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This book offers a clear understanding of the concept of
context-aware machine learning including an automated rule-based
framework within the broad area of data science and analytics,
particularly, with the aim of data-driven intelligent decision
making. Thus, we have bestowed a comprehensive study on this topic
that explores multi-dimensional contexts in machine learning
modeling, context discretization with time-series modeling,
contextual rule discovery and predictive analytics, recent-pattern
or rule-based behavior modeling, and their usefulness in various
context-aware intelligent applications and services. The presented
machine learning-based techniques can be employed in a wide range
of real-world application areas ranging from personalized mobile
services to security intelligence, highlighted in the book. As the
interpretability of a rule-based system is high, the automation in
discovering rules from contextual raw data can make this book more
impactful for the application developers as well as researchers.
Overall, this book provides a good reference for both academia and
industry people in the broad area of data science, machine
learning, AI-Driven computing, human-centered computing and
personalization, behavioral analytics, IoT and mobile applications,
and cybersecurity intelligence.
This book presents a review of traditional context-aware computing
research, identifies its limitations in developing social
context-aware pervasive systems, and introduces a new technology
framework to address these limitations. Thus, this book provides a
good reference for developments in context-aware computing and
pervasive social computing. It examines the emerging area of
pervasive social computing, which is a novel collective paradigm
derived from pervasive computing, social media, social networking,
social signal processing and multimodal human-computer interaction.
This book offers a novel approach to model, represent, reason about
and manage different types of social context. It shows how users'
social context information can be acquired from different online
social networks such as Facebook, LinkedIn, Twitter and Google
Calendar. It further presents the use of social context information
in developing innovative smart mobile applications to assist users
in their daily life. The mix of both theoretical and applied
research results makes this book attractive to a variety of readers
from both academia and industry. This book provides a new platform
for implementing different types of socially-aware mobile
applications. The platform hides the complexity of managing social
context, and thus provides essential support to application
developers for the development of socially-aware applications. The
book contains detailed descriptions of how the underlying platform
has been implemented using available technologies such as ontology
and rule engines, and how this platform can be used to develop
socially-aware mobile applications using two exemplar applications.
The book also presents evaluations of the proposed platform and
applications using real-world data from Facebook, LinkedIn and
Twitter. Therefore, this book is a syndication of scientific
research with practical industrial applications, making it useful
to researchers as well as to software engineers.
Stem cell biology has drawn tremendous interest in recent years as
it promises cures for a variety of incurable diseases. This book
deals with the basic and clinical aspects of stem cell research and
involves work on the full spectrum of stem cells isolated today. It
also covers the conversion of stem cell types into a variety of
useful tissues which may be used in the future for transplantation
therapy. It is thus aimed at undergraduates, postgraduates,
scientists, embryologists, doctors, tissue engineers and anyone who
wishes to gain some insight into stem cell biology. This book is
important as it is comprehensive and covers all aspects of stem
cell biology, from basic research to clinical applications. It will
have 33 chapters written by renowned stem cell scientists
worldwide. It will be up-to-date and all the chapters include
self-explanatory figures, color photographs, graphics and tables.
It will be easy to read and give the reader a complete
understanding and state of the art of the exciting science and its
applications.
This book presents a review of traditional context-aware computing
research, identifies its limitations in developing social
context-aware pervasive systems, and introduces a new technology
framework to address these limitations. Thus, this book provides a
good reference for developments in context-aware computing and
pervasive social computing. It examines the emerging area of
pervasive social computing, which is a novel collective paradigm
derived from pervasive computing, social media, social networking,
social signal processing and multimodal human-computer interaction.
This book offers a novel approach to model, represent, reason about
and manage different types of social context. It shows how users'
social context information can be acquired from different online
social networks such as Facebook, LinkedIn, Twitter and Google
Calendar. It further presents the use of social context information
in developing innovative smart mobile applications to assist users
in their daily life. The mix of both theoretical and applied
research results makes this book attractive to a variety of readers
from both academia and industry. This book provides a new platform
for implementing different types of socially-aware mobile
applications. The platform hides the complexity of managing social
context, and thus provides essential support to application
developers for the development of socially-aware applications. The
book contains detailed descriptions of how the underlying platform
has been implemented using available technologies such as ontology
and rule engines, and how this platform can be used to develop
socially-aware mobile applications using two exemplar applications.
The book also presents evaluations of the proposed platform and
applications using real-world data from Facebook, LinkedIn and
Twitter. Therefore, this book is a syndication of scientific
research with practical industrial applications, making it useful
to researchers as well as to software engineers.
Service orchestration techniques combine the benefits of Service
Oriented Architecture (SOA) and Business Process Management (BPM)
to compose and coordinate distributed software services. On the
other hand, Software-as-a-Service (SaaS) is gaining popularity as a
software delivery model through cloud platforms due to the many
benefits to software vendors, as well as their customers.
Multi-tenancy, which refers to the sharing of a single application
instance across multiple customers or user groups (called tenants),
is an essential characteristic of the SaaS model. Written in an
easy to follow style with discussions supported by real-world
examples, Service Orchestration as Organization introduces a novel
approach with associated language, framework, and tool support to
show how service orchestration techniques can be used to engineer
and deploy SaaS applications.
This book offers a clear understanding of the concept of
context-aware machine learning including an automated rule-based
framework within the broad area of data science and analytics,
particularly, with the aim of data-driven intelligent decision
making. Thus, we have bestowed a comprehensive study on this topic
that explores multi-dimensional contexts in machine learning
modeling, context discretization with time-series modeling,
contextual rule discovery and predictive analytics, recent-pattern
or rule-based behavior modeling, and their usefulness in various
context-aware intelligent applications and services. The presented
machine learning-based techniques can be employed in a wide range
of real-world application areas ranging from personalized mobile
services to security intelligence, highlighted in the book. As the
interpretability of a rule-based system is high, the automation in
discovering rules from contextual raw data can make this book more
impactful for the application developers as well as researchers.
Overall, this book provides a good reference for both academia and
industry people in the broad area of data science, machine
learning, AI-Driven computing, human-centered computing and
personalization, behavioral analytics, IoT and mobile applications,
and cybersecurity intelligence.
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