Alani, Mohammed M.

Applications of Big Data Analytics : Trends, Issues, and Challenges. - 1 online resource (219 pages) - eBooks on Demand .

Intro -- Preface -- Organization of the Book -- Contents -- 1 Big Data Environment for Smart Healthcare Applications Over 5G Mobile Network -- 1.1 Introduction -- 1.1.1 Smart Devices -- 1.1.2 Future Challenges -- 1.2 Background -- 1.2.1 5G Enabling Technologies -- 1.2.2 Infrastructure-Based RNs -- 1.2.2.1 Fixed Relay Nodes -- 1.2.2.2 Mobile Relay Nodes -- 1.2.3 5G Network Slicing -- 1.2.3.1 Data Traffic Aggregation Model -- 1.2.4 Resource Allocation Scheme (RAS) -- 1.3 Resource Allocation Scheme Environment -- 1.3.1 Related Works -- 1.3.2 System Models -- 1.3.2.1 Service Slices -- 1.3.2.2 Virtual Network -- 1.3.2.3 Physical Resources -- 1.3.3 Two-Tier Scheme and Resource Allocation -- 1.3.3.1 Services Allocation -- 1.3.3.2 Service Slices Strategy -- 1.3.3.3 Resource Allocation -- 1.4 Simulation Approach -- 1.4.1 Simulation Setup -- 1.4.2 QoS of Radio Bearers -- 1.4.3 Radio Resource Allocation Algorithm -- 1.5 Simulation Scenarios -- 1.5.1 OPNET 5G Model Description -- 1.5.2 Experimental Results -- 1.6 Conclusion -- References -- 2 Challenges and Opportunities of Using Big Data for Assessing Flood Risks -- 2.1 Introduction -- 2.2 Impact of Flood as a Natural Disaster -- 2.3 Big Data for Flood Risk Management -- 2.3.1 How Can Big Data Help? -- 2.4 Opportunities of Big Data in Flood Risk Assessment -- 2.5 Challenges of Predicting Flood Risks -- 2.6 System Architecture Implementing Big Data -- 2.6.1 Framework of the Assessment Model -- 2.7 Current Research on Flood Prediction Using Big Data -- 2.8 Conclusion -- References -- 3 A Neural Networks Design Methodology for Detecting Loss of Coolant Accidents in Nuclear Power Plants -- 3.1 Introduction -- 3.2 Approaches for Monitoring the Safety of Nuclear Power Plants -- 3.3 Large Break Loss of Coolant Accidents of a PHWR -- 3.4 The Neural Networks Training Methodology -- 3.4.1 Performance Measures. 3.4.2 Random Data Split and Normalisation of the Transient Dataset -- 3.4.3 Training of 1-Hidden Layer MLPs and Selection of the Optimised 1-Hidden Layer MLP -- 3.4.4 Training of 2-Hidden Layer MLPs and Selection of the Optimised 2-Hidden Layer MLP -- 3.4.5 Training the Optimised 2-Hidden Layer MLP on Linear Interpolation Dataset and Transient Dataset -- 3.5 Results -- 3.5.1 The Optimised 1-Hidden Layer MLP -- 3.5.2 The Optimised 2-Hidden Layer MLP -- 3.5.3 Training the Optimised 2-Hidden Layer MLP on Linear Interpolation Dataset and Transient Dataset -- 3.5.4 Performance Comparison with the Neural Network of the Previous Work -- 3.5.5 Performance Comparison with Exhaustive Training of All 2-Hidden Layer Architectures -- 3.6 Discussion -- 3.7 Conclusion -- References -- 4 Evolutionary Deployment and Hill Climbing-Based Movements of Multi-UAV Networks in Disaster Scenarios -- 4.1 Introduction -- 4.2 Related Work -- 4.2.1 Deployment Problem -- 4.2.2 Mobility Models for Disaster Scenarios -- 4.3 Modeling Disaster Scenarios -- 4.3.1 Disaster Scenario Layout -- 4.3.2 Mobility of Victims -- 4.3.3 0th Responders -- 4.3.4 Communications in Disaster Scenarios -- 4.4 Our Proposed Approach: Evolutionary Deployment and Hill Climbing-Based Movements -- 4.4.1 Initial Deployment -- 4.4.1.1 Formal Definition of the Problem -- 4.4.1.2 An Evolutionary Algorithm Approach -- 4.4.2 Adaptation to the Changing Conditions -- 4.4.2.1 Formal Definition of the Problem -- 4.4.2.2 A Local Search Algorithms Approach -- 4.5 Simulation and Results -- 4.5.1 Disaster Scenario Description -- 4.5.2 Simulation Setup -- 4.5.3 Results and Analysis -- 4.6 Conclusions -- References -- 5 Detection of Obstructive Sleep Apnea Using DeepNeural Network -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Deep Neural Networks -- 5.3 Methodology -- 5.3.1 Data Collection -- 5.3.2 Data Preprocessing. 5.3.2.1 RR Intervals Detection -- 5.3.2.2 RR Intervals Segmentation -- 5.3.3 Feature Extraction -- 5.3.4 Feature Selection -- 5.3.5 Model Preparation -- 5.3.6 Model Classification -- 5.3.6.1 Deep Learning Approach -- 5.3.6.2 Traditional Classification Algorithms -- 5.4 Results -- 5.4.1 Features Importance -- 5.4.2 Minute-Based Classification -- 5.4.3 Minute-Class-Based Classification -- 5.5 Conclusion -- Appendix -- References -- 6 A Study of Data Classification and Selection Techniques to Diagnose Headache Patients -- 6.1 Introduction -- 6.2 Intelligent-Driven Models -- 6.3 Knowledge-Based Headache CDSMs -- 6.4 Knowledge Acquisition -- 6.4.1 Clinical Guidelines -- 6.4.2 Summarizing Attributes -- 6.4.3 Extracting and Formulating Diagnostic Rules -- 6.5 Summary and Limitations -- 6.6 Conclusion and Future Plan -- References -- 7 Applications of Educational Data Mining and Learning Analytics Tools in Handling Big Data in Higher Education -- 7.1 Introduction -- 7.2 Educational Data Mining and Learning Analytics -- 7.3 Methods in Educational Data Mining and Learning Analytics -- 7.4 Applications of EDM and LA in Education -- 7.5 EDM and LA Tools -- 7.6 Case Studies -- 7.7 Conclusion and Future Directions -- References -- 8 Handling Pregel's Limits in Big Graph Processing in the Presence of High-Degree Vertices -- 8.1 Introduction -- 8.2 MapReduce vs Pregel Programming Model -- 8.3 GPHDV: A Solution for Big Graph Partitioning/Processing in the Presence of High-Degree Vertices -- 8.4 Experiments -- 8.5 Related Work -- 8.6 Conclusion and Future Work -- References -- 9 Nature-Inspired Radar Charts as an Innovative Big Data Analysis Tool -- 9.1 Introduction -- 9.2 Using a Common Visualization Metaphor -- 9.3 Challenges and Problems in Big Data Visualization -- 9.4 A View to the Future: Adopting Artificial Intelligence and Virtual Reality -- References. 10 Search of Similar Programs Using Code Metrics and Big Data-Based Assessment of Software Reliability -- 10.1 Introduction -- 10.2 State of Art -- 10.3 Main Approach -- 10.4 Concept of Software Systems Similarity -- 10.4.1 Metrics and Procedure -- 10.4.2 Search Results of a Similar Program -- 10.5 Map and Reduce Steps: Preliminary Processing and Reducing of Data Set Size -- 10.6 Case Study: The Research of Reliability Metrics for Similar Systems -- 10.6.1 Reliability Metrics and Procedure of Their Research -- 10.6.2 Research of Reliability Similarity for System Versions -- 10.7 Results and Discussion -- 10.8 Conclusion and Future Work -- References -- Index.

9783319764726


Big data..
Medical care-Data processing.


Electronic books.

QA75.5-76.95

005.7