عنوان
پدید آورنده
موضوع
رده
QA76.9.B45
QA76
.
9
.
B45
کتابخانه
محل استقرار
INTERNATIONAL STANDARD BOOK NUMBER
(Number (ISBN
3319764721
(Number (ISBN
9783319764726
Erroneous ISBN
3319764713
Erroneous ISBN
9783319764719
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Applications of big data analytics :
General Material Designation
[Book]
Other Title Information
trends, issues and challenges /
First Statement of Responsibility
Mohammed M. Alani [and 3 others], editors.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Cham, Switzerland :
Name of Publisher, Distributor, etc.
Springer,
Date of Publication, Distribution, etc.
2018.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource
CONTENTS NOTE
Text of 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
Text of Note
1.3.3 Two-Tier Scheme and Resource Allocation1.3.3.1 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?
Text of Note
2.4 Opportunities of Big Data in Flood Risk Assessment2.5 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
Text of Note
3.4.2 Random Data Split and Normalisation of the Transient Dataset3.4.3 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
Text of Note
3.5.4 Performance Comparison with the Neural Network of the Previous Work3.5.5 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
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8
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SUMMARY OR ABSTRACT
Text of Note
This text/reference reviews the state of the art of big data analytics, with a particular focus on practical applications. An authoritative selection of leading international researchers present detailed analyses of existing trends for storing and analyzing big data, together with valuable insights into the challenges inherent in current approaches and systems. This is further supported by real-world examples drawn from a broad range of application areas, including healthcare, education, and disaster management. The text also covers, typically from an application-oriented perspective, advances in data science in such areas as big data collection, searching, analysis, and knowledge discovery.
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
Springer Nature
Stock Number
com.springer.onix.9783319764726
OTHER EDITION IN ANOTHER MEDIUM
Title
APPLICATIONS OF BIG DATA ANALYTICS.
International Standard Book Number
3319764713
TOPICAL NAME USED AS SUBJECT
Big data.
Education-- Data processing.
Medical care-- Data processing.
Nuclear power plants-- Design and construction.
Algorithms & data structures.
Big data.
Business mathematics & systems.
COMPUTER SCIENCE-- General.
Databases.
Education-- Data processing.
Information retrieval.
Medical care-- Data processing.
Network hardware.
Nuclear power plants-- Design and construction.
Pattern recognition.
(SUBJECT CATEGORY (Provisional
COM-- 000000
UN
UN
DEWEY DECIMAL CLASSIFICATION
Number
005
.
7
Edition
23
LIBRARY OF CONGRESS CLASSIFICATION
Class number
QA76
.
9
.
B45
PERSONAL NAME - ALTERNATIVE RESPONSIBILITY
Alani, Mohammed M.
ORIGINATING SOURCE
Date of Transaction
20200823111032.0
Cataloguing Rules (Descriptive Conventions))
pn
ELECTRONIC LOCATION AND ACCESS
Electronic name
مطالعه متن کتاب [Book]
Y
