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The success of many companies through the assistance of bitcoin
proves that technology continually dominates and transforms how
economics operate. However, a deeper, more conceptual understanding
of how these technologies work to identify innovation opportunities
and how to successfully thrive in an increasingly competitive
environment is needed for the entrepreneurs of tomorrow.
Transforming Businesses With Bitcoin Mining and Blockchain
Applications provides innovative insights into IT infrastructure
and emerging trends in the realm of digital business technologies.
This publication analyzes and extracts information from Bitcoin
networks and provides the necessary steps to designing open
blockchain. Highlighting topics that include financial markets,
risk management, and smart technologies, the research contained
within the title is ideal for entrepreneurs, business
professionals, managers, executives, academicians, researchers, and
business students.
Recent advancements in graph neural networks have expanded their
capacities and expressive power. Furthermore, practical
applications have begun to emerge in a variety of fields including
recommendation systems, fake news detection, traffic prediction,
molecular structure in chemistry, antibacterial discovery physics
simulations, and more. As a result, a boom of research at the
juncture of graph theory and deep learning has revolutionized many
areas of research. However, while graph neural networks have drawn
a lot of attention, they still face many challenges when it comes
to applying them to other domains, from a conceptual understanding
of methodologies to scalability and interpretability in a real
system. Concepts and Techniques of Graph Neural Networks provides a
stepwise discussion, an exhaustive literature review, detailed
analysis and discussion, rigorous experimentation results, and
application-oriented approaches that are demonstrated with respect
to applications of graph neural networks. The book also develops
the understanding of concepts and techniques of graph neural
networks and establishes the familiarity of different real
applications in various domains for graph neural networks. Covering
key topics such as graph data, social networks, deep learning, and
graph clustering, this premier reference source is ideal for
industry professionals, researchers, scholars, academicians,
practitioners, instructors, and students.
In the era of social connectedness, people are becoming
increasingly enthusiastic about interacting, sharing, and
collaborating through online collaborative media. However,
conducting sentiment analysis on these platforms can be
challenging, especially for business professionals who are using
them to collect vital data. Sentiment Analysis and Knowledge
Discovery in Contemporary Business is an essential reference source
that discusses applications of sentiment analysis as well as data
mining, machine learning algorithms, and big data streams in
business environments. Featuring research on topics such as
knowledge retrieval and knowledge updating, this book is ideally
designed for business managers, academicians, business
professionals, researchers, graduate-level students, and technology
developers seeking current research on data collection and
management to drive profit.
Recent advancements in graph neural networks have expanded their
capacities and expressive power. Furthermore, practical
applications have begun to emerge in a variety of fields including
recommendation systems, fake news detection, traffic prediction,
molecular structure in chemistry, antibacterial discovery physics
simulations, and more. As a result, a boom of research at the
juncture of graph theory and deep learning has revolutionized many
areas of research. However, while graph neural networks have drawn
a lot of attention, they still face many challenges when it comes
to applying them to other domains, from a conceptual understanding
of methodologies to scalability and interpretability in a real
system. Concepts and Techniques of Graph Neural Networks provides a
stepwise discussion, an exhaustive literature review, detailed
analysis and discussion, rigorous experimentation results, and
application-oriented approaches that are demonstrated with respect
to applications of graph neural networks. The book also develops
the understanding of concepts and techniques of graph neural
networks and establishes the familiarity of different real
applications in various domains for graph neural networks. Covering
key topics such as graph data, social networks, deep learning, and
graph clustering, this premier reference source is ideal for
industry professionals, researchers, scholars, academicians,
practitioners, instructors, and students.
The success of many companies through the assistance of bitcoin
proves that technology continually dominates and transforms how
economics operate. However, a deeper, more conceptual understanding
of how these technologies work to identify innovation opportunities
and how to successfully thrive in an increasingly competitive
environment is needed for the entrepreneurs of tomorrow.
Transforming Businesses With Bitcoin Mining and Blockchain
Applications provides innovative insights into IT infrastructure
and emerging trends in the realm of digital business technologies.
This publication analyzes and extracts information from Bitcoin
networks and provides the necessary steps to designing open
blockchain. Highlighting topics that include financial markets,
risk management, and smart technologies, the research contained
within the title is ideal for entrepreneurs, business
professionals, managers, executives, academicians, researchers, and
business students.
In the era of social connectedness, people are becoming
increasingly enthusiastic about interacting, sharing, and
collaborating through online collaborative media. However,
conducting sentiment analysis on these platforms can be
challenging, especially for business professionals who are using
them to collect vital data. Sentiment Analysis and Knowledge
Discovery in Contemporary Business is an essential reference source
that discusses applications of sentiment analysis as well as data
mining, machine learning algorithms, and big data streams in
business environments. Featuring research on topics such as
knowledge retrieval and knowledge updating, this book is ideally
designed for business managers, academicians, business
professionals, researchers, graduate-level students, and technology
developers seeking current research on data collection and
management to drive profit.
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