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Aalborg : River Publishers, 2021

1 online resource (348 pages)

ISBN 9781000793550 (electronic bk.)

ISBN 9788770221825

River Publishers Series in Information Science and Technology Ser.

Print version: Pani, Subhendu Kumar Applications of Machine Learning in Big-Data Analytics and Cloud Computing Aalborg : River Publishers,c2021 ISBN 9788770221825

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- List of Contributors -- List of Figures -- List of Tables -- List of Abbreviations -- 1: Pattern Analysis of COVID-19 Death and Recovery Cases Data of Countries Using Greedy Biclustering Algorithm -- 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 -- 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 -- 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 Naive 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.

13.7 Applications.

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 -- 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.

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 as well as Development -- 9.4.7 Management of Data -- 9.4.8 Due to Privacy Storing of Off-Chain Data -- 9.4.9 Collaboration of Patient Data -- 9.5 Solutions of Blockchain For Big Data in Health Care -- 9.6 Conclusion and Future Scope -- References -- 10: Cloud Resource Management for Network Cameras -- 10.1 Introduction -- 10.2 Resource Analysis -- 10.2.1 Network Cameras -- 10.2.2 Resource Management on Cloud Environment -- 10.2.3 Image and Video Analysis -- 10.3 Cloud Resource Management Problems -- 10.4 Cloud Resource Manager -- 10.4.1 Evaluation of Performance -- 10.4.2 View of Resource Requirements -- 10.5 Bin Packing -- 10.5.1 Analysis of Dynamic Bin Packing -- 10.5.2 MinTotal DBP Problem -- 10.6 Resource Monitoring and Scaling -- 10.7 Conclusion -- References -- 11: Software-Defined Networking for Healthcare Internet of Things -- 11.1 Introduction -- 11.2 Healthcare Internet of Things -- 11.2.1 Challenges in H-IoT -- 11.3 Software-Defined Networking -- 11.4 Opportunities, Challenges, and Possible Solutions -- 11.5 Conclusion -- References -- 12: Cloud Computing in the Public Sector: A Study -- 12.1 Introduction -- 12.2 History and Evolution of Cloud Computing -- 12.3 Application of Cloud Computing -- 12.4 Advantages of Cloud Computing -- 12.5 Challenges -- 12.6 Conclusion -- 13: Big Data Analytics: An overview -- 13.1 Introduction -- 13.2 Related Work -- 13.2.1 Big Data: What Is It? -- 13.2.1.1 Characteristics of Big Data -- 13.2.2 Big Data Analytics: What Is It? -- 13.3 Hadoop and Big Data -- 13.4 Big Data Analytics Framework -- 13.5 Big Data Analytics Techniques -- 13.5.1 Partitioning on Big Data -- 13.5.2 Sampling on Big Data -- 13.5.3 Sampling-Based Approximation -- 13.6 Big Social Data Analytics.

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 -- 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 -- 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.

7: Role of Machine Learning in Big Data -- 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 -- 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 -- 9.1 Introduction -- 9.2 Blockchain for Health care -- 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 Health Care -- 9.3.1 Big Data in Healthcare -- 9.3.2 Blockchain in Health Care -- 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.

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..

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(Au-PeEL)EBL6705008

(MiAaPQ)EBC6705008

(OCoLC)1264471175