G Revathy – 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.

Advanced AI and Data Science Applications explores how the latest developments in artificial intelligence (AI) and data science are transforming diverse domains. The book blends theory and practice to serve as a roadmap to help readers understand how these cutting-edge technologies are revolutionizing practices across various fields. By providing a mix of theoretical insights and practical implementations, the book offers a holistic understanding of advanced AI and data science applications. Highlights of the book include:Metaheuristic optimization techniques for solving complex AI model training challengesThe impact of AI and data science on urban developmentImplementing AI for enhanced cybersecurity in industrial control systemsA comparative study of traditional and AI-based methods for English speech recognitionTemporal dependency modeling in real-time data streams using a deep learning modelPredictive analytics for financial fraud detection and risk managementData science in manufacturing for cost reduction and efficiencyAI-driven agricultural analyticsFeaturing such advanced modeling techniques as predictive modeling, simulation, and optimization algorithms, the book presents innovative solutions that emphasize benefits and practicality. With its emphasis on interdisciplinary applications, it showcases successful projects that underscore the synergy between AI and data science domains, empowering readers to harness the power of innovation for enhanced problem-solving and efficiency in interdisciplinary realms.