Artificial Intelligence And Internet Of Things Based Augmented Trends For Data Driven Systems
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Artificial Intelligence and Internet of Things based Augmented Trends for Data Driven Systems
This book comprehensively discusses the role of cloud computing in artificial intelligence‐based data‐driven systems and hybrid cloud computing for large data‐driven applications. It further explores new approaches, paradigms, and frameworks to meet societal challenges by providing solutions for critical insights into data. The text provides Internet of Things‐based frameworks and advanced computing techniques to deal with online/virtual systems. This book: • Covers the aspects of security, authentication, and prediction for data‐driven systems in heterogeneous environments. • Provides data‐driven frameworks in combination with the Internet of Things, artificial intelligence, and computing to provide critical insights and decision‐making for real‐time problems. • Showcases deep learning‐based computer vision algorithms for enhanced pattern detection in different domains based on data‐centric approaches. • Examines the role of the Internet of Things and machine learning algorithms for data‐driven systems. • Highlights the applications of data‐driven systems and cloud computing in enhancing network performance. This book is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, and computer science engineering.
Cognitive Machine Intelligence
Cognitive Machine Intelligence: Applications, Challenges, and Related Technologies offers a compelling exploration of the transformative landscape shaped by the convergence of machine intelligence, artificial intelligence, and cognitive computing. In this book, the authors navigate through the intricate realms of technology, unveiling the profound impact of cognitive machine intelligence on diverse fields such as communication, healthcare, cybersecurity, and smart city development. The chapters present study on robots and drones to the integration of machine learning with wireless communication networks, IoT, quantum computing, and beyond. The book explores the essential role of machine learning in healthcare, security, and manufacturing. With a keen focus on privacy, trust, and the improvement of human lifestyles, this book stands as a comprehensive guide to the novel techniques and applications driving the evolution of cognitive machine intelligence. The vision presented here extends to smart cities, where AI-enabled techniques contribute to optimal decision-making, and future computing systems address end-to-end delay issues with a central focus on Quality-of-Service metrics. Cognitive Machine Intelligence is an indispensable resource for researchers, practitioners, and enthusiasts seeking a deep understanding of the dynamic landscape at the intersection of artificial intelligence and cognitive computing. This book: Covers a comprehensive exploration of cognitive machine intelligence and its intersection with emerging technologies such as federated learning, blockchain, and 6G and beyond. Discusses the integration of machine learning with various technologies such as wireless communication networks, ad-hoc networks, software-defined networks, quantum computing, and big data. Examines the impact of machine learning on various fields such as healthcare, unmanned aerial vehicles, cybersecurity, and neural networks. Provides a detailed discussion on the challenges and solutions to future computer networks like end-to-end delay issues, Quality of Service (QoS) metrics, and security. Emphasizes the need to ensure privacy and trust while implementing the novel techniques of machine intelligence. It is primarily written for senior undergraduate and graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.
AI and Machine Learning Techniques for Wildlife Conservation
Author: Raghav, Yogita Yashveer
language: en
Publisher: IGI Global
Release Date: 2025-02-11
As the world grapples with the alarming rate of biodiversity loss, the potential of cutting-edge technologies, namely machine learning (ML) and artificial intelligence (AI), revolutionize the way we approach wildlife conservation. From sophisticated sensor technologies to innovative AI algorithms, foundational tools driving this paradigm shift provide a comprehensive understanding of their applications in safeguarding biodiversity. The navigation of systems such as the Spatial Monitoring and Reporting Tool (SMART) and advanced animal detection systems can be used to delve into the intricacies of feature extraction and precise identification. This exploration of predictive modeling, data ethics, citizen science, and the integration of satellite data offers a holistic perspective on the dynamic intersection of technology and conservation. AI and Machine Learning Techniques for Wildlife Conservation illustrates the tangible impact of these technologies on addressing pressing conservation challenges and advocates for the engagement of citizen science initiatives with AI. It fosters a collaborative approach to wildlife conservation that leverages the power of technology for a sustainable future. Covering topics including Internet of Things (IoT), satellite data, and predictive ecosystem management, this book is an excellent resource for conservationists, computer scientists, researchers, professionals, academicians, scholars, and more.