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Books > Computing & IT > Applications of computing
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.
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.
In healthcare, a digital twin is a digital representation of a
patient or healthcare system using integrated simulations and
service data. The digital twin tracks a patient's records,
crosschecks them against registered patterns and analyses any
diseases or contra indications. The digital twin uses adaptive
analytics and algorithms to produce accurate prognoses and suggest
appropriate interventions. A digital twin can run various medical
scenarios before treatment is initiated on the patient, thus
increasing patient safety as well as providing the most appropriate
treatments to meet the patient's requirements. Digital Twin
Technologies for Healthcare 4.0 discusses how the concept of the
digital twin can be merged with other technologies, such as
artificial intelligence (AI), machine learning (ML), big data
analytics, IoT and cloud data management, for the improvement of
healthcare systems and processes. The book also focuses on the
various research perspectives and challenges in implementation of
digital twin technology in terms of data analysis, cloud management
and data privacy issues. With chapters on visualisation techniques,
prognostics and health management, this book is a must-have for
researchers, engineers and IT professionals in healthcare as well
as those involved in using digital twin technology, AI, IoT &
big data analytics for novel applications.
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.
Tensors for Data Processing: Theory, Methods and Applications
presents both classical and state-of-the-art methods on tensor
computation for data processing, covering computation theories,
processing methods, computing and engineering applications, with an
emphasis on techniques for data processing. This reference is ideal
for students, researchers and industry developers who want to
understand and use tensor-based data processing theories and
methods. As a higher-order generalization of a matrix, tensor-based
processing can avoid multi-linear data structure loss that occurs
in classical matrix-based data processing methods. This move from
matrix to tensors is beneficial for many diverse application areas,
including signal processing, computer science, acoustics,
neuroscience, communication, medical engineering, seismology,
psychometric, chemometrics, biometric, quantum physics and quantum
chemistry.
Tactile Sensing, Skill Learning and Robotic Dexterous Manipulation
focuses on cross-disciplinary lines of research and groundbreaking
research ideas in three research lines: tactile sensing, skill
learning and dexterous control. The book introduces recent work
about human dexterous skill representation and learning, along with
discussions of tactile sensing and its applications on unknown
objects' property recognition and reconstruction. Sections also
introduce the adaptive control schema and its learning by imitation
and exploration. Other chapters describe the fundamental part of
relevant research, paying attention to the connection among
different fields and showing the state-of-the-art in related
branches. The book summarizes the different approaches and
discusses the pros and cons of each. Chapters not only describe the
research but also include basic knowledge that can help readers
understand the proposed work, making it an excellent resource for
researchers and professionals who work in the robotics industry,
haptics and in machine learning.
Fractional Order Systems: An Overview of Mathematics, Design, and
Applications for Engineers 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.
5G IoT and Edge Computing for Smart Healthcare addresses the
importance of a 5G IoT and Edge-Cognitive-Computing-based system
for the successful implementation and realization of a
smart-healthcare system. The book provides insights on 5G
technologies, along with intelligent processing
algorithms/processors that have been adopted for processing the
medical data that would assist in addressing the challenges in
computer-aided diagnosis and clinical risk analysis on a real-time
basis. Each chapter is self-sufficient, solving real-time problems
through novel approaches that help the audience acquire the right
knowledge. With the progressive development of medical and
communication - computer technologies, the healthcare system has
seen a tremendous opportunity to support the demand of today's new
requirements.
Cognitive Systems and Signal Processing in Image Processing
presents different frameworks and applications of cognitive signal
processing methods in image processing. This book provides an
overview of recent applications in image processing by cognitive
signal processing methods in the context of Big Data and Cognitive
AI. It presents the amalgamation of cognitive systems and signal
processing in the context of image processing approaches in solving
various real-word application domains. This book reports the latest
progress in cognitive big data and sustainable computing. Various
real-time case studies and implemented works are discussed for
better understanding and more clarity to readers. The combined
model of cognitive data intelligence with learning methods can be
used to analyze emerging patterns, spot business opportunities, and
take care of critical process-centric issues for computer vision in
real-time.
Structured Light for Optical Communication highlights principles
and applications in the rapidly evolving field of structured light
in wide-ranging contexts, from classical forms of communication to
new frontiers of quantum communication. Besides the basic
principles and applications, the book covers the background of
structured light in its most common forms, as well as
state-of-the-art developments. Structured light has been hailed as
affording outstanding prospects for the realization of high
bandwidth communication, enhanced tools for more highly secure
cryptography, and exciting opportunities for providing a reliable
platform for quantum computing. This book is a valuable resource
for graduate students and other active researchers, as well as
others who may be interested in learning about this cutting-edge
research field.
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