Artificial Intelligence (AI) is a rapidly developing field of computer science which now plays an increasingly important role in many disciplines. A catalyst for significant change, research into AI is of particular importance in fields such as medicine and education, and as such has become an area to watch for many people worldwide. This book presents the proceedings of AIMEE 2023, the 7th International Conference on Artificial Intelligence, Medical Engineering and Education, held on 9 and 10 November 2023 in Guangzhou, China. The conference brought together top international researchers from around the world to exchange research results and address open issues in AI, medical engineering and education. A total of 238 submissions were received for AIMEE 2023, of which 89 papers were selected for presentation and publication after a rigorous international peer review process. The book is divided into 3 sections, covering artificial intelligence and scientific methodology; systems engineering and analysis: concepts, methods, and applications; and education reform and innovation. Presenting papers which explore and discuss many novel concepts and methodologies contributing to the rapid evolution of artificial intelligence and its applications, the book will be of interest to all those working in the relevant fields.

This book broadly covers a scope of the latest advances for the development of artificial intelligence systems and their applications in various fields from medicine and technology to education. The proceedings comprise refereed papers presented at the Fifth International Conference of Artificial Intelligence, Medical Engineering, and Education (AIMEE2021), which took place at the Mechanical Engineering Institute of the Russian Academy of Sciences, Moscow, Russia, on 1–3 October 2021. Given the rapid development of artificial intelligence systems, the book emphasizes the need for the intensification of training of a growing number of relevant specialists, in particular, in medical engineering to increase the effectiveness of medical diagnosing and treatment. In digital artificial intelligence systems, scientists endeavour to reproduce the innate intellectual abilities of humans and other organisms, and the in-depth study of genetic systems and inherited biological processes can provide new approaches to create more and more effective artificial intelligence methods. Topics of the included papers concern thematic materials in the following spheres: mathematics and biomathematics; medical approaches; technological and educational approaches. The book is a compilation of cutting-edge research papers in the field, covering a comprehensive range of subjects that are relevant to business managers and engineering professionals alike. The breadth and depth of these proceedings make them an excellent resource for asset management practitioners, researchers, and academics, as well as undergraduate and postgraduate students who are interested in artificial intelligence, bioinformatics systems, also their expanding applications. The intended readership includes specialists, students, and other circles of readers who would like to know where artificial intelligence systems can be applied in the future with great benefit.

This book covers the latest advances for the development of artificial intelligence systems and their applications in various fields from medicine and technology to education. The proceedings comprise refereed papers presented at the Fourth International Conference of Artificial Intelligence, Medical Engineering, Education (AIMEE2020), held at the Mechanical Engineering Institute of the Russian Academy of Sciences, Moscow, Russia, in 3–4 October 2020. Given the rapid development of artificial intelligence systems, the book emphasizes the need for the intensification of training of a growing number of relevant specialists, in particular, in medical engineering to increase the effectiveness of medical diagnosing and treatment. In digital artificial intelligence systems, scientists endeavor to reproduce the innate intellectual abilities of human and other organisms, and the in-depth study of genetic systems and inherited biological processes can provide new approaches to create more and more effective artificial intelligence methods. Topics of the included papers concern thematic materials in the following spheres: mathematics and biomathematics; medical approaches; technological and educational approaches. The book is a compilation of state-of-the-art papers in the field, covering a comprehensive range of subjects that is relevant to business managers and engineering professionals alike. The breadth and depth of these proceedings make them an excellent resource for asset management practitioners, researchers, and academics, as well as undergraduate and postgraduate students interested in artificial intelligence and bioinformatics systems as well as their growing applications. Intended readership includes specialists, students, and other circles of readers who would like to know where artificial intelligence systems can be applied in the future with great benefit.

This book includes the proceedings of the Second International Conference of Artificial Intelligence, Medical Engineering, Education (AIMEE2018), held in Moscow, Russia, on 6–8 October 2018. The conference covered advances in the development of artificial intelligence systems and their applications in various fields, from medicine and technology to education. The papers presented in the book discuss topics in mathematics and biomathematics; medical approaches; and technological and educational approaches. Given the rapid development of artificial intelligence systems, the book highlights the need for more intensive training for a growing number of specialists, particularly in medical engineering, to increase the effectiveness of medical diagnosis and treatment. The book is intended for specialists, students and other readers who would like to know where artificial intelligence systems can beneficially be applied in the future.

Deep learning (DL) is a method of machine learning, running over artificial neural networks, that uses multiple layers to extract high-level features from large amounts of raw data. DL methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of DL and its applications in the field of biomedical engineering. DL has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. DL provides computational models of multiple processing layers to learn and represent data with higher levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to many of the hardware architectures that are currently available. The ever-expanding amount of data that can be gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and artificial intelligence techniques such as DL and convolutional neural networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use DL include computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic particle imaging, electroencephalography/magnetoencephalography (EE/MEG), optical microscopy and tomography, photoacoustic tomography, electron tomography, and atomic force microscopy. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides the most complete coverage of DL applications in biomedical engineering available, including detailed real-world applications in areas such as computational neuroscience, neuroimaging, data fusion, medical image processing, neurological disorder diagnosis for diseases such as Alzheimer's, attention deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), tumor prediction, and translational multimodal imaging analysis. Presents a comprehensive handbook of the biomedical engineering applications of DL, including computational neuroscience, neuroimaging, time series data such as MRI, functional MRI, CT, EEG, MEG, and data fusion of biomedical imaging data from disparate sources, such as X-Ray/CT. Helps readers understand key concepts in DL applications for biomedical engineering and health care, including manifold learning, classification, clustering, and regression in neuroimaging data analysis. Provides readers with key DL development techniques such as creation of algorithms and application of DL through artificial neural networks and convolutional neural networks. Includes coverage of key application areas of DL such as early diagnosis of specific diseases such as Alzheimer's, ADHD, and ASD, and tumor prediction through MRI and translational multimodality imaging and biomedical applications such as detection, diagnostic analysis, quantitative measurements, and image guidance of ultrasonography. ~

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.

This book discusses the latest advances in the development of artificial intelligence systems and their applications in various fields, from medicine and technology to education. It comprises papers presented at the Third International Conference of Artificial Intelligence, Medical Engineering, Education (AIMEE2019), held at the Mechanical Engineering Institute of the Russian Academy of Sciences, Moscow, Russia, on 1–3 October 2019. Covering topics such as mathematics and biomathematics; medical approaches; and technological and educational approaches, it is intended for the growing number of specialists and students in this field, as well as other readers interested in discovering where artificial intelligence systems can be applied in the future.

This book presents an overview of the latest artificial intelligence systems and methods, which have a broad spectrum of effective and sometimes unexpected applications in medical, educational and other fields of sciences and technology. In digital artificial intelligence systems, scientists endeavor to reproduce the innate intellectual abilities of human and other organisms, and the in-depth study of genetic systems and inherited biological processes can provide new approaches to create more and more effective artificial intelligence methods. The book focuses on the intensive development of bio-mathematical studies on living organism patents, which ensure the noise immunity of genetic information, its quasi-holographic features, and its connection with the Boolean algebra of logic used in technical artificial intelligence systems. In other words, the study of genetic systems and creation of methods of artificial intelligence go hand in hand, mutually enriching enrich each other. These proceedings comprise refereed papers presented at the 1st International Conference of Artificial Intelligence, Medical Engineering, and Education (AIMEE2017), held at the Mechanical Engineering Institute of the Russian Academy of Sciences, Moscow, Russia on 21–23 August 2017. The topics discussed include advances in thematic mathematics and bio-mathematics; advances in thematica medical approaches; and advances in thematic technological and educational approaches. The book is a compilation of state-of-the-art papers in the field, covering a comprehensive range of subjects that are relevant to business managers and engineering professionals alike. The breadth and depth of these proceedings make them an excellent resource for asset management practitioners, researchers and academics, as well as undergraduate and postgraduate students interested in artificial intelligence and bioinformatics systems as well as their growing applications

This book explores the advancements and future challenges in biomedical application developments using breakthrough technologies like Artificial Intelligence (AI), Internet of Things (IoT), and Signal Processing. It will also contribute to biosensors and secure systems,and related research. Applied Artificial Intelligence: A Biomedical Perspective begins by detailing recent trends and challenges of applied artificial intelligence in biomedical systems. Part I of the book presents the technological background of the book in terms of applied artificial intelligence in the biomedical domain. Part II demonstrates the recent advancements in automated medical image analysis that have opened ample research opportunities in the applications of deep learning to different diseases. Part III focuses on the use of cyberphysical systems that facilitates computing anywhere by using medical IoT and biosensors and the numerous applications of this technology in the healthcare domain. Part IV describes the different signal processing applications in the healthcare domain. It also includes the prediction of some human diseases based on the inputs in signal format. Part V highlights the scope and applications of biosensors and security aspects of biomedical images. The book will be beneficial to the researchers, industry persons, faculty, and students working in biomedical applications of computer science and electronics engineering. It will also be a useful resource for teaching courses like AI/ML, medical IoT, signal processing, biomedical engineering, and medical image analysis.