Description
Taylor and Francis Ltd Machine Learning, Blockchain, and Cyber Security in Smart Environments 1st Edition 2022 Hardbound by Tanwar, Sarvesh
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 securityDiscusses the fundamentals, challenges and architectural overviews with conceptsExplores recent advancements in machine learning, blockchain, and cyber securityExamines recent trends in emerging technologiesThis book is primarily aimed at graduates, researchers, and professionals working in the areas of machine learning, blockchain, and cyber security. 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