Muutke küpsiste eelistusi

Predictive Computing and Information Security 1st ed. 2017 [Kõva köide]

, ,
  • Formaat: Hardback, 162 pages, kõrgus x laius: 235x155 mm, kaal: 4026 g, 57 Illustrations, black and white; XXI, 162 p. 57 illus., 1 Hardback
  • Ilmumisaeg: 05-Oct-2017
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811051062
  • ISBN-13: 9789811051067
Teised raamatud teemal:
  • Kõva köide
  • Hind: 104,29 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 122,69 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
Predictive Computing and Information Security 1st ed. 2017Suurem pilt
  • Formaat: Hardback, 162 pages, kõrgus x laius: 235x155 mm, kaal: 4026 g, 57 Illustrations, black and white; XXI, 162 p. 57 illus., 1 Hardback
  • Ilmumisaeg: 05-Oct-2017
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811051062
  • ISBN-13: 9789811051067
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789811051067.html
Märksõnad:

This book describes various methods and recent advances in predictive computing and information security. It highlights various predictive application scenarios to discuss these breakthroughs in real-world settings. Further, it addresses state-of-art techniques and the design, development and innovative use of technologies for enhancing predictive computing and information security.

Coverage also includes the frameworks for eTransportation and eHealth, security techniques, and algorithms for predictive computing and information security based on Internet-of-Things and Cloud computing. As such, the book offers a valuable resource for graduate students and researchers interested in exploring predictive modeling techniques and architectures to solve information security, privacy and protection issues in future communication.

1 Introduction to Predictive Computing
1(16)
1.1 Introduction
1(2)
1.2 Definitions
3(1)
1.3 Pillars of Predictive Computing
4(3)
1.4 Horizons of Predictive Computing
7(1)
1.5 Role of Information Security and Techniques
7(3)
1.6 Summary
10(7)
References
13(4)
2 Predictive Computing and Information Security: A Technical Review
17(38)
2.1 Introduction
17(1)
2.2 Google Trend Analysis
18(2)
2.3 Predictive Computing Techniques
20(7)
2.3.1 Data Handling Techniques
20(1)
2.3.2 Sustainable Techniques
20(3)
2.3.3 Navigation Techniques
23(1)
2.3.4 Intelligent Agents
24(2)
2.3.5 Smart Objects Based Computing
26(1)
2.4 Predictive Computing Frameworks
27(6)
2.4.1 Healthcare Frameworks
27(3)
2.4.2 Smart Home Frameworks
30(1)
2.4.3 Navigation Framework
31(1)
2.4.4 e-Commerce Framework
32(1)
2.5 Information Security Techniques
33(3)
2.5.1 Security Techniques for Cloud Computing
34(1)
2.5.2 Security Techniques for Internet-of-Things
35(1)
2.6 Information Security-Based Frameworks
36(7)
2.6.1 Cloud Computing-Based Security Frameworks
36(5)
2.6.2 IoT-Based Security Frameworks
41(2)
2.7 Challenges and Discussions
43(3)
2.8 Summary
46(9)
References
47(8)
3 Predictive Computing: A Technical Insight
55(18)
3.1 Introduction
55(2)
3.2 Design Architecture of Predictive Computing
57(1)
3.2.1 Predictive Analytics Process
57(1)
3.3 Predictive Model
58(6)
3.3.1 Predictive Model for e-Transportation
58(5)
3.3.2 Predictive Model for Banking
63(1)
3.4 Algorithms for Predictive Computing
64(2)
3.4.1 Local Learning and Model Fusion for Multiple Information Sources
64(1)
3.4.2 Mining from Sparse, Uncertain and Incomplete Data Representation
65(1)
3.5 Mathematical Modelling and Algorithms
66(1)
3.5.1 Probabilistic Learning Model and Statistical Analysis
66(1)
3.5.2 Fuzzy Rule-Based Expert Systems
66(1)
3.5.3 Rule Base Reduction Techniques
67(1)
3.5.4 Recommendation Mining
67(1)
3.6 Clustering Algorithms
67(2)
3.6.1 k-Means Clustering
68(1)
3.6.2 Centroid Generation Using Canopy Clustering
68(1)
3.6.3 Fuzzy k-Means Clustering Technique
68(1)
3.7 Classification Algorithms
69(1)
3.8 Summary
69(4)
References
70(3)
4 Cloud-Based Predictive Computing
73(18)
4.1 Introduction
73(1)
4.2 Related Work
74(3)
4.2.1 Cloud-Based Healthcare Frameworks
74(3)
4.2.2 Predictive Healthcare Applications
77(1)
4.3 IoT-Based Cloud-Centric Design Architecture
77(6)
4.3.1 Application Architecture
80(1)
4.3.2 Predictive Framework for User Activity
81(2)
4.4 Cloud-Based Predictive Computing Design
83(4)
4.4.1 Predictive Analysis of Physical Activities
84(2)
4.4.2 Predictive Efficiency of Framework
86(1)
4.5 Summary
87(4)
References
87(4)
5 Internet of Things Based Predictive Computing
91(16)
5.1 Introduction
91(1)
5.2 IoT Applications
92(4)
5.2.1 Personal and Home Services
93(1)
5.2.2 Industries and Enterprises
94(1)
5.2.3 IoT-Based Utility Services
94(1)
5.2.4 Internet of Things in Healthcare
95(1)
5.3 Major Issues and Challenges
96(3)
5.3.1 Technical Challenges
96(1)
5.3.2 Device Lifetime and Energy Challenge
97(1)
5.3.3 Representation of Massive Dataand Information Challenge
97(1)
5.3.4 Incomplete Data Representation
98(1)
5.4 IoT-Based Predictive Modelling
99(1)
5.4.1 Predictive Model
99(1)
5.4.2 Descriptive Modelling
100(1)
5.5 IoT-Based Predictive Techniques
100(3)
5.5.1 Probabilistic Learning Model and Statistical Analysis
100(1)
5.5.2 Predictive Analytics and Data Mining Algorithms
101(1)
5.5.3 Data Fusion Approach
101(1)
5.5.4 Failure Prediction Using a Tree Ensemble Classifier
102(1)
5.6 Summary
103(4)
References
103(4)
6 Cloud-Based Information Security
107(30)
6.1 Introduction
107(1)
6.2 Related Work
108(16)
6.2.1 Security Issues
110(1)
6.2.2 Privacy Issues
111(2)
6.2.3 Trust Issues
113(1)
6.2.4 Issues with Cloud Service Models
114(3)
6.2.5 Threats in Cloud Computing
117(2)
6.2.6 Attacks on Cloud
119(2)
6.2.7 Cloud-Based Information Security Models
121(3)
6.3 Framework to Maintain Data Integrity
124(9)
6.3.1 Data Integrity Algorithms
126(1)
6.3.2 Performance Analysis of Security Techniques
127(6)
6.4 Summary
133(4)
References
134(3)
7 Applications of Predictive Computing
137(20)
7.1 Introduction
137(1)
7.2 Applications Based Features of Predictive Computing
138(14)
7.2.1 Smart Mobility
138(3)
7.2.2 e-Health
141(6)
7.2.3 e-Logistics
147(5)
7.3 Summary
152(5)
References
152(5)
Appendix: Datasets 157
Dr. P.K. Gupta, a Post-Doctorate from the University of Pretoria, South Africa (201516) in the Department of Electrical, Electronic and Computer Engineering, is currently a Assistant Professor (Sr. Grade) at Jaypee University of Information Technology (JUIT), Himachal Pradesh (HP), India. He has more than 15 years of national and international experience in the Information Technology (IT) industry and academics. He has authored a number of research papers in peer-reviewed international journals and conferences. Further, Dr. Gupta is an Associate Editor of IEEE Access. His research areas include Internet-of-Things, Cloud Computing, Sustainable Computing, and Storage networks. 

Prof. Vipin Tyagi, Fellow-IETE, is currently working as Professor in Computer Science and Engineering department and Head - Faculty of Mathematical Sciences at Jaypee University of Engineering and Technology, Madhya Pradesh (MP), India. He is Vice President of the Computer Society of

India, Region 3, and is associated with the Societys Special Interest Group on Cyber Forensics. He was President of the Engineering Sciences Section of the Indian Science Congress Association for the term 201011. He has published a number of papers in various reputed journals and advanced research series, and has attended several national and international conferences. He is an expert in the area of Cyber Security, Cyber Forensics and Image Processing. 

Prof. Sanjay Kumar Singh is an Associate Professor in the Department of Computer Science and Engineering, Indian Institute of Technology (IIT), Uttar Pradesh (UP), India. He has been certified as a Novell Engineer (CNE) and Novell Administrator (CNA) by Novell Netware, USA. He is a member of LIMSTE, the IEEE, International Association of Engineers and the ISCE. He has over 70 national and international journal publications, book chapters and conference papers to his credit. His research areas include Biometrics,Computer Vision, Image Processing, Video Processing, Pattern Recognition and Artificial Intelligence.