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Travel 4.0: An Artificial Intelligence In The Modern Tourism Industry
Travel 4.0 is an advanced, AI-powered travel ecosystem that revolutionizes how people explore the world. Built on cutting-edge technologies such as artificial intelligence, big data, IoT, blockchain, AR/VR, and mobile integration, Travel 4.0 offers a hyper-personalized, frictionless, and immersive travel experience from start to finish. Whether you're booking flights, exploring new destinations through virtual tours, checking into a smart hotel, or using real-time language translation abroad, Travel 4.0 ensures every aspect of your journey is smart, secure, sustainable, and tailored to you.
Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019)
This book presents the proceedings of the International Conference on Computing Networks, Big Data and IoT [ICCBI 2019], held on December 19–20, 2019 at the Vaigai College of Engineering, Madurai, India. Recent years have witnessed the intertwining development of the Internet of Things and big data, which are increasingly deployed in computer network architecture. As society becomes smarter, it is critical to replace the traditional technologies with modern ICT architectures. In this context, the Internet of Things connects smart objects through the Internet and as a result generates big data. This has led to new computing facilities being developed to derive intelligent decisions in the big data environment. The book covers a variety of topics, including information management, mobile computing and applications, emerging IoT applications, distributed communication networks, cloud computing, and healthcare big data. It also discusses security and privacy issues, network intrusion detection, cryptography, 5G/6G networks, social network analysis, artificial intelligence, human–machine interaction, smart home and smart city applications.
Intelligent Sustainable Systems
This book features research papers presented at the 4th International Conference on Intelligent Sustainable Systems (ICISS 2021), held at SCAD College of Engineering and Technology, Tirunelveli, Tamil Nadu, India, during February 26–27, 2021. The book discusses the latest research works that discuss the tools, methodologies, practices, and applications of sustainable systems and computational intelligence methodologies. The book is beneficial for readers from both academia and industry.
Intelligent Computing in Engineering
This book comprises select papers from the international conference on Research in Intelligent and Computing in Engineering (RICE 2019) held at Hanoi University of Industry, Hanoi, Vietnam. The volume focuses on current research on various computing models such as centralized, distributed, cluster, grid and cloud. The contents cover recent advances in wireless sensor networks, mobile ad hoc networks, internet of things, machine learning, grid and cloud computing, and their various applications. The book will help researchers as well as professionals to gain insight into the rapidly evolving fields of internet computing and data mining.
Climate Risk and Emerging Markets
Climate Risk and Emerging Markets: Valuation, Volatility, and Portfolio Strategies delves into the unique challenges that emerging economies encounter, including the threats posed by rising sea levels and disruptions to agriculture.
Content-Based Image Retrieval
The book describes several techniques used to bridge the semantic gap and reflects on recent advancements in content-based image retrieval (CBIR). It presents insights into and the theoretical foundation of various essential concepts related to image searches, together with examples of natural and texture image types. The book discusses key challenges and research topics in the context of image retrieval, and provides descriptions of various image databases used in research studies. The area of image retrieval, and especially content-based image retrieval (CBIR), is a very exciting one, both for research and for commercial applications. The book explains the low-level features that can be extracted from an image (such as color, texture, shape) and several techniques used to successfully bridge the semantic gap in image retrieval, making it a valuable resource for students and researchers interested in the area of CBIR alike.
