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This book explores some of the emerging scientific and
technological areas in which the need for data analytics arises and
is likely to play a significant role in the years to come. At the
dawn of the 4th Industrial Revolution, data analytics is emerging
as a force that drives towards dramatic changes in our daily lives,
the workplace and human relationships. Synergies between physical,
digital, biological and energy sciences and technologies, brought
together by non-traditional data collection and analysis, drive the
digital economy at all levels and offer new, previously-unavailable
opportunities. The need for data analytics arises in most modern
scientific disciplines, including engineering; natural-, computer-
and information sciences; economics; business; commerce;
environment; healthcare; and life sciences. Coming as the third
volume under the general title MACHINE LEARNING PARADIGMS, the book
includes an editorial note (Chapter 1) and an additional 12
chapters, and is divided into five parts: (1) Data Analytics in the
Medical, Biological and Signal Sciences, (2) Data Analytics in
Social Studies and Social Interactions, (3) Data Analytics in
Traffic, Computer and Power Networks, (4) Data Analytics for
Digital Forensics, and (5) Theoretical Advances and Tools for Data
Analytics. This research book is intended for both
experts/researchers in the field of data analytics, and readers
working in the fields of artificial and computational intelligence
as well as computer science in general who wish to learn more about
the field of data analytics and its applications. An extensive list
of bibliographic references at the end of each chapter guides
readers to probe further into the application areas of interest to
them.
The topic of this monograph falls within the, so-called,
biologically motivated computing paradigm, in which biology
provides the source of models and inspiration towards the
development of computational intelligence and machine learning
systems. Specifically, artificial immune systems are presented as a
valid metaphor towards the creation of abstract and high level
representations of biological components or functions that lay the
foundations for an alternative machine learning paradigm.
Therefore, focus is given on addressing the primary problems of
Pattern Recognition by developing Artificial Immune System-based
machine learning algorithms for the problems of Clustering,
Classification and One-Class Classification. Pattern
Classification, in particular, is studied within the context of the
Class Imbalance Problem. The main source of inspiration stems from
the fact that the Adaptive Immune System constitutes one of the
most sophisticated biological systems that is exceptionally evolved
in order to continuously address an extremely unbalanced pattern
classification problem, namely, the self / non-self discrimination
process. The experimental results presented in this monograph
involve a wide range of degenerate binary classification problems
where the minority class of interest is to be recognized against
the vast volume of the majority class of negative patterns. In this
context, Artificial Immune Systems are utilized for the development
of personalized software as the core mechanism behind the
implementation of Recommender Systems. The book will be useful to
researchers, practitioners and graduate students dealing with
Pattern Recognition and Machine Learning and their applications in
Personalized Software and Recommender Systems. It is intended for
both the expert/researcher in these fields, as well as for the
general reader in the field of Computational Intelligence and, more
generally, Computer Science who wishes to learn more about the
field of Intelligent Computing Systems and its applications. An
extensive list of bibliographic references at the end of each
chapter guides the reader to probe further into application area of
interest to him/her.
The topic of this monograph falls within the, so-called,
biologically motivated computing paradigm, in which biology
provides the source of models and inspiration towards the
development of computational intelligence and machine learning
systems. Specifically, artificial immune systems are presented as a
valid metaphor towards the creation of abstract and high level
representations of biological components or functions that lay the
foundations for an alternative machine learning paradigm.
Therefore, focus is given on addressing the primary problems of
Pattern Recognition by developing Artificial Immune System-based
machine learning algorithms for the problems of Clustering,
Classification and One-Class Classification. Pattern
Classification, in particular, is studied within the context of the
Class Imbalance Problem. The main source of inspiration stems from
the fact that the Adaptive Immune System constitutes one of the
most sophisticated biological systems that is exceptionally evolved
in order to continuously address an extremely unbalanced pattern
classification problem, namely, the self / non-self discrimination
process. The experimental results presented in this monograph
involve a wide range of degenerate binary classification problems
where the minority class of interest is to be recognized against
the vast volume of the majority class of negative patterns. In this
context, Artificial Immune Systems are utilized for the development
of personalized software as the core mechanism behind the
implementation of Recommender Systems. The book will be useful to
researchers, practitioners and graduate students dealing with
Pattern Recognition and Machine Learning and their applications in
Personalized Software and Recommender Systems. It is intended for
both the expert/researcher in these fields, as well as for the
general reader in the field of Computational Intelligence and, more
generally, Computer Science who wishes to learn more about the
field of Intelligent Computing Systems and its applications. An
extensive list of bibliographic references at the end of each
chapter guides the reader to probe further into application area of
interest to him/her.
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