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Books > Computing & IT > Applications of computing > Artificial intelligence
In the world of mathematics and computer science, technological
advancements are constantly being researched and applied to ongoing
issues. Setbacks in social networking, engineering, and automation
are themes that affect everyday life, and researchers have been
looking for new techniques in which to solve these challenges.
Graph theory is a widely studied topic that is now being applied to
real-life problems. Advanced Applications of Graph Theory in Modern
Society is an essential reference source that discusses recent
developments on graph theory, as well as its representation in
social networks, artificial neural networks, and many complex
networks. The book aims to study results that are useful in the
fields of robotics and machine learning and will examine different
engineering issues that are closely related to fuzzy graph theory.
Featuring research on topics such as artificial neural systems and
robotics, this book is ideally designed for mathematicians,
research scholars, practitioners, professionals, engineers, and
students seeking an innovative overview of graphic theory.
This book presents and discusses innovative ideas in the design,
modelling, implementation, and optimization of hardware platforms
for neural networks. The rapid growth of server, desktop, and
embedded applications based on deep learning has brought about a
renaissance in interest in neural networks, with applications
including image and speech processing, data analytics, robotics,
healthcare monitoring, and IoT solutions. Efficient implementation
of neural networks to support complex deep learning-based
applications is a complex challenge for embedded and mobile
computing platforms with limited computational/storage resources
and a tight power budget. Even for cloud-scale systems it is
critical to select the right hardware configuration based on the
neural network complexity and system constraints in order to
increase power- and performance-efficiency. Hardware Architectures
for Deep Learning provides an overview of this new field, from
principles to applications, for researchers, postgraduate students
and engineers who work on learning-based services and hardware
platforms.
The Easy Introduction to Machine Learning (Ml) for Nontechnical
People--In Business and Beyond Artificial Intelligence for Business
is your plain-English guide to Artificial Intelligence (AI) and
Machine Learning (ML): how they work, what they can and cannot do,
and how to start profiting from them. Writing for nontechnical
executives and professionals, Doug Rose demystifies AI/ML
technology with intuitive analogies and explanations honed through
years of teaching and consulting. Rose explains everything from
early "expert systems" to advanced deep learning networks. First,
Rose explains how AI and ML emerged, exploring pivotal early ideas
that continue to influence the field. Next, he deepens your
understanding of key ML concepts, showing how machines can create
strategies and learn from mistakes. Then, Rose introduces current
powerful neural networks: systems inspired by the structure and
function of the human brain. He concludes by introducing leading AI
applications, from automated customer interactions to event
prediction. Throughout, Rose stays focused on business: applying
these technologies to leverage new opportunities and solve real
problems. Compare the ways a machine can learn, and explore current
leading ML algorithms Start with the right problems, and avoid
common AI/ML project mistakes Use neural networks to automate
decision-making and identify unexpected patterns Help neural
networks learn more quickly and effectively Harness AI chatbots,
virtual assistants, virtual agents, and conversational AI
applications
There is not a single industry which will not be transformed by
machine learning and Internet of Things (IoT). IoT and machine
learning have altogether changed the technological scenario by
letting the user monitor and control things based on the prediction
made by machine learning algorithms. There has been substantial
progress in the usage of platforms, technologies and applications
that are based on these technologies. These breakthrough
technologies affect not just the software perspective of the
industry, but they cut across areas like smart cities, smart
healthcare, smart retail, smart monitoring, control, and others.
Because of these "game changers," governments, along with top
companies around the world, are investing heavily in its research
and development. Keeping pace with the latest trends, endless
research, and new developments is paramount to innovate systems
that are not only user-friendly but also speak to the growing needs
and demands of society. This volume is focused on saving energy at
different levels of design and automation including the concept of
machine learning automation and prediction modeling. It also deals
with the design and analysis for IoT-enabled systems including
energy saving aspects at different level of operation. The editors
and contributors also cover the fundamental concepts of IoT and
machine learning, including the latest research, technological
developments, and practical applications. Valuable as a learning
tool for beginners in this area as well as a daily reference for
engineers and scientists working in the area of IoT and machine
technology, this is a must-have for any library.
Autism spectrum disorder (ASD) is known as a neuro-disorder in
which a person may face problems in interaction and communication
with people, amongst other challenges. As per medical experts, ASD
can be diagnosed at any stage or age but is often noticeable within
the first two years of life. If caught early enough, therapies and
services can be provided at this early stage instead of waiting
until it is too late. ASD occurrences appear to have increased over
the last couple of years leading to the need for more research in
the field. It is crucial to provide researchers and clinicians with
the most up-to-date information on the clinical features,
etiopathogenesis, and therapeutic strategies for patients as well
as to shed light on the other psychiatric conditions often
associated with ASD. In addition, it is equally important to
understand how to detect ASD in individuals for accurate diagnosing
and early detection. Artificial Intelligence for Accurate Analysis
and Detection of Autism Spectrum Disorder discusses the early
detection and diagnosis of autism spectrum disorder enabled by
artificial intelligence technologies, applications, and therapies.
This book will focus on the early diagnosis of ASD through
artificial intelligence, such as deep learning and machine learning
algorithms, for confirming diagnosis or suggesting the need for
further evaluation of individuals. The chapters will also discuss
the use of artificial intelligence technologies, such as medical
robots, for enhancing the communication skills and the social and
emotional skills of children who have been diagnosed with ASD. This
book is ideally intended for IT specialists, data scientists,
academicians, scholars, researchers, policymakers, medical
practitioners, and students interested in how artificial
intelligence is impacting the diagnosis and treatment of autism
spectrum disorder.
The book's core argument is that an artificial intelligence that
could equal or exceed human intelligence-sometimes called
artificial general intelligence (AGI)-is for mathematical reasons
impossible. It offers two specific reasons for this claim: Human
intelligence is a capability of a complex dynamic system-the human
brain and central nervous system. Systems of this sort cannot be
modelled mathematically in a way that allows them to operate inside
a computer. In supporting their claim, the authors, Jobst Landgrebe
and Barry Smith, marshal evidence from mathematics, physics,
computer science, philosophy, linguistics, and biology, setting up
their book around three central questions: What are the essential
marks of human intelligence? What is it that researchers try to do
when they attempt to achieve "artificial intelligence" (AI)? And
why, after more than 50 years, are our most common interactions
with AI, for example with our bank's computers, still so
unsatisfactory? Landgrebe and Smith show how a widespread fear
about AI's potential to bring about radical changes in the nature
of human beings and in the human social order is founded on an
error. There is still, as they demonstrate in a final chapter, a
great deal that AI can achieve which will benefit humanity. But
these benefits will be achieved without the aid of systems that are
more powerful than humans, which are as impossible as AI systems
that are intrinsically "evil" or able to "will" a takeover of human
society.
The internet of things (IoT) is quickly growing into a large
industry with a huge economic impact expected in the near future.
However, the users' needs go beyond the existing web-like services,
which do not provide satisfactory intelligence levels. Ambient
intelligence services in IoT environments is an emerging research
area that can change the way that technology and services are
perceived by the users. Ambient Intelligence Services in IoT
Environments: Emerging Research and Opportunities is a unique
source that systemizes recent trends and advances for service
development with such key technological enablers of modern ICT as
ambient intelligence, IoT, web of things, and cyber-physical
systems. The considered concepts and models are presented using a
smart spaces approach with a particular focus on the Smart-M3
platform, which is now shaping into an open source technology for
creating ontology-based smart spaces and is shifting towards the
development of web of things applications and socio-cyber-physical
systems. Containing coverage on a broad range of topics such as fog
computing, smart environments, and virtual reality, multitudes of
researchers, students, academicians, and professionals will benefit
from this timely reference.
As environmental issues remain at the forefront of energy research,
renewable energy is now an all-important field of study. And as
smart technology continues to grow and be refined, its applications
broaden and increase in their potential to revolutionize
sustainability studies. This potential can only be fully realized
with a thorough understanding of the most recent breakthroughs in
the field. Research Advancements in Smart Technology, Optimization,
and Renewable Energy is a collection of innovative research that
explores the recent steps forward for smart applications in
sustainability. Featuring coverage on a wide range of topics
including energy assessment, neural fuzzy control, and
biogeography, this book is ideally designed for advocates,
policymakers, engineers, software developers, academicians,
researchers, and students.
The clinical use of Artificial Intelligence (AI) in radiation
oncology is in its infancy. However, it is certain that AI is
capable of making radiation oncology more precise and personalized
with improved outcomes. Radiation oncology deploys an array of
state-of-the-art technologies for imaging, treatment, planning,
simulation, targeting, and quality assurance while managing the
massive amount of data involving therapists, dosimetrists,
physicists, nurses, technologists, and managers. AI consists of
many powerful tools which can process a huge amount of
inter-related data to improve accuracy, productivity, and
automation in complex operations such as radiation oncology.This
book offers an array of AI scientific concepts, and AI technology
tools with selected examples of current applications to serve as a
one-stop AI resource for the radiation oncology community. The
clinical adoption, beyond research, will require ethical
considerations and a framework for an overall assessment of AI as a
set of powerful tools.30 renowned experts contributed to sixteen
chapters organized into six sections: Define the Future, Strategy,
AI Tools, AI Applications, and Assessment and Outcomes. The future
is defined from a clinical and a technical perspective and the
strategy discusses lessons learned from radiology experience in AI
and the role of open access data to enhance the performance of AI
tools. The AI tools include radiomics, segmentation, knowledge
representation, and natural language processing. The AI
applications discuss knowledge-based treatment planning and
automation, AI-based treatment planning, prediction of radiotherapy
toxicity, radiomics in cancer prognostication and treatment
response, and the use of AI for mitigation of error propagation.
The sixth section elucidates two critical issues in the clinical
adoption: ethical issues and the evaluation of AI as a
transformative technology.
In recent years, artificial intelligence (AI) has drawn significant
attention with respect to its applications in several scientific
fields, varying from big data handling to medical diagnosis. A
tremendous transformation has taken place with the emerging
application of AI. AI can provide a wide range of solutions to
address many challenges in civil engineering. Artificial
Intelligence and Machine Learning Techniques for Civil Engineering
highlights the latest technologies and applications of AI in
structural engineering, transportation engineering, geotechnical
engineering, and more. It features a collection of innovative
research on the methods and implementation of AI and machine
learning in multiple facets of civil engineering. Covering topics
such as damage inspection, safety risk management, and information
modeling, this premier reference source is an essential resource
for engineers, government officials, business leaders and
executives, construction managers, students and faculty of higher
education, librarians, researchers, and academicians.
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Makupedia
(Hardcover)
Peter K Matthews - Akukalia
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R1,776
Discovery Miles 17 760
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Ships in 10 - 15 working days
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FLINS, an acronym originally for Fuzzy Logic and Intelligent
Technologies in Nuclear Science, was inaugurated by Prof. Da Ruan
of the Belgian Nuclear Research Center (SCK*CEN) in 1994 with the
purpose of providing PhD and Postdoc researchers with a platform to
present their research ideas in fuzzy logic and artificial
intelligence. For more than 28 years, FLINS has been expanded to
include research in both theoretical and practical development of
computational intelligent systems.With this successful conference
series: FLINS1994 and FLINS1996 in Mol, FLINS1998 in Antwerp,
FLINS2000 in Bruges, FLINS2002 in Gent, FLINS2004 in Blankenberge,
FLINS2006 in Genova, FLINS2008 in Marid, FLINS2010 in Chengdu,
FLINS2012 in Istanbul, FLINS2014 in Juan Pesoa, FLINS2016 in
Roubaix, FLINS2018 in Belfast and FLINS2020 in Cologne, FLINS2022
was organized by Nankai University, and co-organized by Southwest
Jiaotong University, University of Technology Sydney and Ecole
Nationale Superieure des Arts et Industries Textiles of University
of Lille. This unique international research collaboration has
provided researchers with a platform to share and exchange ideas on
state-of-art development in machine learning, multi agent and cyber
physical systems.Following the wishes of Prof. Da Ruan, FLINS2022
offered an international platform that brought together
mathematicians, computer scientists, and engineers who are actively
involved in machine learning, intelligent systems, data analysis,
knowledge engineering and their applications, to share their latest
innovations and developments, exchange notes on the
state-of-the-art research ideas, especially in the areas of
industrial microgrids, intelligent wearable systems, sustainable
development, logistics, supply chain and production optimization,
evaluation systems and performance analysis, as well as risk and
security management, that have now become part and parcel of Fuzzy
Logic and Intelligent Technologies in Nuclear Science.This
FLINS2022 Proceedings has selected 78 conference papers that cover
the following seven areas of interests:
Machine Learning in Bioinformatics of Protein Sequences guides
readers around the rapidly advancing world of cutting-edge machine
learning applications in the protein bioinformatics field. Edited
by bioinformatics expert, Dr Lukasz Kurgan, and with contributions
by a dozen of accomplished researchers, this book provides a
holistic view of the structural bioinformatics by covering a broad
spectrum of algorithms, databases and software resources for the
efficient and accurate prediction and characterization of
functional and structural aspects of proteins. It spotlights key
advances which include deep neural networks, natural language
processing-based sequence embedding and covers a wide range of
predictions which comprise of tertiary structure, secondary
structure, residue contacts, intrinsic disorder, protein, peptide
and nucleic acids-binding sites, hotspots, post-translational
modification sites, and protein function. This volume is loaded
with practical information that identifies and describes leading
predictive tools, useful databases, webservers, and modern software
platforms for the development of novel predictive tools.
There is a tremendous need for computer scientists, data
scientists, and software developers to learn how to develop
Socratic problem-solving applications. While the amount of data and
information processing has been accelerating, our ability to learn
and problem-solve with that data has fallen behind. Meanwhile,
problems have become too complex to solve in the workplace without
a concerted effort to follow a problem-solving process. This
problem-solving process must be able to deal with big and disparate
data. Furthermore, it must solve problems that do not have a "rule"
to apply in solving them. Moreover, it must deal with ambiguity and
help humans use informed judgment to build on previous steps and
create new understanding. Computer-based Socratic problem-solving
systems answer this need for a problem-solving process using big
and disparate data. Furthermore, computer scientists, data
scientists, and software developers need the knowledge to develop
these systems. Socrates Digital (TM) for Learning and Problem
Solving presents the rationale for developing a Socratic
problem-solving application. It describes how a computer-based
Socratic problem-solving system called Socrates DigitalTM can keep
problem-solvers on track, document the outcome of a problem-solving
session, and share those results with problem-solvers and larger
audiences. In addition, Socrates DigitalTM assists problem-solvers
to combine evidence about their quality of reasoning for individual
problem-solving steps and their overall confidence in the solution.
Socrates DigitalTM also captures, manages, and distributes this
knowledge across organizations to improve problem-solving. This
book also presents how to build a Socrates DigitalTM system by
detailing the four phases of design and development: Understand,
Explore, Materialize, and Realize. The details include flow charts
and pseudo-code for readers to implement Socrates DigitalTM in a
general-purpose programming language. The completion of the design
and development process results in a Socrates DigitalTM system that
leverages artificial intelligence services from providers that
include Apple, Microsoft, Google, IBM, and Amazon. In addition, an
appendix provides a demonstration of a no-code implementation of
Socrates DigitalTM in Microsoft Power Virtual Agent.
As digital technology continues to revolutionize the world,
businesses are also evolving by adopting digital technologies such
as artificial intelligence, digital marketing, and analytical
methods into their daily practices. Due to this growing adoption,
further study on the potential solutions modern technology provides
to businesses is required to successfully apply it across
industries. AI-Driven Intelligent Models for Business Excellence
explores various artificial intelligence models and methods for
business applications and considers algorithmic approaches for
business excellence across numerous fields and applications.
Covering topics such as business analysis, deep learning, machine
learning, and analytical methods, this reference work is ideal for
managers, business owners, computer scientists, industry
professionals, researchers, scholars, practitioners, academicians,
instructors, and students.
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