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Mohanty: Evolution and Applications of
Quantum Computing

Evolution and Applications of Quantum Computing

Encyclopedia of Optimization

Leveraging AI, Data and Analytics in Travel

Technology-Led Tourism

Intelligent automation is widely considered to have the greatest potential for Industry 4.0 innovations for corporations. Industrial machinery is increasingly being upgraded to intelligent machines that can perceive, act, evolve, and interact in an industrial environment. The innovative technologies featured in this machinery include the Internet of Things, cyber-physical systems, and artificial intelligence. Artificial intelligence enables computer systems to learn from experience, adapt to new input data, and perform intelligent tasks. The significance of AI is not found in its computational models, but in how humans can use them. Consistently observing equipment to keep it from malfunctioning is the procedure of predictive maintenance. Predictive maintenance includes a periodic maintenance schedule and anticipates equipment failure rather than responding to equipment problems. Currently, the industry is struggling to adopt a viable and trustworthy predictive maintenance plan for machinery. The goal of predictive maintenance is to reduce the amount of unanticipated downtime that a machine experiences due to a failure in a highly automated manufacturing line. In recent years, manufacturing across the globe has increasingly embraced the Industry 4.0 concept. Greater solutions than those offered by conventional maintenance are promised by machine learning, revealing precisely how AI and machine learning-based models are growing more prevalent in numerous industries for intelligent performance and greater productivity. Artificial and Machine Learning for Industry 4.0 emphasizes technological developments that could have great influence on an industrial revolution and introduces the fundamental technologies responsible for directing the development of innovative firms. Decision-making requires a vast intake of data and customization in the manufacturing process, which managers and machines both deal with on a regular basis. One of the biggest issues in this field is the capacity to foresee when maintenance of assets is necessary. Leaders in the sector will have to make careful decisions about how, when, and where to employ these technologies. Artificial and Machine Learning for Industry 4.0 offers contemporary technological advancements in AI and machine learning from an Industry 4.0 perspective, looking at their prospects, obstacles, and potential applications.

Artificial Intelligence and Machine Learning for Industry 4.0

Digital Geographies

Global Digital Divides in the COVID-19 Era examines global digital divides prior to and during the pandemic, emphasizing internet and technology trends, spatial distributions, determinants, explanations, policy implications, and the aspiration of an equitable digital society. It analyzes purposeful use of the internet in the world and the majority of its major regions and countries. For digitally underserved economies, such as sub-Saharan Africa and parts of the Middle East, it is still appropriate to consider the traditional measures of technology access and use. In advanced economies, AI and machine learning are examined. This book identifies where deficits are located geographically, and also where the hot spots and cold spots are for levels of use and purposeful use of technologies. It points out the impacts of these technologies before and during the COVID-19 pandemic. For mature, inclusive, and equitable digital societies worldwide, attention has shifted from gaps in access to gaps in purposeful use of these technologies. For broadband, purposeful uses include e-commerce, e-health, e-entertainment, e-communication, e-education, and remote work. This book provides actual accounts of the digital divide challenges and successes in several case studies, such as the trailing usage of ICTs in Bolivia, the heightened purposeful use of e-commerce in Sweden and Denmark, and AI use in the US Silicon Valley. Finally, it recommends policies and steps that various national, state, and provincial governments can develop to narrow digital divides and improve the equity and inclusion of all their diverse citizens.

Purposeful Use of the Internet and Technologies

Global Digital Divides in the COVID-19 Era

Specialty Crops and Climate Change Modeling

Artificial Intelligence and Data Science in Agriculture

Algorithms have become the key organizer through which power is enacted in our society. A huge amount of data regarding our daily routines are monitored and analyzed to make recommendations that manage, control, and lead our behaviors in everyday life. AI, Humans, and Misinformation: How Does AI Alter Human Behavior and How Do Humans Influence Algorithmic Misinformation? is a guide to understanding the dynamics of AI and misinformation in human contexts by addressing meaningful questions-How does AI alter human behavior and how do humans influence algorithmic decision-making? In answering these questions, this book examines the role of misinformation, disinformation, and fake news, and shows readers how to develop AI methods and algorithms that combat misinformation by using AI design choices that provide users and developers alike with meaningful control over AI. This book brings together various perspectives on algorithms into an integrated conceptual framework, and provides a broad socio-technical analysis, addressing critical and ethical issues of misinformation and fake news. The book offers a compelling insight into the misinformation phenomenon and the future of AI-based society. Readers will find an integrated technical analysis of the logic and social implications of algorithmic processes. Reporting from the cutting edge of critical technical methods and research, the result is useful and constructive for developing the relations between algorithms and humans. This is an imperative methodology for understanding what is at stake as industry and government use AI to reshape the world.

Decoding Bias, Algorithms, and Trust

Minds, Machines, and Misinformation

Gain the Confidence You Need to Succeed

Data and AI Skills

Intelligent IoT-based Diagnostic and Assistive Systems for Neurological Disorders

Medical Robots and Devices: New Developments and Advances

Medical Imaging Application in Health Assessment and Disease Management is an all-encompassing book that explores the transformative power of medical imaging in various fields of medicine. It showcases the latest advancements and applications of medical imaging modalities, ranging from neurology and oncology to audiology and osteoporosis. The book highlights the role of medical imaging in understanding and treating neurological conditions, assessing bone health, unraveling hearing disorders, and diagnosing and treating oncological conditions. It also delves into the potential of artificial intelligence and machine learning in improving cancer diagnosis and treatment. The book explores the use of medical imaging in observing mental health conditions such as autism spectrum disorder and stress-related behavioral changes. This comprehensive resource is essential for researchers and professional engineers in the fields of medical image computing/processing, computer science, artificial intelligence, radiology, neuroscience, and biomedical research.

Medical Imaging Application in Health Assessment and Disease Management

Analytics and AI for Healthcare

This edited volume examines the transformative impact of AI technologies on global healthcare systems, with a focus on enhancing efficiency and accessibility. A comprehensive guide for healthcare professionals, AI researchers, and those seeking to develop effective AI-driven healthcare solutions that address global health challenges.

Advancing AI Solutions for Global Health Challenges

Scalable Artificial Intelligence for Healthcare

Environmental Monitoring Using Artificial Intelligence is a vital resource for anyone looking to leverage cutting-edge technologies in artificial intelligence and sensor systems to effectively address environmental challenges, offering innovative solutions and insights essential for creating a sustainable future. Environmental Monitoring Using Artificial Intelligence provides a comprehensive exploration of the cutting-edge technologies transforming environmental monitoring. This book bridges the gap between artificial intelligence (AI), natural language processing (NLP), and sensor-based systems, highlighting their potential to revolutionize the way we address pressing environmental challenges. Each chapter presents innovative case studies, real-world applications, and the latest research on how these technologies are being utilized to monitor and manage ecosystems, water resources, air quality, and urban sustainability. From advanced sensor networks to machine learning models, this book covers a broad spectrum of topics, including smart water solutions, biodiversity conservation, waste management, and agricultural sustainability. It offers an interdisciplinary approach, making it an essential resource for environmental engineers, data scientists, researchers, and policymakers. Whether you're exploring smart city innovations, renewable energy monitoring, or AI-driven solutions for environmental protection, Environmental Monitoring Using Artificial Intelligence equips readers with the knowledge and tools to leverage technology for a sustainable future.

Environmental Monitoring Using Artificial Intelligence

New Directions in Contemporary Art

The field of AI Art is a hotbed for strange, uneasy partnerships between big tech, big art and critical culture. Not since Walter Benjamin's Age of Mechanical Reproduction has there been a similar challenge to humanist art criticism. This book examines how a contemporary critic should best engage with, contextualise and effectively critique machine-learning-based art. In considering this question, Nora Khan looks at the rush of institutions to place AI Art within an art-historical lineage while they simultaneously accept significant funding from technology companies. She discusses the scale and speed at which technological production, machine learning, and AI have abraded the individual's capacity for critical evaluation, moving us to consider what a shared, collective criticism of AI might sound like.

The field of AI Art is a hotbed for strange, uneasy partnerships between big tech, big art and critical culture. Not since Walter Benjamin's Age of Mechanical Reproduction has there been a similar challenge to humanist art criticism. This book examines how a contemporary critic should best engage with, contextualise and effectively critique machine-learning-based art. In considering this question, Nora Khan looks at the rush of institutions to place AI Art within an art-historical lineage while they simultaneously accept significant funding from technology companies. She discusses the scale and speed at which technological production, machine learning, and AI have abraded the individual’s capacity for critical evaluation, moving us to consider what a shared, collective criticism of AI might sound like.

AI Art, Machine Learning and the Stakes for Art Criticism

Evolution and Applications of Quantum Computing

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