Neural Engineering for Autism Spectrum Disorder, Volume One: Imaging and Signal Analysis Techniques presents the latest advances in neural engineering and biomedical engineering as applied to the clinical diagnosis and treatment of Autism Spectrum Disorder (ASD). Advances in the role of neuroimaging, infrared spectroscopy, sMRI, fMRI, DTI, social behaviors and suitable data analytics useful for clinical diagnosis and research applications for Autism Spectrum Disorder are covered, including relevant case studies. The application of brain signal evaluation, EEG analytics, feature selection, and analysis of blood oxygen level-dependent (BOLD) signals are presented for detection and estimation of the degree of ASD. - Presents applications of Neural Engineering and other Machine Learning techniques for the diagnosis of Autism Spectrum Disorder (ASD) - Includes in-depth technical coverage of imaging and signal analysis techniques, including coverage of functional MRI, neuroimaging, infrared spectroscopy, sMRI, fMRI, DTI, and neuroanatomy of autism - Covers Signal Analysis for the detection and estimation of Autism Spectrum Disorder (ASD), including brain signal analysis, EEG analytics, feature selection, and analysis of blood oxygen level-dependent (BOLD) signals for ASD - Written to help engineers, computer scientists, researchers and clinicians understand the technology and applications of Neural Engineering for the detection and diagnosis of Autism Spectrum Disorder (ASD)

Neural Engineering for Autism Spectrum Disorder, Volume Two: Diagnosis and Clinical Analysis presents the latest advances in neural engineering and biomedical engineering as applied to the clinical diagnosis and treatment of Autism Spectrum Disorder (ASD). Advances in the role of neuroimaging, magnetic resonance spectroscopy, MRI, fMRI, DTI, video analysis of sensory-motor and social behaviors, and suitable data analytics useful for clinical diagnosis and research applications for Autism Spectrum Disorder are covered, including relevant case studies. The application of brain signal evaluation, EEG analytics, fuzzy model and temporal fractal analysis of rest state BOLD signals and brain signals are also presented. A clinical guide for general practitioners is provided along with a variety of assessment techniques such as magnetic resonance spectroscopy. The book is presented in two volumes, including Volume One: Imaging and Signal Analysis Techniques comprised of two Parts: Autism and Medical Imaging, and Autism and Signal Analysis. Volume Two: Diagnosis and Treatment includes Autism and Clinical Analysis: Diagnosis, and Autism and Clinical Analysis: Treatment. - Presents applications of Neural Engineering techniques for diagnosis of Autism Spectrum Disorder (ASD) - Includes in-depth technical coverage of assessment techniques, such as the functional and structural networks underlying visuospatial vs. linguistic reasoning in autism - Covers treatment techniques for Autism Spectrum Disorder (ASD), including social skills intervention, behavioral treatment, evidence-based treatments, and technical tools such as Magnetic Resonance Spectroscopy for ASD - Written by engineers for engineers, computer scientists, researchers and clinicians who need to understand the technology and applications of Neural Engineering for the detection and diagnosis of Autism Spectrum Disorder (ASD)

This book presents high-quality peer-reviewed papers from the International Conference on Electronics, Biomedical Engineering, and Health Informatics (ICEBEHI) 2022 held at Surabaya, Indonesia, virtually. The contents are broadly divided into three parts: (a) Electronics, (b) Biomedical Engineering, and (c) Health Informatics. The major focus is on emerging technologies and their applications in the domain of biomedical engineering. It includes papers based on original theoretical, practical, and experimental simulations, development, applications, measurements, and testing. Featuring the latest advances in the field of biomedical engineering applications, this book serves as a definitive reference resource for researchers, professors, and practitioners interested in exploring advanced techniques in the fields of electronics, biomedical engineering, and health informatics. The applications and solutions discussed here provide excellent reference material for future product development.

This book constitutes selected papers presented at the First International Conference on Artificial Intelligence and Data Science, ICAIDS 2021, held in Hyderabad, India, in December 2021. The 43 papers presented in this volume were thoroughly reviewed and selected from the 195 submissions. They focus on topics of artificial intelligence for intelligent applications and data science for emerging technologies.

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. Deep Learning 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 Deep Learning and its applications in the field of Biomedical Engineering. Deep learning has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. Deep Learning 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 AI techniques such as Deep Learning and Convolutional Neural Networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use Deep Learning include: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Magnetic Particle Imaging, 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 Deep Learning 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, ADHD, and ASD, tumor prediction, as well as 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 presents cutting-edge research and developments in the field of Biomedical Engineering. It describes both fundamental and clinically-oriented findings, highlighting advantages and challenges of innovative methods and technologies, such as artificial intelligence, wearable devices and neuroengineering, important issues related to health technology management and human factors in health, and new findings in biomechanical analysis and modeling. Gathering the proceedings of the XXVII Brazilian Congress on Biomedical Engineering, CBEB 2020, held on October 26-30, 2020, in Vitória, Brazil, and promoted by the Brazilian Society of Biomedical Engineering – SBEB, this book gives emphasis to research and developments carried out by Brazilian scientists, institutions and professionals. It offers an extensive overview on new trends and clinical implementation of technologies, and it is intended to foster communication and collaboration between medical scientists, engineers, and researchers inside and outside the country.

The book highlights new trends and challenges in research on agents and the new digital and knowledge economy. It includes papers on business process management, agent-based modeling and simulation and anthropic-oriented computing that were originally presented at the 16th International KES Conference on Agents and Multi-Agent Systems: Technologies and Applications (KES-AMSTA 2022), held at Rhodes, Greece in June 20–22, 2022. The respective papers cover topics such as software agents, multi-agent systems, agent modeling, mobile and cloud computing, big data analysis, business intelligence, artificial intelligence, social systems, computer embedded systems and nature inspired manufacturing, all of which contribute to the modern digital economy.

Academic scholars and professionals are currently grappling with hurdles in optimizing diagnostic processes, as traditional methodologies prove insufficient in managing the intricate and voluminous nature of medical data. The diverse range of imaging techniques, spanning from endoscopy to magnetic resonance imaging, necessitates a more unified and efficient approach. This complexity has created a pressing need for streamlined methodologies and innovative solutions. Academic scholars find themselves at the forefront of addressing these challenges, seeking ways to leverage AI's full potential in improving the accuracy of medical imaging diagnostics and, consequently, enhancing overall patient outcomes. Future of AI in Medical Imaging, stands as a solution to the challenges faced by academic scholars in the realm of medical imaging. The book lays a solid groundwork for understanding the complexities of medical imaging systems. Through an exploration of various imaging modalities, it not only addresses the current issues but also serves as a guide for scholars to navigate the landscape of AI-integrated medical diagnostics. This collaborative effort not only illuminates the existing hurdles of medical imaging but also looks towards a future where AI-driven diagnostics and personalized medicine become indispensable tools, significantly elevating patient outcomes.

This book constitutes the refereed post-conference proceedings of the Second International Conference on Pan-African Intelligence and Smart Systems, PAAISS 2022, which was held in Dakar, Senegal, in November 2022. The 27 revised full papers presented were carefully selected from 70 submissions. The theme of PAAISS 2022 was: ​IoT and Enabling Smart System Technologies, Special Topics of African Interest, Artificial Intelligence Theory and Methods, Artificial Intelligence Applications in Medicine, Remote sensing and AI in Agriculture, AI applications and Smart Systems technologies, Affective Computing, Intelligent Transportation systems.

This book is a comprehensive, hands-on guide to the basics of data mining and machine learning with a special emphasis on supervised and unsupervised learning methods. The book lays stress on the new ways of thinking needed to master in machine learning based on the Python, R, and Java programming platforms. This book first provides an understanding of data mining, machine learning and their applications, giving special attention to classification and clustering techniques. The authors offer a discussion on data mining and machine learning techniques with case studies and examples. The book also describes the hands-on coding examples of some well-known supervised and unsupervised learning techniques using three different and popular coding platforms: R, Python, and Java. This book explains some of the most popular classification techniques (K-NN, Naïve Bayes, Decision tree, Random forest, Support vector machine etc,) along with the basic description of artificial neural network and deep neural network. The book is useful for professionals, students studying data mining and machine learning, and researchers in supervised and unsupervised learning techniques.