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E-raamat: Applications of Machine Learning in Big-Data Analytics and Cloud Computing

(The American University in the Emirates, UAE), (Indian Institute of Technology, India), (Georgian Technical University, Russia), (National Institute of Technology Durgapur, India), (Principal, Krupajal Computer Academy, India)
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Applications of Machine Learning in Big-Data Analytics and Cloud ComputingSuurem pilt
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This book introduces the state-of-the-art trends and advances in the use of Machine Learning in Cloud and Big Data Analytics.The book will serve as a reference for Data Scientists, systems architects, developers, new researchers and graduate level students in Computer and Data Science. The book will describe the concepts necessary to understand current Machine Learning issues, challenges and possible solutions as well as upcoming trends in Big Data Analytics.

Cloud Computing and Big Data technologies have becomethe new descriptors of the digital age. The global amount of digital data hasincreased more than nine times in volume in just five years and by 2030 itsvolume may reach a staggering 65 trillion gigabytes. This explosion of data hasled to opportunities and transformation in various areas such as healthcare,enterprises, industrial manufacturing and transportation. New Cloud Computingand Big Data tools endow researchers and analysts with novel techniques andopportunities to collect, manage and analyze the vast quantities of data.

 

In Cloud and Big Data Analytics, Swarm Intelligence andDeep Learning are two developing type of Machine Learning techniques that showenormous potential for solving complex business problems. Deep Learning enablescomputers to analyze large quantities of unstructured and binary data and todeduce relationships without requiring specific models or programminginstructions.

 

This book introduces the state-of-the-art trends andadvances in the use of Machine Learning in Cloud and Big Data Analytics. Thebook will serve as a reference for data scientists, systems architects,developers, new researchers and graduate level students in Computer and DataScience. The book will describe the concepts necessary to understand currentMachine Learning issues, challenges and possible solutions as well as upcomingtrends in Big Data Analytics.

Preface xv
List of Contributors
xxi
List of Figures
xxv
List of Tables
xxix
List of Abbreviations
xxxi
1 Pattern Analysis of COVID-19 Death and Recovery Cases Data of Countries Using Greedy Biclustering Algorithm
1(22)
S. Dhamodharavadhani
R. Rathipriya
1.1 Introduction
2(1)
1.2 Problem Description
3(4)
1.2.1 Greedy Approach: Bicluster Size Maximization Based Fitness Function
4(1)
1.2.2 Data Description
5(2)
1.3 Proposed Work: COVID 19 Pattern Identification Using Greedy Biclustering
7(1)
1.4 Results and Discussions
8(10)
1.5 Conclusion
18(1)
1.6 Acknowledgements
18(5)
References
18(5)
2 Artificial Fish Swarm Optimization Algorithm with Hill Climbing Based Clustering Technique for Throughput Maximization in Wireless Multimedia Sensor Network
23(20)
N. Krishnaraj
T. Jayasankar
N. V. Kousik
A. Daniel
2.1 Introduction
24(3)
2.2 The Proposed AFSA-HC Technique
27(7)
2.2.1 AFSA-HC Based Clustering Phase
28(5)
2.2.2 Deflate-Based Data Aggregation Phase
33(1)
2.2.3 Hybrid Data Transmission Phase
34(1)
2.3 Performance Validation
34(6)
2.4 Conclusion
40(3)
References
40(3)
3 Analysis of Machine Learning Techniques for Spam Detection
43(20)
Supriya Raheja
Shreya Kasturia
3.1 Introduction
44(1)
3.1.1 Ham Messages
44(1)
3.1.2 Spam Messages
44(1)
3.2 Types of Spam Attack
45(1)
3.2.1 Email Phishing
45(1)
3.2.2 Spear Phishing
45(1)
3.2.3 Whaling
46(1)
3.3 Spammer Methods
46(1)
3.4 Some Prevention Methods From User End
46(2)
3.4.1 Protect Email Addresses
46(1)
3.4.2 Preventing Spam from Being Sent
47(1)
3.4.3 Block Spam to be Delivered
48(1)
3.4.4 Identify and Separate Spam After Delivery
48(1)
3.4.4.1 Targeted Link Analysis
48(1)
3.4.4.2 Bayesian Filters
48(1)
3.4.5 Report Spam
48(1)
3.5 Machine Learning Algorithms
48(3)
3.5.1 Naive Bayes (NB)
48(1)
3.5.2 Random Forests (RF)
49(1)
3.5.3 Support Vector Machine (SVM)
49(1)
3.5.4 Logistic Regression (LR)
50(1)
3.6 Methodology
51(1)
3.6.1 Database Used
51(1)
3.6.2 Work Flow
51(1)
3.7 Results and Analysis
52(7)
3.7.1 Performance Metric
52(1)
3.7.2 Experimental Results
52(2)
3.7.2.1 Cleaning Data by Removing Punctuations, White Spaces, and Stop Words
54(1)
3.7.2.2 Stemming the Messages
55(1)
3.7.2.3 Analyzing the Common Words from the Spam and Ham Messages
55(1)
3.7.3 Analyses of Machine Learning Algorithms
55(1)
3.7.3.1 Accuracy Score Before Stemming
55(1)
3.7.3.2 Accuracy Score After Stemming
55(1)
3.7.3.3 Splitting Dataset into Train and Test Data
56(2)
3.7.3.4 Mapping Confusion Matrix
58(1)
3.7.3.5 Accuracy
58(1)
3.8 Conclusion and Future Work
59(4)
References
59(4)
4 Smart Sensor Based Prognostication of Cardiac Disease Prediction Using Machine Learning Techniques
63(18)
V. Vijayaganth
M. Naveenkumar
4.1 Introduction
64(1)
4.2 Literature Survey
65(2)
4.3 Proposed Method
67(1)
4.4 Data Collection in IoT
67(5)
4.4.1 Fetching Data from Sensors
68(1)
4.4.2 K-Nearest Neighbor Classifier
69(1)
4.4.3 Random Forest Classifier
70(1)
4.4.4 Decision Tree Classifier
70(1)
4.4.5 Extreme Gradient Boost Classifier
71(1)
4.5 Results and Discussions
72(6)
4.6 Conclusion
78(1)
4.7 Acknowledgements
78(3)
References
78(3)
5 Assimilate Machine Learning Algorithms in Big Data Analytics: Review
81(34)
Sona D. Solanki
Asha D. Solanki
Samarjeet Borah
5.1 Introduction
82(4)
5.2 Literature Survey
86(3)
5.3 Big Data
89(3)
5.4 Machine Learning
92(3)
5.5 File Categories
95(1)
5.6 Storage And Expenses
95(1)
5.7 The Device Learning Anatomy
96(1)
5.8 Machine Learning Technology Methods in Big Data Analytics
97(1)
5.9 Structure Mapreduce
97(1)
5.10 Associated Investigations
98(1)
5.11 Multivariate Data Coterie in Machine Learning
99(1)
5.12 Machine Learning Algorithm
99(7)
5.12.1 Machine Learning Framework
99(1)
5.12.2 Parametric and Non-Parametric Techniques in Machine Learning
99(1)
5.12.2.1 Bias
100(1)
5.12.2.2 Variance
100(1)
5.12.3 Parametric Techniques
101(1)
5.12.3.1 Linear Regression
101(1)
5.12.3.2 Decision Tree
101(1)
5.12.3.3 Naive Bayes
102(1)
5.12.3.4 Support Vector Machine
102(1)
5.12.3.5 Random Forest
102(1)
5.12.3.6 K-Nearest Neighbor
103(1)
5.12.3.7 Deep Learning
104(1)
5.12.3.8 Linear Vector Quantization (LVQ)
104(1)
5.12.3.9 Transfer Learning
104(1)
5.12.4 Non-Parametric Techniques
105(1)
5.12.4.1 ff-Means Clustering
105(1)
5.12.4.2 Principal Component Analysis
105(1)
5.12.4.3 A Priori Algorithm
105(1)
5.12.4.4 Reinforcement Learning (RL)
105(1)
5.12.4.5 Semi-Supervised Learning
106(1)
5.13 Machine Learning Technology Assessment Parameters
106(3)
5.13.1 Ranking Performance
106(1)
5.13.2 Loss in Logarithmic Form
106(1)
5.13.3 Assessment Measures
107(1)
5.13.3.1 Accuracy
107(1)
5.13.3.2 Precision/Specificity
107(1)
5.13.3.3 Recall
107(1)
5.13.3.4 F-Measure
108(1)
5.13.4 Mean Definite Error (MAE)
108(1)
5.13.5 Mean Quadruple Error (MSE)
108(1)
5.14 Correlation of Outcomes of ML Algorithms
109(1)
5.15 Applications
109(3)
5.15.1 Economical Facilities
109(1)
5.15.2 Business and Endorsement
110(1)
5.15.3 Government Bodies
110(1)
5.15.4 Hygiene
110(1)
5.15.5 Transport
110(1)
5.15.6 Fuel and Energy
111(1)
5.15.7 Spoken Validation
111(1)
5.15.8 Perception of the Device
111(1)
5.15.9 Bio-Surveillance
111(1)
5.15.10 Mechanization or Realigning
111(1)
5.15.11 Mining Text
112(1)
5.16 Conclusion
112(3)
References
113(2)
6 Resource Allocation Methodologies in Cloud Computing: A Review and Analysis
115(24)
Pandaba Pradhan
Prafulla Ku. Behera
B. N. B. Ray
6.1 Introduction
116(5)
6.1.1 Cloud Services Models
116(1)
6.1.1.1 Infrastructure as a Service
117(1)
6.1.1.2 Platform as a Service
118(1)
6.1.1.3 Software as a Service
118(1)
6.1.2 Types of Cloud Computing
118(1)
6.1.2.1 Public Cloud
119(1)
6.1.2.2 Private Cloud
119(1)
6.1.2.3 Community Cloud
120(1)
6.1.2.4 Hybrid Cloud
121(1)
6.2 Resource Allocations in Cloud Computing
121(2)
6.2.1 Static Allocation
122(1)
6.2.2 Dynamic Allocation
122(1)
6.3 Dynamic Resource Allocation Models in Cloud Computing
123(7)
6.3.1 Service-Level Agreement Based Dynamic Resource Allocation Models
124(1)
6.3.2 Market-Based Dynamic Resource Allocation Models
125(1)
6.3.3 Utilization-Based Dynamic Resource Allocation Models
126(1)
6.3.4 Task Scheduling in Cloud Computing
127(3)
6.4 Research Challenges
130(1)
6.5 Future Research Paths
131(1)
6.6 Advantages and Disadvantages
131(4)
6.7 Conclusion
135(4)
References
135(4)
7 Role of Machine Learning in Big Data
139(26)
Nilanjana Das
Santwana Sagnika
Bhabani Shankar Prasad Mishra
7.1 Introduction
140(1)
7.2 Related Work
141(1)
7.3 Tools in Big Data
142(3)
7.3.1 Batch Analysis Big Data Tools
142(1)
7.3.2 Stream Analysis Big Data Tools
143(1)
7.3.3 Interactive Analysis Big Data Tools
144(1)
7.4 Machine Learning Algorithms in Big Data
145(6)
7.5 Applications of Machine Learning in Big Data
151(3)
7.6 Challenges of Machine Learning in Big Data
154(6)
7.6.1 Volume
154(2)
7.6.2 Variety
156(1)
7.6.3 Velocity
157(2)
7.6.4 Veracity
159(1)
7.7 Conclusion
160(5)
References
161(4)
8 Healthcare System for COVID-19: Challenges and Developments
165(18)
Arun Kumar
Sharad Sharma
8.1 Introduction
166(1)
8.2 Related Work
167(2)
8.3 IoT with Architecture
169(1)
8.4 IoHT Security Requirements and Challenges
170(2)
8.5 COVID-19 (Coronavirus Disease 2019)
172(1)
8.6 The Potential of IoHT in COVID-19 Like Disease Control
173(2)
8.7 The Current Applications of IoHT During COVID-19
175(2)
8.7.1 Using IoHT to Dissect an Outbreak
175(1)
8.7.2 Using IoHT to Ensure Compliance to Quarantine
176(1)
8.7.3 Using IoHT to Manage Patient Care
176(1)
8.8 IoHT Development for COVID-19
177(2)
8.8.1 Smart Home
178(1)
8.8.2 Smart Office
178(1)
8.8.3 Smart Hotel
178(1)
8.8.4 Smart Hospitals
178(1)
8.9 Conclusion
179(4)
References
179(4)
9 An Integrated Approach of Blockchain & Big Data in Health Care Sector
183(24)
Nitin Tyagi
Bhafat Bhushan
Siddharth Gautam
Nikhil Sharma
Santosh Kumar
9.1 Introduction
184(1)
9.2 Blockchain for Health care
185(6)
9.2.1 Healthcare data sharing through gem Network
186(1)
9.2.2 OmniPHR
187(1)
9.2.3 Medrec
188(1)
9.2.4 PSN (Pervasive Social Network) System
189(1)
9.2.5 Healthcare Data Gateway
190(1)
9.2.6 Resources that are virtual
190(1)
9.3 Overview of Blockchain & Big data in health care
191(3)
9.3.1 Big Data in Healthcare
191(1)
9.3.2 Blockchain in Health Care
192(1)
9.3.3 Benefits of Blockchain in Healthcare
193(1)
9.3.3.1 Master patient indices
193(1)
9.3.3.2 Supply chain management
193(1)
9.3.3.3 Claims adjudication
193(1)
9.3.3.4 Interoperability
194(1)
9.3.3.5 Single, longitudinal patient records
194(1)
9.4 Application of Big Data for Blockchain
194(3)
9.4.1 Smart Ecosystem
194(1)
9.4.2 Digital Trust
195(1)
9.4.3 Cybersecurity
195(1)
9.4.4 Fighting Drugs
195(1)
9.4.5 Online Accessing of Patient's Data
196(1)
9.4.6 Research as well as Development
196(1)
9.4.7 Management of Data
196(1)
9.4.8 Due to privacy storing of off-chain data
196(1)
9.4.9 Collaboration of patient data
197(1)
9.5 Solutions of Blockchain For Big Data in Health Care
197(1)
9.6 Conclusion and Future Scope
198(9)
References
199(8)
10 Cloud Resource Management for Network Cameras
207(24)
Hemanta Kumar Bhuyan
Subhendu Kumar Pani
10.1 Introduction
207(3)
10.2 Resource Analysis
210(4)
10.2.1 Network Cameras
210(1)
10.2.2 Resource Management on Cloud Environment
210(3)
10.2.3 Image and Video Analysis
213(1)
10.3 Cloud Resource Management Problems
214(2)
10.4 Cloud Resource Manager
216(2)
10.4.1 Evaluation of Performance
217(1)
10.4.2 View of Resource Requirements
217(1)
10.5 Bin Packing
218(4)
10.5.1 Analysis of Dynamic Bin Packing
219(1)
10.5.2 MinTotal DBP Problem
220(2)
10.6 Resource Monitoring and Scaling
222(2)
10.7 Conclusion
224(7)
References
225(6)
11 Software-Defined Networking for Healthcare Internet of Things
231(18)
Rinki Sharma
11.1 Introduction
231(2)
11.2 Healthcare Internet of Things
233(6)
11.2.1 Challenges in H-IoT
238(1)
11.3 Software-Defined Networking
239(4)
11.4 Opportunities, challenges, and possible solutions
243(2)
11.5 Conclusion
245(4)
References
246(3)
12 Cloud Computing in the Public Sector: A Study
249(22)
Amita Verma
Anukampa
12.1 Introduction
250(1)
12.2 History and Evolution of Cloud Computing
251(1)
12.3 Application of Cloud Computing
252(6)
12.4 Advantages of Cloud Computing
258(5)
12.5 Challenges
263(6)
12.6 Conclusion
269(2)
13 Big Data Analytics: An overview
271(18)
Dipalika Das
Maya Nayak
13.1 Introduction
271(1)
13.2 Related Work
272(6)
13.2.1 Big Data: What Is It?
275(1)
13.2.1.1 Characteristics of Big Data
276(1)
13.2.2 Big Data Analytics: What Is It?
277(1)
13.3 Hadoop and Big Data
278(1)
13.4 Big Data Analytics Framework
279(1)
13.5 Big Data Analytics Techniques
280(1)
13.5.1 Partitioning on Big Data
280(1)
13.5.2 Sampling on Big Data
281(1)
13.5.3 Sampling-Based Approximation
281(1)
13.6 Big Social Data Analytics
281(1)
13.7 Applications
282(2)
13.7.1 Manufacturing Production
282(1)
13.7.2 Smart Grid
283(1)
13.7.3 Outbreak of Flu Prediction from Social Site
283(1)
13.7.4 Sentiment Analysis of Twitter Data
283(1)
13.8 Electricity Price Forecasting
284(1)
13.9 Security Situational Analysis for Smart Grid
285(1)
13.10 FutureScope
285(1)
13.11 Challenges
285(1)
13.12 Conclusion
286(3)
References
286(3)
14 Video Usefulness Detection in Big Surveillance Systems
289(20)
Hemanta Kumar Bhuyan
Subhendu Kumar Pani
14.1 Introduction
290(2)
14.1.1 Challenges of Video Usefulness Detection
291(1)
14.1.2 Video Usefulness Model
292(1)
14.2 Background
292(3)
14.2.1 (a) Quality of Video Services (QoS)
292(2)
14.2.2 Edge Computing
294(1)
14.3 Failure of Video Data in Video Surveillance Systems
295(2)
14.4 Approaches of Video Failure Detection
297(1)
14.5 Failure Detection and Scheduling
298(3)
14.5.1 Failure Detection Approaches in Domains
298(1)
14.5.1.1 Failure Detection in Fedge Domain
298(2)
14.5.1.2 Failure Detection in the Fuser Domain
300(1)
14.5.1.3 Failure Detection in the Fcloud Domain
300(1)
14.6 Methodological Analysis
301(1)
14.6.1 Test of Video Usefulness Model
301(1)
14.7 Conclusion
302(7)
References
303(6)
Index 309(2)
About the Editors 311
Subhendu Kumar Pani, Somanath Tripathy, George Jandieri, Sumit Kundu, Talal Ashraf Butt
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