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'For a long time, researchers in the fields of psychology and neuroscience have been interested in discovering ways to boost productivity in traditionally'healthy,''clinical,'and'military'populations. However, one of the biggest challenges in reaching this objective is developing personalised performance phenotypes that can be used to build interventions that are specifically catered to each individual's needs. Impact: Thanks to AI's recent advancements, we can now create individualised training, preparation, and recovery plans that are tailored to each person's unique cognitive and biological profile. Concurrent AI advancements, especially in the psychological and neurological sciences, have demonstrated the importance of incorporating domain expertise and stakeholder inputs into the development of trustworthy AI. Value: This Patterns themed issue seeks original empirical work, literature reviews, and methodological papers that establish and validate precision artificial intelligence methods for human performance optimization, with a focus on modelling individual differences via state-of-the-art computational methods'--

In the fields of psychological and brain sciences, there is a pressing need to enhance individual performance through tailored interventions, considering the unique cognitive and biological traits of humans. However, discovering such precision approaches has proven to be a challenging task. Traditional methods often fall short in providing personalized solutions; the lack of a comprehensive integration of domain expertise and human judgment inevitably limits the effectiveness of artificial intelligence (AI) in optimizing human performance. As the demand for individualized training, health monitoring, and cognitive improvement rises, it becomes imperative to bridge the gap between AI capabilities and the specific needs of human subjects. Advances in Artificial and Human Intelligence in the Modern Era offers a groundbreaking solution to address the existing challenges in the intersection of AI and human performance optimization. This book delves into the latest empirical research, literature reviews, and methodological papers to present precision AI methods for personalized interventions. By exploring how domain expertise and human insights can improve AI's performance, the book establishes a comprehensive framework to model individual differences and develop effective, tailored AI strategies. Designed with care to be useful to academic scholars and researchers in psychological and brain sciences, computer science, and related disciplines, this book offers a multifaceted exploration of innovative advancements in artificial and human intelligence convergence. Through its diverse chapters covering topics such as detecting mental health issues, incorporating human intelligence into AI tools, improving trustworthiness, and exploring data standards, the book provides a definitive resource for harnessing the full potential of AI to optimize human performance. By merging expertise from both fields, the book sets the stage for a new era of individualized interventions that can revolutionize how we approach human cognitive enhancement, training, and overall well-being.

Modern businesses face the challenge of how to most effectively harness the power of Artificial Intelligence (AI) to enhance customer engagement and streamline operations. The proliferation of AI tools like ChatGPT offers immense potential. Yet, businesses often need help to navigate the complexities of implementation and maximize the benefits. This gap between AI's promise and its practical application highlights the need for a comprehensive resource that offers practical insights and innovative strategies. Real-Time Data Decisions With AI and ChatGPT Techniques is a groundbreaking book that addresses this critical challenge. By providing a detailed analysis of ChatGPT and other AI tools, this book equips businesses with the knowledge and strategies needed to leverage AI effectively. From algorithmic enhancements to real-world applications, each chapter offers valuable insights and actionable recommendations, making this book an indispensable guide for businesses seeking to capitalize on AI's transformative potential. With contributions from leading experts in the field, this book covers a wide range of topics, including algorithmic challenges, use cases in various industries, security concerns, and future trends. Whether you're a business leader looking to enhance customer engagement or a researcher exploring the frontiers of AI, this book offers a comprehensive and insightful exploration of ChatGPT and its impact on the future of business.

The rise of Artificial Intelligence (AI) amidst the backdrop of a world that is changing at lightning speed presents a whole new set of challenges. One of our biggest hurdles is more transparency in AI solutions. It's a complex issue, but one that we need to address if we want to ensure that the benefits of AI are accessible to everyone. Across diverse sectors such as healthcare, surveillance, and human-computer interaction, the inability to understand and evaluate AI's decision-making processes hinders progress and raises concerns about accountability. Cross-Industry AI Applications is a groundbreaking solution to illuminate the mysteries of AI-driven human behavior analysis. This pioneering book addresses the necessity of transparency and explainability in AI systems, particularly in analyzing human behavior. Compiling insights from leading experts and academics offers a comprehensive exploration of state-of-the-art methodologies, benchmarks, and systems for understanding and predicting human behavior using AI. Each chapter delves into novel approaches and real-world applications, from facial expressions to gesture recognition, providing invaluable knowledge for scholars and professionals alike. With its focus on explainable AI and its implications for decision-making, Cross-Industry AI Applications serves as a roadmap for navigating the complexities of human behavior analysis in the age of AI. By offering clarity and insight into AI algorithms and their impact on diverse industries, this book empowers readers to harness the full potential of AI while ensuring accountability and ethical practice. Join the forefront of AI research and innovation – order your copy today and embark on a transformative journey into the future of human-AI interaction.

As society transitions into the digital age, the demand for advanced robotics and autonomous systems has remained unchanged. However, the field faces significant challenges bridging the gap between current capabilities and the potential for brilliant, autonomous machines. While exact and efficient, current robotic systems need more sophistication and adaptability of human intelligence. This limitation restricts their application in complex and dynamic environments, hindering their ability to realize their potential fully. Multidisciplinary Applications of AI Robotics and Autonomous Systems addresses these challenges by presenting cutting-edge research and innovative robotics and autonomous systems solutions. By exploring topics such as digital transformation, IoT, AI, and cloud-native computing paradigms, readers will understand the latest advancements in the field. The book delves into theoretical frameworks, computational models, and experimental approaches, offering insights to help researchers and practitioners develop more intelligent and autonomous machines. Through a multidisciplinary approach, this book aims to inspire and guide researchers, engineers, and policymakers in advancing the field of robotics. By highlighting autonomous systems'importance and potential economic impact, the book seeks to motivate further research and development in this crucial area. With contributions from leading experts, this book provides a roadmap for the future of robotics and autonomous systems, offering innovative solutions to today's and tomorrow's challenges.

Industry 5.0 is poised to redefine the collaboration between humans and machines, marking a crucial moment in technological evolution. However, as we stand at the threshold of this transformative era, a critical challenge emerges – the integration of emotional intelligence into the industrial landscape. Organizations grapple with the urgent need to understand, strategize, and ethically deploy artificial emotional intelligence (AEI) in Industry 5.0. This pivotal juncture calls for a comprehensive resource that explores the theoretical foundations but offers practical insights into the applications, challenges, and responsible deployment of AEI. The absence of a cohesive guide addressing the intricacies of AEI in Industry 5.0 leaves a void in academic scholarship. Organizations, researchers, and policymakers lack a singular, authoritative source to navigate the complexities of emotional intelligence integration, impacting Industry 5.0 strategies, sustainability plans, and customer services. The challenge lies in managing the delicate balance between human and machine collaboration while ensuring ethical considerations are at the forefront of AI deployment. As the demand for emotional intelligence in the industrial landscape intensifies, the need for a unifying resource becomes increasingly apparent. Harnessing Artificial Emotional Intelligence for Improved Human-Computer Interactions is an innovative book crafted by leading researchers and practitioners across disciplines that serves as the solution to the challenges posed by the AEI revolution. By offering a comprehensive exploration of emotion recognition, affective computing, and human-robot interaction, the book equips readers with the knowledge needed to navigate the complexities of AEI in Industry 5.0. With a focus on practical implementations, ethical considerations, and the real-world impact on factories, collaborative robotics, and industrial automation, this book positions itself as the go-to resource for scholars, industry professionals, and decision-makers seeking to harness the transformative power of emotional intelligence in the Fifth Industrial Revolution.

In the rapidly transforming landscape of fast-paced technology evolution, the fusion of artificial intelligence (AI) and humanoid robotics is set to redefine academia as we know it. From advancements in AI, humanoid robotics, nano and bio technologies, and smart medicine, the vision of an ultra-smart cyberspace is becoming a tangible reality. Yet, amid this transformative potential, scholars face a pressing challenge – how to navigate the complexities of these cutting-edge technologies to drive impactful research and innovation. Applied AI and Humanoid Robotics for the Ultra-Smart Cyberspace beckons scholars to harness the full potential of applied AI and humanoid robotics in academia. This book illuminates the most effective applications of these technologies across various disciplines such as industry, business, health, government, military, and critical cyber infrastructure. Through rigorously peer-reviewed chapters, the book addresses key issues, provides technical solutions, and guides future research directions, fostering a collaborative bridge between academia and industry. Whether you're a seasoned academic expert or a graduate student embarking on your academic journey, this book offers invaluable insights into the transformative power of applied AI and humanoid robotics. This book is tailored for academic scholars, researchers, and educators who want to stay ahead of technological innovation. Moreover, it serves as an essential reference for decision-makers worldwide across government, military, business, and industry, providing strategic guidance in navigating the evolving landscape of AI-driven technologies.

In the digital age, the relentless growth of data centers and cloud computing has given rise to a pressing dilemma. The power consumption of these facilities is spiraling out of control, emitting massive amounts of carbon dioxide, and contributing to the ever-increasing threat of global warming. Studies show that data centers alone are responsible for nearly eighty million metric tons of CO2 emissions worldwide, and this figure is poised to skyrocket to a staggering 8000 TWh by 2030 unless we revolutionize our approach to computing resource management. The root of this problem lies in inefficient resource allocation within cloud environments, as service providers often over-provision computing resources to avoid Service Level Agreement (SLA) violations, leading to both underutilization of resources and a significant increase in energy consumption. Computational Intelligence for Green Cloud Computing and Digital Waste Management stands as a beacon of hope in the face of the environmental and technological challenges we face. It introduces the concept of green computing, dedicated to creating an eco-friendly computing environment. The book explores innovative, intelligent resource management methods that can significantly reduce the power consumption of data centers. From machine learning and deep learning solutions to green virtualization technologies, this comprehensive guide explores innovative approaches to address the pressing challenges of green computing. Whether you are an educator teaching about green computing, an environmentalist seeking sustainability solutions, an industry professional navigating the digital landscape, a resolute researcher, or simply someone intrigued by the intersection of technology and sustainability, this book offers an indispensable resource. It strives to not only educate, but also inspire and empower readers to contribute to the development of green computing solutions. Through in-depth research, case studies, and a roadmap for the future, Computational Intelligence for Green Cloud Computing and Digital Waste Management paves the way for a greener, more efficient digital and physical world.

The applications of rapidly advancing intelligent systems are so varied that many are still yet to be discovered. There is often a disconnect between experts in computer science, artificial intelligence, machine learning, robotics, and other specialties, which inhibits the potential for the expansion of this technology and its many benefits. A resource that encourages interdisciplinary collaboration is needed to bridge the gap between these respected leaders of their own fields. Deep Learning, Reinforcement Learning, and the Rise of Intelligent Systems represents an exploration of the forefront of artificial intelligence, navigating the complexities of this field and its many applications. This guide expertly navigates through the intricate domains of deep learning and reinforcement learning, offering an in-depth journey through foundational principles, advanced methodologies, and cutting-edge algorithms shaping the trajectory of intelligent systems. The book covers an introduction to artificial intelligence and its subfields, foundational aspects of deep learning, a demystification of the architecture of neural networks, the mechanics of backpropagation, and the intricacies of critical elements such as activation and loss functions. The exploration continues with an in-depth dive into advanced techniques and algorithms emphasizing the practical applications of these disciplines. From Convolutional Neural Networks (CNNs) transforming image processing to the intricate workings of Recurrent Neural Networks (RNNs) in handling sequential data and the innovative applications of Generative Adversarial Networks (GANs) in data synthesis, the book unfolds a tapestry of state-of-the-art advancements. Additionally, readers will find a robust resource in this book with the latest findings on reinforcement learning, covering Markov Decision Processes (MDPs), value functions, and policies. The exploration advances into sophisticated algorithms like Deep Q-Networks (DQNs), policy gradient methods, and actor-critic models, each unraveling new dimensions in learning and decision-making. With chapters that spotlight real-world applications, a focus on computer vision, natural language processing, and personalized recommendations, this book's narrative extends beyond theoretical frameworks. It provides insights into the practical deployment of intelligent systems in game-playing, robotics, autonomous vehicles, and beyond. The book serves as a valuable educational resource for professionals. Its structured approach makes it an ideal reference for students, researchers, and industry professionals. The book imparts knowledge and prompts critical discourse, ensuring that the next wave of intelligent systems is built on a foundation of ethical principles and responsible practices.

In our increasingly interconnected world, low-resource languages face the threat of oblivion. These linguistic gems, often spoken by marginalized communities, are at risk of fading away due to limited data and resources. The neglect of these languages not only erodes cultural diversity but also hinders effective communication, education, and social inclusion. Academics, practitioners, and policymakers grapple with the urgent need for a comprehensive solution to preserve and empower these vulnerable languages. Empowering Low-Resource Languages With NLP Solutions is a pioneering book that stands as the definitive answer to the pressing problem at hand. It tackles head-on the challenges that low-resource languages face in the realm of Natural Language Processing (NLP). Through real-world case studies, expert insights, and a comprehensive array of topics, this book equips its readers—academics, researchers, practitioners, and policymakers—with the tools, strategies, and ethical considerations needed to address the crisis facing low-resource languages. This book explores the profound social and cultural implications of NLP for low-resource languages, emphasizing linguistic diversity, preservation, and community empowerment. From tailored knowledge on data collection, pre-processing, and feature extraction to sociolinguistic insights and real-world applications, this book empowers readers to champion the cause of linguistic diversity and cultural heritage preservation.

In the world of academia, scholars and researchers are confronted with a rapidly expanding knowledge base in Artificial Intelligence (AI) and nanotechnology. The integration of these two groundbreaking fields presents an intricate web of concepts, innovations, and interdisciplinary applications that can overwhelm even the most astute academic minds. Staying up to date with the latest developments and effectively navigating this complex terrain has become a pressing challenge for those striving to contribute meaningfully to these fields. Artificial Intelligence in the Age of Nanotechnology is a transformative solution meticulously crafted to address the academic community's knowledge gaps and challenges. This comprehensive book serves as the guiding light for scholars, researchers, and students grappling with the dynamic synergy between AI and Nanotechnology. It offers a structured and authoritative exploration of the core principles and transformative applications of these domains across diverse fields. By providing clarity and depth, it empowers academics to stay at the forefront of innovation and make informed contributions. The book serves as a beacon of knowledge, illuminating the intricate intersections of these two fields, from healthcare breakthroughs to energy revolutions. As scholars delve into its content, they will discover a newfound capacity to address real-world problems, contribute to groundbreaking advancements, and shape the future of AI and nanotechnology.

Exploring Ethical Dimensions of Environmental Sustainability and Use of AI is a comprehensive and insightful book that delves into the ethical implications and challenges that emerge at the intersection of environmental sustainability and the utilization of artificial intelligence (AI). With a focus on key ethical dimensions such as transparency, equity, privacy, autonomy, unintended consequences, and trade-offs, this book aims to provide a thorough understanding of the responsible deployment and development of AI in the realm of environmental sustainability. By addressing the ethical aspects and challenges involved, this book contributes to the development of ethical guidelines and frameworks that align AI technologies with the vision of a sustainable and equitable future. Researchers will find immense value in this book as it offers a holistic exploration of the ethical implications, filling a critical gap in the existing literature. Policymakers can gain valuable insights to inform the creation of ethical guidelines and regulations governing AI use in sustainable initiatives. Practitioners, including professionals working in environmental organizations or technology companies, will acquire practical knowledge to guide their decision-making and implementation of AI-driven solutions. The book covers various topics ranging from AI applications in conservation and sustainable resource management to climate change mitigation, responsible AI development, ethical challenges in environmental monitoring, AI in sustainable agriculture, energy solutions, waste management, biodiversity conservation, AI-enabled smart cities, water management, human-AI collaboration, AI ethics governance, and the future of AI and environmental sustainability.

In the rapidly evolving landscape of Industry 5.0, integrating emotional intelligence into the industrial framework is becoming increasingly crucial. Organizations are trying to navigate this uncharted territory and seeking guidance on understanding, implementing, and ethically managing artificial emotional intelligence (AEI). However, the absence of a comprehensive resource addressing these complexities has left a significant void in academic scholarship and industrial practice. Human-Machine Collaboration and Emotional Intelligence in Industry 5.0 offers a holistic exploration of emotion recognition, affective computing, and human-robot interaction. It equips readers with the knowledge and tools to successfully integrate AEI into Industry 5.0, ensuring a harmonious collaboration between humans and machines. This book is a go-to resource for scholars, industry professionals, and decision-makers seeking to leverage emotional intelligence in the Fifth Industrial Revolution by addressing practical implementations, ethical considerations, and real-world impacts. Human-Machine Collaboration and Emotional Intelligence in Industry 5.0 is a transformative guide to a future where machines and humans collaborate in a more empathetic and efficient manner, revolutionizing industries to be more productive and employee-friendly. With a focus on practical solutions and real-world examples, this book is poised to become the authoritative guide on AEI in Industry 5.0, reshaping the future of industrial practices and academic research.

Today, the convergence of Artificial Intelligence (AI) and Big Data has revolutionized industries worldwide, driving business growth and reshaping societies. While these technologies have yielded remarkable benefits, many developing countries face challenges in harnessing their potential due to inadequate data collection and availability. Emerging Advancements in AI and Big Data Technologies in Business and Society delves into the profound impact of AI and Big Data on the digital landscape and their transformative influence on social, economic, and political spheres. With a historical overview of AI's evolution and its operational definition, this book explores interconnected subfields such as problem-solving, intelligent agents, natural language processing, computer vision, and machine learning. AI is hailed as the fourth industrial revolution and the widespread use of AI technologies prompts discussions about their applications, performances, and societal impact. This book sheds light on the critical role of AI and Big Data in accelerating smart healthcare services, exemplified by their significance in managing the COVID-19 pandemic. By showcasing recent advancements, methodologies, and systems, this collection of high-quality research provides valuable insights on leveraging AI and Big Data technologies for the betterment of businesses and societies. Emerging Advancements in AI and Big Data Technologies in Business and Society serves as a comprehensive guide for academics, researchers, and students in universities and engineering schools. It also caters to policymakers, government officers, corporate leaders, technology directors, and managers seeking to understand the potential of AI and Big Data. Additionally, libraries and information centers catering to the needs of these professionals will find this book an essential resource.

In our rapidly evolving digital landscape, the threat of natural disasters looms large, necessitating innovative solutions for effective disaster management. Integrating Artificial Intelligence (AI) and the Internet of Things (IoT) presents a transformative approach to addressing these challenges. However, despite the potential benefits, the field needs more comprehensive resources that explore the full extent of AI and IoT applications in disaster management. AI and IoT for Proactive Disaster Management fills that gap by examining how AI and IoT can revolutionize disaster preparedness, response, and recovery. It offers a deep dive into AI frameworks, IoT infrastructures, and the synergy of these technologies in predicting and managing natural disasters. By showcasing cutting-edge research and practical applications, this book equips readers with the knowledge and tools to harness AI and IoT for more efficient and effective disaster management strategies. Targeted at undergraduate and postgraduate students, academicians, research scholars, industry professionals, and technology enthusiasts, this book serves as a comprehensive guide to understanding the intersection of AI, IoT, and disaster management. It offers insights into emerging trends, ethical considerations, and best practices, making it an essential resource for anyone interested in leveraging technology to mitigate the impact of natural disasters.

Generative Artificial Intelligence (AI), an ever-evolving technology, holds immense promise across various industries, from healthcare to content generation. However, its rapid advancement has also given rise to profound ethical concerns. Illicit black-market industries exploit generative AI for counterfeit imagery, and in educational settings, biases and misinformation perpetuate. These issues underscore the need to grapple with the risks accompanying generative AI integration. Exploring the Ethical Implications of Generative AI emerges as a wellspring of insight for discerning academic scholars. It sets the stage by acknowledging generative AI's multifaceted potential and its capacity to reshape industries. The book addresses these complex ethical concerns, offering a comprehensive analysis and providing a roadmap for responsible AI development and usage. Its intended audience spans business leaders, policymakers, scholars, and individuals passionate about the ethical dimensions of AI. The book's objective is a twofold mission: to identify and dissect the ethical challenges inherent in generative AI while prescribing practical guidelines for its responsible development and utilization. By amalgamating the current research landscape, it aims to provide a comprehensive view of the field. Furthermore, the book introduces a set of practical guidelines that can serve as a guiding light for developers and users in diverse contexts, including workplaces, media, healthcare, education, and the military. By comprehensively addressing issues like bias, privacy, misuse, accountability, and responsibility, this book becomes the definitive guide for academic scholars navigating the ethical dilemmas of generative AI, ultimately safeguarding the responsible evolution of this groundbreaking technology.

In a world where the relentless force of natural and man-made disasters threatens societies, the need for effective disaster management has never been more critical. Predicting Natural Disasters With AI and Machine Learning addresses the challenges of disasters and charts a path toward proactive solutions by applying artificial intelligence (AI) and machine learning (ML). This book begins by interpreting the nature of disasters, clearly distinguishing between natural and man-made hazards. It delves into the intricacies of disaster risk reduction (DRR), emphasizing the human contribution to most disasters. Recognizing the necessity for a multifaceted approach, the book advocates the four ‘R's - Risk Mitigation, Response Readiness, Response Execution, and Recovery - as integral components of comprehensive disaster management. This book explores various AI and ML applications designed to predict, manage, and mitigate the impact of natural disasters, focusing on natural language processing, and early warning systems. The contrast between weak AI, simulating human intelligence for specific tasks, and strong AI, capable of autonomous problem-solving, is thoroughly examined in the context of disaster management. Its chapters systematically address critical issues, including real-world data handling, challenges related to data accessibility, completeness, security, privacy, and ethical considerations. Ideal for academics, public and private organizations, managers, and the wider public, the book speaks to the urgency of adopting AI and ML in disaster management. Providing a comprehensive overview of research in the field serves as a catalyst for further studies, especially among postgraduate students interested in the convergence of AI and ML in predicting and managing natural disasters.

Women encounter multifaceted threats, ranging from personal safety hazards to discrimination deeply embedded in societal structures. The existing landscape demands innovative strategies to ensure women can participate fully in society without fear or impediment. Traditional systems often fall short, necessitating a paradigm shift in our approach to women's safety. Impact of AI on Advancing Women's Safety emerges as a groundbreaking solution to address the pervasive challenges they face. From the shadows of harassment to systemic biases in justice systems, women navigate a complex landscape. This book delves into the pressing issues, unveiling a visionary approach that leverages artificial intelligence to create tangible, transformative solutions. From AI-powered mobile apps providing real-time protection to systems identifying and rectifying biases in justice, the book presents a roadmap for harnessing technology to create a safer world for women. It serves as a vital resource for policymakers, engineers, academics, and activists, as well as anyone enthusiastic about safeguarding women's rights and fostering an environment where women can thrive without compromise. This book not only highlights the potential of AI but actively engages readers in envisioning and contributing to a future where women's safety is a priority, facilitated by the innovative use of emerging technologies.

The rapid advancement of the Internet of Technology (IoT) and smart technologies poses significant challenges for academic scholars. With the emergence of pervasive sensing, machine learning, cloud computing, and energy efficiency, pioneers in this field face the complexity of meeting new requirements such as reliability, quality of service, and energy efficiency. Navigating these challenges and keeping up with the latest advancements can be overwhelming, hindering scholarly contributions in this dynamic area of study. To address these challenges, AIoT and Smart Sensing Technologies for Smart Devices, edited by Fadi Alturjman, offers an invaluable solution for academic scholars. This comprehensive book serves as a knowledge hub, bringing together a diverse range of research and professional contributions. By providing a platform for knowledge exchange and collaboration, it enables scholars to delve into topics such as IoT-oriented applications, sustainable development, machine learning, wireless networks, and more. It is ideal for experts, researchers, and students, as this book equips readers with the necessary insights and understanding to navigate the complexities of IoT and smart technologies. Covering diverse areas like IoT paradigms in sustainable education systems, network security, machine learning approaches, and wireless network protocols, it serves as a guide to unlocking the potential of these new technologies and their many applications. With this resource at their disposal, scholars can stay informed, explore innovative approaches, and make meaningful contributions to the advancement of IoT and smart technologies. AIoT and Smart Sensing Technologies for Smart Devices is a compelling solution for academic scholars grappling with the challenges of AIoT and smart technologies. By consolidating a wealth of research and professional contributions, this book provides a platform for collaboration and knowledge exchange. With its comprehensive coverage of key topics, it empowers experts, researchers, and students to navigate the complexities of AIoT, foster innovation, and contribute to the development of this transformative field.

In the age of immediate technical expansion, our world faces a multifaceted challenge: ensuring the sustainability of our digital transformation. Governments and organizations have wholeheartedly embraced innovative technologies such as artificial intelligence, blockchain, and e-governance, but in doing so, they have encountered a complex web of issues. These range from cybersecurity concerns in an increasingly digitalized world to the need for intelligent systems capable of managing automation infrastructure and interconnected environments. To tackle these challenges effectively, academic scholars and researchers require a comprehensive resource that illuminates the path to a sustainable digital future. Sustainable Development in AI, Blockchain, and E-Governance Applications offers a forward-thinking approach that harnesses the synergy between intelligent systems, machine learning, deep learning, and blockchain methods. It explores data-driven decision-making, automation infrastructure, autonomous transportation, and the creation of connected buildings, all aimed at crafting a sustainable digital future. By delving into topics like machine learning for smart parking, disease classification through neural networks, and the Internet of Things (IoT) for smarter cities, this book equips academic scholars with the tools they need to navigate the complex terrain of technology and governance. Academic scholars and researchers in technology, governance, and sustainability will find this book to be an indispensable resource. It caters to those seeking a comprehensive understanding of current and future trends in the integration of intelligent systems with cybersecurity applications. By offering innovative solutions and a roadmap toward a sustainable and intelligent future, this book serves as a guiding light in a world that demands adaptable, forward-thinking solutions to the challenges of the digital era.

The rise of generative Artificial Intelligence (AI) signifies a momentous stride in the evolution of Large Language Models (LLMs) within the expansive sphere of Natural Language Processing (NLP). This groundbreaking advancement ripples through numerous facets of our existence, with education, AI literacy, and curriculum enhancement emerging as focal points of transformation. Within the pages of Transforming Education With Generative AI: Prompt Engineering and Synthetic Content Creation, readers embark on a journey into the heart of this transformative phenomenon. Generative AI's influence extends deeply into education, touching the lives of educators, administrators, policymakers, and learners alike. Within the pages of this book, we explore the intricate art of prompt engineering, a skill that shapes the quality of AI-generated educational content. As generative AI becomes increasingly accessible, this comprehensive volume empowers its audience, by providing them with the knowledge needed to navigate and harness the potential of this powerful tool. Transforming Education With Generative AI: Prompt Engineering and Synthetic Content Creation offers a comprehensive exploration that delves into an array of topics, from ethical considerations of AI, to inclusivity in education. It further explores personalized learning applications, learning analytics, and cybersecurity in education. Through this in-depth examination, the book reveals how this innovative technology is reshaping the landscape of education in the 21st century, enriching student engagement, and delivering valuable insights into the educational process.

'This book is an essential resource for undergraduate and postgraduate students, researchers, academicians, industry professionals, and scientists working in research laboratories. It provides a comprehensive overview of XAI concepts, advantages over AI, and its applications in smart city development. By showcasing the impact of XAI on various smart city applications, the book enables readers to understand the importance of XAI in creating more sustainable and efficient smart cities. Additionally, the book addresses the open challenges and research issues related to XAI in modern smart cities, providing a roadmap for future research in this field. Overall, this book is a valuable resource for anyone interested in understanding the importance of XAI in smart city applications'--

The Earth Sciences industry faces a new challenge - the need for accurate, efficient, and reliable methods to monitor and predict geological phenomena and environmental changes. As climate change, earthquakes, and other natural disasters become more frequent and severe, the necessity for advanced tools and techniques is paramount. Traditional methods often fall short in providing the precision and speed required to address these critical issues. Geologists and earth scientists who are grappling with the urgent problem of utilizing artificial intelligence (AI) to revolutionize their field, will find the solution within the pages of Novel AI Applications for Advancing Earth Sciences. This book offers the research community concepts expanding upon the fusion of AI technology with earth sciences. By leveraging advanced AI tools, such as convolutional neural networks, support vector machines, artificial neural networks, and the potential of remote sensing satellites, this book transforms the identification of geological features, geological mapping, soil classification, and gas detection. Scientists can now predict earthquakes and assess the probability of climate change with unprecedented accuracy. Additionally, the book explains how the optimization of algorithms for specific tasks substantially reduces the time complexity of earth observations, leading to an unprecedented leap in accuracy and efficiency. The book goes beyond academic insights, and extends its reach to real-world applications. It empowers scholars, researchers, students, and practitioners to monitor climate change, evaluate carbon footprints, and adapt to changes in land use. Furthermore, it plays a pivotal role in optimizing renewable energy production, ensuring the world's transition to clean, reliable energy sources. The data obtained from Earth observation satellites can be harnessed for a multitude of on-ground applications, ranging from agriculture and forestry to mining, water management, rural and urban development, and more. In a world where AI meets earth sciences, this book provides many innovative solutions to the challenges faced by academicians and researchers, illuminating the path toward a more informed future.

In manufacturing, entrenched challenges like costly maintenance, operational inefficiencies, and product defects loom large, casting shadows over industry progress. Despite the promise of Industry 4.0 and the proliferation of data-driven technologies, many enterprises need help to effectively harness the transformative power of artificial intelligence (AI). The gap between AI's potential and its practical application persists, hindering manufacturing companies from achieving optimal efficiency, competitiveness, and sustainability. Industry Applications of Thrust Manufacturing: Convergence with Real-Time Data and AI is a groundbreaking book meticulously crafted to address the pressing needs of academic scholars and industry professionals. Offering a nuanced exploration of AI's role in revolutionizing manufacturing, this book serves as a beacon of clarity amidst the complexities of modern industrial landscapes. Through meticulous research, real-world case studies, and expert insights, readers will embark on a transformative journey, unraveling the intricacies of predictive maintenance, demand forecasting, quality assurance, and beyond. With a strategic focus on AI's applications across manufacturing phases and sub-sectors, coupled with in-depth discussions on emerging technologies like machine learning, deep learning, and natural language processing, this book equips readers with the knowledge and tools to confidently navigate the evolving landscape of Industry 4.0. Whether seeking to optimize operational workflows, mitigate risks, or unlock untapped opportunities, this definitive guide offers invaluable insights and actionable strategies to propel manufacturing enterprises into a future of innovation, efficiency, and sustainable growth.

In the advancing fields of artificial intelligence (AI) and data science, a pressing ethical dilemma arises. As technology continues its relentless march forward, ethical considerations within these domains become increasingly complex and critical. Bias in algorithms, lack of transparency, data privacy breaches, and the broader societal repercussions of AI applications are demanding urgent attention. This ethical quandary poses a formidable challenge for researchers, academics, and industry professionals alike, threatening the very foundation of responsible technological innovation. Navigating this ethical minefield requires a comprehensive understanding of the multifaceted issues at hand. The Ethical Frontier of AI and Data Analysis is an indispensable resource crafted to address the ethical challenges that define the future of AI and data science. Researchers and academics who find themselves at the forefront of this challenge are grappling with the evolving landscape of AI and data science ethics. Underscoring the need for this book is the current lack of clarity on ethical frameworks, bias mitigation strategies, and the broader societal implications, which hinder progress and leave a void in the discourse. As the demand for responsible AI solutions intensifies, the imperative for this reliable guide that consolidates, explores, and advances the dialogue on ethical considerations grows exponentially. Tailored for researchers, academics, and professionals, this publication serves as a beacon of ethical excellence. With a comprehensive exploration of bias, fairness, transparency, and accountability, it guides readers through the intricate web of ethical considerations. From foundational philosophical frameworks to real-world case studies, the book offers a roadmap to not only understand but actively shape the ethical trajectory of AI and data science. It is more than a book; it serves as a transformative tool for those seeking to align technological innovation with ethical standards and societal values.

Climate change, natural resource depletion, and unsustainable agricultural practices pose unprecedented challenges to our planet. The increasing environmental footprint of computer networks, communication systems, and other IT infrastructures exacerbates these issues, contributing significantly to energy consumption and greenhouse gas emissions. Without innovative solutions, these challenges will continue to escalate, threatening the sustainability of our planet for future generations. AI Applications for Business, Medical, and Agricultural Sustainability offers a comprehensive solution by harnessing the power of Artificial Intelligence (AI) and High-Performance Computing (HPC). This book showcases how AI's ability to process vast amounts of data and HPC's capability to perform massive computations in short periods can revolutionize climate modeling, energy optimization, and sustainable agriculture. By introducing new energy models, algorithms, and methodologies, the book provides a roadmap for developing next-generation computing and communication infrastructures that are environmentally sustainable. This book is ideal for educators, environmentalists, industry professionals, researchers, and academics. It explores the potential of AI and HPC in addressing environmental challenges and delves into the ethics and policies surrounding their use. By offering practical insights and innovative solutions, AI Applications for Business, Medical, and Agricultural Sustainability empowers readers to make a difference in mitigating climate change, preserving natural resources, and promoting sustainable agriculture.

In a world where natural disasters wreak havoc with increasing frequency and severity, the need for accurate prediction and effective management has never been more critical. From earthquakes shattering communities to floods submerging vast regions, these events endanger lives and strain resources and infrastructure to their limits. Yet, amidst this turmoil, traditional forecasting methods often need to catch up, leaving us vulnerable and reactive rather than proactive. This comprehensive academic collection provides a beacon of hope in uncertain circumstances: Internet of Things and AI for Natural Disaster Management and Prediction. Within its pages, leading scholars and practitioners converge to explore the transformative potential of machine learning in mitigating the impact of natural calamities. Through a synthesis of cutting-edge research and practical applications, this book serves as a roadmap toward a more resilient future. From leveraging deep learning algorithms, to analyzing satellite imagery for early warning systems, to harnessing artificial neural networks to model complex disaster scenarios, each chapter delves into a specific aspect of machine learning's role in disaster preparedness and response. By bridging the gap between theory and practice, this book empowers academics, policymakers, and practitioners alike to harness the full potential of machine learning in safeguarding lives and livelihoods.

Data security is paramount in our modern world, and the symbiotic relationship between machine learning and cryptography has recently taken center stage. The vulnerability of traditional cryptosystems to human error and evolving cyber threats is a pressing concern. The stakes are higher than ever, and the need for innovative solutions to safeguard sensitive information is undeniable. Innovative Machine Learning Applications for Cryptography emerges as a steadfast resource in this landscape of uncertainty. Machine learning's prowess in scrutinizing data trends, identifying vulnerabilities, and constructing adaptive analytical models offers a compelling solution. The book explores how machine learning can automate the process of constructing analytical models, providing a continuous learning mechanism to protect against an ever-increasing influx of data. This book goes beyond theoretical exploration, and provides a comprehensive resource designed to empower academic scholars, specialists, and students in the fields of cryptography, machine learning, and network security. Its broad scope encompasses encryption, algorithms, security, and more unconventional topics like Quantum Cryptography, Biological Cryptography, and Neural Cryptography. By examining data patterns and identifying vulnerabilities, it equips its readers with actionable insights and strategies that can protect organizations from the dire consequences of security breaches. Innovative Machine Learning Applications for Cryptography bridges the gap between two powerful domains and assists in diminishing the influence of human error on encryption and decryption processes. For academic scholars, engineers, scientists, and students, this book offers a valuable treasure trove of knowledge and actionable strategies in an age where the security of every byte is of utmost importance.

The amalgamation of artificial intelligence (AI), optimization techniques, and blockchain is revolutionizing how to conceptualize, design, and operate aerospace systems. While optimization techniques are pivotal in streamlining aerospace processes, security challenges have recently surfaced. AI and Blockchain Optimization Techniques in Aerospace Engineering delves into the transformative impact of technologies on various facets of the aerospace industry, offering a multidimensional solution to overcome security concerns and enhance the overall efficiency of aerospace systems The book explores how machine learning reshapes aerospace systems by automating complex tasks through self/reinforced learning methods. From air traffic data analysis to flight scheduling, geographical information, and navigation, machine learning has become an indispensable tool, offering valuable insights that enhance aerospace operations. Simultaneously, blockchain technology, with its inherent characteristics of decentralization and tamper-proof ledgers, ensures transparency, accountability, and security in transactions, providing an innovative approach to data integrity and system resilience. Designed for technology development professionals, academicians, data scientists, industrial experts, researchers, and students, the book offers a panoramic view of the latest innovations in the field. The content spans various critical areas, including aerodynamics, flight mechanics, gas dynamic medical imaging, analytics and diagnostics, image segmentation, deep learning-based computer-aided diagnosis systems, natural language processing, robotics-assisted systems, and bio-inspired algorithms for aerospace applications.

Blockchain has become the cornerstone of technologies, supported by others including Big Data and Artificial Intelligence (AI). Originating from cryptocurrency, it transcends boundaries, finding resonance in finance, healthcare, e-governance, and beyond. While blockchain relies on a peer-to-peer approach, enabling nodes to collaborate without the shackles of a central authority, appropriate monitoring and updating of these technologies is a constant necessity. AI systems may generate false alerts of money laundering, leading to wasted resources and unnecessary investigations. Cybersecurity threats are becoming more sophisticated, and AI systems need to evolve accordingly. This is especially true for healthcare data systems, where data privacy and security concerns, especially with sensitive health information are paramount. Threads of automation in artificial intelligence (AI) weave through sectors such as business, finance, healthcare, marketing, and governance. Industrial Applications of Big Data, AI, and Blockchain delves into the pulsating realms of big data, AI, and blockchain. From natural language processing's eloquent interpretation of human language to the prowess of AI algorithms in predictive tasks, this book explores how AI enhances decision-making accuracy, catalyzing a paradigm shift in diverse industries. The book also navigates through big data, a robust infrastructure adorned with the quintessence of the 5V characteristics—velocity, volume, value, variety, and veracity. It is a stronghold for developers, offering an efficient sanctuary for advanced database systems, encompassing services like data cleaning, storage, processing, and analysis. The gravitational pull of big data attracts both researchers and industrialists. This book is ideal for researchers, business visionaries, tech enthusiasts, and curious minds eager to fathom the transformative potential of these technologies. From the AI in money laundering detection to the blockchain in healthcare, each chapter unfurls a narrative stitched.

Unmanned aerial vehicles (UAVs) continue to become more advanced and complex as researchers push the boundaries of other supporting technologies. Applications of Machine Learning in UAV Networks presents a pioneering exploration into the symbiotic relationship between machine learning techniques and UAVs. In an age where UAVs are revolutionizing sectors as diverse as agriculture, environmental preservation, security, and disaster response, this meticulously crafted volume offers an analysis of the manifold ways machine learning drives advancements in UAV network efficiency and efficacy. This book navigates through an expansive array of domains, each demarcating a pivotal application of machine learning in UAV networks. From the precision realm of agriculture and its dynamic role in yield prediction to the ecological sensitivity of biodiversity monitoring and habitat restoration, the contours of each domain are vividly etched. These explorations are not limited to the terrestrial sphere; rather, they extend to the pivotal aerial missions of wildlife conservation, forest fire monitoring, and security enhancement, where UAVs adorned with machine learning algorithms wield an instrumental role. Scholars and practitioners from fields as diverse as machine learning, UAV technology, robotics, and IoT networks will find themselves immersed in a confluence of interdisciplinary expertise. The book's pages cater equally to professionals entrenched in agriculture, environmental studies, disaster management, and beyond. Furthermore, the students and researchers finds knowledge that illuminates the convergence of UAVs and machine learning, arguably one of the most riveting frontiers in contemporary research.

The rapid advancement of generative artificial intelligence (AI) has brought about significant ethical challenges. As machines become more adept at creating human-like content, concerns about misuse, bias, privacy, and accountability have emerged. Without clear guidelines and regulations, there is a risk of unethical use, such as creating deepfake videos or disseminating misinformation, which could have severe societal consequences. Additionally, questions about intellectual property rights and the ownership of AI-generated creations still need to be solved, further complicating the ethical landscape. The book, Generative Artificial Intelligence and Ethics: Standards, Guidelines, and Best Practices, comprehensively solves these ethical challenges. By providing insights into the historical development and key milestones of Generative AI, the book lays a foundation for understanding its complex ethical implications. It examines existing ethical frameworks and proposes new ones tailored to AI's unique characteristics, helping readers apply traditional ethics to AI development and deployment. The book equips readers with the knowledge and tools needed to address these ethical considerations in AI development through in-depth discussions on bias, fairness, privacy, and security. It also explores the role of education in fostering responsible AI practices. It offers practical guidelines for ethical decision-making throughout the AI lifecycle. By promoting ethical awareness and providing actionable solutions, this book empowers readers to navigate the moral challenges of Generative AI, ensuring its responsible and beneficial use for society.

In a rapidly evolving digital landscape, integrating emerging technologies presents unprecedented opportunities and complex challenges across various disciplines. As society navigates this transformation, there is a growing need for comprehensive insights into the implications of these advancements. This book serves as a vital resource, offering a multidimensional exploration of how emerging technologies are reshaping the social sciences, education, law and policy, tourism, health, environment, communication, business and management, and security. Focusing on the intersection of technology and society, the Future of Digital Technology and AI in Social Sectors addresses pressing issues such as ethical dilemmas in technological advancement, the impact of automation on employment, and the moral and legal challenges of AI and data analytics. By providing a platform for researchers and practitioners to delve into these topics, the book aims to foster a deeper understanding of emerging technologies'implications and opportunities across diverse fields. Researchers and practitioners alike will find this book invaluable for gaining new perspectives and insights into the far-reaching effects of emerging technologies. By offering a comprehensive analysis of these advancements, the book aims to contribute to the ongoing dialogue on technology's role in shaping society's future. Scholars, educators, policymakers, and industry professionals can use this reference to explore the transformative power of emerging technologies in today's world.

In a rapidly evolving technological landscape, the integration of antenna technologies, electronics, and artificial intelligence (AI) poses a significant challenge: the complexity of these converging domains often surpasses the capacity of traditional educational resources and has been an area of research that is largely understudied. This creates a challenge for researchers attempting to gain a full understanding of the issue, as the lack of cohesive literature that bridges the gaps between these disciplines is significant. Researchers, engineers, and enthusiasts are hampered in their efforts to grasp the full potential of this convergence. Convergence of Antenna Technologies, Electronics, and AI offers a unique perspective to this problem by synthesizing the fundamental principles, cutting-edge advancements, and practical applications of these interconnected domains. This book serves as a comprehensive guide for navigating the complexities of this multidisciplinary frontier. It provides a cohesive framework that not only elucidates the interconnections between antennas, electronics, and AI but also empowers readers to leverage this knowledge in developing transformative innovations. Through its exploration of foundational principles, contemporary challenges, and future trajectories, this book inspires readers to envision and shape a future where seamless connectivity and intelligent systems redefine the boundaries of what is possible. This book catalyzes the next wave of innovation in communication, connectivity, and smart systems by offering a nuanced understanding of the convergence of technologies using antenna, and artificial intelligence.

In today's rapidly evolving business landscape, organizations are inundated with vast amounts of data, making it increasingly challenging to extract meaningful insights and make informed decisions. The traditional business intelligence (BI) approach must often address the complexity and speed required for effective decision-making in this data-rich environment. As a result, many businesses need help to leverage their data to drive sustainable growth and remain competitive. Intersection of AI and Business Intelligence in Data-Driven Decision-Making presents a transformative solution to this pressing challenge. By exploring the convergence of artificial intelligence (AI) and BI, our book provides a comprehensive framework for leveraging AI-powered BI to revolutionize data analysis, predictive modeling, and decision-making processes. Readers will gain valuable insights into practical applications, emerging trends, and ethical considerations, inspiring and exciting them about the potential of AI in driving business success. Through in-depth discussions, case studies, and best practices, this book equips professionals, researchers, and students with the knowledge and tools needed to navigate the complexities of AI-powered business intelligence. Whether you're looking to predict trends, analyze consumer behavior, or optimize supply chains, this book offers actionable strategies and techniques for implementing AI-powered BI solutions in your organization.

The global demand for clean energy solutions the urgency of addressing climate change continue to intensify, and as such, the need for innovative approaches becomes increasingly paramount. However, navigating the complex landscape of clean energy production and sustainability presents significant challenges. Traditional methods often fall short in efficiently optimizing renewable energy systems and mitigating environmental impacts. Moreover, the integration of artificial intelligence (AI) into the energy sector remains underexplored, despite its potential to revolutionize operations and drive sustainable development. AI Applications for Clean Energy and Sustainability emerges, working to tackle these pressing issues. This comprehensive volume delves into the transformative power of AI in revolutionizing clean energy production, distribution, and management. By harnessing machine learning algorithms, data analytics, and optimization techniques, the book offers innovative solutions to enhance the efficiency, reliability, and scalability of renewable energy systems. Through real-world case studies and practical examples, it illustrates AI's potential to optimize energy infrastructure, monitor marine ecosystems, and predict climate change impacts, thereby paving the way for a more sustainable future. Designed for researchers, policymakers, industry professionals, and students alike, this book serves as a roadmap towards a greener and more resilient world. By providing insights into cutting-edge AI technologies and their applications in the energy sector, the book empowers stakeholders to address the challenges of climate change and advance sustainability goals. With its interdisciplinary approach and emphasis on practical solutions, this book inspires collaboration and innovation, driving meaningful progress towards a cleaner and more sustainable energy future.

The landscape of Industry 6.0 is rapidly evolving, and businesses are facing a significant challenge of integrating sustainable practices into their operations. This task is further complicated by the need to navigate the complex web of sustainability goals and green technologies. Many companies need assistance understanding how Industry 6.0 can be leveraged to meet these goals, promote innovation, and drive economic growth. Sustainable Innovation for Industry 6.0 provides diverse insights on the intricate relationships between Industry 6.0's sustainable innovation functions, empowering companies to capitalize on sustainability and manage sustainable development effectively by providing a comprehensive roadmap. It offers a unique perspective on how businesses can use Industry 6.0 technologies to incorporate sustainability into their practices, thus ensuring long-term success and environmental stewardship. This book is a must-read for undergraduate and postgraduate students in various disciplines and research scholars working in sustainable management. It provides a thorough understanding of sustainable development and offers practical insights into how Industry 6.0 can be harnessed to create a more sustainable future. With its innovative approach and comprehensive coverage, Sustainable Innovation for Industry 6.0 is poised to become a seminal work in sustainable management.

In today's rapidly evolving digital landscape, extended reality (XR) technologies have emerged as transformative tools that blur the boundaries between the physical and digital worlds. However, with this innovation comes a host of complex challenges. From ethical considerations in virtual reality research to legal and regulatory hurdles in extended reality, navigating this dynamic terrain requires a deep understanding of technology and human behavior. The lack of comprehensive resources that bridge these disciplines poses a significant barrier to those seeking to harness the full potential of immersive technologies. Multidisciplinary Applications of Extended Reality for Human Experience addresses this critical gap by offering a multidisciplinary exploration of XR's impact on society. Through expert contributions from diverse fields, including psychology, sociology, design, and culture, this book provides a comprehensive understanding of how XR technologies are reshaping human perceptions and interactions. It serves as a foundational text for academics, researchers, and industry professionals seeking to navigate the complexities of immersive technologies and their profound implications for human life. By delving into topics such as the psychological effects of immersive experiences, the design principles guiding XR content creation, and the transformative potential of these technologies in various fields, this book equips readers with the knowledge and insights needed to harness XR's full potential. Whether you are a researcher exploring the ethical dimensions of virtual reality or a technologist developing immersive applications, this book offers practical guidance and scholarly analysis to help you navigate the evolving landscape of extended reality.

The rapid progress of artificial intelligence (AI) technologies has resulted in a complicated landscape for researchers and practitioners. Understanding and navigating the complexities of AI applications, particularly in the context of ChatGPT and its interactions with other AI tools, can be challenging. Researchers and academics need guidance to keep up with these technologies'evolving trends and implications, which leads to gaps in knowledge and implementation strategies. Additionally, the ethical and societal impacts of integrating AI into various domains remain a significant concern, requiring a comprehensive approach to address. Applications, Challenges, and the Future of ChatGPT provide a comprehensive solution to these issues by offering a detailed analysis of the current research trends in AI, focusing on ChatGPT and its interactions with other AI tools. The book delves into how we can effectively utilize ChatGPT and other AI tools to address complex problems by exploring AI applications'collaborative potentials and emerging paradigms. By identifying research gaps and suggesting future directions, this book equips researchers and practitioners with the knowledge and tools necessary to navigate the evolving landscape of AI. The book goes beyond theoretical discussions and examines the transformative impact of incorporating ChatGPT into various domains, including education. It highlights the practical role of ChatGPT in shaping teaching strategies and outcomes, providing real-world examples of its applications. The book also delves into the history and popularity of ChatGPT since its introduction, offering a nuanced understanding of its functionalities and limitations. With a strong focus on the ethical and societal implications of integrating AI, this book offers a holistic approach to understanding the synergistic possibilities in intelligent systems, providing practical guidance for its implementation.