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Human Action Recognition is a challenging area presently. The vigor
of research effort directed towards this domain is self indicative
of this. With the ever-increasing involvement of Computational
Intelligence in our day to day applications, the necessity of human
activity recognition has been able to make its presence felt to the
concerned research community. The primary drive of such an effort
is to equip the computing system capable of recognizing and
interpreting human activities from posture, pose, gesture, facial
expression etc. The intent of human activity recognition is a
formidable component of cognitive science in which researchers are
actively engaged of late. Features: A systematic overview of the
state-of-the-art in computational intelligence techniques for human
action recognition. Emphasized on different intelligent techniques
to recognize different human actions. Discussed about the
automation techniques to handle human action recognition. Recent
research results and some pointers to future advancements in this
arena. In the present endeavour the editors intend to come out with
a compilation that reflects the concerns of relevant research
community. The readers would be able to come across some of the
latest findings of active researchers of the concerned field. It is
anticipated that this treatise shall be useful to the readership
encompassing students at undergraduate and postgraduate level,
researchers active as well as aspiring, not to speak of the senior
researchers.
Human Action Recognition is a challenging area presently. The vigor
of research effort directed towards this domain is self indicative
of this. With the ever-increasing involvement of Computational
Intelligence in our day to day applications, the necessity of human
activity recognition has been able to make its presence felt to the
concerned research community. The primary drive of such an effort
is to equip the computing system capable of recognizing and
interpreting human activities from posture, pose, gesture, facial
expression etc. The intent of human activity recognition is a
formidable component of cognitive science in which researchers are
actively engaged of late. Features: A systematic overview of the
state-of-the-art in computational intelligence techniques for human
action recognition. Emphasized on different intelligent techniques
to recognize different human actions. Discussed about the
automation techniques to handle human action recognition. Recent
research results and some pointers to future advancements in this
arena. In the present endeavour the editors intend to come out with
a compilation that reflects the concerns of relevant research
community. The readers would be able to come across some of the
latest findings of active researchers of the concerned field. It is
anticipated that this treatise shall be useful to the readership
encompassing students at undergraduate and postgraduate level,
researchers active as well as aspiring, not to speak of the senior
researchers.
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Quantum Machine Learning (Hardcover)
Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Sourav De, Elizabeth Behrman, …
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R4,328
Discovery Miles 43 280
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Ships in 10 - 15 working days
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Quantum-enhanced machine learning refers to quantum algorithms that
solve tasks in machine learning, thereby improving a classical
machine learning method. Such algorithms typically require one to
encode the given classical dataset into a quantum computer, so as
to make it accessible for quantum information processing. After
this, quantum information processing routines can be applied and
the result of the quantum computation is read out by measuring the
quantum system. While many proposals of quantum machine learning
algorithms are still purely theoretical and require a full-scale
universal quantum computer to be tested, others have been
implemented on small-scale or special purpose quantum devices.
This book proposes soft computing techniques for segmenting
real-life images in applications such as image processing, image
mining, video surveillance, and intelligent transportation systems.
The book suggests hybrids deriving from three main approaches:
fuzzy systems, primarily used for handling real-life problems that
involve uncertainty; artificial neural networks, usually applied
for machine cognition, learning, and recognition; and evolutionary
computation, mainly used for search, exploration, efficient
exploitation of contextual information, and optimization. The
contributed chapters discuss both the strengths and the weaknesses
of the approaches, and the book will be valuable for researchers
and graduate students in the domains of image processing and
computational intelligence.
This book explains efficient solutions for segmenting the intensity
levels of different types of multilevel images. The authors present
hybrid soft computing techniques, which have advantages over
conventional soft computing solutions as they incorporate data
heterogeneity into the clustering/segmentation procedures. This is
a useful introduction and reference for researchers and graduate
students of computer science and electronics engineering,
particularly in the domains of image processing and computational
intelligence.
This book explains efficient solutions for segmenting the intensity
levels of different types of multilevel images. The authors present
hybrid soft computing techniques, which have advantages over
conventional soft computing solutions as they incorporate data
heterogeneity into the clustering/segmentation procedures. This is
a useful introduction and reference for researchers and graduate
students of computer science and electronics engineering,
particularly in the domains of image processing and computational
intelligence.
This book proposes soft computing techniques for segmenting
real-life images in applications such as image processing, image
mining, video surveillance, and intelligent transportation systems.
The book suggests hybrids deriving from three main approaches:
fuzzy systems, primarily used for handling real-life problems that
involve uncertainty; artificial neural networks, usually applied
for machine cognition, learning, and recognition; and evolutionary
computation, mainly used for search, exploration, efficient
exploitation of contextual information, and optimization. The
contributed chapters discuss both the strengths and the weaknesses
of the approaches, and the book will be valuable for researchers
and graduate students in the domains of image processing and
computational intelligence.
Applied Smart Health Care Informatics Explores how intelligent
systems offer new opportunities for optimizing the acquisition,
storage, retrieval, and use of information in healthcare Applied
Smart Health Care Informatics explores how health information
technology and intelligent systems can be integrated and deployed
to enhance healthcare management. Edited and authored by leading
experts in the field, this timely volume introduces modern
approaches for managing existing data in the healthcare sector by
utilizing artificial intelligence (AI), meta-heuristic algorithms,
deep learning, the Internet of Things (IoT), and other smart
technologies. Detailed chapters review advances in areas including
machine learning, computer vision, and soft computing techniques,
and discuss various applications of healthcare management systems
such as medical imaging, electronic medical records (EMR), and drug
development assistance. Throughout the text, the authors propose
new research directions and highlight the smart technologies that
are central to establishing proactive health management, supporting
enhanced coordination of care, and improving the overall quality of
healthcare services. Provides an overview of different deep
learning applications for intelligent healthcare informatics
management Describes novel methodologies and emerging trends in
artificial intelligence and computational intelligence and their
relevance to health information engineering and management Proposes
IoT solutions that disseminate essential medical information for
intelligent healthcare management Discusses mobile-based healthcare
management, content-based image retrieval, and computer-aided
diagnosis using machine and deep learning techniques Examines the
use of exploratory data analysis in intelligent healthcare
informatics systems Applied Smart Health Care Informatics: A
Computational Intelligence Perspective is an invaluable text for
graduate students, postdoctoral researchers, academic lecturers,
and industry professionals working in the area of healthcare and
intelligent soft computing.
Interdisciplinary approaches using Machine Learning and Deep
Learning techniques are smartly addressing real life challenges and
have emerged as an inseparable element of disruption in current
times. Applications of Disruptive Technology in Management
practices are an ever interesting domain for researchers and
professionals. This volume entitled Emerging Trends in Disruptive
Technology Management for Sustainable Development has attempted to
collate five different interesting research approaches that have
innovatively reflected diverse potential of disruptive trends in
the era of 4th. Industrial Revolution. The uniqueness of the volume
is going to cater the entrepreneurs and professionals in the domain
of artificial intelligence, machine learning, deep learning etc.
with its unique propositions in each of the chapters. The volume is
surely going to be a significant source of knowledge and
inspiration to those aspiring minds endeavouring to shape their
futures in the area of applied research in machine learning and
computer vision. The expertise and experiences of the contributing
authors to this volume is encompassing different fields of
proficiencies. This has set an excellent prelude to discover the
correlation among multidisciplinary approaches of innovation.
Covering a broad range of topics initiating from IoT based
sustainable development to crowd sourcing concepts with a blend of
applied machine learning approaches has made this volume a must
read to inquisitive wits. Features Assorted approaches to
interdisciplinary research using disruptive trends Focus on
application of disruptive technology in technology management Focus
on role of disruptive technology on sustainable development
Promoting green IT with disruptive technology The book is meant to
benefit several categories of students and researchers. At the
students' level, this book can serve as a treatise/reference book
for the special papers at the masters level aimed at inspiring
possibly future researchers. Newly inducted PhD aspirants would
also find the contents of this book useful as far as their
compulsory course-works are concerned. At the researchers' level,
those interested in interdisciplinary research would also be
benefited from the book. After all, the enriched interdisciplinary
contents of the book would always be a subject of interest to the
faculties, existing research communities and new research aspirants
from diverse disciplines of the concerned departments of premier
institutes across the globe. This is expected to bring different
research backgrounds (due to its cross platform characteristics)
close to one another to form effective research groups all over the
world. Above all, availability of the book should be ensured to as
much universities and research institutes as possible through
whatever graceful means it may be. Hope this volume will cater as a
ready reference to your quest for diving deep into the ocean of
technology management for 4th. Industrial Revolution.
This book gathers extended versions of papers presented at DoSIER
2021 (the 2021 Third Doctoral Symposium on Intelligence Enabled
Research, held at Cooch Behar Government Engineering College, West
Bengal, India, during November 12–13, 2021). The papers address
the rapidly expanding research area of computational intelligence,
which, no longer limited to specific computational fields, has
since made inroads in signal processing, smart manufacturing,
predictive control, robot navigation, smart cities, and sensor
design, to name but a few. Presenting chapters written by experts
active in these areas, the book offers a valuable reference guide
for researchers and industrial practitioners alike and inspires
future studies.
Advanced Data Mining Tools and Methods for Social Computing
explores advances in the latest data mining tools, methods,
algorithms and the architectures being developed specifically for
social computing and social network analysis. The book reviews
major emerging trends in technology that are supporting current
advancements in social networks, including data mining techniques
and tools. It also aims to highlight the advancement of
conventional approaches in the field of social networking. Chapter
coverage includes reviews of novel techniques and state-of-the-art
advances in the area of data mining, machine learning, soft
computing techniques, and their applications in the field of social
network analysis.
This book gathers extended versions of papers presented at DoSIER
2021 (the 2021 Third Doctoral Symposium on Intelligence Enabled
Research, held at Cooch Behar Government Engineering College, West
Bengal, India, during November 12-13, 2021). The papers address the
rapidly expanding research area of computational intelligence,
which, no longer limited to specific computational fields, has
since made inroads in signal processing, smart manufacturing,
predictive control, robot navigation, smart cities, and sensor
design, to name but a few. Presenting chapters written by experts
active in these areas, the book offers a valuable reference guide
for researchers and industrial practitioners alike and inspires
future studies.
Computer vision and machine intelligence paradigms are prominent in
the domain of medical image applications, including computer
assisted diagnosis, image guided radiation therapy, landmark
detection, imaging genomics, and brain connectomics. Medical image
analysis and understanding are daunting tasks owing to the massive
influx of multi-modal medical image data generated during routine
clinal practice. Advanced computer vision and machine intelligence
approaches have been employed in recent years in the field of image
processing and computer vision. However, due to the unstructured
nature of medical imaging data and the volume of data produced
during routine clinical processes, the applicability of these
meta-heuristic algorithms remains to be investigated. Advanced
Machine Vision Paradigms for Medical Image Analysis presents an
overview of how medical imaging data can be analyzed to provide
better diagnosis and treatment of disease. Computer vision
techniques can explore texture, shape, contour and prior knowledge
along with contextual information, from image sequence and 3D/4D
information which helps with better human understanding. Many
powerful tools have been developed through image segmentation,
machine learning, pattern classification, tracking, and
reconstruction to surface much needed quantitative information not
easily available through the analysis of trained human specialists.
The aim of the book is for medical imaging professionals to acquire
and interpret the data, and for computer vision professionals to
learn how to provide enhanced medical information by using computer
vision techniques. The ultimate objective is to benefit patients
without adding to already high healthcare costs.
This volume comprises six well-versed contributed chapters devoted
to report the latest fi ndings on the applications of machine
learning for big data analytics. Big data is a term for data sets
that are so large or complex that traditional data processing
application software is inadequate to deal with them. The possible
challenges in this direction include capture, storage, analysis,
data curation, search, sharing, transfer, visualization, querying,
updating and information privacy. Big data analytics is the process
of examining large and varied data sets - i.e., big data - to
uncover hidden patterns, unknown correlations, market trends,
customer preferences and other useful information that can help
organizations make more-informed business decisions. This volume is
intended to be used as a reference by undergraduate and post
graduate students of the disciplines of computer science,
electronics and telecommunication, information science and
electrical engineering. THE SERIES: FRONTIERS IN COMPUTATIONAL
INTELLIGENCE The series Frontiers In Computational Intelligence is
envisioned to provide comprehensive coverage and understanding of
cutting edge research in computational intelligence. It intends to
augment the scholarly discourse on all topics relating to the
advances in artifi cial life and machine learning in the form of
metaheuristics, approximate reasoning, and robotics. Latest
research fi ndings are coupled with applications to varied domains
of engineering and computer sciences. This field is steadily
growing especially with the advent of novel machine learning
algorithms being applied to different domains of engineering and
technology. The series brings together leading researchers that
intend to continue to advance the fi eld and create a broad
knowledge about the most recent research.
Image data has portrayed immense potential as a foundation of
information for numerous applications. Recent trends in multimedia
computing have witnessed a rapid growth in digital image
collections, resulting in a need for increased image data
management. Feature Dimension Reduction for Content-Based Image
Identification is a pivotal reference source that explores the
contemporary trends and techniques of content-based image
recognition. Including research covering topics such as feature
extraction, fusion techniques, and image segmentation, this book
explores different theories to facilitate timely identification of
image data and managing, archiving, maintaining, and extracting
information. This book is ideally designed for engineers, IT
specialists, researchers, academicians, and graduate-level students
seeking interdisciplinary research on image processing and
analysis.
As the most natural and convenient means of conveying or
transmitting information, images play a vital role in our daily
lives. Image processing is now of paramount importance in the
computer vision research community, and proper processing of
two-dimensional (2D) real-life images plays a key role in many
real-life applications as well as commercial developments.
Intelligent Multidimensional Data and Image Processing is a vital
research publication that contains an in-depth exploration of image
processing techniques used in various applications, including how
to handle noise removal, object segmentation, object extraction,
and the determination of the nearest object classification and its
associated confidence level. Featuring coverage on a broad range of
topics such as object detection, machine vision, and image
conversion, this book provides critical research for scientists,
computer engineers, professionals, researchers, and academicians
seeking current research on solutions for new challenges in 2D and
3D image processing.
Multimedia represents information in novel and varied formats. One
of the most prevalent examples of continuous media is video.
Extracting underlying data from these videos can be an arduous
task. From video indexing, surveillance, and mining, complex
computational applications are required to process this data.
Intelligent Analysis of Multimedia Information is a pivotal
reference source for the latest scholarly research on the
implementation of innovative techniques to a broad spectrum of
multimedia applications by presenting emerging methods in
continuous media processing and manipulation. This book offers a
fresh perspective for students and researchers of information
technology, media professionals, and programmers.
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