Applications of Machine Learning in Big Data Analytics and Cloud Computing 1st edition by River Publishers 9781000796322 1000796329

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ISBN 10: 1000796329
ISBN 13: 9781000796322 
Author: River Publishers

Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to opportunities and transformation in various areas such as healthcare, enterprises, industrial manufacturing and transportation. New Cloud Computing and Big Data tools endow researchers and analysts with novel techniques and opportunities to collect, manage and analyze the vast quantities of data. In Cloud and Big Data Analytics, the two areas of Swarm Intelligence and Deep Learning are a developing type of Machine Learning techniques that show enormous potential for solving complex business problems. Deep Learning enables computers to analyze large quantities of unstructured and binary data and to deduce relationships without requiring specific models or programming instructions. 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.

Applications of Machine Learning in Big Data Analytics and Cloud Computing 1st Table of contents:

1. Pattern Analysis of COVID-19 Death and Recovery Cases Data of Countries Using Greedy Biclustering Algorithm

• Abstract
• 1.1 Introduction
• 1.2 Problem Description
  • 1.2.1 Greedy Approach: Bicluster Size Maximization Based Fitness Function
  • 1.2.2 Data Description
• 1.3 Proposed Work: COVID-19 Pattern Identification Using Greedy Biclustering
• 1.4 Results and Discussions
• 1.5 Conclusion
• 1.6 Acknowledgements
• References

2. Artificial Fish Swarm Optimization Algorithm with Hill Climbing Based Clustering Technique for Throughput Maximization in Wireless Multimedia Sensor Network

• Abstract
• 2.1 Introduction
• 2.2 The Proposed AFSA-HC Technique
  • 2.2.1 AFSA-HC Based Clustering Phase
  • 2.2.2 Deflate-Based Data Aggregation Phase
  • 2.2.3 Hybrid Data Transmission Phase
• 2.3 Performance Validation
• 2.4 Conclusion
• References

3. Analysis of Machine Learning Techniques for Spam Detection

• Abstract
• 3.1 Introduction
  • 3.1.1 Ham Messages
  • 3.1.2 Spam Messages
• 3.2 Types of Spam Attack
  • 3.2.1 Email Phishing
  • 3.2.2 Spear Phishing
  • 3.2.3 Whaling
• 3.3 Spammer Methods
• 3.4 Some Prevention Methods From User End
  • 3.4.1 Protect Email Addresses
  • 3.4.2 Preventing Spam from Being Sent
  • 3.4.3 Block Spam to be Delivered
  • 3.4.4 Identify and Separate Spam After Delivery
    • 3.4.4.1 Targeted Link Analysis
    • 3.4.4.2 Bayesian Filters
  • 3.4.5 Report Spam
• 3.5 Machine Learning Algorithms
  • 3.5.1 Naïve Bayes (NB)
  • 3.5.2 Random Forests (RF)
  • 3.5.3 Support Vector Machine (SVM)
  • 3.5.4 Logistic Regression (LR)
• 3.6 Methodology
  • 3.6.1 Database Used
  • 3.6.2 Work Flow
• 3.7 Results and Analysis
  • 3.7.1 Performance Metric
  • 3.7.2 Experimental Results
    • 3.7.2.1 Cleaning Data by Removing Punctuations, White Spaces, and Stop Words
    • 3.7.2.2 Stemming the Messages
    • 3.7.2.3 Analyzing the Common Words from the Spam and Ham Messages
  • 3.7.3 Analyses of Machine Learning Algorithms
    • 3.7.3.1 Accuracy Score Before Stemming
    • 3.7.3.2 Accuracy Score After Stemming
    • 3.7.3.3 Splitting Dataset into Train and Test Data
    • 3.7.3.4 Mapping Confusion Matrix
    • 3.7.3.5 Accuracy
• 3.8 Conclusion and Future Work
• References

4. Smart Sensor Based Prognostication of Cardiac Disease Prediction Using Machine Learning Techniques

• Abstract
• 4.1 Introduction
• 4.2 Literature Survey
• 4.3 Proposed Method
• 4.4 Data Collection in IoT
  • 4.4.1 Fetching Data from Sensors
  • 4.4.2 K-Nearest Neighbor Classifier
  • 4.4.3 Random Forest Classifier
  • 4.4.4 Decision Tree Classifier
  • 4.4.5 Extreme Gradient Boost Classifier
• 4.5 Results and Discussions
• 4.6 Conclusion
• 4.7 Acknowledgements
• References

5. Assimilate Machine Learning Algorithms in Big Data Analytics: Review

• Abstract
• 5.1 Introduction
• 5.2 Literature Survey
• 5.3 Big Data
• 5.4 Machine Learning
• 5.5 File Categories
• 5.6 Storage And Expenses
• 5.7 The Device Learning Anatomy
• 5.8 Machine Learning Technology Methods in Big Data Analytics
• 5.9 Structure Mapreduce
• 5.10 Associated Investigations
• 5.11 Multivariate Data Coterie in Machine Learning
• 5.12 Machine Learning Algorithm
  • 5.12.1 Machine Learning Framework
  • 5.12.2 Parametric and Non-Parametric Techniques in Machine Learning
    • 5.12.2.1 Bias
    • 5.12.2.2 Variance
  • 5.12.3 Parametric Techniques
    • 5.12.3.1 Linear Regression
    • 5.12.3.2 Decision Tree
    • 5.12.3.3 Naive Bayes
    • 5.12.3.4 Support Vector Machine
    • 5.12.3.5 Random Forest
    • 5.12.3.6 K-Nearest Neighbor
    • 5.12.3.7 Deep Learning
    • 5.12.3.8 Linear Vector Quantization (LVQ)
    • 5.12.3.9 Transfer Learning
  • 5.12.4 Non-Parametric Techniques
    • 5.12.4.1 K-Means Clustering
    • 5.12.4.2 Principal Component Analysis
    • 5.12.4.3 A Priori Algorithm
    • 5.12.4.4 Reinforcement Learning (RL)
    • 5.12.4.5 Semi-Supervised Learning
• 5.13 Machine Learning Technology Assessment Parameters
  • 5.13.1 Ranking Performance
  • 5.13.2 Loss in Logarithmic Form
  • 5.13.3 Assessment Measures
    • 5.13.3.1 Accuracy
    • 5.13.3.2 Precision/Specificity
    • 5.13.3.3 Recall
    • 5.13.3.4 F-Measure
  • 5.13.4 Mean Definite Error (MAE)
  • 5.13.5 Mean Quadruple Error (MSE)
• 5.14 Correlation of Outcomes of ML Algorithms
• 5.15 Applications
  • 5.15.1 Economical Facilities
  • 5.15.2 Business and Endorsement
  • 5.15.3 Government Bodies
  • 5.15.4 Hygiene
  • 5.15.5 Transport
  • 5.15.6 Fuel and Energy
  • 5.15.7 Spoken Validation
  • 5.15.8 Perception of the Device
  • 5.15.9 Bio-Surveillance
  • 5.15.10 Mechanization or Realigning
  • 5.15.11 Mining Text
• 5.16 Conclusion
• References

6. Resource Allocation Methodologies in Cloud Computing: A Review and Analysis

• Abstract
• 6.1 Introduction
  • 6.1.1 Cloud Services Models
    • 6.1.1.1 Infrastructure as a Service
    • 6.1.1.2 Platform as a Service
    • 6.1.1.3 Software as a Service
  • 6.1.2 Types of Cloud Computing
    • 6.1.2.1 Public Cloud
    • 6.1.2.2 Private Cloud
    • 6.1.2.3 Community Cloud
    • 6.1.2.4 Hybrid Cloud
• 6.2 Resource Allocations in Cloud Computing
  • 6.2.1 Static Allocation
  • 6.2.2 Dynamic Allocation
• 6.3 Dynamic Resource Allocation Models in Cloud Computing
  • 6.3.1 Service-Level Agreement Based Dynamic Resource Allocation Models
  • 6.3.2 Market-Based Dynamic Resource Allocation Models
  • 6.3.3 Utilization-Based Dynamic Resource Allocation Models
  • 6.3.4 Task Scheduling in Cloud Computing
• 6.4 Research Challenges
• 6.5 Future Research Paths
• 6.6 Advantages and Disadvantages
• 6.7 Conclusion
• References

7. Role of Machine Learning in Big Data

• Abstract
• 7.1 Introduction
• 7.2 Related Work
• 7.3 Tools in Big Data
  • 7.3.1 Batch Analysis Big Data Tools
  • 7.3.2 Stream Analysis Big Data Tools
  • 7.3.3 Interactive Analysis Big Data Tools
• 7.4 Machine Learning Algorithms in Big Data
• 7.5 Applications of Machine Learning in Big Data
• 7.6 Challenges of Machine Learning in Big Data
  • 7.6.1 Volume
  • 7.6.2 Variety
  • 7.6.3 Velocity
  • 7.6.4 Veracity
• 7.7 Conclusion
• References

8. Healthcare System for COVID-19: Challenges and Developments

• Abstract
• 8.1 Introduction
• 8.2 Related Work
• 8.3 IoT with Architecture
• 8.4 IoHT Security Requirements and Challenges
• 8.5 COVID-19 (Coronavirus Disease 2019)
• 8.6 The Potential of IoHT in COVID-19 Like Disease Control
• 8.7 The Current Applications of IoHT During COVID-19
  • 8.7.1 Using IoHT to Dissect an Outbreak
  • 8.7.2 Using IoHT to Ensure Compliance to Quarantine
  • 8.7.3 Using IoHT to Manage Patient Care
• 8.8 IoHT Development for COVID-19
  • 8.8.1 Smart Home
  • 8.8.2 Smart Office
  • 8.8.3 Smart Hotel
  • 8.8.4 Smart Hospitals
• 8.9 Conclusion
• References

9. An Integrated Approach of Blockchain & Big Data in Health Care Sector

• Abstract
• 9.1 Introduction
• 9.2 Blockchain for Healthcare
  • 9.2.1 Healthcare data sharing through Gem Network
  • 9.2.2 OmniPHR
  • 9.2.3 Medrec
  • 9.2.4 PSN (Pervasive Social Network) System
  • 9.2.5 Healthcare Data Gateway
  • 9.2.6 Resources that are virtual
• 9.3 Overview of Blockchain & Big Data in Healthcare
  • 9.3.1 Big Data in Healthcare
  • 9.3.2 Blockchain in Healthcare
  • 9.3.3 Benefits of Blockchain in Healthcare
    • 9.3.3.1 Master patient indices
    • 9.3.3.2 Supply chain management
    • 9.3.3.3 Claims adjudication
    • 9.3.3.4 Interoperability
    • 9.3.3.5 Single, longitudinal patient records
• 9.4 Application of Big Data for Blockchain
  • 9.4.1 Smart Ecosystem
  • 9.4.2 Digital Trust
  • 9.4.3 Cybersecurity
  • 9.4.4 Fighting Drugs
  • 9.4.5 Online Accessing of Patient’s Data
  • 9.4.6 Research and Development
  • 9.4.7 Management of Data
  • 9.4.8 Privacy Storing of Off-chain Data
  • 9.4.9 Collaboration of Patient Data
• 9.5 Solutions of Blockchain For Big Data in Healthcare
• 9.6 Conclusion and Future Scope
• References

10. Cloud Resource Management for Network Cameras

• Abstract
• 10.1 Introduction
• 10.2 Resource Analysis
  • 10.2.1 Network Cameras

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