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In today’s era, there is a need for a system that can automate
the process of treatment for the patient if medical facilities are
out of reach. Smart healthcare can step in to make the patient more
self-dependent. 6G with its features can be taken as the future of
smart healthcare with IoT and AI. 6G-enabled IoT and AI for Smart
Healthcare: Challenges, Impact, and Analysis offers the
fundamentals, history, reality, and challenges faced in the smart
healthcare industry today. It discusses the concepts, tools, and
techniques of smart healthcare as well as the analysis used. The
book details the role that Machine Learning-based Deep Learning and
6G-enabled IoT concepts play in the automation of smart healthcare
systems. The book goes on to presents applications of smart
healthcare through various real-world examples and includes
chapters on security and privacy in the 6G-enabled and IoT
environment, as well as research on the future prospects of the
smart healthcare industry. This book: Offers the fundamentals,
history, reality, and the challenges faced in the smart healthcare
industry Discusses the concepts, tools, and techniques of smart
healthcare as well as the analysis used Details the role that
Machine Learning-based Deep Learning and 6G enabled IoT concepts
play in the automation of smart healthcare systems Presents
applications of smart healthcare through various real-world
examples Includes topics on security and privacy in 6G enabled IoT,
as well as research and future prospectus of the smart healthcare
industry Interested readers of this book will include anyone
working in or involved in smart healthcare research which includes,
but is not limited to healthcare specialists, Computer Science
Engineers, Electronics Engineers, Systems Engineers, and
Pharmaceutical practitioners.
1) Discusses technical details of the Machine Learning tools and
techniques in the different types of cancers 2) Machine learning
and data mining in healthcare is a very important topic and hence
there would be a demand for such a book 3) As compared to other
titles, the proposed book focuses on different types of cancer
disease and their prediction strategy using machine leaning and
data mining.
With the increase in urban population, it became necessary to keep
track of the object of interest. In favor of SDGs for sustainable
smart city, with the advancement in technology visual tracking
extends to track multi-target present in the scene rather
estimating location for single target only. In contrast to single
object tracking, multi-target introduces one extra step of
detection. Tracking multi-target includes detecting and
categorizing the target into multiple classes in the first frame
and provides each individual target an ID to keep its track in the
subsequent frames of a video stream. One category of multi-target
algorithms exploits global information to track the target of the
detected target. On the other hand, some algorithms consider
present and past information of the target to provide efficient
tracking solutions. Apart from these, deep leaning-based algorithms
provide reliable and accurate solutions. But, these algorithms are
computationally slow when applied in real-time. This book presents
and summarizes the various visual tracking algorithms and
challenges in the domain. The various feature that can be extracted
from the target and target saliency prediction is also covered. It
explores a comprehensive analysis of the evolution from traditional
methods to deep learning methods, from single object tracking to
multi-target tracking. In addition, the application of visual
tracking and the future of visual tracking can also be introduced
to provide the future aspects in the domain to the reader. This
book also discusses the advancement in the area with critical
performance analysis of each proposed algorithm. This book will be
formulated with intent to uncover the challenges and possibilities
of efficient and effective tracking of single or multi-object,
addressing the various environmental and hardware
challenges. The intended audience includes academicians,
engineers, postgraduate students, developers, professionals,
military personals, scientists, data analysts, practitioners, and
people who are interested in exploring more about tracking.·
Another projected audience are the researchers and academicians who
identify and develop methodologies, frameworks, tools, and
applications through reference citations, literature reviews,
quantitative/qualitative results, and discussions.
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