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MACHINE LEARNING AND DATA SCIENCE Written and edited by a team of
experts in the field, this collection of papers reflects the most
up-to-date and comprehensive current state of machine learning and
data science for industry, government, and academia. Machine
learning (ML) and data science (DS) are very active topics with an
extensive scope, both in terms of theory and applications. They
have been established as an important emergent scientific field and
paradigm driving research evolution in such disciplines as
statistics, computing science and intelligence science, and
practical transformation in such domains as science, engineering,
the public sector, business, social science, and lifestyle.
Simultaneously, their applications provide important challenges
that can often be addressed only with innovative machine learning
and data science algorithms. These algorithms encompass the larger
areas of artificial intelligence, data analytics, machine learning,
pattern recognition, natural language understanding, and big data
manipulation. They also tackle related new scientific challenges,
ranging from data capture, creation, storage, retrieval, sharing,
analysis, optimization, and visualization, to integrative analysis
across heterogeneous and interdependent complex resources for
better decision-making, collaboration, and, ultimately, value
creation.
Knowledge Engineering (KE) is a fi eld within artifi cial
intelligence that develops knowledgebased systems. KE is the
process of imitating how a human expert in a specifi c domain would
act and take decisions. It contains large amounts of knowledge,
like metadata and information about a data object that describes
characteristics such as content, quality, and format, structure and
processes. Such systems are computer programs that are the basis of
how a decision is made or a conclusion is reached. It is having all
the rules and reasoning mechanisms to provide solutions to
real-world problems. This book presents an extensive collection of
the recent fi ndings and innovative research in the information
system and KE domain. Highlighting the challenges and diffi culties
in implementing these approaches, this book is a critical reference
source for academicians, professionals, engineers, technology
designers, analysts, undergraduate and postgraduate students in
computing science and related disciplines such as Information
systems, Knowledge Engineering, Intelligent Systems, Artifi cial
Intelligence, Cognitive Neuro - science, and Robotics. In addition,
anyone who is interested or involved in sophisticated information
systems and knowledge engineering developments will fi nd this book
a valuable source of ideas and guidance.
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