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Swarm Intelligence in Cloud Computing is an invaluable treatise for
researchers involved in delivering intelligent optimized solutions
for reliable deployment, infrastructural stability, and security
issues of cloud-based resources. Starting with a bird's eye view on
the prevalent state-of-the-art techniques, this book enriches the
readers with the knowledge of evolving swarm intelligent optimized
techniques for addressing different cloud computing issues
including task scheduling, virtual machine allocation, load
balancing and optimization, deadline handling, power-aware
profiling, fault resilience, cost-effective design, and energy
efficiency. The book offers comprehensive coverage of the most
essential topics, including: Role of swarm intelligence on cloud
computing services Cloud resource sharing strategies Cloud service
provider selection Dynamic task and resource scheduling Data center
resource management. Indrajit Pan is an Associate Professor in
Information Technology of RCC Institute of Information Technology,
India. He received his PhD from Indian Institute of Engineering
Science and Technology, Shibpur, India. With an academic experience
of 14 years, he has published around 40 research publications in
different international journals, edited books, and conference
proceedings. Mohamed Abd Elaziz is a Lecturer in the Mathematical
Department of Zagazig University, Egypt. He received his PhD from
the same university. He is the author of more than 100 articles.
His research interests include machine learning, signal processing,
image processing, cloud computing, and evolutionary algorithms.
Siddhartha Bhattacharyya is a Professor in Computer Science and
Engineering of Christ University, Bangalore. He received his PhD
from Jadavpur University, India. He has published more than 230
research publications in international journals and conference
proceedings in his 20 years of academic experience.
Hybrid computational intelligent techniques are efficient in
dealing with the real-world problems encountered in engineering
fields. The primary objective of this book is to provide an
exhaustive introduction as well as review of the hybrid
computational intelligent paradigm, with supportive case studies.
In addition, it aims to provide a gallery of engineering
applications where this computing paradigm can be effectively use.
Finally, it focuses on the recent quantum inspired hybrid
intelligence to develop intelligent solutions for the future. The
book also incorporates video demonstrations of each application for
better understanding of the subject matter.
Hybrid Intelligent Techniques for Pattern Analysis and
Understanding outlines the latest research on the development and
application of synergistic approaches to pattern analysis in
real-world scenarios. An invaluable resource for lecturers,
researchers, and graduates students in computer science and
engineering, this book covers a diverse range of hybrid intelligent
techniques, including image segmentation, character recognition,
human behavioral analysis, hyperspectral data processing, and
medical image analysis.
This book discusses the recent research trends and upcoming
applications based on artificial intelligence. It includes best
selected research papers presented at the International Conference
on Research and Applications in Artificial Intelligence (RAAI
2020), organized by Department of Information Technology, RCC
Institute of Information technology, Kolkata, West Bengal, India
during 19 - 20, December, 2020. Many versatile fields of artificial
intelligence are categorically addressed through different chapters
of this book. The book is a valuable resource and reference for
researchers, instructors, students, scientists, engineers, managers
and industry practitioners in these important areas.
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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.
The book includes high-quality research papers presented at the
International Conference on Innovative Computing and Communication
(ICICC 2018), which was held at the Guru Nanak Institute of
Management (GNIM), Delhi, India on 5-6 May 2018. 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.
The field of computational intelligence has grown tremendously over
that past five years, thanks to evolving soft computing and
artificial intelligent methodologies, tools and techniques for
envisaging the essence of intelligence embedded in real life
observations. Consequently, scientists have been able to explain
and understand real life processes and practices which previously
often remain unexplored by virtue of their underlying imprecision,
uncertainties and redundancies, and the unavailability of
appropriate methods for describing the incompleteness and vagueness
of information represented. With the advent of the field of
computational intelligence, researchers are now able to explore and
unearth the intelligence, otherwise insurmountable, embedded in the
systems under consideration. Computational Intelligence is now not
limited to only specific computational fields, it has made inroads
in signal processing, smart manufacturing, predictive control,
robot navigation, smart cities, and sensor design to name a few.
Recent Trends in Computational Intelligence Enabled Research:
Theoretical Foundations and Applications explores the use of this
computational paradigm across a wide range of applied domains which
handle meaningful information. Chapters investigate a broad
spectrum of the applications of computational intelligence across
different platforms and disciplines, expanding our knowledge base
of various research initiatives in this direction. This volume aims
to bring together researchers, engineers, developers and
practitioners from academia and industry working in all major areas
and interdisciplinary areas of computational intelligence,
communication systems, computer networks, and soft computing.
This volume comprises eight well-versed contributed chapters
devoted to report the latest findings on the intelligent approaches
to multimedia data analysis. Multimedia data is a combination of
different discrete and continuous content forms like text, audio,
images, videos, animations and interactional data. At least a
single continuous media in the transmitted information generates
multimedia information. Due to these different types of varieties,
multimedia data present varied degrees of uncertainties and
imprecision, which cannot be easy to deal by the conventional
computing paradigm. Soft computing technologies are quite efficient
to handle the imprecision and uncertainty of the multimedia data
and they are flexible enough to process the real-world information.
Proper analysis of multimedia data finds wide applications in
medical diagnosis, video surveillance, text annotation etc. 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 state of the art.
This book presents fascinating, state-of-the-art research findings
in the field of signal and image processing. It includes conference
papers covering a wide range of signal processing applications
involving filtering, encoding, classification, segmentation,
clustering, feature extraction, denoising, watermarking, object
recognition, reconstruction and fractal analysis. It addresses
various types of signals, such as image, video, speech, non-speech
audio, handwritten text, geometric diagram, ECG and EMG signals;
MRI, PET and CT scan images; THz signals; solar wind speed signals
(SWS); and photoplethysmogram (PPG) signals, and demonstrates how
new paradigms of intelligent computing, like quantum computing, can
be applied to process and analyze signals precisely and
effectively. The book also discusses applications of hybrid
methods, algorithms and image filters, which are proving to be
better than the individual techniques or algorithms.
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