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ویرایش: 1 نویسندگان: Sachi Nandan Mohanty (editor), Jyotir Moy Chatterjee (editor), Monika Mangla (editor), Suneeta Satpathy (editor), Sirisha Potluri (editor) سری: ISBN (شابک) : 1119785804, 9781119785804 ناشر: Wiley-Scrivener سال نشر: 2021 تعداد صفحات: 516 زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 29 مگابایت
در صورت تبدیل فایل کتاب Machine Learning Approach for Cloud Data Analytics in IoT به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب روش یادگیری ماشین برای تجزیه و تحلیل داده های ابری در اینترنت اشیا نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
در این دوره از اینترنت اشیا، دستگاههای لبه در هر کسری از ثانیه دادههای عظیمی تولید میکنند. هدف اصلی این شبکه ها استنتاج برخی اطلاعات معنادار از داده های جمع آوری شده است. برای همین، داده های عظیم به ابر منتقل می شود که بسیار گران و وقت گیر است. از این رو، باید مکانیزمی کارآمد برای مدیریت این دادههای عظیم ابداع کند، بنابراین نیاز به تکنیکهای کارآمد مدیریت دادهها دارد. پارادایمهای محاسباتی پایدار مانند ابر و مه برای رسیدگی به مسائل مربوط به عملکرد، قابلیتهای مرتبط با ذخیرهسازی و پردازش، نگهداری، امنیت، کارایی، یکپارچهسازی، هزینه، انرژی و تأخیر مناسب هستند. با این حال، به ابزارهای تحلیلی پیچیده ای نیاز دارد تا به پرس و جوها در زمان بهینه رسیدگی شود. از این رو، تحقیقات دقیقی در جهت ابداع چارچوبی مؤثر و کارآمد برای کسب حداکثر مزیت در حال انجام است.
یادگیری ماشینی برای مدیریت حجم عظیمی از داده ها محبوبیت بی نظیری به دست آورده است و در رشته های مختلف کاربرد دارد. از جمله رسانههای اجتماعی.
رویکرد یادگیری ماشینی برای تجزیه و تحلیل دادههای ابری در اینترنت اشیا جزئیات و همه جنبههای اینترنت اشیا، محاسبات ابری و تجزیه و تحلیل دادهها را از دیدگاههای متنوع ادغام میکند. این گزارش در مورد جدیدترین تحقیقات و موضوعات پیشرفته گزارش می دهد، بنابراین خوانندگان را به روز می کند و به آنها ابزاری برای درک و کاوش در طیف برنامه های IoT، محاسبات ابری و تجزیه و تحلیل داده ها می دهد.
In this era of IoT, edge devices generate gigantic data during every fraction of a second. The main aim of these networks is to infer some meaningful information from the collected data. For the same, the huge data is transmitted to the cloud which is highly expensive and time-consuming. Hence, it needs to devise some efficient mechanism to handle this huge data, thus necessitating efficient data handling techniques. Sustainable computing paradigms like cloud and fog are expedient to capably handle the issues of performance, capabilities allied to storage and processing, maintenance, security, efficiency, integration, cost, energy and latency. However, it requires sophisticated analytics tools so as to address the queries in an optimized time. Hence, rigorous research is taking place in the direction of devising effective and efficient framework to garner utmost advantage.
Machine learning has gained unmatched popularity for handling massive amounts of data and has applications in a wide variety of disciplines, including social media.
Machine Learning Approach for Cloud Data Analytics in IoT details and integrates all aspects of IoT, cloud computing and data analytics from diversified perspectives. It reports on the state-of-the-art research and advanced topics, thereby bringing readers up to date and giving them a means to understand and explore the spectrum of applications of IoT, cloud computing and data analytics.
Cover Half-Title Page Series Page Title Page Copyright Page Contents Preface Acknowledgment 1 Machine Learning–Based Data Analysis 1.1 Introduction 1.2 Machine Learning for the Internet of Things Using Data Analysis 1.2.1 Computing Framework 1.2.2 Fog Computing 1.2.3 Edge Computing 1.2.4 Cloud Computing 1.2.5 Distributed Computing 1.3 Machine Learning Applied to Data Analysis 1.3.1 Supervised Learning Systems 1.3.2 Decision Trees 1.3.3 Decision Tree Types 1.3.4 Unsupervised Machine Learning 1.3.5 Association Rule Learning 1.3.6 Reinforcement Learning 1.4 Practical Issues in Machine Learning 1.5 Data Acquisition 1.6 Understanding the Data Formats Used in Data Analysis Applications 1.7 Data Cleaning 1.8 Data Visualization 1.9 Understanding the Data Analysis Problem-Solving Approach 1.10 Visualizing Data to Enhance Understanding and Using Neural Networks in Data Analysis 1.11 Statistical Data Analysis Techniques 1.11.1 Hypothesis Testing 1.11.2 Regression Analysis 1.12 Text Analysis and Visual and Audio Analysis 1.13 Mathematical and Parallel Techniques for Data Analysis 1.13.1 Using Map-Reduce 1.13.2 Leaning Analysis 1.13.3 Market Basket Analysis 1.14 Conclusion References 2 Machine Learning for Cyber-Immune IoT Applications 2.1 Introduction 2.2 Some Associated Impactful Terms 2.2.1 IoT 2.2.2 IoT Device 2.2.3 IoT Service 2.2.4 Internet Security 2.2.5 Data Security 2.2.6 Cyberthreats 2.2.7 Cyber Attack 2.2.8 Malware 2.2.9 Phishing 2.2.10 Ransomware 2.2.11 Spear-Phishing 2.2.12 Spyware 2.2.13 Cybercrime 2.2.14 IoT Cyber Security 2.2.15 IP Address 2.3 Cloud Rationality Representation 2.3.1 Cloud 2.3.2 Cloud Data 2.3.3 Cloud Security 2.3.4 Cloud Computing 2.4 Integration of IoT With Cloud 2.5 The Concepts That Rules Over 2.5.1 Artificial Intelligent 2.5.2 Overview of Machine Learning 2.5.3 Applications of Machine Learning in Cyber Security 2.5.4 Applications of Machine Learning in Cybercrime 2.5.5 Adherence of Machine Learning With Cyber Security in Relevance to IoT 2.5.6 Distributed Denial-of-Service 2.6 Related Work 2.7 Methodology 2.8 Discussions and Implications 2.9 Conclusion References 3 Employing Machine Learning Approaches for Predictive Data Analytics in Retail Industry 3.1 Introduction 3.2 Related Work 3.3 Predictive Data Analytics in Retail 3.3.1 ML for Predictive Data Analytics 3.3.2 Use Cases 3.3.3 Limitations and Challenges 3.4 Proposed Model 3.4.1 Case Study 3.5 Conclusion and Future Scope References 4 Emerging Cloud Computing Trends for Business Transformation 4.1 Introduction 4.1.1 Computing Definition Cloud 4.1.2 Advantages of Cloud Computing Over On-Premises IT Operation 4.1.3 Limitations of Cloud Computing 4.2 History of Cloud Computing 4.3 Core Attributes of Cloud Computing 4.4 Cloud Computing Models 4.4.1 Cloud Deployment Model 4.4.2 Cloud Service Model 4.5 Core Components of Cloud Computing Architecture: Hardware and Software 4.6 Factors Need to Consider for Cloud Adoption 4.6.1 Evaluating Cloud Infrastructure 4.6.2 Evaluating Cloud Provider 4.6.3 Evaluating Cloud Security 4.6.4 Evaluating Cloud Services 4.6.5 Evaluating Cloud Service Level Agreements (SLA) 4.6.6 Limitations to Cloud Adoption 4.7 Transforming Business Through Cloud 4.8 Key Emerging Trends in Cloud Computing 4.8.1 Technology Trends 4.8.2 Business Models 4.8.3 Product Transformation 4.8.4 Customer Engagement 4.8.5 Employee Empowerment 4.8.6 Data Management and Assurance 4.8.7 Digitalization 4.8.8 Building Intelligence Cloud System 4.8.9 Creating Hyper-Converged Infrastructure 4.9 Case Study: Moving Data Warehouse to Cloud Boosts Performance for Johnson & Johnson 4.10 Conclusion References 5 Security of Sensitive Data in Cloud Computing 5.1 Introduction 5.1.1 Characteristics of Cloud Computing 5.1.2 Deployment Models for Cloud Services 5.1.3 Types of Cloud Delivery Models 5.2 Data in Cloud 5.2.1 Data Life Cycle 5.3 Security Challenges in Cloud Computing for Data 5.3.1 Security Challenges Related to Data at Rest 5.3.2 Security Challenges Related to Data in Use 5.3.3 Security Challenges Related to Data in Transit 5.4 Cross-Cutting Issues Related to Network in Cloud 5.5 Protection of Data 5.6 Tighter IAM Controls 5.7 Conclusion and Future Scope References 6 Cloud Cryptography for Cloud Data Analytics in IoT 6.1 Introduction 6.2 Cloud Computing Software Security Fundamentals 6.3 Security Management 6.4 Cryptography Algorithms 6.4.1 Types of Cryptography 6.5 Secure Communications 6.6 Identity Management and Access Control 6.7 Autonomic Security 6.8 Conclusion References 7 Issues and Challenges of Classical Cryptography in Cloud Computing 7.1 Introduction 7.1.1 Problem Statement and Motivation 7.1.2 Contribution 7.2 Cryptography 7.2.1 Cryptography Classification 7.3 Security in Cloud Computing 7.3.1 The Need for Security in Cloud Computing 7.3.2 Challenges in Cloud Computing Security 7.3.3 Benefits of Cloud Computing Security 7.3.4 Literature Survey 7.4 Classical Cryptography for Cloud Computing 7.4.1 RSA 7.4.2 AES 7.4.3 DES 7.4.4 Blowfish 7.5 Homomorphic Cryptosystem 7.5.1 Paillier Cryptosystem 7.5.2 RSA Homomorphic Cryptosystem 7.6 Implementation 7.7 Conclusion and Future Scope References 8 Cloud-Based Data Analytics for Monitoring Smart Environments 8.1 Introduction 8.2 Environmental Monitoring for Smart Buildings 8.2.1 Smart Environments 8.3 Smart Health 8.3.1 Description of Solutions in General 8.3.2 Detection of Distress 8.3.3 Green Protection 8.3.4 Medical Preventive/Help 8.4 Digital Network 5G and Broadband Networks 8.4.1 IoT-Based Smart Grid Technologies 8.5 Emergent Smart Cities Communication Networks 8.5.1 RFID Technologies 8.5.2 Identifier Schemes 8.6 Smart City IoT Platforms Analysis System 8.7 Smart Management of Car Parking in Smart Cities 8.8 Smart City Systems and Services Securing: A Risk-Based Analytical Approach 8.9 Virtual Integrated Storage System 8.10 Convolutional Neural Network (CNN) 8.10.1 IEEE 802.15.4 8.10.2 BLE 8.10.3 ITU-T G.9959 (Z-Wave) 8.10.4 NFC 8.10.5 LoRaWAN 8.10.6 Sigfox 8.10.7 NB-IoT 8.10.8 PLC 8.10.9 MS/TP 8.11 Challenges and Issues 8.11.1 Interoperability and Standardization 8.11.2 Customization and Adaptation 8.11.3 Entity Identification and Virtualization 8.11.4 Big Data Issue in Smart Environments 8.12 Future Trends and Research Directions in Big Data Platforms for the Internet of Things 8.13 Case Study 8.14 Conclusion References 9 Performance Metrics for Comparison of Heuristics Task Scheduling Algorithms in Cloud Computing Platform 9.1 Introduction 9.2 Workflow Model 9.3 System Computing Model 9.4 Major Objective of Scheduling 9.5 Task Computational Attributes for Scheduling 9.6 Performance Metrics 9.7 Heuristic Task Scheduling Algorithms 9.7.1 Heterogeneous Earliest Finish Time (HEFT) Algorithm 9.7.2 Critical-Path-on-a-Processor (CPOP) Algorithm 9.7.3 As Late As Possible (ALAP) Algorithm 9.7.4 Performance Effective Task Scheduling (PETS) Algorithm 9.8 Performance Analysis and Results 9.9 Conclusion References 10 Smart Environment Monitoring Models Using Cloud-Based Data Analytics: A Comprehensive Study 10.1 Introduction 10.1.1 Internet of Things 10.1.2 Cloud Computing 10.1.3 Environmental Monitoring 10.2 Background and Motivation 10.2.1 Challenges and Issues 10.2.2 Technologies Used for Designing Cloud-Based Data Analytics 10.2.3 Cloud-Based Data Analysis Techniques and Models 10.2.4 Data Mining Techniques 10.2.5 Machine Learning 10.2.6 Applications 10.3 Conclusion References 11 Advancement of Machine Learning and Cloud Computing in the Field of Smart Health Care 11.1 Introduction 11.2 Survey on Architectural WBAN 11.3 Suggested Strategies 11.3.1 System Overview 11.3.2 Motivation 11.3.3 DSCB Protocol 11.4 CNN-Based Image Segmentation (UNet Model) 11.5 Emerging Trends in IoT Healthcare 11.6 Tier Health IoT Model 11.7 Role of IoT in Big Data Analytics 11.8 Tier Wireless Body Area Network Architecture 11.9 Conclusion References 12 Study on Green Cloud Computing—A Review 12.1 Introduction 12.2 Cloud Computing 12.2.1 Cloud Computing: On-Request Outsourcing-Pay-as-You-Go 12.3 Features of Cloud Computing 12.4 Green Computing 12.5 Green Cloud Computing 12.6 Models of Cloud Computing 12.7 Models of Cloud Services 12.8 Cloud Deployment Models 12.9 Green Cloud Architecture 12.10 Cloud Service Providers 12.11 Features of Green Cloud Computing 12.12 Advantages of Green Cloud Computing 12.13 Limitations of Green Cloud Computing 12.14 Cloud and Sustainability Environmental 12.15 Statistics Related to Cloud Data Centers 12.16 The Impact of Data Centers on Environment 12.17 Virtualization Technologies 12.18 Literature Review 12.19 The Main Objective 12.20 Research Gap 12.21 Research Methodology 12.22 Conclusion and Suggestions 12.23 Scope for Further Research References 13 Intelligent Reclamation of Plantae Affliction Disease 13.1 Introduction 13.2 Existing System 13.3 Proposed System 13.4 Objectives of the Concept 13.5 Operational Requirements 13.6 Non-Operational Requirements 13.7 Depiction Design Description 13.8 System Architecture 13.8.1 Module Characteristics 13.8.2 Convolutional Neural System 13.8.3 User Application 13.9 Design Diagrams 13.9.1 High-Level Design 13.9.2 Low-Level Design 13.9.3 Test Cases 13.10 Comparison and Screenshot 13.11 Conclusion References 14 Prediction of the Stock Market Using Machine Learning–Based Data Analytics 14.1 Introduction of Stock Market 14.1.1 Impact of Stock Prices 14.2 Related Works 14.3 Financial Prediction Systems Framework 14.3.1 Conceptual Financial Prediction Systems 14.3.2 Framework of Financial Prediction Systems Using Machine Learning 14.3.3 Framework of Financial Prediction Systems Using Deep Learning 14.4 Implementation and Discussion of Result 14.4.1 Pharmaceutical Sector 14.4.2 Banking Sector 14.4.3 Fast-Moving Consumer Goods Sector 14.4.4 Power Sector 14.4.5 Automobiles Sector 14.4.6 Comparison of Prediction Using Linear Regression Model and Long-Short-Term Memory Model 14.5 Conclusion 14.5.1 Future Enhancement References Web Citations 15 Pehchaan: Analysis of the ‘Aadhar Dataset’ to Facilitate a Smooth and Efficient Conduct of the Upcoming NPR 15.1 Introduction 15.2 Basic Concepts 15.3 Study of Literature Survey and Technology 15.4 Proposed Model 15.5 Implementation and Results 15.6 Conclusion References 16 Deep Learning Approach for Resource Optimization in Blockchain, Cellular Networks, and IoT: Open Challenges and Current Solutions 16.1 Introduction 16.1.1 Aim 16.1.2 Research Contribution 16.1.3 Organization 16.2 Background 16.2.1 Blockchain 16.2.2 Internet of Things (IoT) 16.2.3 5G Future Generation Cellular Networks 16.2.4 Machine Learning and Deep Learning Techniques 16.2.5 Deep Reinforcement Learning 16.3 Deep Learning for Resource Management in Blockchain, Cellular, and IoT Networks 16.3.1 Resource Management in Blockchain for 5G Cellular Networks 16.3.2 Deep Learning Blockchain Application for Resource Management in IoT Networks 16.4 Future Research Challenges 16.4.1 Blockchain Technology 16.4.2 IoT Networks 16.4.3 5G Future Generation Networks 16.4.4 Machine Learning and Deep Learning 16.4.5 General Issues 16.5 Conclusion and Discussion References 17 Unsupervised Learning in Accordance With New Aspects of Artificial Intelligence 17.1 Introduction 17.2 Applications of Machine Learning in Data Management Possibilities 17.2.1 Terminology of Basic Machine Learning 17.2.2 Rules Based on Machine Learning 17.2.3 Unsupervised vs. Supervised Methodology 17.3 Solutions to Improve Unsupervised Learning Using Machine Learning 17.3.1 Insufficiency of Labeled Data 17.3.2 Overfitting 17.3.3 A Closer Look Into Unsupervised Algorithms 17.3.4 Singular Value Decomposition (SVD) 17.3.5 Dictionary Learning 17.3.6 The Latent Dirichlet Allocation 17.4 Open Source Platform for Cutting Edge Unsupervised Machine Learning 17.4.1 TensorFlow 17.4.2 Keras 17.4.3 Scikit-Learn 17.4.4 Microsoft Cognitive Toolkit 17.4.5 Theano 17.4.6 Caffe 17.4.7 Torch 17.5 Applications of Unsupervised Learning 17.5.1 Regulation of Digital Data 17.5.2 Machine Learning in Voice Assistance 17.5.3 For Effective Marketing 17.5.4 Advancement of Cyber Security 17.5.5 Faster Computing Power 17.5.6 The Endnote 17.6 Applications Using Machine Learning Algos 17.6.1 Linear Regression 17.6.2 Logistic Regression 17.6.3 Decision Tree 17.6.4 Support Vector Machine (SVM) 17.6.5 Naive Bayes 17.6.6 K-Nearest Neighbors 17.6.7 K-Means 17.6.8 Random Forest 17.6.9 Dimensionality Reduction Algorithms 17.6.10 Gradient Boosting Algorithms References 18 Predictive Modeling of Anthropomorphic Gamifying Blockchain-Enabled Transitional Healthcare System 18.1 Introduction 18.1.1 Transitional Healthcare Services and Their Challenges 18.2 Gamification in Transitional Healthcare: A New Model 18.2.1 Anthropomorphic Interface With Gamification 18.2.2 Gamification in Blockchain 18.2.3 Anthropomorphic Gamification in Blockchain: Motivational Factors 18.3 Existing Related Work 18.4 The Framework 18.4.1 Health Player 18.4.2 Data Collection 18.4.3 Anthropomorphic Gamification Layers 18.4.4 Ethereum 18.4.5 Reward Model 18.4.6 Predictive Models 18.5 Implementation 18.5.1 Methodology 18.5.2 Result Analysis 18.5.3 Threats to the Validity 18.6 Conclusion References Index