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Artificial Intelligence for Oral Health Care: Applications and Future Prospects
by Falk Schwendicke Prabhat Kumar Chaudhari Kunaal Dhingra Sergio E. Uribe Manal HamdanArtificial intelligence (AI) is reshaping diagnostics, treatment planning, and patient care across diverse disciplines. Dentistry is now at the forefront of this innovation, with AI's ability to process complex data to improve clinical practice, empower patients, and address public health challenges. This richly illustrated book offers a deeper understanding of the foundational concepts of AI, its practical applications in oral health, and the possibilities that lie ahead. From oral pathology to maxillofacial surgery, prosthodontics to orthodontics, and endodontics to dental education, it presents compelling, evidence-based insights into how AI is changing the landscape of dentistry. Beyond its clinical potential, the book tackles the key risks and challenges associated with AI implementation. It provides a thoughtful roadmap for addressing ethical considerations, encouraging transparency, and developing solutions that prioritize both innovation and patient well-being. Written for dental professionals and an interdisciplinary audience, including educators, researchers, policymakers, and the public, this book serves as both a guide and a call to action for responsibly embracing AI's transformative power. Whether you are a seasoned practitioner, an academic, or simply curious about the intersection of AI and healthcare, this book will inspire and inform your journey into the future of dentistry.
Artificial Intelligence for Personalized Medicine: Promoting Healthy Living and Longevity (Studies in Computational Intelligence #1106)
by Arash Shaban-Nejad Martin Michalowski Simone BiancoThis book aims to highlight the latest achievements in the use of AI in personalized medicine and healthcare delivery. The edited book contains selected papers presented at the 2023 Health Intelligence workshop, co-located with the Thirty-Seven Association for the Advancement of Artificial Intelligence (AAAI) conference, and presents an overview of the issues, challenges, and potentials in the field, along with new research results. This book provides information for researchers, students, industry professionals, clinicians, and public health agencies interested in the applications of AI in medicine and public health.
Artificial Intelligence for Precision Agriculture
by Pethuru Raj N. Gayathri Kathrine, G. Jaspher WillsiePrecision agriculture is a next-generation farming management concept that optimizes resource use, productivity, quality, profitability, and sustainability by observing and responding to crop variability. Precision agriculture employs digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), 5G communication, cybersecurity, edge computing, cloud-native principles, and blockchain to ensure crops and soil receive exactly what they need for optimal health and productivity.Artificial Intelligence for Precision Agriculture explores the latest developments in precision agriculture, detailing how AI contributes to its goals. The book discusses how precision agriculture solutions use IoT devices, data storage, AI analytics, connectivity, and cloud infrastructures to analyze factors such as soil type, terrain, weather, plant growth, and yield data. It also examines edge technologies—sensors, microchips, beacons, RFID tags, robots, drones, and actuators—that collect field data and transmit it to cloud-based AI platforms for analysis. The book shows how AI-driven insights guide actions in the field, such as crop rotation, optimal planting and harvesting times, and soil management, and help farmers apply the right amounts of water, fertilizers, and pesticides, reducing waste and environmental impact. Applications covered in the book include: Drone-based high-resolution field mapping Tracking crops Crop yield assessments Data collection for irrigation, fertilization, and crop management Advanced weather monitoring Equipment management With chapters on AI model development, plant disease detection and remediation, sustainable farming techniques, data integration, AI-enabled data analytics, and knowledge visualization, this book is a comprehensive guide to technologies and applications in precision agriculture.
Artificial Intelligence for Renewable Energy and Climate Change
by Pandian Vasant Gerhard-Wilhelm Weber Joshua Thomas José Antonio Marmolejo-Saucedo Roman Rodriguez-AguilarARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY AND CLIMATE CHANGE Written and edited by a global team of experts in the field, this groundbreaking new volume presents the concepts and fundamentals of using artificial intelligence in renewable energy and climate change, while also covering the practical applications that can be utilized across multiple disciplines and industries, for the engineer, the student, and other professionals and scientists. Renewable energy and climate change are two of the most important and difficult issues facing the world today. The state of the art in these areas is changing rapidly, with new techniques and theories coming online seemingly every day. It is important for scientists, engineers, and other professionals working in these areas to stay abreast of developments, advances, and practical applications, and this volume is an outstanding reference and tool for this purpose. The paradigm in renewable energy and climate change shifts constantly. In today’s international and competitive environment, lean and green practices are important determinants to increase performance. Corresponding production philosophies and techniques help companies diminish lead times and costs of manufacturing, improve delivery on time and quality, and at the same time become more ecological by reducing material use and waste, and by recycling and reusing. Those lean and green activities enhance productivity, lower carbon footprint and improve consumer satisfaction, which in reverse makes firms competitive and sustainable. This practical, new groundbreaking volume: Features coverage on a wide range of topics such as classical and nature-inspired optimization and optimal control, hybrid and stochastic systems Is ideally designed for engineers, scientists, industrialist, academicians, researchers, computer and information technologists, sustainable developers, managers, environmentalists, government leaders, research officers, policy makers, business leaders and students Is useful as a practical tool for practitioners in the fields of sustainable and renewable energy sustainability Includes wide coverage of how artificial intelligence can be used to impact the struggle against global warming and climate change
Artificial Intelligence for Renewable Energy Systems (Artificial Intelligence and Soft Computing for Industrial Transformation)
by Ajay Kumar Vyas S. Balamurugan Kamal Kant Hiran Harsh S. DhimanARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.
Artificial Intelligence for Research and Democracy: First International Workshop, AI4Research 2024, and 4th International Workshop, DemocrAI 2024, Held in Conjunction with IJCAI 2024, Jeju, South Korea, August 5, 2024, Proceedings (Lecture Notes in Computer Science #14917)
by Wenpeng Yin Jihyun Janice Ahn Rui Zhang Lifu Huang Rafik Hadfi Takayuki Ito Susumu Ohnuma Shun ShiramatsuThis book constitutes the proceedings of the First International workshop on AI for Research, AI4Research 2024, and 4th International workshop on Democracy and AI, DemocrAI 2024, held in conjunction with the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024, in Jeju, South Korea, in August 2024. The 9 papers presented here were carefully reviewed and selected from 34 submissions. The AI4Research 2024 proceedings focus on how recent developments in AI are influencing research across various fields in science. The DemocrAI 2024 proceedings focus on the use of AI-based systems in Democracy, especially Democratic online platforms, formal theories of collective decision-making, and advanced methodologies for discourse analysis.
Artificial Intelligence for Risk Mitigation in the Financial Industry
by Narayan C. Debnath Archana Patel Purvi Pokhariyal Shweta Anand Ambrish Kumar MishraArtificial Intelligence for Risk Mitigation in the Financial Industry This book extensively explores the implementation of AI in the risk mitigation process and provides information for auditing, banking, and financial sectors on how to reduce risk and enhance effective reliability. The applications of the financial industry incorporate vast volumes of structured and unstructured data to gain insight into the financial and non-financial performance of companies. As a result of exponentially increasing data, auditors and management professionals need to enhance processing capabilities while maintaining the effectiveness and reliability of the risk mitigation process. The risk mitigation and audit procedures are processes involving the progression of activities to “transform inputs into output.” As AI systems continue to grow mainstream, it is difficult to imagine an aspect of risk mitigation in the financial industry that will not require AI-related assurance or AI-assisted advisory services. AI can be used as a strong tool in many ways, like the prevention of fraud, money laundering, and cybercrime, detection of risks and probability of NPAs at early stages, sound lending, etc. Audience This is an introductory book that provides insights into the advantages of risk mitigation by the adoption of AI in the financial industry. The subject is not only restricted to individuals like researchers, auditors, and management professionals, but also includes decision-making authorities like the government. This book is a valuable guide to the utilization of AI for risk mitigation and will serve as an important standalone reference for years to come.
Artificial Intelligence for Robotics: Build intelligent robots that perform human tasks using AI techniques
by Francis X. GoversBring a new degree of interconnectivity to your world by building your own intelligent robots Key Features Leverage fundamentals of AI and robotics Work through use cases to implement various machine learning algorithms Explore Natural Language Processing (NLP) concepts for efficient decision making in robots Book Description Artificial Intelligence for Robotics starts with an introduction to Robot Operating Systems (ROS), Python, robotic fundamentals, and the software and tools that are required to start out with robotics. You will learn robotics concepts that will be useful for making decisions, along with basic navigation skills. As you make your way through the chapters, you will learn about object recognition and genetic algorithms, which will teach your robot to identify and pick up an irregular object. With plenty of use cases throughout, you will explore natural language processing (NLP) and machine learning techniques to further enhance your robot. In the concluding chapters, you will learn about path planning and goal-oriented programming, which will help your robot prioritize tasks. By the end of this book, you will have learned to give your robot an artificial personality using simulated intelligence. What you will learn Get started with robotics and artificial intelligence Apply simulation techniques to give your robot an artificial personality Understand object recognition using neural networks and supervised learning techniques Pick up objects using genetic algorithms for manipulation Teach your robot to listen using NLP via an expert system Use machine learning and computer vision to teach your robot how to avoid obstacles Understand path planning, decision trees, and search algorithms in order to enhance your robot Who this book is for If you have basic knowledge about robotics and want to build or enhance your existing robot's intelligence, then Artificial Intelligence for Robotics is for you. This book is also for enthusiasts who want to gain knowledge of AI and robotics.
Artificial Intelligence for Robotics and Autonomous Systems Applications (Studies in Computational Intelligence #1093)
by Ahmad Taher Azar Anis KoubaaThis book addresses many applications of artificial intelligence in robotics, namely AI using visual and motional input. Robotic technology has made significant contributions to daily living, industrial uses, and medicinal applications. Machine learning, in particular, is critical for intelligent robots or unmanned/autonomous systems such as UAVs, UGVs, UUVs, cooperative robots, and so on. Humans are distinguished from animals by capacities such as receiving visual information, adjusting to uncertain circumstances, and making decisions to take action in a complex system. Significant progress has been made in robotics toward human-like intelligence; yet, there are still numerous unresolved issues. Deep learning, reinforcement learning, real-time learning, swarm intelligence, and other developing approaches such as tiny-ML have been developed in recent decades and used in robotics.Artificial intelligence is being integrated into robots in order to develop advanced robotics capable of performing multiple tasks and learning new things with a better perception of the environment, allowing robots to perform critical tasks with human-like vision to detect or recognize various objects. Intelligent robots have been successfully constructed using machine learning and deep learning AI technology. Robotics performance is improving as higher quality, and more precise machine learning processes are used to train computer vision models to recognize different things and carry out operations correctly with the desired outcome.We believe that the increasing demands and challenges offered by real-world robotic applications encourage academic research in both artificial intelligence and robotics. The goal of this book is to bring together scientists, specialists, and engineers from around the world to present and share their most recent research findings and new ideas on artificial intelligence in robotics.
Artificial Intelligence for Safety and Reliability Engineering: Methods, Applications, and Challenges (Springer Series in Reliability Engineering)
by Kim Phuc TranThis book is a comprehensive exploration of the latest theoretical research, technological advancements, and real-world applications of artificial intelligence (AI) for safety and reliability engineering. Smart manufacturing relies on predictive maintenance (PdM) to ensure sustainable production systems, and the integration of AI has become increasingly prevalent in this field. This book serves as a valuable resource for researchers, practitioners, and decision-makers in manufacturing. By combining theoretical research, practical applications, and case studies, it equips readers with the necessary knowledge and tools to implement AI for safety and reliability engineering effectively in smart manufacturing contexts.
Artificial Intelligence for Science: Frontiers and Perspectives Based on Parallel Intelligence (SpringerBriefs in Service Science)
by Qinghai Miao Fei-Yue WangThis book presents a comprehensive framework for analyzing, evaluating, and guiding AI for Sciences (AI4Sci) research, offering a unified approach that facilitates analysis across various academic fields through a shared set of dimensions and indicators. It provides a systematic overview of recent AI4Sci advances in various disciplines and offers insights into the latest issues in and prospects of AI4Sci. The book is based on the theory of Parallel Intelligence (PI), which forms the foundation for the general AI4Sci framework. By analyzing multiple cases in various academic fields, this framework integrates key elements of AI4Sci, such as real scientific problems, datasets, virtual systems, AI methods, human roles, and organizational mechanisms, from a multidimensional perspective. It also assesses and summarizes the limitations of AI4Sci, incorporating the latest advances in AI for fundamental models. Lastly, it explores the impact of DeSci and DAO, as well as TAO, on AI4Sci ecosystem development and prospects. Through its balanced approach, the book offers readers a goal-oriented perspective, focusing on a concise presentation of the core ideas and reducing detailed descriptions of specific AI4Sci cases to a minimum.
Artificial Intelligence for Science and Engineering Applications
by Shahab D. MohagheghArtificial Intelligence (AI) is defined as the simulation of human intelligence through the mimicking of the human brain for analysis, modeling, and decision‑making. Science and engineering problem solving requires modeling of physical phenomena, and humans approach the solution of scientific and engineering problems differently from other problems. Artificial Intelligence for Science and Engineering Applications addresses the unique differences in how AI should be developed and used in science and engineering. Through the inclusion of definitions and detailed examples, this book describes the actual and realistic requirements as well as what characteristics must be avoided for correct and successful science and engineering applications of AI.This book:• Offers a brief history of AI and covers science and engineering applications• Explores the modeling of physical phenomena using AI• Discusses explainable AI (XAI) applications• Covers the ethics of AI in science and engineering• Features real‑world case studiesOffering a probing view into the unique nature of scientific and engineering exploration, this book will be of interest to generalists and experts looking to expand their understanding of how AI can better tackle and advance technology and developments in scientific and engineering disciplines.
Artificial Intelligence for Scientific Discoveries: Extracting Physical Concepts from Experimental Data Using Deep Learning
by Raban ItenWill research soon be done by artificial intelligence, thereby making human researchers superfluous? This book explains modern approaches to discovering physical concepts with machine learning and elucidates their strengths and limitations. The automation of the creation of experimental setups and physical models, as well as model testing are discussed. The focus of the book is the automation of an important step of the model creation, namely finding a minimal number of natural parameters that contain sufficient information to make predictions about the considered system. The basic idea of this approach is to employ a deep learning architecture, SciNet, to model a simplified version of a physicist's reasoning process. SciNet finds the relevant physical parameters, like the mass of a particle, from experimental data and makes predictions based on the parameters found. The author demonstrates how to extract conceptual information from such parameters, e.g., Copernicus' conclusion that the solar system is heliocentric.
Artificial Intelligence for Security: Enhancing Protection in a Changing World
by Tuomo Sipola Tero Kokkonen Janne Alatalo Monika WolfmayrThis book discusses the use of artificial intelligence (AI) for security purposes. It is divided into three parts: methodological fundamentals of AI, use of AI for critical infrastructure protection and anomaly detection. The first section describes the latest knowledge for creating safe AIs and using them to enhance protection. This book also presents various domains and examples of AI-driven security. The chapters describe potential methods, demonstrate use cases and discuss the challenges of the evolving field. This includes topics such as defensive use of AI to detect threats. It discusses the offensive use of AI to better understand the future threat landscape, the use of AI for automation in critical infrastructure and overall challenges of AI usage for critical tasks. As new threats emerge, the use of AI technologies to protect the world one lives in is topical. New technologies in this space have advanced rapidly, and subsequently, their use in enhancing protection is an evident development. To this effect, this book brings together a group of international researchers and professionals who present their views on how to create security through AI. This book targets postgraduate students, researchers and professionals who want to understand the use of AI for security. Understanding latest advancements in this field will also be useful to those who want to comprehend modern cybersecurity in detail and who want to follow research and latest trends.
Artificial Intelligence for Smart Healthcare (EAI/Springer Innovations in Communication and Computing)
by Parul Agarwal Kavita Khanna Ahmed A. Elngar Ahmed J. Obaid Zdzislaw PolkowskiThis book provides information on interdependencies of medicine and telecommunications engineering and how the two must rely on each other to effectively function in this era. The book discusses new techniques for medical service improvisation such as clear-cut views on medical technologies. The authors provide chapters on communication essentiality in healthcare, processing of medical amenities using medical images, the importance of data and information technology in medicine, and machine learning and artificial intelligence in healthcare. Authors include researchers, academics, and professionals in the field.
Artificial Intelligence for Smart Manufacturing: Methods, Applications, and Challenges (Springer Series in Reliability Engineering)
by Kim Phuc TranThis book provides readers with a comprehensive overview of the latest developments in the field of smart manufacturing, exploring theoretical research, technological advancements, and practical applications of AI approaches. With Industry 4.0 paving the way for intelligent systems and innovative technologies to enhance productivity and quality, the transition to Industry 5.0 has introduced a new concept known as augmented intelligence (AuI), combining artificial intelligence (AI) with human intelligence (HI).As the demand for smart manufacturing continues to grow, this book serves as a valuable resource for professionals and practitioners looking to stay up-to-date with the latest advancements in Industry 5.0. Covering a range of important topics such as product design, predictive maintenance, quality control, digital twin, wearable technology, quantum, and machine learning, the book also features insightful case studies that demonstrate the practical application of these tools in real-world scenarios.Overall, this book provides a comprehensive and up-to-date account of the latest advancements in smart manufacturing, offering readers a valuable resource for navigating the challenges and opportunities presented by Industry 5.0.
Artificial Intelligence for Smart Manufacturing and Industry X.0 (Springer Series in Advanced Manufacturing)
by M. M. Manjurul Islam Marcia L. Baptista Faisal TariqThis book offers a foundational understanding of smart manufacturing (SM) and introduces effective AI methods tailored for smart manufacturing, including supervised, unsupervised, and reinforcement learning techniques. It also features real-world industrial case studies that demonstrate the practical applications of smart manufacturing. Drawing from the invaluable experiences gleaned from the aviation, healthcare, and semiconductors industries, this book provides an in-depth understanding of how AI is driving transformative changes in the manufacturing landscape. In the era of rapid technological advancements, the integration of AI into manufacturing processes has emerged as a game-changer. This book serves as an indispensable guide for navigating this transformation, presenting readers with a multidimensional perspective on the diverse applications, challenges, and opportunities that AI brings to the manufacturing sector. The book explores the emergence of Large Language Models (LLMs) as a valuable tool in manufacturing. It presents how LLMs, especially the GPT series, can process and generate textual data, offering potential applications in areas like smart manufacturing and big-data analysis. It contains detailed case studies, illustrating the practical implementation of smart manufacturing in different industries. The aviation, healthcare, automotive, and semiconductors sectors are examined, highlighting tangible benefits, challenges faced, and lessons learned from each domain. The book addresses the future prospects of Industry 4.0 and beyond—the interconnected, data-driven evolution of manufacturing. It examines the potential impact of emerging technologies such as the Industrial Internet of Things (IIoT), 5G, and advanced robotics on the manufacturing landscape. Challenges and future possibilities pertaining to research and advancement in smart manufacturing within the domains of Aviation, Semiconductors, and Healthcare sectors are also discussed. The chapters will be written in a tutorial style to allow early-career researchers and industry practitioners an in-depth understanding of the various topics. The book serves as a reference for researchers, engineers, and students seeking to understand the synergy between AI, Industry 4.0, LLMs, and real-world applications.
Artificial Intelligence for Societal Issues (Intelligent Systems Reference Library #231)
by Anupam Biswas Vijay Bhaskar Semwal Durgesh SinghArtificial intelligence (AI) has the potential to provide innovative solutions to various societal issues and real-world social challenges. AI is useful in combating some of the seemingly unsolvable social crises facing the world today. Be it disaster awareness and management or demand forecasting, or healthcare informatics or disease outbreaks like COVID-19, the AI plays a pivotal role everywhere. AI has the potential to address some of the societal issues that indirectly pose challenges like cybercrime, agriculture, education, economy, and health. The book covers several applications of AI as solutions to different societal issues, which include economic empowerment, smart education system, COVID-19 detection & management, emotion detection, fraudulent transactions, applications in agriculture and health informatics, etc. The book will be helpful for the academicians and researchers working with various areas of societal issues, data science, artificial intelligence, and machine learning.
Artificial Intelligence for Solar Photovoltaic Systems: Approaches, Methodologies, and Technologies (Explainable AI (XAI) for Engineering Applications)
by Bhavnesh KumarThis book provides a clear explanation of how to apply artificial intelligence (AI) to solve the challenges in solar photovoltaic technology. It introduces readers to new AI-based approaches and technologies that help manage and operate solar photovoltaic systems effectively. It also motivates readers to find new AI-based solutions for these challenges by providing a comprehensive collection of findings on AI techniques.It covers important topics including solar irradiance variability, solar power forecasting, solar irradiance forecasting, maximum power point tracking, hybrid algorithms, swarm optimization, evolutionary optimization, sensor-based sun- tracking systems, single-axis and dual-axis sun-tracking systems, smart metering, frequency regulation using AI, emerging multilevel inverter topologies, and voltage and reactive power control using AI.This book is useful for senior undergraduate students, graduate students, and academic researchers in areas such as electrical engineering, electronics and communication engineering, computer science, and renewable energy.
Artificial Intelligence for Space: Trends, Applications, and Perspectives
by Matteo Madi Olga SokolovaThe new age space value chain is a complex interconnected system with diverse actors, which involves cross-sector and cross-border collaborations. This book helps to enrich the knowledge of Artificial Intelligence (AI) across the value chain in the space-related domains. Advancements of AI and Machine Learning have impactfully supported the space sector transformation as it is shown in the book. "This book embarks on a journey through the fascinating realm of AI in space, exploring its profound implications, emerging trends, and transformative potential." Prof. mult. Dr.med. Dr.rer.nat. Oliver UllrichDirector Innovation Cluster Space and Aviaton (UZH Space Hub), University of Zurich, Switzerland Aimed at space engineers, risk analysts, policy makers, technical experts and non-specialists, this book demonstrates insights into the implementation of AI in the space sector, alongside its limitations and use-case examples. It covers diverse AI-related topics applicable to space technologies or space big data such as AI-based technologies for improving Earth Observation big data, AI for space robotics exploration, AI for astrophysics, AI for emerging in-orbit servicing market, and AI for space tourism safety improvement. Key Features: Provides an interdisciplinary approach, with chapter contributions from expert teams working in the governmental or private space sectors, with valuable contributions from computer scientists and legal experts; Presents insights into AI implementation and how to unlock AI technologies in the field; Up to date with the latest developments and cutting-edge applications Matteo Madi, Ph.D., is an entrepreneur, innovator, business developer and space-tech specialist with many years of experiences in the Swiss and International public and private sectors. He is the founder of Sirin Orbital Systems AG, a Swiss innovative company based in Zurich, focused on the development and commercialization of advanced enabling technologies for the needs of emerging space market and future sustainable space exploration. It also creates innovative solutions for the use of space technologies and satellite-based services for terrestrial applications. Olga Sokolova, Ph.D., is a risk analyst proficient in critical infrastructure risk assessment to natural and technical hazards. She has been engaged in development and analysis of structural risk-management tools towards sustainable future and has a record in raising social awareness of spaceborne risks and opportunities brought to the society by the "New Space" industry developments. Along with Dr. M. Madi, Dr. O. Sokolova is the Co-Editor of the book entitled, "Space Debris Peril: Pathways to Opportunities", published by CRC Press: Taylor and Francis in November 2020 (ISBN 9780367469450).
Artificial Intelligence for Strategic Communication
by Karen E. SutherlandIn an era where AI is revolutionising every aspect of communication, this groundbreaking research monograph provides an essential roadmap for navigating the intersection of artificial intelligence and strategic communication. Drawing on extensive primary research, including interviews with 41 experts and surveys of 400 professionals across three continents and eight countries, this book provides insights from relevant scholars, communication practitioners and AI tool developers. This comprehensive guide combines scholarly rigour with practical application, presenting a data-informed Model for Practice that helps to withstand the constant evolution of AI technology. Each chapter delivers research-informed, actionable tools relating to the multifaceted field of strategic communication including ethical practice, strategy development, content creation, evaluation, and continuous improvement. Bridging the gap between theoretical understanding and practical implementation, AI for Strategic Communication is an invaluable resource for strategic communication scholars, students, and practitioners, essential for advancing careers in the age of AI. This work emerged from the need for a comprehensive source combining scholarly, practitioner and AI developer perspectives on strategic communication from around the globe.
Artificial Intelligence for Supporting Human Cognition and Exploratory Learning in the Digital Age (Cognition and Exploratory Learning in the Digital Age)
by Dirk Ifenthaler Pedro Isaias Demetrios G. SampsonThe Cognition and Exploratory Learning in the Digital Age (CELDA) conference focuses on discussing and addressing the challenges pertaining to the evolution of the learning process, the role of pedagogical approaches and the progress of technological innovation, in the context of the digital age. In each edition, CELDA, gathers researchers and practitioners in an effort to cover both technological and pedagogical issues in ground-breaking studies. Some of CELDA's main topics include: assessment of exploratory learning approaches and technologies, educational psychology, learning paradigms in academia and the corporate sector, student-centered learning and lifelong learning. The CELDA 2023 conference selected and published a selection of papers that focus on the use of Artificial Intelligence and Learning Analytics in the educational context.
Artificial Intelligence for Sustainability: Innovations in Business and Financial Services
by Thomas Walker Stefan Wendt Sherif Goubran Tyler SchwartzIn light of the climate crisis, businesses are expected to embrace sustainability to reduce their negative impacts on the environment and society, while at the same time strengthening their organisations’ positive impacts. Managing this alongside the traditional requirements of business requires careful handling, and it is small wonder that some are heralding the advantages posed by artificial intelligence (AI) as the missing piece of the puzzle. This edited book aims to present a balanced discussion of the benefits and costs of using AI for the natural environment and society, including an analysis of its potential to help meet the UN’s Agenda 2030 and the Sustainable Development Goals. Researchers, practitioners, regulators, and entrepreneurs at the forefront of sustainable AI solutions share insights into the different sustainable applications of AI and highlight how these new developments may affect and contribute to our fight against climate change and address further environmental and social challenges. This volume will be of great interest to scholars and students of digital business and new technologies, sustainability, and strategy.
Artificial Intelligence for Sustainable Applications (Artificial Intelligence and Soft Computing for Industrial Transformation)
by K. Umamaheswari B. Vinoth Kumar S. K. SomasundaramARTIFICAL INTELLIGENCE for SUSTAINABLE APPLICATIONS The objective of this book is to leverage the significance of artificial intelligence in achieving sustainable solutions using interdisciplinary research through innovative ideas. With the advent of recent technologies, the demand for Information and Communication Technology (ICT)-based applications such as artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), health care, data analytics, augmented reality/virtual reality, cyber-physical systems, and future generation networks, has increased drastically. In recent years, artificial intelligence has played a more significant role in everyday activities. While AI creates opportunities, it also presents greater challenges in the sustainable development of engineering applications. Therefore, the association between AI and sustainable applications is an essential field of research. Moreover, the applications of sustainable products have come a long way in the past few decades, driven by social and environmental awareness, and abundant modernization in the pertinent field. New research efforts are inevitable in the ongoing design of sustainable applications, which makes the study of communication between them a promising field to explore. This book highlights the recent advances in AI and its allied technologies with a special focus on sustainable applications. It covers theoretical background, a hands-on approach, and real-time use cases with experimental and analytical results. Audience AI researchers as well as engineers in information technology and computer science.
Artificial Intelligence for Sustainable Development (EAI/Springer Innovations in Communication and Computing)
by Anandakumar Haldorai Babitha Lincy R Suriya Murugan Minu BalakrishnanThis book delves into the synergy between AI and sustainability. This comprehensive guide illuminates the latest trends and cutting-edge techniques, offering invaluable insights for researchers, practitioners, and policymakers interested in the cross-section of AI and sustainability. The authors illustrate how AI-driven innovations are revolutionizing environmental conservation, urban planning, healthcare, and more. The book also considers the ethical considerations and governance frameworks crucial to harnessing AI's potential for global benefit. Whether a seasoned expert or a curious newcomer, this book empowers readers to navigate the dynamic landscape of AI and sustainability, paving the way for a more eco-conscious and equitable world.