AudienceResearchers and industry engineers in computer science and artificial intelligence, IT professionals, network administrators, cybersecurity experts. Sachi Nandan Mohanty received his PhD from IIT Kharagpur 2015 and he is now an associate professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education, Hyderabad, India. Jyotir Moy Chatterjee is an assistant professor in the IT Department at Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation), Kathmandu, Nepal. Monika Mangla received her PhD from Thapar Institute of Engineering & Technology, Patiala, Punjab in 2019, and is now an assistant professor in the Department of Computer Engineering at Lokmanya Tilak College of Engineering (LTCoE), Navi Mumbai, India. Suneeta Satpathy received her PhD from Utkal University, Bhubaneswar, Odisha in 2015, and is now an associate professor in the Department of Computer Science & Engineering at College of Engineering Bhubaneswar (CoEB), Bhubaneswar, India. Ms. Sirisha Potluri is an assistant professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education, Hyderabad, India.

• Preface xixAcknowledgment xxiii1 Machine Learning–Based Data Analysis 1M. Deepika and K. Kalaiselvi1.1 Introduction 11.2 Machine Learning for the Internet of Things Using Data Analysis 41.2.1 Computing Framework 61.2.2 Fog Computing 61.2.3 Edge Computing 61.2.4 Cloud Computing 71.2.5 Distributed Computing 71.3 Machine Learning Applied to Data Analysis 71.3.1 Supervised Learning Systems 81.3.2 Decision Trees 91.3.3 Decision Tree Types 91.3.4 Unsupervised Machine Learning 101.3.5 Association Rule Learning 101.3.6 Reinforcement Learning 101.4 Practical Issues in Machine Learning 111.5 Data Acquisition 121.6 Understanding the Data Formats Used in Data Analysis Applications 131.7 Data Cleaning 141.8 Data Visualization 151.9 Understanding the Data Analysis Problem-Solving Approach 151.10 Visualizing Data to Enhance Understanding and Using Neural Networks in Data Analysis 161.11 Statistical Data Analysis Techniques 171.11.1 Hypothesis Testing 181.11.2 Regression Analysis 181.12 Text Analysis and Visual and Audio Analysis 181.13 Mathematical and Parallel Techniques for Data Analysis 191.13.1 Using Map-Reduce 201.13.2 Leaning Analysis 201.13.3 Market Basket Analysis 211.14 Conclusion 21References 222 Machine Learning for Cyber-Immune IoT Applications 25Suchismita Sahoo and Sushree Sangita Sahoo2.1 Introduction 252.2 Some Associated Impactful Terms 272.2.1 IoT 272.2.2 IoT Device 282.2.3 IoT Service 292.2.4 Internet Security 292.2.5 Data Security 302.2.6 Cyberthreats 312.2.7 Cyber Attack 312.2.8 Malware 322.2.9 Phishing 322.2.10 Ransomware 332.2.11 Spear-Phishing 332.2.12 Spyware 342.2.13 Cybercrime 342.2.14 IoT Cyber Security 352.2.15 IP Address 362.3 Cloud Rationality Representation 362.3.1 Cloud 362.3.2 Cloud Data 372.3.3 Cloud Security 382.3.4 Cloud Computing 382.4 Integration of IoT With Cloud 402.5 The Concepts That Rules Over 412.5.1 Artificial Intelligent 412.5.2 Overview of Machine Learning 412.5.2.1 Supervised Learning 412.5.2.2 Unsupervised Learning 422.5.3 Applications of Machine Learning in Cyber Security 432.5.4 Applications of Machine Learning in Cybercrime 432.5.5 Adherence of Machine Learning With Cyber Security in Relevance to IoT 432.5.6 Distributed Denial-of-Service 442.6 Related Work 452.7 Methodology 462.8 Discussions and Implications 482.9 Conclusion 49References 493 Employing Machine Learning Approaches for Predictive Data Analytics in Retail Industry 53Rakhi Akhare, Sanjivani Deokar, Monika Mangla and Hardik Deshmukh3.1 Introduction 533.2 Related Work 553.3 Predictive Data Analytics in Retail 563.3.1 ML for Predictive Data Analytics 583.3.2 Use Cases 593.3.3 Limitations and Challenges 613.4 Proposed Model 613.4.1 Case Study 633.5 Conclusion and Future Scope 68References 694 Emerging Cloud Computing Trends for Business Transformation 71Prasanta Kumar Mahapatra, Alok Ranjan Tripathy and Alakananda Tripathy4.1 Introduction 714.1.1 Computing Definition Cloud 724.1.2 Advantages of Cloud Computing Over On-Premises IT Operation 734.1.3 Limitations of Cloud Computing 744.2 History of Cloud Computing 744.3 Core Attributes of Cloud Computing 754.4 Cloud Computing Models 774.4.1 Cloud Deployment Model 774.4.2 Cloud Service Model 794.5 Core Components of Cloud Computing Architecture: Hardware and Software 834.6 Factors Need to Consider for Cloud Adoption 844.6.1 Evaluating Cloud Infrastructure 844.6.2 Evaluating Cloud Provider 854.6.3 Evaluating Cloud Security 864.6.4 Evaluating Cloud Services 864.6.5 Evaluating Cloud Service Level Agreements (SLA) 874.6.6 Limitations to Cloud Adoption 874.7 Transforming Business Through Cloud 884.8 Key Emerging Trends in Cloud Computing 894.8.1 Technology Trends 904.8.2 Business Models 924.8.3 Product Transformation 924.8.4 Customer Engagement 924.8.5 Employee Empowerment 934.8.6 Data Management and Assurance 934.8.7 Digitalization 934.8.8 Building Intelligence Cloud System 934.8.9 Creating Hyper-Converged Infrastructure 944.9 Case Study: Moving Data Warehouse to Cloud Boosts Performance for Johnson & Johnson 944.10 Conclusion 95References 965 Security of Sensitive Data in Cloud Computing 99Kirti Wanjale, Monika Mangla and Paritosh Marathe5.1 Introduction 1005.1.1 Characteristics of Cloud Computing 1005.1.2 Deployment Models for Cloud Services 1015.1.3 Types of Cloud Delivery Models 1025.2 Data in Cloud 1025.2.1 Data Life Cycle 1035.3 Security Challenges in Cloud Computing for Data 1055.3.1 Security Challenges Related to Data at Rest 1065.3.2 Security Challenges Related to Data in Use 1075.3.3 Security Challenges Related to Data in Transit 1075.4 Cross-Cutting Issues Related to Network in Cloud 1085.5 Protection of Data 1095.6 Tighter IAM Controls 1145.7 Conclusion and Future Scope 117References 1176 Cloud Cryptography for Cloud Data Analytics in IoT 119N. Jayashri and K. Kalaiselvi6.1 Introduction 1206.2 Cloud Computing Software Security Fundamentals 1206.3 Security Management 1226.4 Cryptography Algorithms 1236.4.1 Types of Cryptography 1236.5 Secure Communications 1276.6 Identity Management and Access Control 1336.7 Autonomic Security 1376.8 Conclusion 139References 1397 Issues and Challenges of Classical Cryptography in Cloud Computing 143Amrutanshu Panigrahi, Ajit Kumar Nayak and Rourab Paul7.1 Introduction 1447.1.1 Problem Statement and Motivation 1457.1.2 Contribution 1467.2 Cryptography 1467.2.1 Cryptography Classification 1477.2.1.1 Classical Cryptography 1477.2.1.2 Homomorphic Encryption 1497.3 Security in Cloud Computing 1507.3.1 The Need for Security in Cloud Computing 1517.3.2 Challenges in Cloud Computing Security 1527.3.3 Benefits of Cloud Computing Security 1537.3.4 Literature Survey 1547.4 Classical Cryptography for Cloud Computing 1577.4.1 RSA 1577.4.2 AES 1577.4.3 DES 1587.4.4 Blowfish 1587.5 Homomorphic Cryptosystem 1587.5.1 Paillier Cryptosystem 1597.5.1.1 Additive Homomorphic Property 1597.5.2 RSA Homomorphic Cryptosystem 1607.5.2.1 Multiplicative Homomorphic Property 1607.6 Implementation 1607.7 Conclusion and Future Scope 162References 1628 Cloud-Based Data Analytics for Monitoring Smart Environments 167D. Karthika8.1 Introduction 1678.2 Environmental Monitoring for Smart Buildings 1698.2.1 Smart Environments 1698.3 Smart Health 1718.3.1 Description of Solutions in General 1718.3.2 Detection of Distress 1728.3.3 Green Protection 1738.3.4 Medical Preventive/Help 1748.4 Digital Network 5G and Broadband Networks 1748.4.1 IoT-Based Smart Grid Technologies 1748.5 Emergent Smart Cities Communication Networks 1758.5.1 RFID Technologies 1778.5.2 Identifier Schemes 1778.6 Smart City IoT Platforms Analysis System 1778.7 Smart Management of Car Parking in Smart Cities 1788.8 Smart City Systems and Services Securing: A Risk-Based Analytical Approach 1788.9 Virtual Integrated Storage System 1798.10 Convolutional Neural Network (CNN) 1818.10.1 IEEE 802.15.4 1828.10.2 BLE 1828.10.3 ITU-T G.9959 (Z-Wave) 1838.10.4 NFC 1838.10.5 LoRaWAN 1848.10.6 Sigfox 1848.10.7 NB-IoT 1848.10.8 PLC 1848.10.9 MS/TP 1848.11 Challenges and Issues 1858.11.1 Interoperability and Standardization 1858.11.2 Customization and Adaptation 1868.11.3 Entity Identification and Virtualization 1878.11.4 Big Data Issue in Smart Environments 1878.12 Future Trends and Research Directions in Big Data Platforms for the Internet of Things 1888.13 Case Study 1898.14 Conclusion 191References 1919 Performance Metrics for Comparison of Heuristics Task Scheduling Algorithms in Cloud Computing Platform 195Nidhi Rajak and Ranjit Rajak9.1 Introduction 1959.2 Workflow Model 1979.3 System Computing Model 1989.4 Major Objective of Scheduling 1989.5 Task Computational Attributes for Scheduling 1989.6 Performance Metrics 2009.7 Heuristic Task Scheduling Algorithms 2019.7.1 Heterogeneous Earliest Finish Time (HEFT) Algorithm 2029.7.2 Critical-Path-on-a-Processor (CPOP) Algorithm 2089.7.3 As Late As Possible (ALAP) Algorithm 2139.7.4 Performance Effective Task Scheduling (PETS) Algorithm 2179.8 Performance Analysis and Results 2209.9 Conclusion 224References 22410 Smart Environment Monitoring Models Using Cloud-Based Data Analytics: A Comprehensive Study 227Pradnya S. Borkar and Reena Thakur10.1 Introduction 22810.1.1 Internet of Things 22910.1.2 Cloud Computing 23010.1.3 Environmental Monitoring 23210.2 Background and Motivation 23410.2.1 Challenges and Issues 23410.2.2 Technologies Used for Designing Cloud-Based Data Analytics 24010.2.2.1 Communication Technologies 24110.2.3 Cloud-Based Data Analysis Techniques and Models 24310.2.3.1 MapReduce for Data Analysis 24310.2.3.2 Data Analysis Workflows 24610.2.3.3 NoSQL Models 24710.2.4 Data Mining Techniques 24810.2.5 Machine Learning 25110.2.5.1 Significant Importance of Machine Learning and Its Algorithms 25310.2.6 Applications 25310.3 Conclusion 261References 26211 Advancement of Machine Learning and Cloud Computing in the Field of Smart Health Care 273Aradhana Behura, Shibani Sahu and Manas Ranjan Kabat11.1 Introduction 27411.2 Survey on Architectural WBAN 27811.3 Suggested Strategies 28011.3.1 System Overview 28011.3.2 Motivation 28111.3.3 DSCB Protocol 28111.3.3.1 Network Topology 28211.3.3.2 Starting Stage 28211.3.3.3 Cluster Evolution 28211.3.3.4 Sensed Information Stage 28311.3.3.5 Choice of Forwarder Stage 28311.3.3.6 Energy Consumption as Well as Routing Stage 28511.4 CNN-Based Image Segmentation (UNet Model) 28711.5 Emerging Trends in IoT Healthcare 29011.6 Tier Health IoT Model 29411.7 Role of IoT in Big Data Analytics 29411.8 Tier Wireless Body Area Network Architecture 29611.9 Conclusion 303References 30312 Study on Green Cloud Computing—A Review 307Meenal Agrawal and Ankita Jain12.1 Introduction 30712.2 Cloud Computing 30812.2.1 Cloud Computing: On-Request Outsourcing-Pay-as-You-Go 30812.3 Features of Cloud Computing 30912.4 Green Computing 30912.5 Green Cloud Computing 30912.6 Models of Cloud Computing 31012.7 Models of Cloud Services 31012.8 Cloud Deployment Models 31112.9 Green Cloud Architecture 31212.10 Cloud Service Providers 31212.11 Features of Green Cloud Computing 31312.12 Advantages of Green Cloud Computing 31312.13 Limitations of Green Cloud Computing 31412.14 Cloud and Sustainability Environmental 31512.15 Statistics Related to Cloud Data Centers 31512.16 The Impact of Data Centers on Environment 31512.17 Virtualization Technologies 31612.18 Literature Review 31612.19 The Main Objective 31812.20 Research Gap 31912.21 Research Methodology 31912.22 Conclusion and Suggestions 32012.23 Scope for Further Research 320References 32113 Intelligent Reclamation of Plantae Affliction Disease 323Reshma Banu, G.F Ali Ahammed and Ayesha Taranum13.1 Introduction 32413.2 Existing System 32713.3 Proposed System 32713.4 Objectives of the Concept 32813.5 Operational Requirements 32813.6 Non-Operational Requirements 32913.7 Depiction Design Description 33013.8 System Architecture 33013.8.1 Module Characteristics 33113.8.2 Convolutional Neural System 33213.8.3 User Application 33213.9 Design Diagrams 33313.9.1 High-Level Design 33313.9.2 Low-Level Design 33313.9.3 Test Cases 33513.10 Comparison and Screenshot 33513.11 Conclusion 342References 34214 Prediction of Stock Market Using Machine Learning–Based Data Analytics 347Maheswari P. and Jaya A.14.1 Introduction of Stock Market 34814.1.1 Impact of Stock Prices 34914.2 Related Works 35014.3 Financial Prediction Systems Framework 35214.3.1 Conceptual Financial Prediction Systems 35214.3.2 Framework of Financial Prediction Systems Using Machine Learning 35314.3.2.1 Algorithm to Predicting the Closing Price of the Given Stock Data Using Linear Regression 35514.3.3 Framework of Financial Prediction Systems Using Deep Learning 35514.3.3.1 Algorithm to Predict the Closing Price of the Given Stock Using Long Short-Term Memory 35614.4 Implementation and Discussion of Result 35714.4.1 Pharmaceutical Sector 35714.4.1.1 Cipla Limited 35714.4.1.2 Torrent Pharmaceuticals Limited 35914.4.2 Banking Sector 35914.4.2.1 ICICI Bank 35914.4.2.2 State Bank of India 35914.4.3 Fast-Moving Consumer Goods Sector 36214.4.3.1 ITC 36314.4.3.2 Hindustan Unilever Limited 36314.4.4 Power Sector 36314.4.4.1 Adani Power Limited 36314.4.4.2 Power Grid Corporation of India Limited 36414.4.5 Automobiles Sector 36814.4.5.1 Mahindra & Mahindra Limited 36814.4.5.2 Maruti Suzuki India Limited 36814.4.6 Comparison of Prediction Using Linear Regression Model and Long-Short-Term Memory Model 36814.5 Conclusion 37114.5.1 Future Enhancement 372References 372Web Citations 37315 Pehchaan: Analysis of the ‘Aadhar Dataset’ to Facilitate a Smooth and Efficient Conduct of the Upcoming NPR 375Soumyadev Mukherjee, Harshit Anand, Nishan Acharya, Subham Char, Pritam Ghosh and MinakhiRout15.1 Introduction 37615.2 Basic Concepts 37715.3 Study of Literature Survey and Technology 38015.4 Proposed Model 38115.5 Implementation and Results 38315.6 Conclusion 389References 38916 Deep Learning Approach for Resource Optimization in Blockchain, Cellular Networks, and IoT: Open Challenges and Current Solutions 391Upinder Kaur and Shalu16.1 Introduction 39216.1.1 Aim 39316.1.2 Research Contribution 39516.1.3 Organization 39616.2 Background 39616.2.1 Blockchain 39716.2.2 Internet of Things (IoT) 39816.2.3 5G Future Generation Cellular Networks 39816.2.4 Machine Learning and Deep Learning Techniques 39916.2.5 Deep Reinforcement Learning 39916.3 Deep Learning for Resource Management in Blockchain, Cellular, and IoT Networks 40116.3.1 Resource Management in Blockchain for 5G Cellular Networks 40216.3.2 Deep Learning Blockchain Application for Resource Management in IoT Networks 40216.4 Future Research Challenges 41316.4.1 Blockchain Technology 41316.4.1.1 Scalability 41416.4.1.2 Efficient Consensus Protocols 41516.4.1.3 Lack of Skills and Experts 41516.4.2 IoT Networks 41616.4.2.1 Heterogeneity of IoT and 5G Data 41616.4.2.2 Scalability Issues 41616.4.2.3 Security and Privacy Issues 41616.4.3 5G Future Generation Networks 41616.4.3.1 Heterogeneity 41616.4.3.2 Security and Privacy 41716.4.3.3 Resource Utilization 41716.4.4 Machine Learning and Deep Learning 41716.4.4.1 Interpretability 41816.4.4.2 Training Cost for ML and DRL Techniques 41816.4.4.3 Lack of Availability of Data Sets 41816.4.4.4 Avalanche Effect for DRL Approach 41916.4.5 General Issues 41916.4.5.1 Security and Privacy Issues 41916.4.5.2 Storage 41916.4.5.3 Reliability 42016.4.5.4 Multitasking Approach 42016.5 Conclusion and Discussion 420References 42217 Unsupervised Learning in Accordance With New Aspects of Artificial Intelligence 429Riya Sharma, Komal Saxena and Ajay Rana17.1 Introduction 43017.2 Applications of Machine Learning in Data Management Possibilities 43117.2.1 Terminology of Basic Machine Learning 43217.2.2 Rules Based on Machine Learning 43417.2.3 Unsupervised vs. Supervised Methodology 43417.3 Solutions to Improve Unsupervised Learning Using Machine Learning 43617.3.1 Insufficiency of Labeled Data 43617.3.2 Overfitting 43717.3.3 A Closer Look Into Unsupervised Algorithms 43717.3.3.1 Reducing Dimensionally 43717.3.3.2 Principal Component Analysis 43817.3.4 Singular Value Decomposition (SVD) 43917.3.4.1 Random Projection 43917.3.4.2 Isomax 43917.3.5 Dictionary Learning 43917.3.6 The Latent Dirichlet Allocation 44017.4 Open Source Platform for Cutting Edge Unsupervised Machine Learning 44017.4.1 TensorFlow 44117.4.2 Keras 44117.4.3 Scikit-Learn 44117.4.4 Microsoft Cognitive Toolkit 44217.4.5 Theano 44217.4.6 Caffe 44217.4.7 Torch 44217.5 Applications of Unsupervised Learning 44317.5.1 Regulation of Digital Data 44317.5.2 Machine Learning in Voice Assistance 44317.5.3 For Effective Marketing 44417.5.4 Advancement of Cyber Security 44417.5.5 Faster Computing Power 44417.5.6 The Endnote 44517.6 Applications Using Machine Learning Algos 44517.6.1 Linear Regression 44517.6.2 Logistic Regression 44617.6.3 Decision Tree 44617.6.4 Support Vector Machine (SVM) 44617.6.5 Naive Bayes 44617.6.6 K-Nearest Neighbors 44717.6.7 K-Means 44717.6.8 Random Forest 44717.6.9 Dimensionality Reduction Algorithms 44817.6.10 Gradient Boosting Algorithms 448References 44918 Predictive Modeling of Anthropomorphic Gamifying Blockchain-Enabled Transitional Healthcare System 461Deepa Kumari, B.S.A.S. Rajita, Medindrao Raja Sekhar, Ritika Garg and Subhrakanta Panda18.1 Introduction 46218.1.1 Transitional Healthcare Services and Their Challenges 46218.2 Gamification in Transitional Healthcare: A New Model 46318.2.1 Anthropomorphic Interface With Gamification 46418.2.2 Gamification in Blockchain 46518.2.3 Anthropomorphic Gamification in Blockchain: Motivational Factors 46618.3 Existing Related Work 46818.4 The Framework 47818.4.1 Health Player 47918.4.2 Data Collection 48018.4.3 Anthropomorphic Gamification Layers 48018.4.4 Ethereum 48018.4.4.1 Ethereum-Based Smart Contracts for Healthcare 48118.4.4.2 Installation of Ethereum Smart Contract 48118.4.5 Reward Model 48218.4.6 Predictive Models 48218.5 Implementation 48318.5.1 Methodology 48318.5.2 Result Analysis 48418.5.3 Threats to the Validity 48618.6 Conclusion 487References 487Index 491

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