The recent advancements in the field of the internet of things (IoT), AI, big data, blockchain, augmented reality (AR)/virtual reality (VR), cloud platforms, quantum computing, cybersecurity, and telecommunication technology enabled the promotion of conventional computer-aided industry to the metaverse ecosystem that is powered by AR/VR-driven technologies. In this paradigm shift, the integrated technologies of IoT and AI play a vital role to connect the cyberspace of computing systems and virtual environments. AR/VR supports a huge range of industrial applications such as logistics, the food industry, and manufacturing utilities. AI-Based Technologies and Applications in the Era of the Metaverse discusses essential components of the metaverse ecosystem such as concepts, methodologies, technologies, modeling, designs, statistics, implementation, and maintenance. Covering key topics such as machine learning, deep learning, quantum computing, and blockchain, this premier reference source is ideal for computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.

Those entrenched in academia often have daunting processes of formulating research questions, data collection, analysis, and scholarly paper composition. Artificial intelligence (AI) emerges as an invaluable ally, simplifying these processes and elevating the quality of scholarly output. Where the pursuit of knowledge meets the cutting edge of technology, Utilizing AI Tools in Academic Research Writing unfolds a transformative journey through the symbiotic relationship between AI and academic inquiry. It offers practical insights into the myriad ways AI can revolutionize academic pursuits. This book extends beyond theoretical discussions, delving into practical dimensions of AI integration, demonstrating how it facilitates topic identification, refines research design, empowers data analysis, and enriches literature reviews. Readers will explore AI's indispensable role in precise hypothesis development, enhancing the very foundation of academic inquiry. The book introduces AI-powered tools that streamline writing and editing, ensuring research papers meet the highest standards of clarity and correctness. Ethical considerations in AI-integrated research take center stage, emphasizing responsible and transparent practices. This book is ideal for doctoral candidates, master's students, undergraduates, or seasoned faculty members.

This book delves into the dynamic synergy between AI and IoT, offering a comprehensive exploration of their transformative potential. With a keen eye on the present and future landscapes, this book navigates through real-world applications, showcasing how AI enriches IoT ecosystems, amplifying their capabilities across diverse sectors. From smart homes and cities to industrial automation and healthcare, each chapter unfolds compelling case studies illustrating how AI augments IoT devices to optimize processes, enhance decision-making, and drive innovation. As the technological horizon expands, the book anticipates emerging trends, paving the way for readers to grasp the profound impact AI will continue to wield on the IoT landscape. Whether you're a seasoned professional or an enthusiast curious about the intersection of AI and IoT, this book offers invaluable insights into the boundless opportunities that await in today's interconnected world and the possibilities that lie ahead.

Artificial Intelligence and Machine Learning for Sustainable Development is a comprehensive exploration of how artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing the field of sustainable development. The book examines cutting-edge innovations, practical applications, and potential challenges in harnessing AI and ML to address global sustainability issues. It offers insights into how these technologies can optimize resource management, improve environmental monitoring, enhance decision-making processes, and promote equitable, eco-friendly solutions. This book would be of special interest to researchers, policymakers, and practitioners seeking to leverage cutting-edge technology for a more sustainable future.

The text comprehensively discusses machine-to-machine communication in real-time, low-power system design and estimation using field programmable gate arrays, PID, hardware, accelerators, and software integration for service applications. It further covers the recent advances in embedded computing and IoT for healthcare systems. The text explains the use of low-power devices such as microcontrollers in executing deep neural networks, and other machine learning techniques. This book: Discusses the embedded system software and hardware methodologies for system-on-chip and FPGA Illustrates low-power embedded applications, AI-based system design, PID control design, and CNN hardware design Highlights the integration of advanced 5G communication technologies with embedded systems Explains weather prediction modeling, embedded machine learning, and RTOS Highlights the significance of machine-learning techniques on the Internet of Things (IoT), real-time embedded system design, communication, and healthcare applications, and provides insights on IoT applications in education, fault attacks, security concerns, AI integration, banking, blockchain, intelligent tutoring systems, and smart technologies It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, and computer engineering.

In recent years, the advent of biosensors and bioelectronics has facilitated the swift and precise detection of numerous biomolecules or pathogens in animal samples, aquatic habitats, and plants. These technologies have emerged as potent assets for the agriculture and aquaculture industries. Many experts have raised the question; how might the integration of biosensors into portable devices or automated monitoring systems enable on-site and real-time detection of diverse biomolecules or pathogens? Agriculture and Aquaculture Applications of Biosensors and Bioelectronics presents the recent developments in biological recognition elements, transducer materials, and signal processing techniques for biosensors and bioelectronics used in agriculture and aquaculture applications. Sophisticated biosensors and bioelectronics operate through the immobilization of biological recognition components—such as enzymes or antibodies—on a transducer surface. This immobilization process allows for the targeted recognition and binding of biomolecules or pathogens. The resulting electrical, optical, or chemical changes triggered by the recognition event are quantifiable through various methods and are often enhanced by applications utilizing artificial intelligence (AI). The necessity for high sensitivity and selectivity, the optimization of biocompatibility and stability, and the integration of biosensors with AI-aided solutions are just some of the challenges and opportunities in developing biosensors for high-tech agriculture and aquaculture. This book targets a mixed audience of biotechnology engineers, biosensors scientists, bioelectronics researchers, high-tech agriculture analysts, Ph.D. scholars, researchers, academics, professionals, engineers, and students.

The rapid advancements in Artificial Intelligence (AI), specifically in Natural Language Processing (NLP) and Generative AI, pose a challenge for academic scholars. Staying current with the latest techniques and applications in these fields is difficult due to their dynamic nature, while the lack of comprehensive resources hinders scholars' ability to effectively utilize these technologies. Advanced Applications of Generative AI and Natural Language Processing Models offers an effective solution to address these challenges. This comprehensive book delves into cutting-edge developments in NLP and Generative AI. It provides insights into the functioning of these technologies, their benefits, and associated challenges. Targeting students, researchers, and professionals in AI, NLP, and computer science, this book serves as a vital reference for deepening knowledge of advanced NLP techniques and staying updated on the latest advancements in generative AI. By providing real-world examples and practical applications, scholars can apply their learnings to solve complex problems across various domains. Embracing Advanced Applications of Generative AI and Natural Language Processing Modelsequips academic scholars with the necessary knowledge and insights to explore innovative applications and unleash the full potential of generative AI and NLP models for effective problem-solving.