Applications of Big Data Analytics : (Record no. 1052277)

001 - CONTROL NUMBER
control field EBC5475199
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
additional material characteristics m o d |
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr cnu||||||||
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200119s2018 xx o ||||0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783319764726
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Cancelled/invalid ISBN 9783319764719
035 ## - SYSTEM CONTROL NUMBER
System control number (MiAaPQ)EBC5475199
035 ## - SYSTEM CONTROL NUMBER
System control number (Au-PeEL)EBL5475199
035 ## - SYSTEM CONTROL NUMBER
System control number (CaPaEBR)ebr11595299
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1046634135
040 ## - CATALOGING SOURCE
Original cataloging agency MiAaPQ
Language of cataloging eng
Description conventions rda
-- pn
Transcribing agency MiAaPQ
Modifying agency MiAaPQ
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA75.5-76.95
082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.7
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (OCLC)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) QA75.5-76.95
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Alani, Mohammed M.
245 10 - TITLE STATEMENT
Title Applications of Big Data Analytics :
Remainder of title Trends, Issues, and Challenges.
264 #1 -
-- Cham :
-- Springer,
-- 2018.
264 #4 -
-- ©2018.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (219 pages)
336 ## - Content
Term text
Code txt
Content rdacontent
337 ## - Media
Term computer
Code c
Media rdamedia
338 ## - Carrier
Term online resource
Code cr
Carrier rdacarrier
490 0# - SERIES STATEMENT
Series statement eBooks on Demand
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 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.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 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.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 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.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 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.
588 ## -
-- Description based on publisher supplied metadata and other sources.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Big data..
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Medical care-Data processing.
655 #4 - INDEX TERM--GENRE/FORM
Genre/form data or focus term Electronic books.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Tawfik, Hissam.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Saeed, Mohammed.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Anya, Obinna.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Print version:
Main entry heading Alani, Mohammed M.
Title Applications of Big Data Analytics : Trends, Issues, and Challenges
Place, publisher, and date of publication Cham : Springer,c2018
International Standard Book Number 9783319764719
797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN)
Corporate name or jurisdiction name as entry element ProQuest (Firm)
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=5475199">https://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=5475199</a>
Link text Click here to view this ebook.
901 ## - LOCAL DATA ELEMENT A, LDA (RLIN)
Platform EBC
901 ## - LOCAL DATA ELEMENT A, LDA (RLIN)
Platform EBL
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Electronic Book
Source of classification or shelving scheme
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Electronic Book
Source of classification or shelving scheme
Holdings
Withdrawn status Lost item Source of classification or shelving scheme Damaged status Not for loan Permanent Location Current Location Shelving location Date acquired Full call number Barcode Date last seen Uniform Resource Identifier Price effective from Koha item type
          UT Tyler Online UT Tyler Online Online 2020-01-23 QA75.5-76.95 EBC5475199 2020-01-23 https://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=5475199 2020-01-23 Electronic Book