Schweitzer Fachinformationen
Wenn es um professionelles Wissen geht, ist Schweitzer Fachinformationen wegweisend. Kunden aus Recht und Beratung sowie Unternehmen, öffentliche Verwaltungen und Bibliotheken erhalten komplette Lösungen zum Beschaffen, Verwalten und Nutzen von digitalen und gedruckten Medien.
Machine Learning Approach for Cloud Data Analytics in IoT
The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications
Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology.
Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.
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.
Preface xix
Acknowledgment xxiii
1 Machine Learning-Based Data Analysis 1M. 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 25Suchismita 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 53Rakhi 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 71Prasanta Kumar Mahapatra, Alok Ranjan Tripathy and Alakananda Tripathy
4.1 Introduction 71
4.1.1 Computing Definition Cloud 72
4.1.2 Advantages of Cloud Computing Over On-Premises IT Operation 73
4.1.3 Limitations of Cloud Computing 74
4.2 History of Cloud Computing 74
4.3 Core Attributes of Cloud Computing 75
4.4 Cloud Computing Models 77
4.4.1 Cloud Deployment Model 77
4.4.2 Cloud Service Model 79
4.5 Core Components of Cloud Computing Architecture: Hardware and Software 83
4.6 Factors Need to Consider for Cloud Adoption 84
4.6.1 Evaluating Cloud Infrastructure 84
4.6.2 Evaluating Cloud Provider 85
4.6.3 Evaluating Cloud Security 86
4.6.4 Evaluating Cloud Services 86
4.6.5 Evaluating Cloud Service Level Agreements (SLA) 87
4.6.6 Limitations to Cloud Adoption 87
4.7 Transforming Business Through Cloud 88
4.8 Key Emerging Trends in Cloud Computing 89
4.8.1 Technology Trends 90
4.8.2 Business Models 92
4.8.3 Product Transformation 92
4.8.4 Customer Engagement 92
4.8.5 Employee Empowerment 93
4.8.6 Data Management and Assurance 93
4.8.7 Digitalization 93
4.8.8 Building Intelligence Cloud System 93
4.8.9 Creating Hyper-Converged Infrastructure 94
4.9 Case Study: Moving Data Warehouse to Cloud Boosts Performance for Johnson & Johnson 94
4.10 Conclusion 95
References 96
5 Security of Sensitive Data in Cloud Computing 99Kirti Wanjale, Monika Mangla and Paritosh Marathe
5.1 Introduction 100
5.1.1 Characteristics of Cloud Computing 100
5.1.2 Deployment Models for Cloud Services 101
5.1.3 Types of Cloud Delivery Models 102
5.2 Data in Cloud 102
5.2.1 Data Life Cycle 103
5.3 Security Challenges in Cloud Computing for Data 105
5.3.1 Security Challenges Related to Data at Rest 106
5.3.2 Security Challenges Related to Data in Use 107
5.3.3 Security Challenges Related to Data in Transit 107
5.4 Cross-Cutting Issues Related to Network in Cloud 108
5.5 Protection of Data 109
5.6 Tighter IAM Controls 114
5.7 Conclusion and Future Scope 117
References 117
6 Cloud Cryptography for Cloud Data Analytics in IoT 119N. Jayashri and K. Kalaiselvi
6.1 Introduction 120
6.2 Cloud Computing Software Security Fundamentals 120
6.3 Security Management 122
6.4 Cryptography Algorithms 123
6.4.1 Types of Cryptography 123
6.5 Secure Communications 127
6.6 Identity Management and Access Control 133
6.7 Autonomic Security 137
6.8 Conclusion 139
References 139
7 Issues and Challenges of Classical Cryptography in Cloud Computing 143Amrutanshu Panigrahi, Ajit Kumar Nayak and Rourab Paul
7.1 Introduction 144
7.1.1 Problem Statement and Motivation 145
7.1.2 Contribution 146
7.2 Cryptography 146
7.2.1 Cryptography Classification 147
7.2.1.1 Classical Cryptography 147
7.2.1.2 Homomorphic Encryption 149
7.3 Security in Cloud Computing 150
7.3.1 The Need for Security in Cloud Computing 151
7.3.2 Challenges in Cloud Computing Security 152
7.3.3 Benefits of Cloud Computing Security 153
7.3.4 Literature Survey 154
7.4 Classical Cryptography for Cloud Computing 157
7.4.1 RSA 157
7.4.2 AES 157
7.4.3 DES 158
7.4.4 Blowfish 158
7.5 Homomorphic Cryptosystem 158
7.5.1 Paillier Cryptosystem 159
7.5.1.1 Additive Homomorphic Property 159
7.5.2 RSA Homomorphic Cryptosystem 160
7.5.2.1 Multiplicative Homomorphic Property 160
7.6 Implementation 160
7.7 Conclusion and Future Scope 162
References 162
8 Cloud-Based Data Analytics for Monitoring Smart Environments 167D. Karthika
8.1 Introduction 167
8.2 Environmental Monitoring for Smart Buildings 169
8.2.1 Smart Environments 169
8.3 Smart Health 171
8.3.1 Description of Solutions in General 171
8.3.2 Detection of Distress 172
8.3.3 Green Protection 173
8.3.4 Medical Preventive/Help 174
8.4 Digital Network 5G and Broadband Networks 174
8.4.1 IoT-Based Smart Grid Technologies 174
8.5 Emergent Smart Cities Communication Networks 175
8.5.1 RFID Technologies 177
8.5.2 Identifier Schemes 177
8.6 Smart City IoT Platforms Analysis System 177
8.7 Smart Management of Car Parking in Smart Cities 178
8.8 Smart City Systems and Services Securing: A Risk-Based Analytical Approach 178
8.9 Virtual Integrated Storage System 179
8.10 Convolutional Neural Network (CNN) 181
8.10.1 IEEE 802.15.4 182
8.10.2 BLE 182
8.10.3 ITU-T G.9959 (Z-Wave) 183
8.10.4 NFC 183
8.10.5 LoRaWAN 184
8.10.6 Sigfox 184
8.10.7 NB-IoT 184
8.10.8 PLC 184
8.10.9 MS/TP 184
8.11 Challenges and Issues 185
8.11.1 Interoperability and Standardization 185
8.11.2 Customization and Adaptation 186
8.11.3 Entity Identification and Virtualization 187
8.11.4 Big Data Issue in Smart Environments 187
8.12 Future Trends and Research Directions in Big Data Platforms for the Internet of Things 188
8.13 Case Study 189
8.14 Conclusion 191
References 191
9 Performance Metrics for Comparison of Heuristics Task Scheduling Algorithms in Cloud Computing Platform 195Nidhi Rajak and Ranjit Rajak
9.1 Introduction 195
9.2 Workflow Model 197
9.3 System Computing Model 198
9.4 Major Objective of Scheduling 198
9.5 Task Computational Attributes for Scheduling 198
9.6 Performance Metrics 200
9.7 Heuristic Task Scheduling Algorithms 201
9.7.1 Heterogeneous Earliest Finish Time (HEFT) Algorithm 202
9.7.2 Critical-Path-on-a-Processor (CPOP) Algorithm 208
9.7.3 As Late As Possible (ALAP) Algorithm 213
9.7.4 Performance Effective Task Scheduling (PETS) Algorithm 217
9.8 Performance Analysis and Results 220
9.9 Conclusion 224
References 224
10 Smart Environment Monitoring Models Using Cloud-Based Data Analytics: A Comprehensive Study 227Pradnya S. Borkar and Reena Thakur
10.1 Introduction 228
10.1.1 Internet of Things 229
10.1.2 Cloud Computing 230
10.1.3 Environmental Monitoring 232
10.2 Background and Motivation 234
10.2.1 Challenges and Issues 234
10.2.2 Technologies Used for Designing Cloud-Based Data Analytics 240
10.2.2.1 Communication Technologies 241
10.2.3 Cloud-Based Data Analysis Techniques and Models 243
10.2.3.1 MapReduce for Data Analysis 243
10.2.3.2 Data Analysis Workflows 246
10.2.3.3 NoSQL Models 247
10.2.4 Data Mining Techniques 248
10.2.5 Machine Learning 251
10.2.5.1 Significant Importance of Machine Learning and Its Algorithms 253
10.2.6 Applications 253
10.3 Conclusion 261
References 262
11 Advancement of Machine Learning and Cloud Computing in the Field of Smart Health Care 273Aradhana Behura, Shibani Sahu and Manas Ranjan Kabat
11.1 Introduction 274
11.2 Survey on Architectural WBAN 278
11.3 Suggested Strategies 280
11.3.1 System Overview 280
11.3.2 Motivation 281
11.3.3 DSCB Protocol 281
11.3.3.1 Network Topology 282
11.3.3.2 Starting Stage 282
11.3.3.3 Cluster Evolution 282
11.3.3.4 Sensed Information Stage 283
11.3.3.5 Choice of Forwarder Stage 283
11.3.3.6 Energy Consumption as Well as Routing Stage 285
11.4 CNN-Based Image Segmentation (UNet Model) 287
11.5 Emerging Trends in IoT Healthcare 290
11.6 Tier Health IoT Model 294
11.7 Role of IoT in Big Data Analytics 294
11.8 Tier Wireless Body Area Network Architecture 296
11.9 Conclusion 303
References 303
12 Study on Green Cloud Computing-A Review 307Meenal Agrawal and Ankita Jain
12.1 Introduction 307
12.2 Cloud Computing 308
12.2.1 Cloud Computing: On-Request Outsourcing-Pay-as-You-Go 308
12.3 Features of Cloud Computing 309
12.4 Green Computing 309
12.5 Green Cloud Computing 309
12.6 Models of Cloud Computing 310
12.7 Models of Cloud Services 310
12.8 Cloud Deployment Models 311
12.9 Green Cloud Architecture 312
12.10 Cloud Service Providers 312
12.11 Features of Green Cloud Computing 313
12.12 Advantages of Green Cloud Computing 313
12.13 Limitations of Green Cloud Computing 314
12.14 Cloud and Sustainability Environmental 315
12.15 Statistics Related to Cloud Data Centers 315
12.16 The Impact of Data Centers on Environment 315
12.17 Virtualization Technologies 316
12.18 Literature Review 316
12.19 The Main Objective 318
12.20 Research Gap 319
12.21 Research Methodology 319
12.22 Conclusion and Suggestions 320
12.23 Scope for Further Research 320
References 321
13 Intelligent Reclamation of Plantae Affliction Disease 323Reshma Banu, G.F Ali Ahammed and Ayesha Taranum
13.1 Introduction 324
13.2 Existing System 327
13.3 Proposed System 327
13.4 Objectives of the Concept 328
13.5 Operational Requirements 328
13.6 Non-Operational Requirements 329
13.7 Depiction Design Description 330
13.8 System Architecture 330
13.8.1 Module Characteristics 331
13.8.2 Convolutional Neural System 332
13.8.3 User Application 332
13.9 Design Diagrams 333
13.9.1 High-Level Design 333
13.9.2 Low-Level Design 333
13.9.3 Test Cases 335
13.10 Comparison and Screenshot 335
13.11 Conclusion 342
References 342
14 Prediction of Stock Market Using Machine Learning-Based Data Analytics 347Maheswari P. and Jaya A.
14.1 Introduction of Stock Market 348
14.1.1 Impact of Stock Prices 349
14.2 Related Works 350
14.3 Financial Prediction Systems Framework 352
14.3.1 Conceptual Financial Prediction Systems 352
14.3.2 Framework of Financial Prediction Systems Using Machine Learning 353
14.3.2.1 Algorithm to Predicting the Closing Price of the Given Stock Data Using Linear Regression 355
14.3.3 Framework of Financial Prediction Systems Using Deep Learning 355
14.3.3.1 Algorithm to Predict the Closing Price of the Given Stock Using Long Short-Term Memory 356
14.4 Implementation and Discussion of Result 357
14.4.1 Pharmaceutical Sector 357
14.4.1.1 Cipla Limited 357
14.4.1.2 Torrent Pharmaceuticals Limited 359
14.4.2 Banking Sector 359
14.4.2.1 ICICI Bank 359
14.4.2.2 State Bank of India 359
14.4.3 Fast-Moving Consumer Goods Sector 362
14.4.3.1 ITC 363
14.4.3.2 Hindustan Unilever Limited 363
14.4.4 Power Sector 363
14.4.4.1 Adani Power Limited 363
14.4.4.2 Power Grid Corporation of India Limited 364
14.4.5 Automobiles Sector 368
14.4.5.1 Mahindra & Mahindra Limited 368
14.4.5.2 Maruti Suzuki India Limited 368
14.4.6 Comparison of Prediction Using Linear Regression Model and Long-Short-Term Memory Model 368
14.5 Conclusion 371
14.5.1 Future Enhancement 372
References 372
Web Citations 373
15 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 MinakhiRout
15.1 Introduction 376
15.2 Basic Concepts 377
15.3 Study of Literature Survey and Technology 380
15.4 Proposed Model 381
15.5 Implementation and Results 383
15.6 Conclusion 389
References 389
16 Deep Learning Approach for Resource Optimization in Blockchain, Cellular Networks, and IoT: Open Challenges and Current Solutions 391Upinder Kaur and Shalu
16.1 Introduction 392
16.1.1 Aim 393
16.1.2 Research Contribution 395
16.1.3 Organization 396
16.2 Background 396
16.2.1 Blockchain 397
16.2.2 Internet of Things (IoT) 398
16.2.3 5G Future Generation Cellular Networks 398
16.2.4 Machine Learning and Deep Learning Techniques 399
16.2.5 Deep Reinforcement Learning 399
16.3 Deep Learning for Resource Management in Blockchain, Cellular, and IoT Networks 401
16.3.1 Resource Management in Blockchain for 5G Cellular Networks 402
16.3.2 Deep Learning Blockchain Application for Resource Management in IoT Networks 402
16.4 Future Research Challenges 413
16.4.1 Blockchain Technology 413
16.4.1.1 Scalability 414
16.4.1.2 Efficient Consensus Protocols 415
16.4.1.3 Lack of Skills and Experts 415
16.4.2 IoT Networks 416
16.4.2.1 Heterogeneity of IoT and 5G Data 416
16.4.2.2 Scalability Issues 416
16.4.2.3 Security and Privacy Issues 416
16.4.3 5G Future Generation Networks 416
16.4.3.1 Heterogeneity 416
16.4.3.2 Security and Privacy 417
16.4.3.3 Resource Utilization 417
16.4.4 Machine Learning and Deep Learning 417
16.4.4.1 Interpretability 418
16.4.4.2 Training Cost for ML and DRL Techniques 418
16.4.4.3 Lack of Availability of Data Sets 418
16.4.4.4 Avalanche Effect for DRL Approach 419
16.4.5 General Issues 419
16.4.5.1 Security and Privacy Issues 419
16.4.5.2 Storage 419
16.4.5.3 Reliability 420
16.4.5.4 Multitasking Approach 420
16.5 Conclusion and Discussion 420
References 422
17 Unsupervised Learning in Accordance With New Aspects of Artificial Intelligence 429Riya Sharma, Komal Saxena and Ajay Rana
17.1 Introduction 430
17.2 Applications of Machine Learning in Data Management Possibilities 431
17.2.1 Terminology of Basic Machine Learning 432
17.2.2 Rules Based on Machine Learning 434
17.2.3 Unsupervised vs. Supervised Methodology 434
17.3 Solutions to Improve Unsupervised Learning Using Machine Learning 436
17.3.1 Insufficiency of Labeled Data 436
17.3.2 Overfitting 437
17.3.3 A Closer Look Into Unsupervised Algorithms 437
17.3.3.1 Reducing Dimensionally 437
17.3.3.2 Principal Component Analysis 438
17.3.4 Singular Value Decomposition (SVD) 439
17.3.4.1 Random Projection 439
17.3.4.2 Isomax 439
17.3.5 Dictionary Learning 439
17.3.6 The Latent Dirichlet Allocation 440
17.4 Open Source Platform for Cutting Edge Unsupervised Machine Learning 440
17.4.1 TensorFlow 441
17.4.2 Keras 441
17.4.3 Scikit-Learn 441
17.4.4 Microsoft Cognitive Toolkit 442
17.4.5 Theano 442
17.4.6 Caffe 442
17.4.7 Torch 442
17.5 Applications of Unsupervised Learning 443
17.5.1 Regulation of Digital Data 443
17.5.2 Machine Learning in Voice Assistance 443
17.5.3 For Effective Marketing 444
17.5.4 Advancement of Cyber Security 444
17.5.5 Faster Computing Power 444
17.5.6 The Endnote 445
17.6 Applications Using Machine Learning Algos 445
17.6.1 Linear Regression 445
17.6.2 Logistic Regression 446
17.6.3 Decision Tree 446
17.6.4 Support Vector Machine (SVM) 446
17.6.5 Naive Bayes 446
17.6.6 K-Nearest Neighbors 447
17.6.7 K-Means 447
17.6.8 Random Forest 447
17.6.9 Dimensionality Reduction Algorithms 448
17.6.10 Gradient Boosting Algorithms 448
References 449
18 Predictive Modeling of Anthropomorphic Gamifying Blockchain-Enabled Transitional Healthcare System 461Deepa Kumari, B.S.A.S. Rajita, Medindrao Raja Sekhar, Ritika Garg and Subhrakanta Panda
18.1 Introduction 462
18.1.1 Transitional Healthcare Services and Their Challenges 462
18.2 Gamification in Transitional Healthcare: A New Model 463
18.2.1 Anthropomorphic Interface With Gamification 464
18.2.2 Gamification in Blockchain 465
18.2.3 Anthropomorphic Gamification in Blockchain: Motivational Factors 466
18.3 Existing Related Work 468
18.4 The Framework 478
18.4.1 Health Player 479
18.4.2 Data Collection 480
18.4.3 Anthropomorphic Gamification Layers 480
18.4.4 Ethereum 480
18.4.4.1 Ethereum-Based Smart Contracts for Healthcare 481
18.4.4.2 Installation of Ethereum Smart Contract 481
18.4.5 Reward Model 482
18.4.6 Predictive Models 482
18.5 Implementation 483
18.5.1 Methodology 483
18.5.2 Result Analysis 484
18.5.3 Threats to the Validity 486
18.6 Conclusion 487
References 487
Index 491
Dateiformat: PDFKopierschutz: Adobe-DRM (Digital Rights Management)
Systemvoraussetzungen:
Das Dateiformat PDF zeigt auf jeder Hardware eine Buchseite stets identisch an. Daher ist eine PDF auch für ein komplexes Layout geeignet, wie es bei Lehr- und Fachbüchern verwendet wird (Bilder, Tabellen, Spalten, Fußnoten). Bei kleinen Displays von E-Readern oder Smartphones sind PDF leider eher nervig, weil zu viel Scrollen notwendig ist. Mit Adobe-DRM wird hier ein „harter” Kopierschutz verwendet. Wenn die notwendigen Voraussetzungen nicht vorliegen, können Sie das E-Book leider nicht öffnen. Daher müssen Sie bereits vor dem Download Ihre Lese-Hardware vorbereiten.
Bitte beachten Sie: Wir empfehlen Ihnen unbedingt nach Installation der Lese-Software diese mit Ihrer persönlichen Adobe-ID zu autorisieren!
Weitere Informationen finden Sie in unserer E-Book Hilfe.
