypothetical Porous Pareto (80/20) Partition -- 2.11 Conundrums -- 2.12 Stigma of Partition vs. Astigmatism of Vision -- 2.13 The Illusion of Data, Delusion of Big Data, and the Absence of Intelligence in AI -- 2.14 In Service of Society -- 2.15 Data Science in Service of Society: Knowledge and Performance from PEAS -- 2.16 Temporary Conclusion -- Acknowledgements -- References -- Chapter 3 Machine Learning Techniques for IoT Data Analytics -- 3.1 Introduction -- 3.2 Taxonomy of Machine Learning Techniques -- 3.2.1 Supervised ML Algorithm -- 3.2.1.1 Classification -- 3.2.1.2 Regression Analysis -- 3.2.1.3 Classification and Regression Tasks -- 3.2.2 Unsupervised Machine Learning Algorithms -- 3.2.2.1 Clustering -- 3.2.2.2 Feature Extraction -- 3.2.3 Conclusion -- References -- Chapter 4 IoT Data Analytics Using Cloud Computing -- 4.1 Introduction -- 4.2 IoT Data Analytics -- 4.2.1 Process of IoT Analytics -- 4.2.2 Types of Analytics -- 4.3 Cloud Computing for IoT -- 4.3.1 Deployment Models for Cloud.
4.3.1.1 Private Cloud -- 4.3.1.2 Public Cloud -- 4.3.1.3 Hybrid Cloud -- 4.3.1.4 Community Cloud -- 4.3.2 Service Models for Cloud Computing -- 4.3.2.1 Software as a Service (SaaS) -- 4.3.2.2 Platform as a Service (PaaS) -- 4.3.2.3 Infrastructure as a Service (IaaS) -- 4.3.3 Data Analytics on Cloud -- 4.4 Cloud-Based IoT Data Analytics Platform -- 4.4.1 Atos Codex -- 4.4.2 AWS IoT -- 4.4.3 IBM Watson IoT -- 4.4.4 Hitachi Vantara Pentaho, Lumada -- 4.4.5 Microsoft Azure IoT -- 4.4.6 Oracle IoT Cloud Services -- 4.5 Machine Learning for IoT Analytics in Cloud -- 4.5.1 ML Algorithms for Data Analytics -- 4.5.2 Types of Predictions Supported by ML and Cloud -- 4.6 Challenges for Analytics Using Cloud -- 4.7 Conclusion -- References -- Chapter 5 Deep Learning Architectures for IoT Data Analytics -- 5.1 Introduction -- 5.1.1 Types of Learning Algorithms -- 5.1.1.1 Supervised Learning -- 5.1.1.2 Unsupervised Learning -- 5.1.1.3 Semi-Supervised Learning -- 5.1.1.4 Reinforcement Learning -- 5.1.2 Steps Involved in Solving a Problem -- 5.1.2.1 Basic Terminology -- 5.1.2.2 Training Process -- 5.1.3 Modeling in Data Science -- 5.1.3.1 Generative -- 5.1.3.2 Discriminative -- 5.1.4 Why DL and IoT? -- 5.2 DL Architectures -- 5.2.1 Restricted Boltzmann Machine -- 5.2.1.1 Training Boltzmann Machine -- 5.2.1.2 Applications of RBM -- 5.2.2 Deep Belief Networks (DBN) -- 5.2.2.1 Training DBN -- 5.2.2.2 Applications of DBN -- 5.2.3 Autoencoders -- 5.2.3.1 Training of AE -- 5.2.3.2 Applications of AE -- 5.2.4 Convolutional Neural Networks (CNN) -- 5.2.4.1 Layers of CNN -- 5.2.4.2 Activation Functions Used in CNN -- 5.2.5 Generative Adversarial Network (GANs) -- 5.2.5.1 Training of GANs -- 5.2.5.2 Variants of GANs -- 5.2.5.3 Applications of GANs -- 5.2.6 Recurrent Neural Networks (RNN) -- 5.2.6.1 Training of RNN -- 5.2.6.2 Applications of RNN.
5.2.7 Long Short-Term Memory (LSTM) -- 5.2.7.1 Training of LSTM -- 5.2.7.2 Applications of LSTM -- 5.3 Conclusion -- References -- Chapter 6 Adding Personal Touches to IoT: A User-Centric IoT Architecture -- 6.1 Introduction -- 6.2 Enabling Technologies for BDA of IoT Systems -- 6.3 Personalizing the IoT -- 6.3.1 Personalization for Business -- 6.3.2 Personalization for Marketing -- 6.3.3 Personalization for Product Improvement and Service Optimization -- 6.3.4 Personalization for Automated Recommendations -- 6.3.5 Personalization for Improved User Experience -- 6.4 Related Work -- 6.5 User Sensitized IoT Architecture -- 6.6 The Tweaked Data Layer -- 6.7 The Personalization Layer -- 6.7.1 The Characterization Engine -- 6.7.2 The Sentiment Analyzer -- 6.8 Concerns and Future Directions -- 6.9 Conclusions -- References -- Chapter 7 Smart Cities and the Internet of Things -- 7.1 Introduction -- 7.2 Development of Smart Cities and the IoT -- 7.3 The Combination of the IoT with Development of City Architecture to Form Smart Cities -- 7.3.1 Unification of the IoT -- 7.3.2 Security of Smart Cities -- 7.3.3 Management of Water and Related Amenities -- 7.3.4 Power Distribution and Management -- 7.3.5 Revenue Collection and Administration -- 7.3.6 Management of City Assets and Human Resources -- 7.3.7 Environmental Pollution Management -- 7.4 How Future Smart Cities Can Improve Their Utilization of the Internet of All Things, with Examples -- 7.5 Conclusion -- References -- Chapter 8 A Roadmap for Application of IoT-Generated Big Data in Environmental Sustainability -- 8.1 Background and Motivation -- 8.2 Execution of the Study -- 8.2.1 Role of Big Data in Sustainability -- 8.2.2 Present Status and Future Possibilities of IoT in Environmental Sustainability -- 8.3 Proposed Roadmap -- 8.4 Identification and Prioritizing the Barriers in the Process.
8.4.1 Internet Infrastructure -- 8.4.2 High Hardware and Software Cost -- 8.4.3 Less Qualified Workforce -- 8.5 Conclusion and Discussion -- References -- Chapter 9 Application of High-Performance Computing in Synchrophasor Data Management and Analysis for Power Grids -- 9.1 Introduction -- 9.2 Applications of Synchrophasor Data -- 9.2.1 Voltage Stability Analysis -- 9.2.2 Transient Stability -- 9.2.3 Out of Step Splitting Protection -- 9.2.4 Multiple Event Detection -- 9.2.5 State Estimation -- 9.2.6 Fault Detection -- 9.2.7 Loss of Main (LOM) Detection -- 9.2.8 Topology Update Detection -- 9.2.9 Oscillation Detection -- 9.3 Utility Big Data Issues Related to PMU-Driven Applications -- 9.3.1 Heterogeneous Measurement Integration -- 9.3.2 Variety and Interoperability -- 9.3.3 Volume and Velocity -- 9.3.4 Data Quality and Security -- 9.3.5 Utilization and Analytics -- 9.3.6 Visualization of Data -- 9.4 Big Data Analytics Platforms for PMU Data Processing -- 9.4.1 Hadoop -- 9.4.2 Apache Spark -- 9.4.3 Apache HBase -- 9.4.4 Apache Storm -- 9.4.5 Cloud-Based Platforms -- 9.5 Conclusions -- References -- Chapter 10 Intelligent Enterprise-Level Big Data Analytics for Modeling and Management in Smart Internet of Roads -- 10.1 Introduction -- 10.2 Fully Convolutional Deep Neural Network for Autonomous Vehicle Identification -- 10.2.1 Detection of the Bounding Box of the License Plate -- 10.2.2 Segmentation Objective -- 10.2.3 Spatial Invariances -- 10.2.4 Model Framework -- 10.2.4.1 Increasing the Layer of Transformation -- 10.2.4.2 Data Format of Sample Images -- 10.2.4.3 Applying Batch Normalization -- 10.2.4.4 Network Architecture -- 10.2.5 Role of Data -- 10.2.6 Synthesizing Samples -- 10.2.7 Invariances -- 10.2.8 Reducing Number of Features -- 10.2.9 Choosing Number of Classes -- 10.3 Experimental Setup and Results -- 10.3.1 Sparse Softmax Loss.
10.3.2 Mean Intersection Over Union -- 10.4 Practical Implementation of Enterprise-Level Big Data Analytics for Smart City -- 10.5 Conclusion -- References -- Chapter 11 Predictive Analysis of Intelligent Sensing and Cloud-Based Integrated Water Management System -- 11.1 Introduction -- 11.2 Literature Survey -- 11.3 Proposed Six-Tier Data Framework -- 11.3.1 Primary Components -- 11.3.2 Contact Unit (FC-37) -- 11.3.3 Internet of Things Communicator (ESP8266) -- 11.3.4 GSM-Based ARM and Control System -- 11.3.5 Methodology -- 11.3.6 Proposed Algorithm -- 11.4 Implementation and Result Analysis -- 11.4.1 Water Report for Home 1 and Home 2 Modules -- 11.5 Conclusion -- References -- Chapter 12 Data Security in the Internet of Things: Challenges and Opportunities -- 12.1 Introduction -- 12.2 IoT: Brief Introduction -- 12.2.1 Challenges in a Secure IoT -- 12.2.2 Security Requirements in IoT Architecture -- 12.2.2.1 Sensing Layer -- 12.2.2.2 Network Layer -- 12.2.2.3 Interface Layer -- 12.2.3 Common Attacks in IoT -- 12.3 IoT Security Classification -- 12.3.1 Application Domain -- 12.3.1.1 Authentication -- 12.3.1.2 Authorization -- 12.3.1.3 Depletion of Resources -- 12.3.1.4 Establishment of Trust -- 12.3.2 Architectural Domain -- 12.3.2.1 Authentication in IoT Architecture -- 12.3.2.2 Authorization in IoT Architecture -- 12.3.3 Communication Channel -- 12.4 Security in IoT Data -- 12.4.1 IoT Data Security: Requirements -- 12.4.1.1 Data: Confidentiality, Integrity, and Authentication -- 12.4.1.2 Data Privacy -- 12.4.2 IoT Data Security: Research Directions -- 12.5 Conclusion -- References -- Chapter 13 DDoS Attacks: Tools, Mitigation Approaches, and Probable Impact on Private Cloud Environment -- 13.1 Introduction -- 13.1.1 State of the Art -- 13.1.2 Contribution -- 13.1.3 Organization -- 13.2 Cloud and DDoS Attack -- 13.2.1 Cloud Deployment Models.
13.2.1.1 Differences Between Private Cloud and Public Cloud.
Record Nr. UNINA-9910554829703321
Hoboken, New Jersey : , : Wiley, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Big data analytics for Internet of things / / edited by Tausifa Jan Saleem, Mohammad Ahsan Chishti
Big data analytics for Internet of things / / edited by Tausifa Jan Saleem, Mohammad Ahsan Chishti
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , [2021]
Descrizione fisica 1 online resource (xx, 376 pages) : illustrations
Disciplina 005.7
Soggetto topico Big data
ISBN 1-119-74077-0
1-119-74078-9
1-119-74076-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- List of Contributors -- List of Abbreviations -- Chapter 1 Big Data Analytics for the Internet of Things: An Overview -- Chapter 2 Data, Analytics and Interoperability Between Systems (IoT) is Incongruous with the Economics of Technology: Evolution of Porous Pareto Partition (P3) -- 2.1 Context -- 2.2 Models in the Background -- 2.3 Problem Space: Are We Asking the Correct Questions? -- 2.4 Solutions Approach: The Elusive Quest to Build Bridges Between Data and Decisions -- 2.5 Avoid This Space: The Deception Space -- 2.6 Explore the Solution Space: Necessary to Ask Questions That May Not Have Answers, Yet -- 2.7 Solution Economy: Will We Ever Get There? -- 2.8 Is This Faux Naïveté in Its Purest Distillate? -- 2.9 Reality Check: Data Fusion -- 2.10 "Double A" Perspective of Data and Tools vs. The Hypothetical Porous Pareto (80/20) Partition -- 2.11 Conundrums -- 2.12 Stigma of Partition vs. Astigmatism of Vision -- 2.13 The Illusion of Data, Delusion of Big Data, and the Absence of Intelligence in AI -- 2.14 In Service of Society -- 2.15 Data Science in Service of Society: Knowledge and Performance from PEAS -- 2.16 Temporary Conclusion -- Acknowledgements -- References -- Chapter 3 Machine Learning Techniques for IoT Data Analytics -- 3.1 Introduction -- 3.2 Taxonomy of Machine Learning Techniques -- 3.2.1 Supervised ML Algorithm -- 3.2.1.1 Classification -- 3.2.1.2 Regression Analysis -- 3.2.1.3 Classification and Regression Tasks -- 3.2.2 Unsupervised Machine Learning Algorithms -- 3.2.2.1 Clustering -- 3.2.2.2 Feature Extraction -- 3.2.3 Conclusion -- References -- Chapter 4 IoT Data Analytics Using Cloud Computing -- 4.1 Introduction -- 4.2 IoT Data Analytics -- 4.2.1 Process of IoT Analytics -- 4.2.2 Types of Analytics -- 4.3 Cloud Computing for IoT -- 4.3.1 Deployment Models for Cloud.
4.3.1.1 Private Cloud -- 4.3.1.2 Public Cloud -- 4.3.1.3 Hybrid Cloud -- 4.3.1.4 Community Cloud -- 4.3.2 Service Models for Cloud Computing -- 4.3.2.1 Software as a Service (SaaS) -- 4.3.2.2 Platform as a Service (PaaS) -- 4.3.2.3 Infrastructure as a Service (IaaS) -- 4.3.3 Data Analytics on Cloud -- 4.4 Cloud-Based IoT Data Analytics Platform -- 4.4.1 Atos Codex -- 4.4.2 AWS IoT -- 4.4.3 IBM Watson IoT -- 4.4.4 Hitachi Vantara Pentaho, Lumada -- 4.4.5 Microsoft Azure IoT -- 4.4.6 Oracle IoT Cloud Services -- 4.5 Machine Learning for IoT Analytics in Cloud -- 4.5.1 ML Algorithms for Data Analytics -- 4.5.2 Types of Predictions Supported by ML and Cloud -- 4.6 Challenges for Analytics Using Cloud -- 4.7 Conclusion -- References -- Chapter 5 Deep Learning Architectures for IoT Data Analytics -- 5.1 Introduction -- 5.1.1 Types of Learning Algorithms -- 5.1.1.1 Supervised Learning -- 5.1.1.2 Unsupervised Learning -- 5.1.1.3 Semi-Supervised Learning -- 5.1.1.4 Reinforcement Learning -- 5.1.2 Steps Involved in Solving a Problem -- 5.1.2.1 Basic Terminology -- 5.1.2.2 Training Process -- 5.1.3 Modeling in Data Science -- 5.1.3.1 Generative -- 5.1.3.2 Discriminative -- 5.1.4 Why DL and IoT? -- 5.2 DL Architectures -- 5.2.1 Restricted Boltzmann Machine -- 5.2.1.1 Training Boltzmann Machine -- 5.2.1.2 Applications of RBM -- 5.2.2 Deep Belief Networks (DBN) -- 5.2.2.1 Training DBN -- 5.2.2.2 Applications of DBN -- 5.2.3 Autoencoders -- 5.2.3.1 Training of AE -- 5.2.3.2 Applications of AE -- 5.2.4 Convolutional Neural Networks (CNN) -- 5.2.4.1 Layers of CNN -- 5.2.4.2 Activation Functions Used in CNN -- 5.2.5 Generative Adversarial Network (GANs) -- 5.2.5.1 Training of GANs -- 5.2.5.2 Variants of GANs -- 5.2.5.3 Applications of GANs -- 5.2.6 Recurrent Neural Networks (RNN) -- 5.2.6.1 Training of RNN -- 5.2.6.2 Applications of RNN.
5.2.7 Long Short-Term Memory (LSTM) -- 5.2.7.1 Training of LSTM -- 5.2.7.2 Applications of LSTM -- 5.3 Conclusion -- References -- Chapter 6 Adding Personal Touches to IoT: A User-Centric IoT Architecture -- 6.1 Introduction -- 6.2 Enabling Technologies for BDA of IoT Systems -- 6.3 Personalizing the IoT -- 6.3.1 Personalization for Business -- 6.3.2 Personalization for Marketing -- 6.3.3 Personalization for Product Improvement and Service Optimization -- 6.3.4 Personalization for Automated Recommendations -- 6.3.5 Personalization for Improved User Experience -- 6.4 Related Work -- 6.5 User Sensitized IoT Architecture -- 6.6 The Tweaked Data Layer -- 6.7 The Personalization Layer -- 6.7.1 The Characterization Engine -- 6.7.2 The Sentiment Analyzer -- 6.8 Concerns and Future Directions -- 6.9 Conclusions -- References -- Chapter 7 Smart Cities and the Internet of Things -- 7.1 Introduction -- 7.2 Development of Smart Cities and the IoT -- 7.3 The Combination of the IoT with Development of City Architecture to Form Smart Cities -- 7.3.1 Unification of the IoT -- 7.3.2 Security of Smart Cities -- 7.3.3 Management of Water and Related Amenities -- 7.3.4 Power Distribution and Management -- 7.3.5 Revenue Collection and Administration -- 7.3.6 Management of City Assets and Human Resources -- 7.3.7 Environmental Pollution Management -- 7.4 How Future Smart Cities Can Improve Their Utilization of the Internet of All Things, with Examples -- 7.5 Conclusion -- References -- Chapter 8 A Roadmap for Application of IoT-Generated Big Data in Environmental Sustainability -- 8.1 Background and Motivation -- 8.2 Execution of the Study -- 8.2.1 Role of Big Data in Sustainability -- 8.2.2 Present Status and Future Possibilities of IoT in Environmental Sustainability -- 8.3 Proposed Roadmap -- 8.4 Identification and Prioritizing the Barriers in the Process.
8.4.1 Internet Infrastructure -- 8.4.2 High Hardware and Software Cost -- 8.4.3 Less Qualified Workforce -- 8.5 Conclusion and Discussion -- References -- Chapter 9 Application of High-Performance Computing in Synchrophasor Data Management and Analysis for Power Grids -- 9.1 Introduction -- 9.2 Applications of Synchrophasor Data -- 9.2.1 Voltage Stability Analysis -- 9.2.2 Transient Stability -- 9.2.3 Out of Step Splitting Protection -- 9.2.4 Multiple Event Detection -- 9.2.5 State Estimation -- 9.2.6 Fault Detection -- 9.2.7 Loss of Main (LOM) Detection -- 9.2.8 Topology Update Detection -- 9.2.9 Oscillation Detection -- 9.3 Utility Big Data Issues Related to PMU-Driven Applications -- 9.3.1 Heterogeneous Measurement Integration -- 9.3.2 Variety and Interoperability -- 9.3.3 Volume and Velocity -- 9.3.4 Data Quality and Security -- 9.3.5 Utilization and Analytics -- 9.3.6 Visualization of Data -- 9.4 Big Data Analytics Platforms for PMU Data Processing -- 9.4.1 Hadoop -- 9.4.2 Apache Spark -- 9.4.3 Apache HBase -- 9.4.4 Apache Storm -- 9.4.5 Cloud-Based Platforms -- 9.5 Conclusions -- References -- Chapter 10 Intelligent Enterprise-Level Big Data Analytics for Modeling and Management in Smart Internet of Roads -- 10.1 Introduction -- 10.2 Fully Convolutional Deep Neural Network for Autonomous Vehicle Identification -- 10.2.1 Detection of the Bounding Box of the License Plate -- 10.2.2 Segmentation Objective -- 10.2.3 Spatial Invariances -- 10.2.4 Model Framework -- 10.2.4.1 Increasing the Layer of Transformation -- 10.2.4.2 Data Format of Sample Images -- 10.2.4.3 Applying Batch Normalization -- 10.2.4.4 Network Architecture -- 10.2.5 Role of Data -- 10.2.6 Synthesizing Samples -- 10.2.7 Invariances -- 10.2.8 Reducing Number of Features -- 10.2.9 Choosing Number of Classes -- 10.3 Experimental Setup and Results -- 10.3.1 Sparse Softmax Loss.
10.3.2 Mean Intersection Over Union -- 10.4 Practical Implementation of Enterprise-Level Big Data Analytics for Smart City -- 10.5 Conclusion -- References -- Chapter 11 Predictive Analysis of Intelligent Sensing and Cloud-Based Integrated Water Management System -- 11.1 Introduction -- 11.2 Literature Survey -- 11.3 Proposed Six-Tier Data Framework -- 11.3.1 Primary Components -- 11.3.2 Contact Unit (FC-37) -- 11.3.3 Internet of Things Communicator (ESP8266) -- 11.3.4 GSM-Based ARM and Control System -- 11.3.5 Methodology -- 11.3.6 Proposed Algorithm -- 11.4 Implementation and Result Analysis -- 11.4.1 Water Report for Home 1 and Home 2 Modules -- 11.5 Conclusion -- References -- Chapter 12 Data Security in the Internet of Things: Challenges and Opportunities -- 12.1 Introduction -- 12.2 IoT: Brief Introduction -- 12.2.1 Challenges in a Secure IoT -- 12.2.2 Security Requirements in IoT Architecture -- 12.2.2.1 Sensing Layer -- 12.2.2.2 Network Layer -- 12.2.2.3 Interface Layer -- 12.2.3 Common Attacks in IoT -- 12.3 IoT Security Classification -- 12.3.1 Application Domain -- 12.3.1.1 Authentication -- 12.3.1.2 Authorization -- 12.3.1.3 Depletion of Resources -- 12.3.1.4 Establishment of Trust -- 12.3.2 Architectural Domain -- 12.3.2.1 Authentication in IoT Architecture -- 12.3.2.2 Authorization in IoT Architecture -- 12.3.3 Communication Channel -- 12.4 Security in IoT Data -- 12.4.1 IoT Data Security: Requirements -- 12.4.1.1 Data: Confidentiality, Integrity, and Authentication -- 12.4.1.2 Data Privacy -- 12.4.2 IoT Data Security: Research Directions -- 12.5 Conclusion -- References -- Chapter 13 DDoS Attacks: Tools, Mitigation Approaches, and Probable Impact on Private Cloud Environment -- 13.1 Introduction -- 13.1.1 State of the Art -- 13.1.2 Contribution -- 13.1.3 Organization -- 13.2 Cloud and DDoS Attack -- 13.2.1 Cloud Deployment Models.
13.2.1.1 Differences Between Private Cloud and Public Cloud.
Record Nr. UNINA-9910678137803321
Hoboken, New Jersey : , : Wiley, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Big data analytics for large-scale multimedia search / / edited by Stefanos Vrochidis [and three others]
Big data analytics for large-scale multimedia search / / edited by Stefanos Vrochidis [and three others]
Autore Vrochidis Stefanos <1975->
Pubbl/distr/stampa Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , [2019]
Descrizione fisica 1 online resource (375 pages)
Disciplina 005.7
Collana THEi Wiley ebooks.
Soggetto topico Multimedia data mining
Big data
ISBN 1-119-37699-8
1-119-37698-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910544580603321
Vrochidis Stefanos <1975->  
Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , [2019]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Big data analytics for large-scale multimedia search / / edited by Stefanos Vrochidis [and three others]
Big data analytics for large-scale multimedia search / / edited by Stefanos Vrochidis [and three others]
Autore Vrochidis Stefanos <1975->
Pubbl/distr/stampa Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , [2019]
Descrizione fisica 1 online resource (375 pages)
Disciplina 005.7
Collana THEi Wiley ebooks.
Soggetto topico Multimedia data mining
Big data
ISBN 1-119-37699-8
1-119-37698-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910824409003321
Vrochidis Stefanos <1975->  
Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , [2019]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Big data analytics for large-scale multimedia search / / edited by Stefanos Vrochidis [and three others]
Big data analytics for large-scale multimedia search / / edited by Stefanos Vrochidis [and three others]
Pubbl/distr/stampa Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , [2019]
Descrizione fisica 1 online resource (375 pages)
Disciplina 005.7
Soggetto topico Multimedia data mining
Big data
Soggetto genere / forma Electronic books.
ISBN 1-119-37699-8
1-119-37698-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910467073603321
Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , [2019]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Big Data Analytics for Smart Transport and Healthcare Systems / / Saeid Pourroostaei Ardakani and Ali Cheshmehzangi
Big Data Analytics for Smart Transport and Healthcare Systems / / Saeid Pourroostaei Ardakani and Ali Cheshmehzangi
Autore Pourroostaei Ardakani Saeid
Edizione [First edition.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore Pte Ltd., , [2023]
Descrizione fisica 1 online resource (197 pages)
Disciplina 005.7
Collana Urban Sustainability Series
Soggetto topico Big data
Medical care - Data processing
Transportation - Data processing
ISBN 981-9966-20-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto The Role of Big Data Analytics in Urban Systems: Review and Prospect for Smart Transport and Healthcare Systems -- Smart Transport -- Big Data Analysis for an Optimised Classification for Flight Status: Prediction Analysis using Machine Learning Classifiers -- On-Board Unit Freight Transport Data Analysis and Prediction: Big Data Analysis for Data Pre-processing and Result Accuracy -- Data-driven Multi-target Prediction Analysis for Driving Pattern Recognition: A Machine Learning Approach to enhance Prediction Accuracy -- A Predictive Data Analysis for Traffic Accidents: Real-time Data use for Mobility Improvement and Accident Reduction -- Smart Healthcare -- Healthcare Infrastructure Development and Pandemic Prevention: An Optimal Model for Healthcare Investment using Big Data -- Big Data for Social Media Analysis during the COVID-19 Pandemic: An Emotion Analysis based on Influences from Social Networks -- Big Data-enabled Time Series analysis for Climate Change Analysis in Brazil: An Artificial Neural Network Machine Learning Model -- Optimized Clustering Model for Healthcare Sentiments on Twitter: A Big Data Analysis Approach -- Big Data Analytics and the Future of Smart Transport and Healthcare Systems.
Record Nr. UNINA-9910768440903321
Pourroostaei Ardakani Saeid  
Singapore : , : Springer Nature Singapore Pte Ltd., , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Big Data Analytics in Astronomy, Science, and Engineering [[electronic resource] ] : 10th International Conference on Big Data Analytics, BDA 2022, Aizu, Japan, December 5–7, 2022, Proceedings / / edited by Shelly Sachdeva, Yutaka Watanobe, Subhash Bhalla
Big Data Analytics in Astronomy, Science, and Engineering [[electronic resource] ] : 10th International Conference on Big Data Analytics, BDA 2022, Aizu, Japan, December 5–7, 2022, Proceedings / / edited by Shelly Sachdeva, Yutaka Watanobe, Subhash Bhalla
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (XII, 246 p. 136 illus., 112 illus. in color.)
Disciplina 005.7
Collana Lecture Notes in Computer Science
Soggetto topico Big data
Big Data
ISBN 3-031-28350-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Data Science: Systems -- Architectures -- Big Data Analytics in Healthcare Support Systems -- Information Interchange of Web Data Resources -- Business Analytics.
Record Nr. UNISA-996517752603316
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui

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