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Technological advancements have enhanced all functions of society
and revolutionized the healthcare field. Smart healthcare
applications and practices have grown within the past decade,
strengthening overall care. Biomedical signals observe
physiological activities, which provide essential information to
healthcare professionals. Biomedical signal processing can be
optimized through artificial intelligence (AI) and machine learning
(ML), presenting the next step towards smart healthcare. AI-Enabled
Smart Healthcare Using Biomedical Signals will not only cover the
mathematical description of the AI- and ML-based methods, but also
analyze and demonstrate the usability of different AI methods for a
range of biomedical signals. The book covers all types of
biomedical signals helpful for smart healthcare applications.
Covering topics such as automated diagnosis, emotion
identification, and frequency discrimination techniques, this
premier reference source is an excellent resource for healthcare
administration, biomedical engineers, medical laboratory
technicians, medical technology assistants, computer scientists,
libraries, students and faculty of higher education, researchers,
and academicians.
Football and fascism: the politics of popular culture in Portugal
tells the hidden history of football and discusses its political,
social and cultural foundations, during the longest running
authoritarian regime in Europe. Theoretically grounded on
Bourdieu's field theory, and using a multi-scalar methodology, this
award-winning research explores the political tensions between the
nationalization of sports envisaged by the Portuguese "New State"
and the integration of national football in a globalized urban
popular culture. Mobilizing unexplored archival sources, and a wide
array of primary materials, this groundbreaking work offers new
insight on the administrative structures of the corporativist
state, the making of an authoritarian cultural program, and the
relation between state institutions and civil society. Besides
broadening the scope of existing transnational histories of
football, this study also puts into question the conventional
geographies and political chronologies adopted in sports history.
For close to forty years now T.M. Scanlon has been one of the most
important contributors to moral and political philosophy in the
Anglo-American world. Through both his writing and his teaching, he
has played a central role in shaping the questions with which
research in moral and political philosophy now grapples.
Reasons and Recognition brings together fourteen new papers on an
array of topics from the many areas to which Scanlon has made
path-breaking contributions, each of which develops a distinctive
and independent position while critically engaging with central
themes from Scanlon's own work in the area. Contributors include
well-known senior figures in moral and political philosophy as well
as important younger scholars whose work is just beginning to gain
wider recognition. Taken together, these papers make evident the
scope and lasting interest of Scanlon's contributions to moral and
political philosophy while contributing to a deeper understanding
of the issues addressed in his work.
This book presents and argues for a suitably articulated version of
consensualism as a form of Kantian moral theory with an ability to
powerfully illuminate the moral intuitions to which Kantian and
utilitarian theories have traditionally appealed.
Biomarkers for Environmental Biomonitoring: An Integrated
Perspective provides a holistic view of the biomonitoring of
environmental degradation, accumulated toxicity, and associated
human health concerns. Organized into two sections, the book
incorporates theoretical and practical aspects of the biomonitoring
of environmental pollution and the health surveillance of
ecological communities using samples from living organisms which
are analyzed for contaminants and toxin levels. In the first half,
the book provides a general overview if the different types of
biomarkers, their significance as bioindicators for contaminants
and detection of toxicity, as well as how they can be utilized in
the restoration of degraded ecosystems. The second half of the book
discusses molecular biomarkers and how they are used as diagnostic
and prognostic tools for pollution monitoring. It reviews
analytical tools used to validate the biomarkers in the detection
and monitoring of pollution and disease. The book also delves into
how novel approaches like genetic ecotoxicology; Big Data, and
artificial intelligence calculates the potential consequences of
environmental pollution on the ecosystems and on human health.
One of the principle weaknesses of the non-consequentialist moral theories is that they fail to adequately address the intuitions to which consequentialist views appeal for their strength. For this reason, those who expect a moral theory to provide insight into these intuitions continue to be drawn to consequentialism, despite well-known ways in which its theories can be deeply counter-intuitive. This study shows how this challenge can be met by non-consequentialists. Articulating and defending a form of Scanlonian contractualism, called Consensualism, Kumar argues that consensualism also has the resources to powerfully illuminate those commonsense moral intuitions to which only consequentialism normally appeals.
Existing human beings stand in a unique relationship of
asymmetrical influence over future generations. Our choices now can
settle whether there are any human beings in the further future;
how many will exist; what capacities and abilities they might have;
and what the character of the natural world they inhabit is like.
This volume, with contributions from both new voices and prominent,
established figures in moral and political philosophy, examines
three generally underexplored themes concerning morality and our
relationship to future generations. First, would it be morally
wrong to allow humanity to go extinct? Or do we have moral reasons
to try and ensure that humanity continues into the indefinite
future? Second, if humanity is to continue into the future, how
many people should there be? And is it morally important whether
they have lives that are of high quality or are just barely worth
living? And third, how can we best make sense of the intuitive idea
that by not taking action on climate change and preserving natural
resources, we are in some way wronging future generations? This
book was originally published as a special issue of the Canadian
Journal of Philosophy.
Reparations is an idea whose time has come. From civilian victims
of war in Iraq and South America to descendents of slaves in the US
to citizens of colonized nations in Africa and south Asia to
indigenous peoples around the world - these groups and their
advocates are increasingly arguing for the importance of addressing
historical injustices that have long been either ignored or denied.
This volume aims to contribute to these debates by focusing the
attention of a group of highly distinguished international experts
on the ways that reparations claims figure in contemporary
political and social justice movements. Four broad types of
reparations claims are examined, those involving indigenous
peoples, the legacy of slavery in the United States, victims of war
and conflict, and colonialism. In each instance, scholars and
activists argue about the character of the injustice for which
reparations are owed, why it is important to take these demands
seriously, and what form redress should take. The aim is not
consensus but to exhibit better the complexity of the issues
involved - a goal which the interdisciplinary nature of the volume
furthers - as well as the importance of taking seriously both
conceptual issues and the actual politics of reparations.
Insightful projects to master deep learning and neural network
architectures using Python and Keras Key Features Explore deep
learning across computer vision, natural language processing (NLP),
and image processing Discover best practices for the training of
deep neural networks and their deployment Access popular deep
learning models as well as widely used neural network architectures
Book DescriptionDeep learning has been gradually revolutionizing
every field of artificial intelligence, making application
development easier. Python Deep Learning Projects imparts all the
knowledge needed to implement complex deep learning projects in the
field of computational linguistics and computer vision. Each of
these projects is unique, helping you progressively master the
subject. You'll learn how to implement a text classifier system
using a recurrent neural network (RNN) model and optimize it to
understand the shortcomings you might experience while implementing
a simple deep learning system. Similarly, you'll discover how to
develop various projects, including word vector representation,
open domain question answering, and building chatbots using
seq-to-seq models and language modeling. In addition to this,
you'll cover advanced concepts, such as regularization, gradient
clipping, gradient normalization, and bidirectional RNNs, through a
series of engaging projects. By the end of this book, you will have
gained knowledge to develop your own deep learning systems in a
straightforward way and in an efficient way What you will learn Set
up a deep learning development environment on Amazon Web Services
(AWS) Apply GPU-powered instances as well as the deep learning AMI
Implement seq-to-seq networks for modeling natural language
processing (NLP) Develop an end-to-end speech recognition system
Build a system for pixel-wise semantic labeling of an image Create
a system that generates images and their regions Who this book is
forPython Deep Learning Projects is for you if you want to get
insights into deep learning, data science, and artificial
intelligence. This book is also for those who want to break into
deep learning and develop their own AI projects. It is assumed that
you have sound knowledge of Python programming
Your hands-on reference guide to developing, training, and
optimizing your machine learning models Key Features Your guide to
learning efficient machine learning processes from scratch Explore
expert techniques and hacks for a variety of machine learning
concepts Write effective code in R, Python, Scala, and Spark to
solve all your machine learning problems Book DescriptionMachine
learning makes it possible to learn about the unknowns and gain
hidden insights into your datasets by mastering many tools and
techniques. This book guides you to do just that in a very compact
manner. After giving a quick overview of what machine learning is
all about, Machine Learning Quick Reference jumps right into its
core algorithms and demonstrates how they can be applied to
real-world scenarios. From model evaluation to optimizing their
performance, this book will introduce you to the best practices in
machine learning. Furthermore, you will also look at the more
advanced aspects such as training neural networks and work with
different kinds of data, such as text, time-series, and sequential
data. Advanced methods and techniques such as causal inference,
deep Gaussian processes, and more are also covered. By the end of
this book, you will be able to train fast, accurate machine
learning models at your fingertips, which you can easily use as a
point of reference. What you will learn Get a quick rundown of
model selection, statistical modeling, and cross-validation Choose
the best machine learning algorithm to solve your problem Explore
kernel learning, neural networks, and time-series analysis Train
deep learning models and optimize them for maximum performance
Briefly cover Bayesian techniques and sentiment analysis in your
NLP solution Implement probabilistic graphical models and causal
inferences Measure and optimize the performance of your machine
learning models Who this book is forIf you're a machine learning
practitioner, data scientist, machine learning developer, or
engineer, this book will serve as a reference point in building
machine learning solutions. You will also find this book useful if
you're an intermediate machine learning developer or data scientist
looking for a quick, handy reference to all the concepts of machine
learning. You'll need some exposure to machine learning to get the
best out of this book.
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