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Artificial Intelligence in Higher Education and Scientific Research: Future Development (Bridging Human and Machine: Future Education with Intelligence)
by Fatima RoumateThis book explains the interaction between artificial intelligence and higher education. It explores artificial intelligence’s tangible and intangible impact on higher education and scientific research and discusses how higher education and scientific research enhance the progress of artificial intelligence technologies. Based on systematic analysis with a multidisciplinary approach and a combination of theory and practice, the book brings original perspectives from the massive use of artificial intelligence in higher education and scientific research since the appearance of COVID-19. This book also discusses ethics in artificial intelligence, taking into consideration the recommendation on ethics of artificial intelligence adopted by UNESCO. This book explains the importance of technological sovereignty and new strategies to face current and future challenges related to e-learning, deep learning, and machine learning.
Artificial Intelligence in Highway Safety
by Subasish DasArtificial Intelligence in Highway Safety provides cutting-edge advances in highway safety using AI. The author is a highway safety expert. He pursues highway safety within its contexts, while drawing attention to the predictive powers of AI techniques in solving complex problems for safety improvement. This book provides both theoretical and practical aspects of highway safety. Each chapter contains theory and its contexts in plain language with several real-life examples. It is suitable for anyone interested in highway safety and AI and it provides an illuminating and accessible introduction to this fast-growing research trend. Material supplementing the book can be found at https://github.com/subasish/AI_in_HighwaySafety. It offers a variety of supplemental materials, including data sets and R codes.
Artificial Intelligence in Industrial Applications: Approaches to Solve the Intrinsic Industrial Optimization Problems (Learning and Analytics in Intelligent Systems #25)
by Steven Lawrence Fernandes Tarun K. SharmaThis book highlights the analytics and optimization issues in industry, to propose new approaches, and to present applications of innovative approaches in real facilities. In the past few decades there has been an exponential rise in the application of artificial intelligence for solving complex and intricate problems arising in industrial domain. The versatility of these techniques have made them a favorite among scientists and researchers working in diverse areas. The book is edited to serve a broad readership, including computer scientists, medical professionals, and mathematicians interested in studying computational intelligence and their applications. It will also be helpful for researchers, graduate and undergraduate students with an interest in the fields of Artificial Intelligence and Industrial problems. This book will be a useful resource for researchers, academicians as well as professionals interested in the highly interdisciplinary field of Artificial Intelligence.
Artificial Intelligence in Industry 4.0: A Collection of Innovative Research Case-studies that are Reworking the Way We Look at Industry 4.0 Thanks to Artificial Intelligence (Studies in Computational Intelligence #928)
by Alexiei Dingli Foaad Haddod Christina KlüverThis book is intended to help management and other interested parties such as engineers, to understand the state of the art when it comes to the intersection between AI and Industry 4.0 and get them to realise the huge possibilities which can be unleashed by the intersection of these two fields. We have heard a lot about Industry 4.0, but most of the time, it focuses mainly on automation. In this book, the authors are going a step further by exploring advanced applications of Artificial Intelligence (AI) techniques, ranging from the use of deep learning algorithms in order to make predictions, up to an implementation of a full-blown Digital Triplet system. The scope of the book is to showcase what is currently brewing in the labs with the hope of migrating these technologies towards the factory floors. Chairpersons and CEOs must read these papers if they want to stay at the forefront of the game, ahead of their competition, while also saving huge sums of money in the process.
Artificial Intelligence in Industry 4.0 and 5G Technology
by Pandian Vasant Elias Munapo J. Joshua Thomas Gerhard-Wilhelm WeberArtificial Intelligence in Industry 4.0 and 5G Technology Explores innovative and value-added solutions for application problems in the commercial, business, and industry sectors As the pace of Artificial Intelligence (AI) technology innovation continues to accelerate, identifying the appropriate AI capabilities to embed in key decision processes has never been more critical to establishing competitive advantage. New and emerging analytics tools and technologies can be configured to optimize business value, change how an organization gains insights, and significantly improve the decision-making process across the enterprise. Artificial Intelligence in Industry 4.0 and 5G Technology helps readers solve real-world technological engineering optimization problems using evolutionary and swarm intelligence, mathematical programming, multi-objective optimization, and other cutting-edge intelligent optimization methods. Contributions from leading experts in the field present original research on both the theoretical and practical aspects of implementing new AI techniques in a variety of sectors, including Big Data analytics, smart manufacturing, renewable energy, smart cities, robotics, and the Internet of Things (IoT). Presents detailed information on meta-heuristic applications with a focus on technology and engineering sectors such as smart manufacturing, smart production, innovative cities, and 5G networks. Offers insights into the use of metaheuristic strategies to solve optimization problems in business, economics, finance, and industry where uncertainty is a factor. Provides guidance on implementing metaheuristics in different applications and hybrid technological systems. Describes various AI approaches utilizing hybrid meta-heuristics optimization algorithms, including meta-search engines for innovative research and hyper-heuristics algorithms for performance measurement. Artificial Intelligence in Industry 4.0 and 5G Technology is a valuable resource for IT specialists, industry professionals, managers and executives, researchers, scientists, engineers, and advanced students an up-to-date reference to innovative computing, uncertainty management, and optimization approaches.
Artificial Intelligence in Information and Communication Technologies, Healthcare and Education: A Roadmap Ahead
by Parikshit N. Mahalle Rajendra S. Talware Ganesh C. Patil Sachin R. Sakhare Yogesh H. Dandawate Pravin R. FutaneArtificial Intelligence in Information and Communication Technologies, Healthcare and Education: A Roadmap Ahead is designed as a reference text and discusses inter-dependability, communication and effective control for the betterment of services through artificial intelligence (AI), as well as the challenges and path ahead for AI in computing and control across different domains of business and human life. The book accommodates technologies and application domains including backbone hardware, systems and methods for deployment, which help incorporating intelligence through different supervised and probabilistic learning approaches. Features The book attempts to establish a connection between hardware, software technologies and algorithmic intelligence for data analysis and decision support in domains such as healthcare, education and other aspects of business and mobility. It presents various recent applications of artificial intelligence in information and communication technologies such as search and optimization methods, machine learning, data representation and ontologies, and multi-agent systems. The book provides a collection of different case studies with experimentation results than mere theoretical and generalized approaches. Covers most of the applications using the trending technologies like machine learning (ML), data science (DS), Internet of Things (IoT), and underlying information and communication technologies. The book is aimed primarily at advanced undergraduates and postgraduate students studying computer science, computer applications, and information technology. Researchers and professionals will also find this book useful.
Artificial Intelligence in Intelligent Systems: Proceedings of 10th Computer Science On-line Conference 2021, Vol. 2 (Lecture Notes in Networks and Systems #229)
by Radek SilhavyThis book constitutes the refereed proceedings of the artificial intelligence in intelligent systems section of the 10th Computer Science Online Conference 2021 (CSOC 2021), held online in April 2021. Artificial intelligence in intelligent systems topics are presented in this book. Modern hybrid and bio-inspired algorithms and their application are discussed in selected papers.
Artificial Intelligence in Internet of Things: Proceedings of 8th International Conference on Internet of Things and Connected Technologies (ICIoTCT 2023) (Lecture Notes in Networks and Systems #1072)
by Frank Lin David Pastor Nishtha Kesswani Ashok Patel Sushanta Bordoloi Chaitali KoleyThis book is a collection of high-quality research papers presented at 8th International Conference on Internet of Things and Connected Technologies (ICIoTCT 2023), held at National Institute of Technology (NIT), Mizoram, India, during 29–30 September 2023. This book presents recent advances on IoT and connected technologies. This book is designed for marketing managers, business professionals, researchers, academicians, and graduate-level students seeking to learn how IoT and connecting technologies increase the amount of data gained through devices, enhance customer experience, and widen the scope of IoT analytics in enhancing customer marketing outcomes.
Artificial Intelligence in IoT (Transactions on Computational Science and Computational Intelligence)
by Fadi Al-TurjmanThis book provides an insight into IoT intelligence in terms of applications and algorithmic challenges. The book is dedicated to addressing the major challenges in realizing the artificial intelligence in IoT-based applications including challenges that vary from cost and energy efficiency to availability to service quality in multidisciplinary fashion. The aim of this book is hence to focus on both the algorithmic and practical parts of the artificial intelligence approaches in IoT applications that are enabled and supported by wireless sensor networks and cellular networks. Targeted readers are from varying disciplines who are interested in implementing the smart planet/environments vision via intelligent wireless/wired enabling technologies.Includes the most up-to-date research and applications related to IoT artificial intelligence (AI);Provides new and innovative operational ideas regarding the IoT artificial intelligence that help advance the telecommunications industry;Presents AI challenges facing the IoT scientists and provides potential ways to solve them in critical daily life issues.
Artificial Intelligence in IoT and Cyborgization (Studies in Computational Intelligence #1103)
by Rajesh Kumar Dhanaraj Bharat S. Rawal Sathya Krishnamoorthi Balamurugan BalusamyThis book introduces the concept of combining artificial intelligence (AI) and Internet of things (IoT) with real human organs to form a cybernetic organism or cyborg. It is a concept of man–machine mixture which helps in restoring or enhancing the ability of a body part by integrating some technology or artificial component with that body part. These smart artificial organs act as a substitute for real organs having various capabilities like scanning the body, detecting and transmitting the diagnostic data to machines. For example, an artificial heart is capable of monitoring the overall health of a person, and lungs can inform the doctor of abnormalities. This book benefits academic researchers and industrialist who work in the field cyborgization and IoT within human bodies.
Artificial Intelligence in Label-free Microscopy: Biological Cell Classification by Time Stretch
by Ata Mahjoubfar Claire Lifan Chen Bahram JalaliThis book introduces time-stretch quantitative phase imaging (TS-QPI), a high-throughput label-free imaging flow cytometer developed for big data acquisition and analysis in phenotypic screening. TS-QPI is able to capture quantitative optical phase and intensity images simultaneously, enabling high-content cell analysis, cancer diagnostics, personalized genomics, and drug development. The authors also demonstrate a complete machine learning pipeline that performs optical phase measurement, image processing, feature extraction, and classification, enabling high-throughput quantitative imaging that achieves record high accuracy in label -free cellular phenotypic screening and opens up a new path to data-driven diagnosis.
Artificial Intelligence in Management: An Analysis of the Impact of AI on Product Change Management in the Medical Technology Industry (BestMasters)
by Jana M. WürteleIn an era of constant global change, companies are challenged to adapt quickly to evolving consumer demands and technological innovations. However, the complexity of product changes in the medical technology industry is particularly high. Leveraging expertise of interview partners from Germany, Switzerland, Liechtenstein, and India, this book examines the perception of digital transformation within the medical technology industry and the increasing prominence of artificial intelligence as a transformative force. Thus, it provides a critical analysis of the potential opportunities for optimization and risks of integrating this technology into product change management in this sector, offering a comprehensive perspective for management practice.
Artificial Intelligence in Manufacturing: Enabling Intelligent, Flexible and Cost-Effective Production Through AI
by John SoldatosThis open access book presents a rich set of innovative solutions for artificial intelligence (AI) in manufacturing. The various chapters of the book provide a broad coverage of AI systems for state of the art flexible production lines including both cyber-physical production systems (Industry 4.0) and emerging trustworthy and human-centered manufacturing systems (Industry 5.0). From a technology perspective, the book addresses a wide range of AI paradigms such as deep learning, reinforcement learning, active learning, agent-based systems, explainable AI, industrial robots, and AI-based digital twins. Emphasis is put on system architectures and technologies that foster human-AI collaboration based on trusted interactions between workers and AI systems. From a manufacturing applications perspective, the book illustrates the deployment of these AI paradigms in a variety of use cases spanning production planning, quality control, anomaly detection, metrology, workers’ training, supply chain management, as well as various production optimization scenarios. This is an open access book.
Artificial Intelligence in Material Science: Advances
by Mohamed Arezki MellalArtificial intelligence (AI) in the form of machine learning and nature-inspired optimization algorithms are vastly used in material science. These techniques improve many quality metrics, such as reliability and ergonomics.This book highlights the recent challenges in this field and helps readers to understand the subject and develop future works. It reviews the latest methods and applications of AI in material science. It covers a wide range of topics, including Material processing; Properties prediction; Conventional machining, such as turning, boring, grinding, and milling; non-conventional machining, such as electrical discharge machining, electrochemical machining, laser machining, plasma machining, ultrasonic machining, chemical machining, and water-jet machining; Machine tools, such as programming, design, and maintenance. AI techniques reviewed in the book include Machine learning, Fuzzy logic, Genetic algorithms, Particle swarm optimization, Cuckoo search, Grey wolf optimizer, and Ant colony optimization.
Artificial Intelligence in Mechanical and Industrial Engineering (Artificial Intelligence (AI) in Engineering)
by Kaushik Kumar Divya Zindani J. Paulo DavimArtificial Intelligence in Mechanical and Industrial Engineering offers a unified platform for the dissemination of basic and applied knowledge on the integration of artificial intelligence within the realm of mechanical and industrial engineering. The book covers the tools and information needed to build successful careers and a source of knowledge for those working with AI within these domains. The book offers a systematic approach to explicate fundamentals as well as recent advances. It incorporates various case studies for major topics as well as numerous examples. It will also include real-time intelligent automation and associated supporting methodologies and techniques, and cover decision-support systems, as well as applications of Chaos Theory and Fractals. The book will give scientists, researchers, instructors, students, and practitioners the tools and information needed to build successful careers and to be an impetus to advancements in next-generation mechanical and industrial engineering domains.
Artificial Intelligence in Mechatronics and Civil Engineering: Bridging the Gap (Emerging Trends in Mechatronics)
by Ehsan Momeni Danial Jahed Armaghani Aydin AziziRecent studies highlight the application of artificial intelligence, machine learning, and simulation techniques in engineering. This book covers the successful implementation of different intelligent techniques in various areas of engineering focusing on common areas between mechatronics and civil engineering. The power of artificial intelligence and machine learning techniques in solving some examples of real-life problems in engineering is highlighted in this book. The implementation process to design the optimum intelligent models is discussed in this book.
Artificial Intelligence in Medical Imaging: From Theory to Clinical Practice
by Lia Morra Silvia Delsanto Loredana CorrealeChoice Recommended Title, January 2021 This book, written by authors with more than a decade of experience in the design and development of artificial intelligence (AI) systems in medical imaging, will guide readers in the understanding of one of the most exciting fields today. After an introductory description of classical machine learning techniques, the fundamentals of deep learning are explained in a simple yet comprehensive manner. The book then proceeds with a historical perspective of how medical AI developed in time, detailing which applications triumphed and which failed, from the era of computer aided detection systems on to the current cutting-edge applications in deep learning today, which are starting to exhibit on-par performance with clinical experts. In the last section, the book offers a view on the complexity of the validation of artificial intelligence applications for commercial use, describing the recently introduced concept of software as a medical device, as well as good practices and relevant considerations for training and testing machine learning systems for medical use. Open problematics on the validation for public use of systems which by nature continuously evolve through new data is also explored. The book will be of interest to graduate students in medical physics, biomedical engineering and computer science, in addition to researchers and medical professionals operating in the medical imaging domain, who wish to better understand these technologies and the future of the field. Features: An accessible yet detailed overview of the field Explores a hot and growing topic Provides an interdisciplinary perspective
Artificial Intelligence in Medical Imaging: Opportunities, Applications And Risks
by Erik R. Ranschaert Sergey Morozov Paul R. AlgraThis book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
Artificial Intelligence in Medical Imaging in China
by Shiyuan LiuThis book overviews the latest development of Artificial Intelligence in medical imaging in China. Consisted of thirteen chapters, this book discusses development, status, achievements, prospects, visions, bottlenecks, and future challenges affecting development of artificial intelligence in medical imaging from different aspects of government supervision, industrialization, education, academic research and application implementation. It will facilitate better communication between China and foreign countries in all directions of medical imaging AI for all stakeholders.
Artificial Intelligence in Medical Imaging Technology: An Introduction
by Euclid Seeram Vijay KanadeThis book covers the principles, concepts, and applications of artificial intelligence in medical imaging technologies, specifically in the context of diagnostic imaging, such as radiography and radiological technology. First, artificial intelligence and its subsets machine learning and deep learning are described followed by a discussion of applications of these AI principles in medical imaging technologies. Finally, ethical questions, regulatory aspects, and future trends and challenges are also reviewed in this textbook. This book is intended for both students and practitioners in radiological technology, radiography, radiation therapy, nuclear medicine technology, diagnostic medical sonography, and biomedical engineering technology. Furthermore, residents in radiology, and medical physics students and related healthcare personnel (administrators and managers for example) may find this book useful.
Artificial Intelligence in Medical Sciences and Psychology: With Application of Machine Language, Computer Vision, and NLP Techniques
by Tshepo Chris NokeriGet started with artificial intelligence for medical sciences and psychology. This book will help healthcare professionals and technologists solve problems using machine learning methods, computer vision, and natural language processing (NLP) techniques. The book covers ways to use neural networks to classify patients with diseases. You will know how to apply computer vision techniques and convolutional neural networks (CNNs) to segment diseases such as cancer (e.g., skin, breast, and brain cancer) and pneumonia. The hidden Markov decision making process is presented to help you identify hidden states of time-dependent data. In addition, it shows how NLP techniques are used in medical records classification. This book is suitable for experienced practitioners in varying medical specialties (neurology, virology, radiology, oncology, and more) who want to learn Python programming to help them work efficiently. It is also intended for data scientists, machine learning engineers, medical students, and researchers. What You Will Learn Apply artificial neural networks when modelling medical data Know the standard method for Markov decision making and medical data simulation Understand survival analysis methods for investigating data from a clinical trial Understand medical record categorization Measure personality differences using psychological models Who This Book Is ForMachine learning engineers and software engineers working on healthcare-related projects involving AI, including healthcare professionals interested in knowing how AI can improve their work setting
Artificial Intelligence in Medical Virology (Medical Virology: From Pathogenesis to Disease Control)
by Jyotir Moy Chatterjee Shailendra K. SaxenaThis book comprehensively reviews the potential of Artificial Intelligence (AI) in biomedical research and healthcare, with a major emphasis on virology. The initial chapter presents the applications of machine learning methods for structured data, such as the classical support vector machine and neural network, modern deep learning, and natural language processing for unstructured data in biomedical research and healthcare. The subsequent chapters explore the applications of AI in tackling COVID-19, analysis of the pandemic, viral infection, disease spread, and control. The book further identifies the potential applications of machine learning in the field of virology with a focus on the key aspects of infection: diagnosis, transmission, response to treatment, and resistance. The book also discusses progress and challenges in developing viral vaccines and examines the application of viruses in translational research and human healthcare. Furthermore, the book covers the applications of artificial intelligence-mediated diagnosis and the development of drugs to treat the disease. Towards the end, the book summarizes the ethical and legal challenges posed by AI in healthcare and biomedical research. This book is an invaluable source for researchers, medical and industry practitioners, academicians, and students exploring the applications of AI in biomedical research and healthcare.
Artificial Intelligence in Medicine: 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, June 21-24, 2017, Proceedings (Lecture Notes in Computer Science #10259)
by Annette ten Teije, Christian Popow, John H. Holmes and Lucia SacchiThis book constitutes the refereed proceedings of the 16th Conference on Artificial Intelligence in Medicine, AIME 2017, held in Vienna, Austria, in June 2017.The 21 revised full and 23 short papers presented were carefully reviewed and selected from 113 submissions. The papers are organized in the following topical sections: ontologies and knowledge representation; Bayesian methods; temporal methods; natural language processing; health care processes; and machine learning, and a section with demo papers.
Artificial Intelligence in Medicine
by Hutan Ashrafian Niklas LidströmerThis book provides a structured and analytical guide to the use of artificial intelligence in medicine. Covering all areas within medicine, the chapters give a systemic review of the history, scientific foundations, present advances, potential trends, and future challenges of artificial intelligence within a healthcare setting. Artificial Intelligence in Medicine aims to give readers the required knowledge to apply artificial intelligence to clinical practice. The book is relevant to medical students, specialist doctors, and researchers whose work will be affected by artificial intelligence.
Artificial Intelligence in Medicine: 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings (Lecture Notes in Computer Science #13897)
by Jose M. Juarez Mar Marcos Gregor Stiglic Allan TuckerThis book constitutes the refereed proceedings of the 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, held in Portoroz, Slovenia, in June12–15, 2023.The 23 full papers and 21 short papers presented together with 3 demonstration papers were selected from 108 submissions. The papers are grouped in topical sections on: machine learning and deep learning; explainability and transfer learning; natural language processing; image analysis and signal analysis; data analysis and statistical models; knowledge representation and decision support.