Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models focuses on the relationship between three different multidisciplinary branches of engineering: Biomedical Engineering, Cognitive Science and Computer Science through Artificial Intelligence models. These models will be used to study how the nervous system and musculoskeletal system obey movement orders from the brain, as well as the mental processes of the information during cognition when injuries and neurologic diseases are present in the human body.
The interaction between these three areas are studied in this book with the objective of obtaining AI models on injuries and neurologic diseases of the human body, studying diseases of the brain, spine and the nerves that connect them with the musculoskeletal system. There are more than 600 diseases of the nervous system, including brain tumors, epilepsy, Parkinson's disease, stroke, and many others. These diseases affect the human cognitive system that sends orders from the central nervous system (CNS) through the peripheral nervous systems (PNS) to do tasks using the musculoskeletal system. These actions can be detected by many Bioinstruments (Biomedical Instruments) and cognitive device data, allowing us to apply AI using Machine Learning-Deep Learning-Cognitive Computing models through algorithms to analyze, detect, classify, and forecast the process of various illnesses, diseases, and injuries of the human body.
Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models provides readers with the study of injuries, illness, and neurological diseases of the human body through Artificial Intelligence using Machine Learning (ML), Deep Learning (DL) and Cognitive Computing (CC) models based on algorithms developed with MATLAB(R) and IBM Watson(R).
Описание: With this practical book, author Lomit Patel shows you how to use AI and automation to provide an operational layer atop those acquisition solutions to deliver amazing results for your company.
Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy.
In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering.
This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source.
Автор: Bohr, Adam Название: Artificial Intelligence In Healthcare ISBN: 0128184388 ISBN-13(EAN): 9780128184387 Издательство: Elsevier Science Рейтинг: Цена: 16505.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Artificial Intelligence in Healthcare Data is more than a comprehensive introduction to artificial intelligence and machine learning. The book is split into two sections with an introduction to current healthcare data challenges that is followed by specific applications and case studies. The editors explore how AI is used as a tool in the analysis of healthcare data, specifically focusing on machine learning, deep learning, natural language processing. data privacy, cybersecurity and the ethics. Other sections explore how AI tools can help to interrogate data across a range of healthcare applications, including AI driven wearables and sensors and AI assisted surgery.
This book will be useful for researchers, graduate students and practitioners in computer science, data science, bioinformatics, health informatics, biomedical engineering and clinical engineering.
Showcases the latest trends in new virtual/augmented reality healthcare and medical applications and provides an overview of the economic, psychological, educational and organizational impacts of these new applications and how we work, teach, learn and provide care.
With the current advances in technology innovation, the field of medicine and healthcare is rapidly expanding and, as a result, many different areas of human health diagnostics, treatment and care are emerging. Wireless technology is getting faster and 5G mobile technology allows the Internet of Medical Things (IoMT) to greatly improve patient care and more effectively prevent illness from developing.
This book provides an overview and review of the current and anticipated changes in medicine and healthcare due to new technologies and faster communication between users and devices.
The groundbreaking book presents state-of-the-art chapters on many subjects including:
A review of the implications of Virtual Reality (VR) and Augmented Reality (AR) healthcare applications
A review of current augmenting dental care
An overview of typical human-computer interaction (HCI) that can help inform the development of user interface designs and novel ways to evaluate human behavior to responses in VR and other new technologies
A review of telemedicine technologies
Building empathy in young children using augmented reality
AI technologies for mobile health of stroke monitoring & rehabilitation robotics control
Mobile doctor brain AI App
An artificial intelligence mobile cloud computing tool
Development of a robotic teaching aid for disabled children
Training system design of lower limb rehabilitation robot based on virtual reality
Автор: Thomas Heinrich Musiolik, Adrian David Cheok Название: Analyzing future applications of ai, sensors, and robotics in society ISBN: 1799835006 ISBN-13(EAN): 9781799835004 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 22869.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Presents research exploring the potential uses and future challenges of intelligent technological advancements and their impact in education, finance, politics, business, healthcare, and engineering. The book includes coverage on a broad range of topics, such as neuronal networks, cognitive computing, and e-health.
Автор: Fa–Long Luo Название: Machine Learning for Future Wireless Communications ISBN: 1119562252 ISBN-13(EAN): 9781119562252 Издательство: Wiley Рейтинг: Цена: 18683.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A comprehensive review to the theory, application and research of machine learning for future wireless communications
In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities.
Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author - a noted expert on the topic - covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource:
Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks
Covers a range of topics from architecture and optimization to adaptive resource allocations
Reviews state-of-the-art machine learning based solutions for network coverage
Includes an overview of the applications of machine learning algorithms in future wireless networks
Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing
Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.
Build an end-to-end business solution in the cognitive automation lifecycle and explore UiPath Document Understanding, UiPath AI Center, and Druid
Key Features:
Explore out-of-the-box (OOTB) AI Models in UiPath
Learn how to deploy, manage, and continuously improve machine learning models using UiPath AI Center
Deploy UiPath-integrated chatbots and master UiPath Document Understanding
Book Description:
Artificial intelligence (AI) enables enterprises to optimize business processes that are probabilistic, highly variable, and require cognitive abilities with unstructured data. Many believe there is a steep learning curve with AI, however, the goal of our book is to lower the barrier to using AI. This practical guide to AI with UiPath will help RPA developers and tech-savvy business users learn how to incorporate cognitive abilities into business process optimization. With the hands-on approach of this book, you'll quickly be on your way to implementing cognitive automation to solve everyday business problems.
Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will help you understand the power of AI and give you an overview of the relevant out-of-the-box models. You'll learn about cognitive AI in the context of RPA, the basics of machine learning, and how to apply cognitive automation within the development lifecycle. You'll then put your skills to test by building three use cases with UiPath Document Understanding, UiPath AI Center, and Druid.
By the end of this AI book, you'll be able to build UiPath automations with the cognitive capabilities of intelligent document processing, machine learning, and chatbots, while understanding the development lifecycle.
What You Will Learn:
Discover how to bridge the gap between RPA and cognitive automation
Understand how to configure, deploy, and maintain ML models in UiPath
Explore OOTB models to manage documents, chats, emails, and more
Prepare test data and test cases for user acceptance testing (UAT)
Build a UiPath automation to act upon Druid responses
Find out how to connect custom models to RPA
Who this book is for:
AI Engineers and RPA developers who want to upskill and deploy out-of-the-box models using UiPath's AI capabilities will find this guide useful. A basic understanding of robotic process automation and machine learning will be beneficial but not mandatory to get started with this UiPath book.
Описание: On the basis of this study, Steinhoff develops a Marxist analysis to argue that the popular theory of immaterial labour, which holds that information technologies increase the autonomy of workers from capital, tending towards a post-capitalist economy, does not adequately describe the situation of high-tech digital labour today.
Within modern forensic science and criminal investigation, experts face several challenges including managing huge amounts of data, handling miniscule pieces of evidence in a chaotic and complex environment, navigating traditional laboratory structures, and, sometimes, dealing with insufficient knowledge. These challenges must be overcome to avoid failure in investigation or miscarriage of justice.
Technologies to Advance Automation in Forensic Science and Criminal Investigation provides a platform for researchers to present state-of-the-art technologies within forensic science and criminal investigation. Covering topics such as financial fraud, machine learning, and source camera identification, this book is an essential reference for criminal investigators, justice departments, law enforcement, legislators, computer scientists, automation professionals, researchers, academicians, and students and educators in higher education.
Описание: Should we regulate artificial intelligence? Can we? From self-driving cars and high-speed trading to algorithmic decision-making, the way we live, work, and play is increasingly dependent on AI systems. This book examines how our laws are dealing with AI, as well as what additional rules and institutions are needed.
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