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This book examines the use of biomedical signal processing—EEG,
EMG, and ECG—in analyzing and diagnosing various medical
conditions, particularly diseases related to the heart and brain.
In combination with machine learning tools and other optimization
methods, the analysis of biomedical signals greatly benefits the
healthcare sector by improving patient outcomes through early,
reliable detection. The discussion of these modalities promotes
better understanding, analysis, and application of biomedical
signal processing for specific diseases. The major highlights of
Biomedical Signal Processing for Healthcare Applications include
biomedical signals, acquisition of signals, pre-processing and
analysis, post-processing and classification of the signals, and
application of analysis and classification for the diagnosis of
brain- and heart-related diseases. Emphasis is given to brain and
heart signals because incomplete interpretations are made by
physicians of these aspects in several situations, and these
partial interpretations lead to major complications. FEATURES
Examines modeling and acquisition of biomedical signals of
different disorders Discusses CAD-based analysis of diagnosis
useful for healthcare Includes all important modalities of
biomedical signals, such as EEG, EMG, MEG, ECG, and PCG Includes
case studies and research directions, including novel approaches
used in advanced healthcare systems This book can be used by a wide
range of users, including students, research scholars, faculty, and
practitioners in the field of biomedical engineering and medical
image analysis and diagnosis.
Brain and Behavior Computing offers insights into the functions of
the human brain. This book provides an emphasis on brain and
behavior computing with different modalities available such as
signal processing, image processing, data sciences, statistics
further it includes fundamental, mathematical model, algorithms,
case studies, and future research scopes. It further illustrates
brain signal sources and how the brain signal can process,
manipulate, and transform in different domains allowing researchers
and professionals to extract information about the physiological
condition of the brain. Emphasizes real challenges in brain signal
processing for a variety of applications for analysis,
classification, and clustering. Discusses data sciences and its
applications in brain computing visualization. Covers all the most
recent tools for analysing the brain and it’s working. Describes
brain modeling and all possible machine learning methods and their
uses. Augments the use of data mining and machine learning to brain
computer interface (BCI) devices. Includes case studies and actual
simulation examples. This book is aimed at researchers,
professionals, and graduate students in image processing and
computer vision, biomedical engineering, signal processing, and
brain and behavior computing.
Artificial intelligence (AI) and machine learning (ML) techniques
play an important role in our daily lives by enhancing predictions
and decision-making for the public in several fields such as
financial services, real estate business, consumer goods, social
media, etc. Despite several studies that have proved the efficacy
of AI/ML tools in providing improved healthcare solutions, it has
not gained the trust of health-care practitioners and medical
scientists. This is due to poor reporting of the technology,
variability in medical data, small datasets, and lack of standard
guidelines for application of AI. Therefore, the development of new
AI/ML tools for various domains of medicine is an ongoing field of
research. Machine Learning in Healthcare: Fundamentals and Recent
Applications discusses how to build various ML algorithms and how
they can be applied to improve healthcare systems. Healthcare
applications of AI are innumerable: medical data analysis, early
detection and diagnosis of disease, providing objective-based
evidence to reduce human errors, curtailing inter- and
intra-observer errors, risk identification and interventions for
healthcare management, real-time health monitoring, assisting
clinicians and patients for selecting appropriate medications, and
evaluating drug responses. Extensive demonstrations and discussion
on the various principles of machine learning and its application
in healthcare is provided, along with solved examples and
exercises. This text is ideal for readers interested in machine
learning without any background knowledge and looking to implement
machine-learning models for healthcare systems.
Provides the details of state-of-the-art machine learning methods
used in VLSI Design. Discusses hardware implementation and device
modeling pertaining to machine learning algorithms. Explores
machine learning for various VLSI architectures and reconfigurable
computing. Illustrate latest techniques for device size and feature
optimization. Highlight latest case studies and reviews of the
methods used for hardware implementation.
The term "data" being mostly used, experimented, analyzed, and
researched, "Data Science and its Applications" finds relevance in
all domains of research studies including science, engineering,
technology, management, mathematics, and many more in wide range of
applications such as sentiment analysis, social medial analytics,
signal processing, gene analysis, market analysis, healthcare,
bioinformatics etc. The book on Data Science and its applications
discusses about data science overview, scientific methods, data
processing, extraction of meaningful information from data, and
insight for developing the concept from different domains,
highlighting mathematical and statistical models, operations
research, computer programming, machine learning, data
visualization, pattern recognition and others. The book also
highlights data science implementation and evaluation of
performance in several emerging applications such as information
retrieval, cognitive science, healthcare, and computer vision. The
data analysis covers the role of data science depicting different
types of data such as text, image, biomedical signal etc. useful
for a wide range of real time applications. The salient features of
the book are: Overview, Challenges and Opportunities in Data
Science and Real Time Applications Addressing Big Data Issues
Useful Machine Learning Methods Disease Detection and Healthcare
Applications utilizing Data Science Concepts and Deep Learning
Applications in Stock Market, Education, Behavior Analysis, Image
Captioning, Gene Analysis and Scene Text Analysis Data Optimization
Due to multidisciplinary applications of data science concepts, the
book is intended for wide range of readers that include Data
Scientists, Big Data Analysists, Research Scholars engaged in Data
Science and Machine Learning applications.
Covers different modalities for improvement of healthcare system
Describes implementation strategies and their applications in
diagnosis of modalities Reviews automatic identification of related
disorders using medical modality Discusses bio-potential signals
and their appropriate analysis for studying different disorders
Includes case studies, real-time examples and research directions
Brain and Behavior Computing offers insights into the functions of
the human brain. This book provides an emphasis on brain and
behavior computing with different modalities available such as
signal processing, image processing, data sciences, statistics
further it includes fundamental, mathematical model, algorithms,
case studies, and future research scopes. It further illustrates
brain signal sources and how the brain signal can process,
manipulate, and transform in different domains allowing researchers
and professionals to extract information about the physiological
condition of the brain. Emphasizes real challenges in brain signal
processing for a variety of applications for analysis,
classification, and clustering. Discusses data sciences and its
applications in brain computing visualization. Covers all the most
recent tools for analysing the brain and it's working. Describes
brain modeling and all possible machine learning methods and their
uses. Augments the use of data mining and machine learning to brain
computer interface (BCI) devices. Includes case studies and actual
simulation examples. This book is aimed at researchers,
professionals, and graduate students in image processing and
computer vision, biomedical engineering, signal processing, and
brain and behavior computing.
This book examines the use of biomedical signal processing-EEG,
EMG, and ECG-in analyzing and diagnosing various medical
conditions, particularly diseases related to the heart and brain.
In combination with machine learning tools and other optimization
methods, the analysis of biomedical signals greatly benefits the
healthcare sector by improving patient outcomes through early,
reliable detection. The discussion of these modalities promotes
better understanding, analysis, and application of biomedical
signal processing for specific diseases. The major highlights of
Biomedical Signal Processing for Healthcare Applications include
biomedical signals, acquisition of signals, pre-processing and
analysis, post-processing and classification of the signals, and
application of analysis and classification for the diagnosis of
brain- and heart-related diseases. Emphasis is given to brain and
heart signals because incomplete interpretations are made by
physicians of these aspects in several situations, and these
partial interpretations lead to major complications. FEATURES
Examines modeling and acquisition of biomedical signals of
different disorders Discusses CAD-based analysis of diagnosis
useful for healthcare Includes all important modalities of
biomedical signals, such as EEG, EMG, MEG, ECG, and PCG Includes
case studies and research directions, including novel approaches
used in advanced healthcare systems This book can be used by a wide
range of users, including students, research scholars, faculty, and
practitioners in the field of biomedical engineering and medical
image analysis and diagnosis.
Computer-aided design (CAD) plays a key role in improving
biomedical systems for various applications. It also helps in the
detection, identification, predication, analysis, and
classification of diseases, in the management of chronic
conditions, and in the delivery of health services. This book
discusses the uses of CAD to solve real-world problems and
challenges in biomedical systems with the help of appropriate case
studies and research simulation results. Aiming to overcome the gap
between CAD and biomedical science, it describes behaviors,
concepts, fundamentals, principles, case studies, and future
directions for research, including the automatic identification of
related disorders using CAD. Features: Proposes CAD for the study
of biomedical signals to understand physiology and to improve
healthcare systems' ability to diagnose and identify health
disorders. Presents concepts of CAD for biomedical modalities in
different disorders. Discusses design and simulation examples,
issues, and challenges. Illustrates bio-potential signals and their
appropriate use in studying different disorders. Includes case
studies, practical examples, and research directions.
Computer-Aided Design and Diagnosis Methods for Biometrical
Applications is aimed at researchers, graduate students in
biomedical engineering, image processing, biomedical technology,
medical imaging, and health informatics.
This book provides a comprehensive overview of the theory and
praxis of Big Data Analytics and how these are used to extract
cognition-related information from social media and literary texts.
It presents analytics that transcends the borders of
discipline-specific academic research and focuses on knowledge
extraction, prediction, and decision-making in the context of
individual, social, and national development. The content is
divided into three main sections: the first of which discusses
various approaches associated with Big Data Analytics, while the
second addresses the security and privacy of big data in social
media, and the last focuses on the literary text as the literary
data in Big Data Analytics. Sharing valuable insights into the
etiology behind human cognition and its reflection in social media
and literary texts, the book benefits all those interested in
analytics that can be applied to literature, history, philosophy,
linguistics, literary theory, media & communication studies and
computational/digital humanities.
This book provides a framework for robust and novel biometric
techniques, along with implementation and design strategies. The
theory, principles, pragmatic and modern methods, and future
directions of biometrics are presented, along with in-depth
coverage of biometric applications in driverless cars, automated
and AI-based systems, IoT, and wearable devices. Additional
coverage includes computer vision and pattern recognition,
cybersecurity, cognitive computing, soft biometrics, and the social
impact of biometric technology. The book will be a valuable
reference for researchers, faculty, and practicing professionals
working in biometrics and related fields, such as image processing,
computer vision, and artificial intelligence. Highlights robust and
novel biometrics techniques Provides implementation strategies and
future research directions in the field of biometrics Includes case
studies and emerging applications
Statistical Modeling in Machine Learning: Concepts and Applications
presents the basic concepts and roles of statistics, exploratory
data analysis and machine learning. The various aspects of Machine
Learning are discussed along with basics of statistics. Concepts
are presented with simple examples and graphical representation for
better understanding of techniques. This book takes a holistic
approach - putting key concepts together with an in-depth treatise
on multi-disciplinary applications of machine learning. New case
studies and research problem statements are discussed, which will
help researchers in their application areas based on the concepts
of statistics and machine learning. Statistical Modeling in Machine
Learning: Concepts and Applications will help statisticians,
machine learning practitioners and programmers solving various
tasks such as classification, regression, clustering, forecasting,
recommending and more.
This book provides a comprehensive overview of the theory and
praxis of Big Data Analytics and how these are used to extract
cognition-related information from social media and literary texts.
It presents analytics that transcends the borders of
discipline-specific academic research and focuses on knowledge
extraction, prediction, and decision-making in the context of
individual, social, and national development. The content is
divided into three main sections: the first of which discusses
various approaches associated with Big Data Analytics, while the
second addresses the security and privacy of big data in social
media, and the last focuses on the literary text as the literary
data in Big Data Analytics. Sharing valuable insights into the
etiology behind human cognition and its reflection in social media
and literary texts, the book benefits all those interested in
analytics that can be applied to literature, history, philosophy,
linguistics, literary theory, media & communication studies and
computational/digital humanities.
Artificial Intelligence-Based Brain Computer Interface provides
concepts of AI for the modeling of non-invasive modalities of
medical signals such as EEG, MRI and FMRI. These modalities and
their AI-based analysis are employed in BCI and related
applications. The book emphasizes the real challenges in
non-invasive input due to the complex nature of the human brain and
for a variety of applications for analysis, classification and
identification of different mental states. Each chapter starts with
a description of a non-invasive input example and the need and
motivation of the associated AI methods, along with discussions to
connect the technology through BCI. Major topics include different
AI methods/techniques such as Deep Neural Networks and Machine
Learning algorithms for different non-invasive modalities such as
EEG, MRI, FMRI for improving the diagnosis and prognosis of
numerous disorders of the nervous system, cardiovascular system,
musculoskeletal system, respiratory system and various organs of
the body. The book also covers applications of AI in the management
of chronic conditions, databases, and in the delivery of health
services.
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Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT - 5th International Conference, ICETCE 2022, Jaipur, India, February 4-5, 2022, Revised Selected Papers (Paperback, 1st ed. 2022)
Valentina E. Balas, G. R. Sinha, Basant Agarwal, Tarun Kumar Sharma, Pankaj Dadheech, …
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R3,869
Discovery Miles 38 690
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the 5th
International Conference on Emerging Technologies in Computer
Engineering, ICETCE 2021, held in Jaipur, India, in February
2022.The 40 revised full papers along with 20 short papers
presented were carefully reviewed and selected from 235
submissions. The papers are organized according to the following
topical headings: cognitive computing; Internet of Things (IoT);
machine learning and applications; soft computing; data science and
big data analytics; blockchain and cyber security.
This book provides a framework for robust and novel biometric
techniques, along with implementation and design strategies. The
theory, principles, pragmatic and modern methods, and future
directions of biometrics are presented, along with in-depth
coverage of biometric applications in driverless cars, automated
and AI-based systems, IoT, and wearable devices. Additional
coverage includes computer vision and pattern recognition,
cybersecurity, cognitive computing, soft biometrics, and the social
impact of biometric technology. The book will be a valuable
reference for researchers, faculty, and practicing professionals
working in biometrics and related fields, such as image processing,
computer vision, and artificial intelligence. Highlights robust and
novel biometrics techniques Provides implementation strategies and
future research directions in the field of biometrics Includes case
studies and emerging applications
Cognitive sensing systems combined with IoTs and smart technologies
are used in countless applications such as industrial robotics,
computer-aided diagnosis, brain-computer interface (BCI),
human-computer interaction (HCI), telemedicine, driverless cars and
smart energy systems. With contributions from an international team
of experts from a wide range of research areas including sensing,
computer vision, signal processing and device and control
applications, this book highlights the emerging role of cognitive
sensors in a growing number of real time applications including
smart health, smart cities, smart transportation and smart
agriculture. The volume will be suitable for a broad audience of
researchers in the fields of smart sensing, signal processing,
automation and robotics, environmental engineering, energy
engineering, biomedical engineering and allied disciplines where
smart sensors are part of the curriculum.
Cognitive Informatics, Computer Modelling, and Cognitive Science:
Volume Two, Application to Neural Engineering, Robotics, and STEM
presents the practical, real-world applications of Cognitive
Science to help readers understand how it can help them in their
research, engineering and academic pursuits. The book is presented
in two volumes, covering Introduction and Theoretical Background,
Philosophical and Psychological Theory, and Cognitive Informatics
and Computing. Volume Two includes Statistics for Cognitive
Science, Cognitive Applications and STEM Case Studies. Other
sections cover Cognitive Informatics, Computer Modeling and
Cognitive Science: Application to Neural Engineering, Robotics, and
STEM. The book's authors discuss the current status of research in
the field of Cognitive Science, including cognitive language
processing that paves the ways for developing numerous tools for
helping physically challenged persons, and more.
Multinational organizations have begun to realize that sentiment
mining plays an important role for decision making and market
strategy. The revolutionary growth of digital marketing not only
changes the market game, but also brings forth new opportunities
for skilled professionals and expertise. Currently, the
technologies are rapidly changing, and artificial intelligence (AI)
and machine learning are contributing as game-changing
technologies. These are not only trending but are also increasingly
popular among data scientists and data analysts. New Opportunities
for Sentiment Analysis and Information Processing provides
interdisciplinary research in information retrieval and sentiment
analysis including studies on extracting sentiments from textual
data, sentiment visualization-based dimensionality reduction for
multiple features, and deep learning-based multi-domain sentiment
extraction. The book also optimizes techniques used for sentiment
identification and examines applications of sentiment analysis and
emotion detection. Covering such topics as communication networks,
natural language processing, and semantic analysis, this book is
essential for data scientists, data analysts, IT specialists,
scientists, researchers, academicians, and students.
The COVID-19 pandemic has shifted the teaching-learning experience
dramatically, creating an opportunity for new online and blended
learning techniques and tools. This has also added a new dimension
to practices and methods already adopted for achieving sustainable
development goals (SDGs) within education. This requires a new
paradigm shift in the teaching-learning process through the
systemic and pragmatic assessment of student learning outcomes so
that employability skills and competence can be developed in
students for competing at the global level. Development of
Employability Skills Through Pragmatic Assessment of Student
Learning Outcomes discusses the best practices in the assessment of
student learning objectives (SLOs), the mapping of SLOs, and the
ways of developing employability skills in young minds so that SDGs
may be achieved. It elaborates the theory, practice, and importance
of developing employability skills through research-based learning.
Covering topics such as graduate employability, outcome-based
education, and technical undergraduate programs, this premier
reference source is an essential resource for employers, libraries,
students and educators of higher education, faculty and
administration of higher education, pre-service teachers,
government organizations, business leaders and managers, human
resource managers, researchers, and academicians.
In educational institutions, outcome-based education (OBE) remains
crucial in measuring how certain teaching techniques are impacting
the students' ability to learn. Currently, these changes in
students are mapped by analyzing the objectives and outcomes of
certain learning processes. International accreditation agencies
and quality assessment networks are all focusing on mapping between
outcomes and objectives. The need of assessment tools arises that
can provide a genuine mapping in the global context so that
students or learners can achieve expected objectives. Assessment
Tools for Mapping Learning Outcomes With Learning Objectives is a
pivotal reference source that provides vital research on the
implementation of quality assessment methods for measuring the
outcomes of select learning processes on students. While
highlighting topics such as quality assessment, effective
employability, and student learning objectives, this book is
ideally designed for students, administrators, policymakers,
researchers, academicians, practitioners, managers, executives,
strategists, and educators seeking current research on the
application of modern mapping tools for assessing student learning
outcomes in higher education.
Multinational organizations have begun to realize that sentiment
mining plays an important role for decision making and market
strategy. The revolutionary growth of digital marketing not only
changes the market game, but also brings forth new opportunities
for skilled professionals and expertise. Currently, the
technologies are rapidly changing, and artificial intelligence (AI)
and machine learning are contributing as game-changing
technologies. These are not only trending but are also increasingly
popular among data scientists and data analysts. New Opportunities
for Sentiment Analysis and Information Processing provides
interdisciplinary research in information retrieval and sentiment
analysis including studies on extracting sentiments from textual
data, sentiment visualization-based dimensionality reduction for
multiple features, and deep learning-based multi-domain sentiment
extraction. The book also optimizes techniques used for sentiment
identification and examines applications of sentiment analysis and
emotion detection. Covering such topics as communication networks,
natural language processing, and semantic analysis, this book is
essential for data scientists, data analysts, IT specialists,
scientists, researchers, academicians, and students.
In educational institutions, outcome-based education (OBE) remains
crucial in measuring how certain teaching techniques are impacting
the students' ability to learn. Currently, these changes in
students are mapped by analyzing the objectives and outcomes of
certain learning processes. International accreditation agencies
and quality assessment networks are all focusing on mapping between
outcomes and objectives. The need of assessment tools arises that
can provide a genuine mapping in the global context so that
students or learners can achieve expected objectives. Assessment
Tools for Mapping Learning Outcomes With Learning Objectives is a
pivotal reference source that provides vital research on the
implementation of quality assessment methods for measuring the
outcomes of select learning processes on students. While
highlighting topics such as quality assessment, effective
employability, and student learning objectives, this book is
ideally designed for students, administrators, policymakers,
researchers, academicians, practitioners, managers, executives,
strategists, and educators seeking current research on the
application of modern mapping tools for assessing student learning
outcomes in higher education.
The book presents a unique overview of recent as well as
traditional image enhancement methods. We have demonstrated
practical examples of frequency domain image enhancement
techniques. Most of the book is related to 2D images but
generalization to 3D is also shown in most cases. Automatic object
recognition has become an established discipline inside image
analysis. Moments and moment invariants play a very important role
as features in invariant recognition. Our main purpose in writing
this book was to bridge this gap between theoretical concepts and
practical implementations of image enhancement techniques. In this
book, the reader finds both a survey of all important theoretical
results as well as a description of how to use them in various
image processing applications. The target readership of the book is
academic researchers and R&D engineers from all application
areas who need to work with 2D images extracted from binary/gray
level/color images. The book is also intended for university
lecturers and graduate students of image analysis and pattern
recognition.
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