Im Zeitalter des Internet of Things (IoT) erzeugen Edge-Geräte in jedem Sekundenbruchteil gigantische Datenmengen. Dabei besteht das Hauptziel dieser Netzwerke darin, aus den gesammelten Daten sinnvolle Informationen abzuleiten. Gleichzeitig werden gewaltige Datenmengen in die Cloud übertragen, was extrem teuer und zeitaufwändig ist. Es ist somit notwendig, effiziente Mechanismen für die Verarbeitung dieser gewaltigen Datenmengen zu entwickeln, und dafür sind effiziente Datenverarbeitungstechniken erforderlich. Nachhaltige Paradigmen wie Cloud Computing und Fog Computing tragen zu einem…mehr
Im Zeitalter des Internet of Things (IoT) erzeugen Edge-Geräte in jedem Sekundenbruchteil gigantische Datenmengen. Dabei besteht das Hauptziel dieser Netzwerke darin, aus den gesammelten Daten sinnvolle Informationen abzuleiten. Gleichzeitig werden gewaltige Datenmengen in die Cloud übertragen, was extrem teuer und zeitaufwändig ist. Es ist somit notwendig, effiziente Mechanismen für die Verarbeitung dieser gewaltigen Datenmengen zu entwickeln, und dafür sind effiziente Datenverarbeitungstechniken erforderlich. Nachhaltige Paradigmen wie Cloud Computing und Fog Computing tragen zu einem geschickten Umgang mit Themen wie Leistung, Speicher- und Verarbeitungskapazitäten, Wartung, Sicherheit, Effizienz, Integration, Kosten, Energieverbrauch und Latenzzeiten bei. Allerdings werden ausgefeilte Analysetools benötigt, um die Anfragen in einer optimalen Zeit zu bearbeiten. Daher wird derzeit eifrig an der Entwicklung eines effektiven und effizienten Rahmens geforscht, um den größtmöglichen Nutzen zu erhalten.
Bei der Verarbeitung der gewaltigen Datenmengen steht das maschinelle Lernen besonders hoch im Kurs und wird in zahlreichen Disziplinen angewandt, auch in den sozialen Medien.
In Machine Learning Approach for Cloud Data Analytics in IoT werden sämtliche Aspekte des IoT, des Cloud Computing und der Datenanalyse ausführlich erläutert und aus verschiedenen Perspektiven betrachtet. Das Buch präsentiert den neuesten Stand der Forschung und fortschrittliche Themen. So erhalten die Leserinnen und Leser aktuelle Informationen und können das gesamte Spektrum der Anwendungen von IoT, Cloud Computing und Datenanalyse erfassen.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Audience Researchers 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.
Inhaltsangabe
Preface xix
Acknowledgment xxiii
1 Machine Learning-Based Data Analysis 1 M. Deepika and K. Kalaiselvi
1.1 Introduction 1
1.2 Machine Learning for the Internet of Things Using Data Analysis 4
1.2.1 Computing Framework 6
1.2.2 Fog Computing 6
1.2.3 Edge Computing 6
1.2.4 Cloud Computing 7
1.2.5 Distributed Computing 7
1.3 Machine Learning Applied to Data Analysis 7
1.3.1 Supervised Learning Systems 8
1.3.2 Decision Trees 9
1.3.3 Decision Tree Types 9
1.3.4 Unsupervised Machine Learning 10
1.3.5 Association Rule Learning 10
1.3.6 Reinforcement Learning 10
1.4 Practical Issues in Machine Learning 11
1.5 Data Acquisition 12
1.6 Understanding the Data Formats Used in Data Analysis Applications 13
1.7 Data Cleaning 14
1.8 Data Visualization 15
1.9 Understanding the Data Analysis Problem-Solving Approach 15
1.10 Visualizing Data to Enhance Understanding and Using Neural Networks in Data Analysis 16
1.11 Statistical Data Analysis Techniques 17
1.11.1 Hypothesis Testing 18
1.11.2 Regression Analysis 18
1.12 Text Analysis and Visual and Audio Analysis 18
1.13 Mathematical and Parallel Techniques for Data Analysis 19
1.13.1 Using Map-Reduce 20
1.13.2 Leaning Analysis 20
1.13.3 Market Basket Analysis 21
1.14 Conclusion 21
References 22
2 Machine Learning for Cyber-Immune IoT Applications 25 Suchismita Sahoo and Sushree Sangita Sahoo
2.1 Introduction 25
2.2 Some Associated Impactful Terms 27
2.2.1 IoT 27
2.2.2 IoT Device 28
2.2.3 IoT Service 29
2.2.4 Internet Security 29
2.2.5 Data Security 30
2.2.6 Cyberthreats 31
2.2.7 Cyber Attack 31
2.2.8 Malware 32
2.2.9 Phishing 32
2.2.10 Ransomware 33
2.2.11 Spear-Phishing 33
2.2.12 Spyware 34
2.2.13 Cybercrime 34
2.2.14 IoT Cyber Security 35
2.2.15 IP Address 36
2.3 Cloud Rationality Representation 36
2.3.1 Cloud 36
2.3.2 Cloud Data 37
2.3.3 Cloud Security 38
2.3.4 Cloud Computing 38
2.4 Integration of IoT With Cloud 40
2.5 The Concepts That Rules Over 41
2.5.1 Artificial Intelligent 41
2.5.2 Overview of Machine Learning 41
2.5.2.1 Supervised Learning 41
2.5.2.2 Unsupervised Learning 42
2.5.3 Applications of Machine Learning in Cyber Security 43
2.5.4 Applications of Machine Learning in Cybercrime 43
2.5.5 Adherence of Machine Learning With Cyber Security in Relevance to IoT 43
2.5.6 Distributed Denial-of-Service 44
2.6 Related Work 45
2.7 Methodology 46
2.8 Discussions and Implications 48
2.9 Conclusion 49
References 49
3 Employing Machine Learning Approaches for Predictive Data Analytics in Retail Industry 53 Rakhi Akhare, Sanjivani Deokar, Monika Mangla and Hardik Deshmukh
3.1 Introduction 53
3.2 Related Work 55
3.3 Predictive Data Analytics in Retail 56
3.3.1 ML for Predictive Data Analytics 58
3.3.2 Use Cases 59
3.3.3 Limitations and Challenges 61
3.4 Proposed Model 61
3.4.1 Case Study 63
3.5 Conclusion and Future Scope 68
References 69
4 Emerging Cloud Computing Trends for Business Transformation 71 Prasanta Kumar Mahapatr
1 Machine Learning-Based Data Analysis 1 M. Deepika and K. Kalaiselvi
1.1 Introduction 1
1.2 Machine Learning for the Internet of Things Using Data Analysis 4
1.2.1 Computing Framework 6
1.2.2 Fog Computing 6
1.2.3 Edge Computing 6
1.2.4 Cloud Computing 7
1.2.5 Distributed Computing 7
1.3 Machine Learning Applied to Data Analysis 7
1.3.1 Supervised Learning Systems 8
1.3.2 Decision Trees 9
1.3.3 Decision Tree Types 9
1.3.4 Unsupervised Machine Learning 10
1.3.5 Association Rule Learning 10
1.3.6 Reinforcement Learning 10
1.4 Practical Issues in Machine Learning 11
1.5 Data Acquisition 12
1.6 Understanding the Data Formats Used in Data Analysis Applications 13
1.7 Data Cleaning 14
1.8 Data Visualization 15
1.9 Understanding the Data Analysis Problem-Solving Approach 15
1.10 Visualizing Data to Enhance Understanding and Using Neural Networks in Data Analysis 16
1.11 Statistical Data Analysis Techniques 17
1.11.1 Hypothesis Testing 18
1.11.2 Regression Analysis 18
1.12 Text Analysis and Visual and Audio Analysis 18
1.13 Mathematical and Parallel Techniques for Data Analysis 19
1.13.1 Using Map-Reduce 20
1.13.2 Leaning Analysis 20
1.13.3 Market Basket Analysis 21
1.14 Conclusion 21
References 22
2 Machine Learning for Cyber-Immune IoT Applications 25 Suchismita Sahoo and Sushree Sangita Sahoo
2.1 Introduction 25
2.2 Some Associated Impactful Terms 27
2.2.1 IoT 27
2.2.2 IoT Device 28
2.2.3 IoT Service 29
2.2.4 Internet Security 29
2.2.5 Data Security 30
2.2.6 Cyberthreats 31
2.2.7 Cyber Attack 31
2.2.8 Malware 32
2.2.9 Phishing 32
2.2.10 Ransomware 33
2.2.11 Spear-Phishing 33
2.2.12 Spyware 34
2.2.13 Cybercrime 34
2.2.14 IoT Cyber Security 35
2.2.15 IP Address 36
2.3 Cloud Rationality Representation 36
2.3.1 Cloud 36
2.3.2 Cloud Data 37
2.3.3 Cloud Security 38
2.3.4 Cloud Computing 38
2.4 Integration of IoT With Cloud 40
2.5 The Concepts That Rules Over 41
2.5.1 Artificial Intelligent 41
2.5.2 Overview of Machine Learning 41
2.5.2.1 Supervised Learning 41
2.5.2.2 Unsupervised Learning 42
2.5.3 Applications of Machine Learning in Cyber Security 43
2.5.4 Applications of Machine Learning in Cybercrime 43
2.5.5 Adherence of Machine Learning With Cyber Security in Relevance to IoT 43
2.5.6 Distributed Denial-of-Service 44
2.6 Related Work 45
2.7 Methodology 46
2.8 Discussions and Implications 48
2.9 Conclusion 49
References 49
3 Employing Machine Learning Approaches for Predictive Data Analytics in Retail Industry 53 Rakhi Akhare, Sanjivani Deokar, Monika Mangla and Hardik Deshmukh
3.1 Introduction 53
3.2 Related Work 55
3.3 Predictive Data Analytics in Retail 56
3.3.1 ML for Predictive Data Analytics 58
3.3.2 Use Cases 59
3.3.3 Limitations and Challenges 61
3.4 Proposed Model 61
3.4.1 Case Study 63
3.5 Conclusion and Future Scope 68
References 69
4 Emerging Cloud Computing Trends for Business Transformation 71 Prasanta Kumar Mahapatr
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