Data Driven Intelligence In Wireless Networks
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Data-Driven Intelligence in Wireless Networks
This book highlights the importance of data-driven techniques to solve wireless communication problems. It presents a number of problems (e.g., related to performance, security, and social networking), and provides solutions using various data-driven techniques, including machine learning, deep learning, federated learning, and artificial intelligence. This book details wireless communication problems that can be solved by data-driven solutions. It presents a generalized approach toward solving problems using specific data-driven techniques. The book also develops a taxonomy of problems according to the type of solution presented and includes several case studies that examine data-driven solutions for issues such as quality of service (QoS) in heterogeneous wireless networks, 5G/6G networks, and security in wireless networks. The target audience of this book includes professionals, researchers, professors, and students working in the field of networking, communications, machine learning, and related fields.
Intelligent Data-Driven Techniques for Security of Digital Assets
The book covers the role of emerging technologies such as blockchain technology, machine learning, IoT, cryptography, etc., in digital asset management. It further discusses digital asset management applications in different domains such as healthcare, travel industry, image processing, and our daily life activities to maintain privacy and confidentiality. This book: • Discusses techniques for securing and protecting digital assets in collaborative environments, where multiple organizations need access to the same resources. • Explores how artificial intelligence can be used to automate the management of digital assets, and how it can be used to improve security and privacy. • Explains the role of emerging technology such as blockchain technology for transforming conventional business models. • Highlights the importance of machine learning techniques in maintaining the privacy and security of data. • Covers encryption and decryption techniques, their advantages and role in improving the privacy of data. The text is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer science and engineering, information technology, and business management.
Handbook of Security and Privacy of AI-Enabled Healthcare Systems and Internet of Medical Things
The fast-growing number of patients suffering from various ailments has overstretched the carrying capacity of traditional healthcare systems. This handbook addresses the increased need to tackle security issues and preserve patients’ privacy concerns in Artificial Intelligence of Medical Things (AIoMT) devices and systems. Handbook of Security and Privacy of AI-Enabled Healthcare Systems and the Internet of Medical Things provides new insights into the deployment, application, management, and benefits of AIoMT by examining real-world scenarios. The handbook takes a critical look at existing security designs and offers solutions to revamp traditional security architecture, including the new design of effi cient intrusion detection algorithms, attack prevention techniques, and both cryptographic and noncryptographic solutions. The handbook goes on to discuss the critical security and privacy issues that affect all parties in the healthcare ecosystem and provides practical AI-based solutions. This handbook offers new and valuable information that will be highly beneficial to educators, researchers, and others.