Machine Learning Blockchain and Cyber Security in Smart Environments Application and Challenges 1st Edition by Sarvesh Tanwar, Sumit Badotra, Ajay Rana ISBN 1000623912 9781000623918

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Product details:

ISBN 10: 1000623912

ISBN 13: 9781000623918

Author:  Sarvesh Tanwar, Sumit Badotra, Ajay Rana 

Machine Learning, Blockchain, and Cyber Security in Smart Environments: Application and Challenges 1st Edition: 

Machine Learning, Cyber Security, and Blockchain in Smart Environment: Application and Challenges provides far-reaching insights into the recent techniques forming the backbone of smart environments, and addresses the vulnerabilities that give rise to the challenges in real-word implementation. The book focuses on the benefits related to the emerging applications such as machine learning, blockchain and cyber security.

Key Features:

• Introduces the latest trends in the fields of machine learning, blockchain and cyber security
• Discusses the fundamentals, challenges and architectural overviews with concepts
• Explores recent advancements in machine learning, blockchain, and cyber security
• Examines recent trends in emerging technologies

This book is primarily aimed at graduates, researchers, and professionals working in the areas of machine learning, blockchain, and cyber security.

 

Machine Learning, Blockchain, and Cyber Security in Smart Environments: Application and Challenges 1st Edition Table of contents:

1. Intelligent Green Internet of Things: An Investigation

   • Introduction
   • Green IoT
   • Related Surveys
   • IoT Layered Architecture
   • Applications
   • IoT Protocols
   • Limitations and Future Research Directions
   • Issues in Energy Conservation
   • Energy Preservation Approaches
   • Conclusion

2. The Role of Artificial Intelligence in the Education Sector: Possibilities and Challenges

   • Introduction
   • Background to the Study
   • Literature Survey
   • Findings and Discussion
   • Conclusion
   • Future Work

3. Multidisciplinary Applications of Machine Learning

   • Introduction
   • Machine Learning: Workflow and Features
   • Classifications of Machine Learning
   • Applications of Machine Learning in Various Fields
   • Artificial Intelligence and Deep Learning Overview
   • Conclusion

4. Prediction of Diabetes in the Early Stages Using Machine-Learning Tools and Microsoft Azure AI Services

   • Introduction
   • Dataset Collection and Tools Used
   • Data Cleansing
   • Dataset Visualization
   • Model Implementations and Comparisons
   • Conclusion and Future Scope

5. Advanced Agricultural Systems: Identification, Crop Yields, and Recommendations Using Image-Processing Techniques and Machine-Learning Algorithms

   • Introduction
   • Literature Survey
   • Proposed Machine-Learning System
   • Dataset Preparation and Model Evaluation
   • Classification Algorithms
   • Conclusion

6. SP-IMLA: Stroke Prediction Using an Integrated Machine-Learning Approach

   • Introduction
   • Problem Statement and Motivation
   • Review of Relevant Literature
   • Methodology and Technology Used
   • Algorithms/Techniques
   • Conclusion and Future Work

7. Multi-Modal Medical Image Fusion Using Laplacian Re-Decomposition

   • Introduction
   • Related Work
   • Proposed Methodology
   • Fusion Method and Results
   • Conclusion

8. Blockchain Technology-Enabled Healthcare IoT to Increase Security and Privacy Using Fog Computing

   • Introduction
   • Blockchain with Healthcare IoT and Ethereum
   • Supply-Chain Management in Healthcare
   • Genomic Data and Blockchain Applications
   • Conclusion

9. Blockchain in Healthcare, Supply-Chain Management, and Government Policies

   • Introduction
   • Blockchain Applications in Healthcare
   • Blockchain in Supply Chain Management
   • Blockchain in Government Policies
   • Conclusion

10. Electricity and Hardware Resource Consumption in Cryptocurrency Mining

    • Introduction
    • Literature Survey
    • Methodology
    • Discussion
    • Advantages and Disadvantages of Cryptocurrency Mining
    • Conclusion and Future Scope

11. Cryptographic Hash Functions and Attack Complexity Analysis

    • Introduction
    • Literature Review
    • Analysis and Results
    • Password Storage and Brute-Force Attack Complexity
    • Conclusion and Future Work

12. Mixed Deep Learning and Statistical Approach to Network Anomaly Detection

    • Introduction
    • Network Anomaly Detection and Dataset Preparation
    • Feature Selection and Statistical Analysis
    • Deep Learning Model Architecture and Training
    • Conclusion

13. Intrusion Detection System Using Deep Learning Asymmetric Autoencoder (DLAA)

    • Introduction
    • Literature Survey
    • Proposed Method
    • Experimentation and Results
    • Conclusion and Future Work

 

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