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Since its first appearance, artificial intelligence has been
ensuring revolutionary outcomes in the context of real-world
problems. At this point, it has strong relations with biomedical
and today’s intelligent systems compete with human capabilities
in medical tasks. However, advanced use of artificial intelligence
causes intelligent systems to be black-box. That situation is not
good for building trustworthy intelligent systems in medical
applications. For a remarkable amount of time, researchers have
tried to solve the black-box issue by using modular additions,
which have led to the rise of the term: interpretable artificial
intelligence. As the literature matured (as a result of, in
particular, deep learning), that term transformed into explainable
artificial intelligence (XAI). This book provides an essential
edited work regarding the latest advancements in explainable
artificial intelligence (XAI) for biomedical applications. It
includes not only introductive perspectives but also applied
touches and discussions regarding critical problems as well as
future insights. Topics discussed in the book include: XAI for the
applications with medical images XAI use cases for alternative
medical data/task Different XAI methods for biomedical applications
Reviews for the XAI research for critical biomedical problems.
Explainable Artificial Intelligence for Biomedical Applications is
ideal for academicians, researchers, students, engineers, and
experts from the fields of computer science, biomedical, medical,
and health sciences. It also welcomes all readers of different
fields to be informed about use cases of XAI in black-box
artificial intelligence. In this sense, the book can be used for
both teaching and reference source purposes.
New prospects for biomedical and healthcare engineering are being
created by the rapid development of Robotic and Artificial
Intelligence techniques. Innovative technologies such as Artificial
Intelligence, Deep Learning, Robotics, and IoT are currently under
huge influence in today's modern world. For instance, a micro-nano
robot allows us to study the fundamental problems at a cellular
scale owing to its precise positioning and manipulation ability;
the medical robot paves a new way for the low-invasive and
high-efficient clinical operation, and rehabilitation robotics is
able to improve the rehabilitative efficacy of patients. This book
aims at exhibiting the latest research achievements, findings, and
ideas in the field of robotics in biomedical and healthcare
engineering, primarily focusing on the walking assistive robot,
telerobotic surgery, upper/lower limb rehabilitation, and
radiosurgery. As a result, a wide range of robots are being
developed to serve a variety of roles within the medical
environment. Robots specializing in human treatment include
surgical robots and rehabilitation robots. The field of assistive
and therapeutic robotic devices is also expanding rapidly. These
include robots that help patients rehabilitate from severe
conditions like strokes, empathic robots that assist in the care of
older or physically/mentally challenged individuals, and industrial
robots that take on a variety of routine tasks, such as sterilizing
rooms and delivering medical supplies and equipment, including
medications. The objectives of the book are in terms of advancing
the state-of-the-art of robotic techniques and addressing the
challenging problems in biomedical and healthcare engineering. This
book Lays a good foundation for the core concepts and principles of
robotics in biomedical and healthcare engineering, walking the
reader through the fundamental ideas with expert ease. Progresses
on the topics in a step-by-step manner and reinforces theory with a
full-fledged pedagogy designed to enhance students' understanding
and offer them a practical insight into the applications of it.
Features chapters that introduce and cover novel ideas in
healthcare engineering like Applications of Robots in Surgery,
Microrobots and Nanorobots in Healthcare Practices, Intelligent
Walker for Posture Monitoring, AI-Powered Robots in Biomedical and
Hybrid Intelligent Systems for Medical Diagnosis, and so on. Deepak
Gupta is an Assistant Professor at the Maharaja Agrasen Institute
of Technology, GGSIPU, Delhi, India. Moolchand Sharma is an
Assistant Professor at the Maharaja Agrasen Institute of
Technology, GGSIPU, Delhi, India. Vikas Chaudhary is a Professor at
the JIMS Engineering Management Technical Campus, GGSIPU, Greater
Noida, India. Ashish Khanna currently works at the Maharaja Agrasen
Institute of Technology, GGSIPU, Delhi, India.
This book discusses research in Artificial Intelligence for the
Internet of Health Things. It investigates and explores the
possible applications of machine learning, deep learning, soft
computing, and evolutionary computing techniques in design,
implementation, and optimization of challenging healthcare
solutions. This book features a wide range of topics such as AI
techniques, IoT, cloud, wearables, and secured data transmission.
Written for a broad audience, this book will be useful for
clinicians, health professionals, engineers, technology developers,
IT consultants, researchers, and students interested in the
AI-based healthcare applications. Provides a deeper understanding
of key AI algorithms and their use and implementation within the
wider healthcare sector Explores different disease diagnosis models
using machine learning, deep learning, healthcare data analysis,
including machine learning, and data mining and soft computing
algorithms Discusses detailed IoT, wearables, and cloud-based
disease diagnosis model for intelligent systems and healthcare
Reviews different applications and challenges across the design,
implementation, and management of intelligent systems and
healthcare data networks Introduces a new applications and case
studies across all areas of AI in healthcare data K. Shankar
(Member, IEEE) is a Postdoctoral Fellow of the Department of
Computer Applications, Alagappa University, Karaikudi, India.
Eswaran Perumal is an Assistant Professor of the Department of
Computer Applications, Alagappa University, Karaikudi, India. Dr.
Deepak Gupta is an Assistant Professor of the Department Computer
Science & Engineering, Maharaja Agrasen Institute of Technology
(GGSIPU), Delhi, India.
This book discusses research in Artificial Intelligence for the
Internet of Health Things. It investigates and explores the
possible applications of machine learning, deep learning, soft
computing, and evolutionary computing techniques in design,
implementation, and optimization of challenging healthcare
solutions. This book features a wide range of topics such as AI
techniques, IoT, cloud, wearables, and secured data transmission.
Written for a broad audience, this book will be useful for
clinicians, health professionals, engineers, technology developers,
IT consultants, researchers, and students interested in the
AI-based healthcare applications. Provides a deeper understanding
of key AI algorithms and their use and implementation within the
wider healthcare sector Explores different disease diagnosis models
using machine learning, deep learning, healthcare data analysis,
including machine learning, and data mining and soft computing
algorithms Discusses detailed IoT, wearables, and cloud-based
disease diagnosis model for intelligent systems and healthcare
Reviews different applications and challenges across the design,
implementation, and management of intelligent systems and
healthcare data networks Introduces a new applications and case
studies across all areas of AI in healthcare data K. Shankar
(Member, IEEE) is a Postdoctoral Fellow of the Department of
Computer Applications, Alagappa University, Karaikudi, India.
Eswaran Perumal is an Assistant Professor of the Department of
Computer Applications, Alagappa University, Karaikudi, India. Dr.
Deepak Gupta is an Assistant Professor of the Department Computer
Science & Engineering, Maharaja Agrasen Institute of Technology
(GGSIPU), Delhi, India.
New prospects for biomedical and healthcare engineering are being
created by the rapid development of Robotic and Artificial
Intelligence techniques. Innovative technologies such as Artificial
Intelligence, Deep Learning, Robotics, and IoT are currently under
huge influence in today’s modern world. For instance, a
micro-nano robot allows us to study the fundamental problems at a
cellular scale owing to its precise positioning and manipulation
ability; the medical robot paves a new way for the low-invasive and
high-efficient clinical operation, and rehabilitation robotics is
able to improve the rehabilitative efficacy of patients. This book
aims at exhibiting the latest research achievements, findings, and
ideas in the field of robotics in biomedical and healthcare
engineering, primarily focusing on the walking assistive robot,
telerobotic surgery, upper/lower limb rehabilitation, and
radiosurgery. As a result, a wide range of robots are being
developed to serve a variety of roles within the medical
environment. Robots specializing in human treatment include
surgical robots and rehabilitation robots. The field of assistive
and therapeutic robotic devices is also expanding rapidly. These
include robots that help patients rehabilitate from severe
conditions like strokes, empathic robots that assist in the care of
older or physically/mentally challenged individuals, and industrial
robots that take on a variety of routine tasks, such as sterilizing
rooms and delivering medical supplies and equipment, including
medications. The objectives of the book are in terms of advancing
the state-of-the-art of robotic techniques and addressing the
challenging problems in biomedical and healthcare engineering. This
book Lays a good foundation for the core concepts and principles of
robotics in biomedical and healthcare engineering, walking the
reader through the fundamental ideas with expert ease. Progresses
on the topics in a step-by-step manner and reinforces theory with a
full-fledged pedagogy designed to enhance students’ understanding
and offer them a practical insight into the applications of it.
Features chapters that introduce and cover novel ideas in
healthcare engineering like Applications of Robots in Surgery,
Microrobots and Nanorobots in Healthcare Practices, Intelligent
Walker for Posture Monitoring, AI-Powered Robots in Biomedical and
Hybrid Intelligent Systems for Medical Diagnosis, and so on. Deepak
Gupta is an Assistant Professor at the Maharaja Agrasen Institute
of Technology, GGSIPU, Delhi, India. Moolchand Sharma is an
Assistant Professor at the Maharaja Agrasen Institute of
Technology, GGSIPU, Delhi, India. Vikas Chaudhary is a Professor at
the JIMS Engineering Management Technical Campus, GGSIPU, Greater
Noida, India. Ashish Khanna currently works at the Maharaja Agrasen
Institute of Technology, GGSIPU, Delhi, India.
Wearable Telemedicine Technology for the Healthcare Industry:
Product Design and Development focuses on recent advances and
benefits of wearable telemedicine techniques for remote health
monitoring and prevention of chronic conditions, providing real
time feedback and help with rehabilitation and biomedical
applications. Readers will learn about various techniques used by
software engineers, computer scientists and biomedical engineers to
apply intelligent systems, artificial intelligence, machine
learning, virtual reality and augmented reality to gather,
transmit, analyze and deliver real-time clinical and biological
data to clinicians, patients and researchers. Wearable telemedicine
technology is currently establishing its place with large-scale
impact in many healthcare sectors because information about patient
health conditions can be gathered anytime and anywhere outside of
traditional clinical settings, hence saving time, money and even
lives.
Deep Learning for Medical Applications with Unique Data informs
readers about the most recent deep learning-based medical
applications in which only unique data gathered in real cases are
used. The book provides examples of how deep learning can be used
in different problem areas and frameworks in both clinical and
research settings, including medical image analysis, medical image
registration, time series analysis, medical data synthesis, drug
discovery, and pre-processing operations. The volume discusses not
only positive findings, but also negative ones obtained by deep
learning techniques, including the use of newly developed deep
learning techniques rarely reported in the existing literature. The
book excludes research works with ready data sets and includes only
unique data use to better understand the state of deep learning in
real-world cases, along with the feedback and user experiences from
physicians and medical staff for applied deep learning-based
solutions. Other applications presented in the book include hybrid
solutions with deep learning support, disease diagnosis with deep
learning focusing on rare diseases and cancer, patient care and
treatment, genomics research, as well as research on robotics and
autonomous systems.
Data Science for COVID-19 presents leading-edge research on data
science techniques for the detection, mitigation, treatment and
elimination of COVID-19. Sections provide an introduction to data
science for COVID-19 research, considering past and future
pandemics, as well as related Coronavirus variations. Other
chapters cover a wide range of Data Science applications concerning
COVID-19 research, including Image Analysis and Data Processing,
Geoprocessing and tracking, Predictive Systems, Design Cognition,
mobile technology, and telemedicine solutions. The book then covers
Artificial Intelligence-based solutions, innovative treatment
methods, and public safety. Finally, readers will learn about
applications of Big Data and new data models for mitigation.
Data Science for COVID-19, Volume 2: Societal and Medical
Perspectives presents the most current and leading-edge research
into the applications of a variety of data science techniques for
the detection, mitigation, treatment and elimination of the
COVID-19 virus. At this point, Cognitive Data Science is the most
powerful tool for researchers to fight COVID-19. Thanks to instant
data-analysis and predictive techniques, including Artificial
Intelligence, Machine Learning, Deep Learning, Data Mining, and
computational modeling for processing large amounts of data,
recognizing patterns, modeling new techniques, and improving both
research and treatment outcomes is now possible.
Intelligent Data Analysis for Biomedical Applications: Challenges
and Solutions presents specialized statistical, pattern
recognition, machine learning, data abstraction and visualization
tools for the analysis of data and discovery of mechanisms that
create data. It provides computational methods and tools for
intelligent data analysis, with an emphasis on problem-solving
relating to automated data collection, such as computer-based
patient records, data warehousing tools, intelligent alarming,
effective and efficient monitoring, and more. This book provides
useful references for educational institutions, industry
professionals, researchers, scientists, engineers and practitioners
interested in intelligent data analysis, knowledge discovery, and
decision support in databases.
This book presents a set of soft computing approaches and their
application in data analytics, classification model, and control.
The basics of fuzzy logic implementation for advanced hybrid fuzzy
driven optimization methods has been covered in the book. The
various soft computing techniques, including Fuzzy Logic, Rough
Sets, Neutrosophic Sets, Type-2 Fuzzy logic, Neural Networks,
Generative Adversarial Networks, and Evolutionary Computation have
been discussed and they are used on variety of applications
including data analytics, classification model, and control. The
book is divided into two thematic parts. The first thematic section
covers the various soft computing approaches for text
classification and data analysis, while the second section focuses
on the fuzzy driven optimization methods for the control systems.
The chapters has been written and edited by active researchers,
which cover hypotheses and practical considerations; provide
insights into the design of hybrid algorithms for applications in
data analytics, classification model, and engineering control.
This book includes original unpublished contributions presented at
the International Conference on Data Analytics and Management
(ICDAM 2021), held at Jan Wyzykowski University, Poland, during
June 2021. The book covers the topics in data analytics, data
management, big data, computational intelligence, and communication
networks. The book presents innovative work by leading academics,
researchers, and experts from industry which is useful for young
researchers and students.
Hybrid Computational Intelligence: Challenges and Utilities is a
comprehensive resource that begins with the basics and main
components of computational intelligence. It brings together many
different aspects of the current research on HCI technologies, such
as neural networks, support vector machines, fuzzy logic and
evolutionary computation, while also covering a wide range of
applications and implementation issues, from pattern recognition
and system modeling, to intelligent control problems and biomedical
applications. The book also explores the most widely used
applications of hybrid computation as well as the history of their
development. Each individual methodology provides hybrid systems
with complementary reasoning and searching methods which allow the
use of domain knowledge and empirical data to solve complex
problems.
This book includes high-quality research papers presented at the
Fourth International Conference on Innovative Computing and
Communication (ICICC 2021), which is held at the Shaheed Sukhdev
College of Business Studies, University of Delhi, Delhi, India, on
February 20-21, 2021. Introducing the innovative works of
scientists, professors, research scholars, students and industrial
experts in the field of computing and communication, the book
promotes the transformation of fundamental research into
institutional and industrialized research and the conversion of
applied exploration into real-time applications.
This book brings together the latest research in smart sensors
technology and exposes the reader to myriad industrial applications
that this technology has enabled. The book emphasizes several
topics in the area of smart sensors in industrial real-world
applications. The contributions in this book give a broader view on
the usage of smart sensor devices covering a wide range of
interdisciplinary areas like Intelligent Transport Systems,
Healthcare, Agriculture, Drone communications and Security. By
presenting an insight into Smart Sensors for Industrial IoT, this
book directs the readers to explore the utility and advancement in
smart sensors and their applications into numerous research fields.
Lastly, the book aims to reach through a mass number of industry
experts, researchers, scientists, engineers, and practitioners and
help them guide and evolve to advance research practices.
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