Subramaniyaswamy V – författare

In this digital era, a smart city can become an intelligent society by using advances in emerging technologies. Specifically, the rapid adoption of deep learning (DL) in fusion with blockchain technology has led to a new digital smart city ecosystem. A broad spectrum of DL and blockchain applications promises solutions for problems in areas ranging from risk management and financial services to cryptocurrency to public and social services. Furthermore, the convergence of artificial intelligence and blockchain technology is revolutionizing the smart city network architecture to build sustainable ecosystems. However, these advances in technology bring both opportunities and challenges in creating sustainable smart cities.

To help planners and developers to meet these challenges and exploit these opportunities, Deep Learning and Blockchain Technology for Smart and Sustainable Cities takes a deep dive into the technologies and applications that enable smart and sustainable cities. It provides a comprehensive literature review of the security issues and problems that impact the deployment of blockchain systems in smart cities. It presents a detailed discussion of key factors in the convergence of blockchain and DL technologies that help form sustainable smart societies. This book also discusses blockchain security enhancement solutions and summarizes main key points necessary for developing various blockchain and DL‑based intelligent transportation systems.

This book concludes with a discussion of open issues and future research direction.These include new security suggestions and guidelines for a sustainable smart city ecosystem. Also discussed is 6G‑enabled DL and blockchain in real‑time applications.

In this digital era, a smart city can become an intelligent society by using advances in emerging technologies. Specifically, the rapid adoption of deep learning (DL) in fusion with blockchain technology has led to a new digital smart city ecosystem. A broad spectrum of DL and blockchain applications promises solutions for problems in areas ranging from risk management and financial services to cryptocurrency to public and social services. Furthermore, the convergence of artificial intelligence and blockchain technology is revolutionizing the smart city network architecture to build sustainable ecosystems. However, these advances in technology bring both opportunities and challenges in creating sustainable smart cities.

To help planners and developers to meet these challenges and exploit these opportunities, Deep Learning and Blockchain Technology for Smart and Sustainable Cities takes a deep dive into the technologies and applications that enable smart and sustainable cities. It provides a comprehensive literature review of the security issues and problems that impact the deployment of blockchain systems in smart cities. It presents a detailed discussion of key factors in the convergence of blockchain and DL technologies that help form sustainable smart societies. This book also discusses blockchain security enhancement solutions and summarizes main key points necessary for developing various blockchain and DL‑based intelligent transportation systems.

This book concludes with a discussion of open issues and future research direction.These include new security suggestions and guidelines for a sustainable smart city ecosystem. Also discussed is 6G‑enabled DL and blockchain in real‑time applications.

The combination of cognitive analytics and reinforcement learning is a transformational force in the field of modern technological breakthroughs, reshaping the decision-making, problem-solving, and innovation landscape; this book offers an examination of the profound overlap between these two fields and illuminates its significant consequences for business, academia, and research.

Cognitive analytics and reinforcement learning are pivotal branches of artificial intelligence. They have garnered increased attention in the research field and industry domain on how humans perceive, interpret, and respond to information. Cognitive science allows us to understand data, mimic human cognitive processes, and make informed decisions to identify patterns and adapt to dynamic situations. The process enhances the capabilities of various applications.

Readers will uncover the latest advancements in AI and machine learning, gaining valuable insights into how these technologies are revolutionizing various industries, including transforming healthcare by enabling smarter diagnosis and treatment decisions, enhancing the efficiency of smart cities through dynamic decision control, optimizing debt collection strategies, predicting optimal moves in complex scenarios like chess, and much more. With a focus on bridging the gap between theory and practice, this book serves as an invaluable resource for researchers and industry professionals seeking to leverage cognitive analytics and reinforcement learning to drive innovation and solve complex problems.

The book’s real strength lies in bridging the gap between theoretical knowledge and practical implementation. It offers a rich tapestry of use cases and examples. Whether you are a student looking to gain a deeper understanding of these cutting-edge technologies, an AI practitioner seeking innovative solutions for your projects, or an industry leader interested in the strategic applications of AI, this book offers a treasure trove of insights and knowledge to help you navigate the complex and exciting world of cognitive analytics and reinforcement learning.

Audience

The book caters to a diverse audience that spans academic researchers, AI practitioners, data scientists, industry leaders, tech enthusiasts, and educators who associate with artificial intelligence, data analytics, and cognitive sciences.

The combination of cognitive analytics and reinforcement learning is a transformational force in the field of modern technological breakthroughs, reshaping the decision-making, problem-solving, and innovation landscape; this book offers an examination of the profound overlap between these two fields and illuminates its significant consequences for business, academia, and research.

Cognitive analytics and reinforcement learning are pivotal branches of artificial intelligence. They have garnered increased attention in the research field and industry domain on how humans perceive, interpret, and respond to information. Cognitive science allows us to understand data, mimic human cognitive processes, and make informed decisions to identify patterns and adapt to dynamic situations. The process enhances the capabilities of various applications.

Readers will uncover the latest advancements in AI and machine learning, gaining valuable insights into how these technologies are revolutionizing various industries, including transforming healthcare by enabling smarter diagnosis and treatment decisions, enhancing the efficiency of smart cities through dynamic decision control, optimizing debt collection strategies, predicting optimal moves in complex scenarios like chess, and much more. With a focus on bridging the gap between theory and practice, this book serves as an invaluable resource for researchers and industry professionals seeking to leverage cognitive analytics and reinforcement learning to drive innovation and solve complex problems.

The book’s real strength lies in bridging the gap between theoretical knowledge and practical implementation. It offers a rich tapestry of use cases and examples. Whether you are a student looking to gain a deeper understanding of these cutting-edge technologies, an AI practitioner seeking innovative solutions for your projects, or an industry leader interested in the strategic applications of AI, this book offers a treasure trove of insights and knowledge to help you navigate the complex and exciting world of cognitive analytics and reinforcement learning.

Audience

The book caters to a diverse audience that spans academic researchers, AI practitioners, data scientists, industry leaders, tech enthusiasts, and educators who associate with artificial intelligence, data analytics, and cognitive sciences.