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The book shares real-world experiences and knowledge about advanced
application of Machine learning, Artificial intelligence and IOT in
healthcare. The book describes issues, trends scenarios for various
domains and industry sectors.
This book focuses on Artificial Intelligence and Machine Learning
technologies and how they are progressively being incorporated into
a wide range of products, including consumer gadgets, "smart"
personal assistants, cutting-edge medical diagnostic systems, and
quantum computing systems. This concise reference book offers a
broad overview of the most important trends and discusses how these
trends and technologies are being created and employed in the
applications they are finding use in. Artificial Intelligence and
Machine Learning: An Intelligent Perspective of Emerging
Technologies offers a broad package involving the incubation of ML
and AI with various emerging technologies such as IoT, Healthcare,
Smart Cities, Robotics, and more. The book discusses various data
collection and data transformation techniques and maps the legal
and ethical issues of data-driven e-healthcare systems while
covering the possible ways to resolve them. The book explores
different techniques on how AI can be used to create better virtual
reality experiences and deals with the techniques and possible ways
to merge the power of AI and IoT to create smart home appliances.
With contributions from experts in the field, this reference book
is useful to healthcare professionals, researchers, and students of
industrial engineering, systems engineering, biomedical, computer
science, electronics, and communications engineering.
This book provides use case scenarios of machine learning,
artificial intelligence, and real-time domains to supplement cyber
security operations and proactively predict attacks and preempt
cyber incidents. The authors discuss cybersecurity incident
planning, starting from a draft response plan, to assigning
responsibilities, to use of external experts, to equipping
organization teams to address incidents, to preparing communication
strategy and cyber insurance. They also discuss classifications and
methods to detect cybersecurity incidents, how to organize the
incident response team, how to conduct situational awareness, how
to contain and eradicate incidents, and how to cleanup and recover.
The book shares real-world experiences and knowledge from authors
from academia and industry.
This book provides use case scenarios of machine learning,
artificial intelligence, and real-time domains to supplement cyber
security operations and proactively predict attacks and preempt
cyber incidents. The authors discuss cybersecurity incident
planning, starting from a draft response plan, to assigning
responsibilities, to use of external experts, to equipping
organization teams to address incidents, to preparing communication
strategy and cyber insurance. They also discuss classifications and
methods to detect cybersecurity incidents, how to organize the
incident response team, how to conduct situational awareness, how
to contain and eradicate incidents, and how to cleanup and recover.
The book shares real-world experiences and knowledge from authors
from academia and industry.
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Paperback
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R398
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Discovery Miles 3 300
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