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Books > Computing & IT > Applications of computing
Artificial Intelligence, Machine Learning, and Mental Health in
Pandemics: A Computational Approach provides a comprehensive guide
for public health authorities, researchers and health professionals
in psychological health. The book takes a unique approach by
exploring how Artificial Intelligence (AI) and Machine Learning
(ML) based solutions can assist with monitoring, detection and
intervention for mental health at an early stage. Chapters include
computational approaches, computational models, machine learning
based anxiety and depression detection and artificial intelligence
detection of mental health. With the increase in number of natural
disasters and the ongoing pandemic, people are experiencing
uncertainty, leading to fear, anxiety and depression, hence this is
a timely resource on the latest updates in the field.
Cognitive and Soft Computing Techniques for the Analysis of
Healthcare Data discusses the insight of data processing
applications in various domains through soft computing techniques
and enormous advancements in the field. The book focuses on the
cross-disciplinary mechanisms and ground-breaking research ideas on
novel techniques and data processing approaches in handling
structured and unstructured healthcare data. It also gives insight
into various information-processing models and many memories
associated with it while processing the information for forecasting
future trends and decision making. This book is an excellent
resource for researchers and professionals who work in the
Healthcare Industry, Data Science, and Machine learning.
Advances in Computers, Volume 126 presents innovations in computer
hardware, software, theory, design and applications, with this
updated volume including new chapters on VLSI for Super-Computing:
Creativity in R+D from Applications and Algorithms to Masks and
Chips, Bulk Bitwise Execution Model in Memory: Mechanisms,
Implementation, and Evaluation, Embracing the Laws of Physics:
Three Reversible Models of Computation, WSNs in Environmental
Monitoring: Data Acquisition and Dissemination Aspects, Energy
efficient implementation of tensor operations using dataflow
paradigm for machine learning, and A Run-Time Job Scheduling
Algorithm for Cluster Architectures with DataFlow Accelerators.
Artificial Intelligence for Healthcare Applications and Management
introduces application domains of various AI algorithms across
healthcare management. Instead of discussing AI first and then
exploring its applications in healthcare afterward, the authors
attack the problems in context directly, in order to accelerate the
path of an interested reader toward building industrial-strength
healthcare applications. Readers will be introduced to a wide
spectrum of AI applications supporting all stages of patient flow
in a healthcare facility. The authors explain how AI supports
patients throughout a healthcare facility, including diagnosis and
treatment recommendations needed to get patients from the point of
admission to the point of discharge while maintaining quality,
patient safety, and patient/provider satisfaction. AI methods are
expected to decrease the burden on physicians, improve the quality
of patient care, and decrease overall treatment costs. Current
conditions affected by COVID-19 pose new challenges for healthcare
management and learning how to apply AI will be important for a
broad spectrum of students and mature professionals working in
medical informatics. This book focuses on predictive analytics,
health text processing, data aggregation, management of patients,
and other fields which have all turned out to be bottlenecks for
the efficient management of coronavirus patients.
Smart homes use Internet-connected devices, artificial
intelligence, protocols and numerous technologies to enable people
to remotely monitor their home, as well as manage various systems
within it via the Internet using a smartphone or a computer. A
smart home is programmed to act autonomously to improve comfort
levels, save energy and potentially ensure safety; the result is a
better way of life. Innovative solutions continue to be developed
by researchers and engineers and thus smart home technologies are
constantly evolving. By the same token, cybercrime is also becoming
more prevalent. Indeed, a smart home system is made up of connected
devices that cybercriminals can infiltrate to access private
information, commit cyber vandalism or infect devices using
botnets. This book addresses cyber attacks such as sniffing, port
scanning, address spoofing, session hijacking, ransomware and
denial of service. It presents, analyzes and discusses the various
aspects of cybersecurity as well as solutions proposed by the
research community to counter the risks. Cybersecurity in Smart
Homes is intended for people who wish to understand the
architectures, protocols and different technologies used in smart
homes.
Build a solid foundation in data analysis skills and pursue a
coveted Data+ certification with this intuitive study guide CompTIA
Data+ Study Guide: Exam DA0-001 delivers easily accessible and
actionable instruction for achieving data analysis competencies
required for the job and on the CompTIA Data+ certification exam.
You'll learn to collect, analyze, and report on various types of
commonly used data, transforming raw data into usable information
for stakeholders and decision makers. With comprehensive coverage
of data concepts and environments, data mining, data analysis,
visualization, and data governance, quality, and controls, this
Study Guide offers: All the information necessary to succeed on the
exam for a widely accepted, entry-level credential that unlocks
lucrative new data analytics and data science career opportunities
100% coverage of objectives for the NEW CompTIA Data+ exam Access
to the Sybex online learning resources, with review questions,
full-length practice exam, hundreds of electronic flashcards, and a
glossary of key terms Ideal for anyone seeking a new career in data
analysis, to improve their current data science skills, or hoping
to achieve the coveted CompTIA Data+ certification credential,
CompTIA Data+ Study Guide: Exam DA0-001 provides an invaluable head
start to beginning or accelerating a career as an in-demand data
analyst.
Cognitive Models for Sustainable Environment reviews the
fundamental concepts of gathering, processing and analyzing data
from batch processes, along with a review of intelligent and
cognitive tools that can be used. The book is centered on evolving
novel intelligent/cognitive models and algorithms to develop
sustainable solutions for the mitigation of environmental
pollution. It unveils intelligent and cognitive models to address
issues related to the effective monitoring of environmental
pollution and sustainable environmental design. As such, the book
focuses on the overall well-being of the global environment for
better sustenance and livelihood. The book covers novel cognitive
models for effective environmental pollution data management at par
with the standards laid down by the World Health Organization.
Every chapter is supported by real-life case studies, illustrative
examples and video demonstrations that enlighten readers.
Blockchain Technology for Emerging Applications: A Comprehensive
Approach explores recent theories and applications of the execution
of blockchain technology. Chapters look at a wide range of
application areas, including healthcare, digital physical
frameworks, web of-things, smart transportation frameworks,
interruption identification frameworks, ballot-casting,
architecture, smart urban communities, and digital rights
administration. The book addresses the engineering, plan
objectives, difficulties, constraints, and potential answers for
blockchain-based frameworks. It also looks at blockchain-based
design perspectives of these intelligent architectures for
evaluating and interpreting real-world trends. Chapters expand on
different models which have shown considerable success in dealing
with an extensive range of applications, including their ability to
extract complex hidden features and learn efficient representation
in unsupervised environments for blockchain security pattern
analysis.
Opinion Mining and Text Analytics on Literary Works and Social
Media introduces the use of artificial intelligence and big data
analytics techniques which can apply opinion mining and text
analytics on literary works and social media. This book focuses on
theories, method and approaches in which data analytic techniques
can be used to analyze data from social media, literary books,
novels, news, texts, and beyond to provide a meaningful pattern.
The subject area of this book is multidisciplinary; related to data
science, artificial intelligence, social science and humanities,
and literature. This is an essential resource for scholars,
Students and lecturers from various fields of data science,
artificial intelligence, social science and humanities, and
literature, university libraries, new agencies, and many more.
Application of Machine Learning in Smart Agriculture is the first
book to present a multidisciplinary look at how technology can not
only improve agricultural output, but the economic efficiency of
that output as well. Through a global lens, the book approaches the
subject from a technical perspective, providing important knowledge
and insights for effective and efficient implementation and
utilization of machine learning. As artificial intelligence
techniques are being used to increase yield through optimal
planting, fertilizing, irrigation, and harvesting, these are only
part of the complex picture which must also take into account the
economic investment and its optimized return. The performance of
machine learning models improves over time as the various
mathematical and statistical models are proven. Presented in three
parts, Application of Machine Learning in Smart Agriculture looks
at the fundamentals of smart agriculture; the economics of the
technology in the agricultural marketplace; and a diverse
representation of the tools and techniques currently available, and
in development. This book is an important resource for advanced
level students and professionals working with artificial
intelligence, internet of things, technology and agricultural
economics.
Machine Learning Algorithms for Signal and Image Processing Enables
readers to understand the fundamental concepts of machine and deep
learning techniques with interactive, real-life applications within
signal and image processing Machine Learning Algorithms for Signal
and Image Processing aids the reader in designing and developing
real-world applications using advances in machine learning to aid
and enhance speech signal processing, image processing, computer
vision, biomedical signal processing, adaptive filtering, and text
processing. It includes signal processing techniques applied for
pre-processing, feature extraction, source separation, or data
decompositions to achieve machine learning tasks. Written by
well-qualified authors and contributed to by a team of experts
within the field, the work covers a wide range of important topics,
such as: Speech recognition, image reconstruction, object
classification and detection, and text processing Healthcare
monitoring, biomedical systems, and green energy How various
machine and deep learning techniques can improve accuracy,
precision rate recall rate, and processing time Real applications
and examples, including smart sign language recognition, fake news
detection in social media, structural damage prediction, and
epileptic seizure detection Professionals within the field of
signal and image processing seeking to adapt their work further
will find immense value in this easy-to-understand yet extremely
comprehensive reference work. It is also a worthy resource for
students and researchers in related fields who are looking to
thoroughly understand the historical and recent developments that
have been made in the field.
Optimum-Path Forest: Theory, Algorithms, and Applications was first
published in 2008 in its supervised and unsupervised versions with
applications in medicine and image classification. Since then, it
has expanded to a variety of other applications such as remote
sensing, electrical and petroleum engineering, and biology. In
recent years, multi-label and semi-supervised versions were also
developed to handle video classification problems. The book
presents the principles, algorithms and applications of
Optimum-Path Forest, giving the theory and state-of-the-art as well
as insights into future directions.
Mobile Edge Artificial Intelligence: Opportunities and Challenges
presents recent advances in wireless technologies and nonconvex
optimization techniques for designing efficient edge AI systems.
The book includes comprehensive coverage on modeling, algorithm
design and theoretical analysis. Through typical examples, the
powerfulness of this set of systems and algorithms is demonstrated,
along with their abilities to make low-latency, reliable and
private intelligent decisions at network edge. With the
availability of massive datasets, high performance computing
platforms, sophisticated algorithms and software toolkits, AI has
achieved remarkable success in many application domains. As such,
intelligent wireless networks will be designed to leverage advanced
wireless communications and mobile computing technologies to
support AI-enabled applications at various edge mobile devices with
limited communication, computation, hardware and energy resources.
Brain-machine interfacing or brain-computer interfacing (BMI/BCI)
is an emerging and challenging technology used in engineering and
neuroscience. The ultimate goal is to provide a pathway from the
brain to the external world via mapping, assisting, augmenting or
repairing human cognitive or sensory-motor functions. In this book
an international panel of experts introduce signal processing and
machine learning techniques for BMI/BCI and outline their practical
and future applications in neuroscience, medicine, and
rehabilitation, with a focus on EEG-based BMI/BCI methods and
technologies. Topics covered include discriminative learning of
connectivity pattern of EEG; feature extraction from EEG
recordings; EEG signal processing; transfer learning algorithms in
BCI; convolutional neural networks for event-related potential
detection; spatial filtering techniques for improving individual
template-based SSVEP detection; feature extraction and
classification algorithms for image RSVP based BCI; decoding music
perception and imagination using deep learning techniques;
neurofeedback games using EEG-based Brain-Computer Interface
Technology; affective computing system and more.
From climate change forecasts and pandemic maps to Lego sets and
Ancestry algorithms, models encompass our world and our lives. In
her thought-provoking new book, Annabel Wharton begins with a
definition drawn from the quantitative sciences and the philosophy
of science but holds that history and critical cultural theory are
essential to a fuller understanding of modeling. Considering
changes in the medical body model and the architectural model, from
the Middle Ages to the twenty-first century, Wharton demonstrates
the ways in which all models are historical and political.
Examining how cadavers have been described, exhibited, and visually
rendered, she highlights the historical dimension of the modified
body and its depictions. Analyzing the varied reworkings of the
Holy Sepulchre in Jerusalem-including by monumental commanderies of
the Knights Templar, Alberti's Rucellai Tomb in Florence,
Franciscans' olive wood replicas, and video game renderings-she
foregrounds the political force of architectural representations.
And considering black boxes-instruments whose inputs we control and
whose outputs we interpret, but whose inner workings are beyond our
comprehension-she surveys the threats posed by such opaque
computational models, warning of the dangers that models pose when
humans lose control of the means by which they are generated and
understood. Engaging and wide-ranging, Models and World Making
conjures new ways of seeing and critically evaluating how we make
and remake the world in which we live.
Fractional-order Modelling of Dynamic Systems with Applications in
Optimization, Signal Processing and Control introduces applications
from a design perspective, helping readers plan and design their
own applications. The book includes the different techniques
employed to design fractional-order systems/devices comprehensively
and straightforwardly. Furthermore, mathematics is available in the
literature on how to solve fractional-order calculus for system
applications. This book introduces the mathematics that has been
employed explicitly for fractional-order systems. It will prove an
excellent material for students and scholars who want to quickly
understand the field of fractional-order systems and contribute to
its different domains and applications. Fractional-order systems
are believed to play an essential role in our day-to-day
activities. Therefore, several researchers around the globe
endeavor to work in the different domains of fractional-order
systems. The efforts include developing the mathematics to solve
fractional-order calculus/systems and to achieve the feasible
designs for various applications of fractional-order systems.
Recent Trends in Computer-aided Diagnostic Systems for Skin
Diseases: Theory, Implementation, and Analysis provides
comprehensive coverage on the development of computer-aided
diagnostic (CAD) systems employing image processing and machine
learning tools for improved, uniform evaluation and diagnosis
(avoiding subjective judgment) of skin disorders. The methods and
tools are described in a general way so that these tools can be
applied not only for skin diseases but also for a wide range of
analogous problems in the domain of biomedical systems. Moreover,
quantification of clinically relevant information that can
associate the findings of physicians/experts is the most
challenging task of any CAD system. This book gives all the details
in a step-by-step form for different modules so that the readers
can develop each of the modules like preprocessing, feature
extraction/learning, disease classification, as well as an entire
expert diagnosis system themselves for their own applications.
Handbook of Pediatric Brain Imaging: Methods and Applications
presents state-of-the-art research on pediatric brain image
acquisition and analysis from a broad range of imaging modalities,
including MRI, EEG and MEG. With rapidly developing methods and
applications of MRI, this book strongly emphasizes pediatric brain
MRI, elaborating on the sub-categories of structure MRI, diffusion
MRI, functional MRI, perfusion MRI and other MRI methods. It
integrates a pediatric brain imaging perspective into imaging
acquisition and analysis methods, covering head motion, small brain
sizes, small cerebral blood flow of neonates, dynamic cortical
gyrification, white matter tract growth, and much more.
Cyber-Physical Systems: AI and COVID-19 highlights original
research which addresses current data challenges in terms of the
development of mathematical models, cyber-physical systems-based
tools and techniques, and the design and development of algorithmic
solutions, etc. It reviews the technical concepts of gathering,
processing and analyzing data from cyber-physical systems (CPS) and
reviews tools and techniques that can be used. This book will act
as a resource to guide COVID researchers as they move forward with
clinical and epidemiological studies on this outbreak, including
the technical concepts of gathering, processing and analyzing data
from cyber-physical systems (CPS). The major problem in the
identification of COVID-19 is detection and diagnosis due to
non-availability of medicine. In this situation, only one method,
Reverse Transcription Polymerase Chain Reaction (RT-PCR) has been
widely adopted and used for diagnosis. With the evolution of
COVID-19, the global research community has implemented many
machine learning and deep learning-based approaches with
incremental datasets. However, finding more accurate identification
and prediction methods are crucial at this juncture.
Intelligent Sensing and Communications for Internet of Everything
introduces three application scenarios of enhanced mobile broadband
(eMBB), large-scale machine connection (mMTC) and ultra reliable
low latency communication (URLLC). A new communication model,
namely backscatter communication (BackCom), intelligent reflector
surface (IRS) and unmanned aerial vehicle (UAV) technology in
Internet of Everything (IoE), is described in detail. Also focusing
on millimeter wave, the book discusses the potential application of
terahertz 6G network spectrum in the Internet of Things (IoT).
Finally, the applications of IoE network in big data, artificial
intelligence (AI) technology and fog/edge computing technology are
proposed.
Deep Learning in Bioinformatics: Techniques and Applications in
Practice introduces the topic in an easy-to-understand way,
exploring how it can be utilized for addressing important problems
in bioinformatics, including drug discovery, de novo molecular
design, sequence analysis, protein structure prediction, gene
expression regulation, protein classification, biomedical image
processing and diagnosis, biomolecule interaction prediction, and
in systems biology. The book also presents theoretical and
practical successes of deep learning in bioinformatics, pointing
out problems and suggesting future research directions. Dr.
Izadkhah provides valuable insights and will help researchers use
deep learning techniques in their biological and bioinformatics
studies.
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