Machine Learning, Blockchain, and Cyber Security in Smart Environments
Application and Challenges
1st Edition
Published on 31. August 2022
Book
Hardback
220 pages
978-1-032-14639-3 (ISBN)
Description
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.
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.
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Academic, Postgraduate, Professional, and Undergraduate Advanced
Illustrations
124 s/w Abbildungen, 42 s/w Photographien bzw. Rasterbilder, 82 s/w Zeichnungen, 26 s/w Tabellen
26 Tables, black and white; 82 Line drawings, black and white; 42 Halftones, black and white; 124 Illustrations, black and white
Dimensions
Height: 260 mm
Width: 183 mm
Thickness: 17 mm
Weight
647 gr
ISBN-13
978-1-032-14639-3 (9781032146393)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Sarvesh Tanwar | Sumit Badotra | Ajay Rana
Machine Learning, Blockchain, and Cyber Security in Smart Environments
Application and Challenges
Book
10/2024
1st Edition
Chapman & Hall/CRC
€72.60
Shipment within 10-20 days
Sarvesh Tanwar | Sumit Badotra | Ajay Rana
Machine Learning, Blockchain, and Cyber Security in Smart Environments
Application and Challenges
E-Book
08/2022
1st Edition
Chapman & Hall/CRC
€68.49
Available for download
Sarvesh Tanwar | Sumit Badotra | Ajay Rana
Machine Learning, Blockchain, and Cyber Security in Smart Environments
Application and Challenges
E-Book
08/2022
1st Edition
Chapman & Hall/CRC
€68.49
Available for download
Persons
Sarvesh Tanwar, Sumit Badotra, Ajay Rana
Content
1. Intelligent Green Internet of Things: An Investigation. 2. The Role of Artificial Intelligence in the Education Sector: Possibilities and Challenges. 3. Multidisciplinary Applications of Machine Learning. 4. Prediction of Diabetes in the Early Stages using Machine-Learning Tools and Microsoft Azure AI Services. 5. Advanced Agricultural Systems: Identification, Crop Yields and Recommendation using Image Processing Techniques and Machine-Learning Algorithms. 6. SP-IMLA: Stroke Prediction using an Integrated Machine Learning Approach. 7. Multimodal Medical Image Fusion using Laplacian Re-Decomposition. 8. Blockchain Technology-Enabled Healthcare IoT to Increase Security and Privacy Using Fog Computing. 9. Blockchain in Healthcare, Supply-Chain Management, and Government Policies. 10. Electricity and Hardware Resource Consumption in Cryptocurrency Mining. 11. Cryptographic Hash Functions and Attack Complexity Analysis. 12. Mixed Deep Learning and Statistical Approach to Network Anomaly Detection. 13. Intrusion Detection System Using Deep Learning Asymmetric Autoencoder