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Books > Computing & IT
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Data Analytics on Graphs
(Hardcover)
Ljubisa Stankovic, Danilo P. Mandic, Milos Dakovic, Milos Brajovic, Bruno Scalzo, …
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R3,426
Discovery Miles 34 260
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Ships in 10 - 15 working days
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The current availability of powerful computers and huge data sets
is creating new opportunities in computational mathematics to bring
together concepts and tools from graph theory, machine learning and
signal processing, creating Data Analytics on Graphs. In discrete
mathematics, a graph is merely a collection of points (nodes) and
lines connecting some or all of them. The power of such graphs lies
in the fact that the nodes can represent entities as diverse as the
users of social networks or financial market data, and that these
can be transformed into signals which can be analyzed using data
analytics tools. Data Analytics on Graphs is a comprehensive
introduction to generating advanced data analytics on graphs that
allows us to move beyond the standard regular sampling in time and
space to facilitate modelling in many important areas, including
communication networks, computer science, linguistics, social
sciences, biology, physics, chemistry, transport, town planning,
financial systems, personal health and many others. The authors
revisit graph topologies from a modern data analytics point of
view, and proceed to establish a taxonomy of graph networks. With
this as a basis, the authors show how the spectral analysis of
graphs leads to even the most challenging machine learning tasks,
such as clustering, being performed in an intuitive and physically
meaningful way. The authors detail unique aspects of graph data
analytics, such as their benefits for processing data acquired on
irregular domains, their ability to finely-tune statistical
learning procedures through local information processing, the
concepts of random signals on graphs and graph shifts, learning of
graph topology from data observed on graphs, and confluence with
deep neural networks, multi-way tensor networks and Big Data.
Extensive examples are included to render the concepts more
concrete and to facilitate a greater understanding of the
underlying principles. Aimed at readers with a good grasp of the
fundamentals of data analytics, this book sets out the fundamentals
of graph theory and the emerging mathematical techniques for the
analysis of a wide range of data acquired on graph environments.
Data Analytics on Graphs will be a useful friend and a helpful
companion to all involved in data gathering and analysis
irrespective of area of application.
Data Ethics of Power takes a reflective and fresh look at the
ethical implications of transforming everyday life and the world
through the effortless, costless, and seamless accumulation of
extra layers of data. By shedding light on the constant tensions
that exist between ethical principles and the interests invested in
this socio-technical transformation, the book bridges the theory
and practice divide in the study of the power dynamics that
underpin these processes of the digitalization of the world. Gry
Hasselbalch expertly draws on nearly two decades of experience in
the field, and key literature, to advance a better understanding of
the challenges faced by big data and AI developers. She provides an
innovative ethical framework for studying and governing Big-Data
and Artificial Intelligence. Offering both a historical account and
a theoretical analysis of power dynamics and their ethical
implications, as well as incisive ideas to guide future research
and governance practices, the book makes a significant contribution
to the establishment of an emerging data and AI ethics discipline.
This timely book is a must-read for scholars studying AI, data, and
technology ethics. Policymakers in the regulatory, governance,
public administration, and management sectors will find the
practical proposals for a human-centric approach to big data and AI
to be a valuable resource for revising and developing future
policies.
This thought-provoking book challenges the way we think about the
regulation of cryptoassets based on cryptographic consensus
technology. Bringing a timely new perspective, Syren Johnstone
critiques the application of a financial regulation narrative to
cryptoassets, questions the assumptions on which it is based, and
considers its impact on industry development. Providing new
insights into the dynamics of oversight regulation, Johnstone
argues that the financial narrative stifles the 'New Prospect' for
the formation of novel commercial relationships and institutional
arrangements. The book asks whether regulations developed in the
20th century remain appropriate to apply to a technology emerging
in the 21st, suggesting it is time to think about how to regulate
for ecosystem development. Johnstone concludes with proposals for
reform, positing a new framework that facilitates industry
aspirations while remaining sustainable and compatible with
regulatory objectives. Rethinking the Regulation of Cryptoassets
will be an invaluable read for policy makers, regulators and
technologists looking for a deeper understanding of the issues
surrounding cryptoasset regulation and possible alternative
approaches. It will also be of interest to scholars and students
researching the intersection of law, technology, regulation and
finance.
Creativity has been integral to the development of the modern
State, and yet it is becoming increasingly sidelined, especially as
a result of the development of new machinic technologies including
3D printing. Arguing that inner creativity has been endangered by
the rise of administrative regulation, James Griffin explores a
number of reforms to ensure that upcoming regulations do take
creativity into account. The State of Creativity examines how the
State has become distanced from individual processes of creativity.
This book investigates how the failure to incorporate creativity
into administrative regulation is, in fact, adversely impacting the
regulation of new technologies such as 3D and 4D printing and
augmented reality, by focusing on issues concerning copyright and
patents. This is an important read for intellectual property law
scholars, as well as those studying computer science who wish to
gain a more in-depth understanding of the current laws surrounding
digital technologies such as 3D printing in our modern world. Legal
practitioners wanting to remain abreast of developments surrounding
3D printing will also benefit from this book.
The medical domain is home to many critical challenges that stand
to be overcome with the use of data-driven clinical decision
support systems (CDSS), and there is a growing set of examples of
automated diagnosis, prognosis, drug design, and testing. However,
the current state of AI in medicine has been summarized as "high on
promise and relatively low on data and proof." If such problems can
be addressed, a data-driven approach will be very important to the
future of CDSSs as it simplifies the knowledge acquisition and
maintenance process, a process that is time-consuming and requires
considerable human effort. Diverse Perspectives and
State-of-the-Art Approaches to the Utilization of Data-Driven
Clinical Decision Support Systems critically reflects on the
challenges that data-driven CDSSs must address to become mainstream
healthcare systems rather than a small set of exemplars of what
might be possible. It further identifies evidence-based, successful
data-driven CDSSs. Covering topics such as automated planning,
diagnostic systems, and explainable artificial intelligence, this
premier reference source is an excellent resource for medical
professionals, healthcare administrators, IT managers, pharmacists,
students and faculty of higher education, librarians, researchers,
and academicians.
Zeroing Neural Networks Describes the theoretical and practical
aspects of finite-time ZNN methods for solving an array of
computational problems Zeroing Neural Networks (ZNN) have become
essential tools for solving discretized sensor-driven time-varying
matrix problems in engineering, control theory, and on-chip
applications for robots. Building on the original ZNN model,
finite-time zeroing neural networks (FTZNN) enable efficient,
accurate, and predictive real-time computations. Setting up
discretized FTZNN algorithms for different time-varying matrix
problems requires distinct steps. Zeroing Neural Networks provides
in-depth information on the finite-time convergence of ZNN models
in solving computational problems. Divided into eight parts, this
comprehensive resource covers modeling methods, theoretical
analysis, computer simulations, nonlinear activation functions, and
more. Each part focuses on a specific type of time-varying
computational problem, such as the application of FTZNN to the
Lyapunov equation, linear matrix equation, and matrix inversion.
Throughout the book, tables explain the performance of different
models, while numerous illustrative examples clarify the advantages
of each FTZNN method. In addition, the book: Describes how to
design, analyze, and apply FTZNN models for solving computational
problems Presents multiple FTZNN models for solving time-varying
computational problems Details the noise-tolerance of FTZNN models
to maximize the adaptability of FTZNN models to complex
environments Includes an introduction, problem description, design
scheme, theoretical analysis, illustrative verification,
application, and summary in every chapter Zeroing Neural Networks:
Finite-time Convergence Design, Analysis and Applications is an
essential resource for scientists, researchers, academic lecturers,
and postgraduates in the field, as well as a valuable reference for
engineers and other practitioners working in neurocomputing and
intelligent control.
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