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This book provides various insights into machine learning
techniques in healthcare system data and its analysis. Recent
technological advancements in the healthcare system represent
cutting-edge innovations and global research successes in
performance modelling, analysis, and applications. The extensive
use of machine learning in numerous industries, including
healthcare, has been made possible by advancements in data
technologies, including storage capacity, processing capability,
and data transit speeds. The need for a personalized medicine or
""precision medicine"" approach to healthcare has been highlighted
by current trends in medicine due to the complexity of providing
effective healthcare to each individual. Personalized medicine aims
to identify, forecast, and analyze diagnostic decisions using vast
volumes of healthcare data so that doctors may then apply them to
each unique patient. These data may include, but are not limited
to, information on a person's genes or family history, medical
imaging data, drug combinations, patient health outcomes at the
community level, and natural language processing of pre-existing
medical documentation. The introduction of digital technology in
the healthcare industry is marked by ongoing difficulties with
implementation and use. Slow progress has been made in unifying
different healthcare systems, and much of the world still lacks a
fully integrated healthcare system. The intrinsic complexity and
development of human biology, as well as the differences across
patients, have repeatedly demonstrated the significance of the
human element in the diagnosis and treatment of illnesses. But as
digital technology develops, healthcare providers will undoubtedly
need to use it more and more to give patients the best treatment
possible.
The development of intelligent transportation systems has become
significant in marine engineering especially Autonomous Underwater
Vehicles with an aim to enhance energy efficiency management and
communication systems. This book covers different aspects of
optimization autonomous underwater vehicles and their propulsion
systems via machine learning techniques. It further analyses
hydrodynamic characteristics including study of experimental
investigation combined with hydrodynamic characteristics backed my
MATLABĀ® codes and simulation study results. Features: Covers
utilization of machine learning techniques with a focus on marine
science and ocean engineering. Details effect of the intelligent
transportation system (ITS) into the sustainable environment and
ecology system. Evaluates performance of particle swarm intelligent
based optimization techniques. Reviews propulsion performance of
the remoted control vehicles based on machine learning techniques.
Includes MATLABĀ® examples and simulation study results. This book
is aimed at graduate students and researchers in marine engineering
and technology, computer science, and control system engineering.
This book focuses on futuristic approaches and designs for
real-time systems and applications, as well as the fundamental
concepts of including advanced techniques and tools in models of
data-driven blockchain ecosystems. The Data-Driven Blockchain
Ecosystem: Fundamentals, Applications, and Emerging Technologies
discusses how to implement and manage processes for releasing and
delivering blockchain applications. It presents the core of
blockchain technology, IoT-based and AI-based blockchain systems,
and various manufacturing areas related to Industry 4.0. The book
illustrates how to apply design principles to develop and manage
blockchain networks, and also covers the role that cloud computing
plays in blockchain applications. All major technologies involved
in blockchain-embedded applications are included in this book,
which makes it useful to engineering students, researchers,
academicians, and professionals interested in the core of
blockchain technology.
Medical images, in various formats, are used by clinicians to
identify abnormalities or markers associated with certain
conditions, such as cancers, diseases, abnormalities or other
adverse health conditions. Deep learning algorithms use vast
volumes of data to train the computer to recognise certain features
in the images that are associated with the disease or condition
that you wish to identify. Whilst analysing the images by eye can
take a lot of time, deep learning algorithms have the benefit of
reviewing medical images at a faster rate than a human can, which
aids the clinician, speeding up diagnoses and freeing up
clinicians' time for other duties. Deep Learning in Medical Image
Processing and Analysis introduces the fundamentals of deep
learning for biomedical image analysis for applications including
ophthalmology, cancer detection and heart disease. The book
considers the principles of multi-instance feature selection, swarm
optimisation, parallel processing models, artificial neural
networks, support vector machines, as well as their design and
optimisation, in biomedical applications. Topics such as data
security, patient confidentiality, effectiveness and reliability
will also be discussed. Written by an international team of
experts, this edited book covers principles and applications for
industry and academic researchers, scientists, engineers,
developers, and designers in the fields of machine learning, deep
learning, AI, image processing, signal processing, computer science
or related fields. It will also be of interest to standards bodies
and regulators, and clinicians using deep learning models.
This book contains 22 essays on various subjects absolutely
belonging to the ambience, the ethos and the lifestyle cradled in
the soft and verdant stretches of India. It also tells tales those
are urban, amusing and nostalgic. The book is worth reading
universally.
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