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As the number of Internet of Things (IoT) elements grows
exponentially, their interactions can generate a massive amount of
raw and multi-structured data. The challenge with this data
explosion is to transform any raw data into information and
knowledge, which can be used by people and systems to make
intelligent decisions. Industrial IoT Application Architectures and
Use Cases explores how artificial intelligence (AI), data
analytics, and IoT technology combine to promote intelligent
decision-making and automation in a range of industries. With
faster, more stable AI algorithms and approaches, knowledge
discovery and dissemination from IoT-device data can be simplified
and streamlined. An era of powerful cognitive technology is
beginning due to cloud-based cognitive systems that are forming the
foundation of game-changing intelligent applications. This book
presents next-generation use cases of IoT and IoT data analytics
for a variety of industrial verticals as given below: An
Intelligent IoT framework for smart water management An IoT-enabled
smart traffic control system for congestion control and smart
traffic management An intelligent airport system for airport
management and security surveillance An IoT framework for
healthcare to integrate and report patient information Fuzzy
scheduling with IoT for tracking and monitoring hotel assets An IoT
system for designing drainage systems and monitoring drainage pipes
Predictive maintenance of plant equipment to decide the actual mean
time to malfunction Integrated neural networks and IoT systems for
predictive equipment maintenance IoT integration in blockchain for
smart waste management This book also includes a chapter on the IoT
paradigm and an overview of uses cases for personal, social, and
industrial applications.
As the number of Internet of Things (IoT) elements grows
exponentially, their interactions can generate a massive amount of
raw and multi-structured data. The challenge with this data
explosion is to transform any raw data into information and
knowledge, which can be used by people and systems to make
intelligent decisions. Industrial IoT Application Architectures and
Use Cases explores how artificial intelligence (AI), data
analytics, and IoT technology combine to promote intelligent
decision-making and automation in a range of industries. With
faster, more stable AI algorithms and approaches, knowledge
discovery and dissemination from IoT-device data can be simplified
and streamlined. An era of powerful cognitive technology is
beginning due to cloud-based cognitive systems that are forming the
foundation of game-changing intelligent applications. This book
presents next-generation use cases of IoT and IoT data analytics
for a variety of industrial verticals as given below: An
Intelligent IoT framework for smart water management An IoT-enabled
smart traffic control system for congestion control and smart
traffic management An intelligent airport system for airport
management and security surveillance An IoT framework for
healthcare to integrate and report patient information Fuzzy
scheduling with IoT for tracking and monitoring hotel assets An IoT
system for designing drainage systems and monitoring drainage pipes
Predictive maintenance of plant equipment to decide the actual mean
time to malfunction Integrated neural networks and IoT systems for
predictive equipment maintenance IoT integration in blockchain for
smart waste management This book also includes a chapter on the IoT
paradigm and an overview of uses cases for personal, social, and
industrial applications.
This book provides an insight into ways of inculcating the need for
applying mobile edge data analytics in bioinformatics and medicine.
The book is a comprehensive reference that provides an overview of
the current state of medical treatments and systems and offers
emerging solutions for a more personalized approach to the
healthcare field. Topics include deep learning methods for
applications in object detection and identification, object
tracking, human action recognition, and cross-modal and multimodal
data analysis. High performance computing systems for applications
in healthcare are also discussed. The contributors also include
information on microarray data analysis, sequence analysis,
genomics based analytics, disease network analysis, and techniques
for big data Analytics and health information technology.
This volume inserts the place of the local in theorizing about
language policies and practices in applied linguistics. While the
effects of globalization around the world are being discussed in
such diverse circles as corporations, law firms, and education, and
while the spread of English has come to largely benefit those in
positions of power, relatively little has been said about the
impact of globalization at the local level, directly or indirectly.
Reclaiming the Local in Language Policy and Practice is unique in
focusing specifically on the outcomes of globalization in and among
the communities affected by these changes. The authors make a case
for why it is important for local social practices, communicative
conventions, linguistic realities, and knowledge paradigms to
actively inform language policies and practices for classrooms and
communities in specific contexts, and to critically inform those
pertaining to other communities. Engaging with the dominant
paradigms in the discipline of applied linguistics, the chapters
include research relating to second language acquisition,
sociolinguistics, literacy, and language planning. The majority of
chapters are case studies of specific contexts and communities,
focused on situations of language teaching. Beyond their local
contexts these studies are important for initiating discussion of
their relevance for other, different communities and contexts.
Taken together, the chapters in this book approach the task of
reclaiming and making space for the local by means of negotiating
with the present and the global. They illuminate the paradox that
the local contains complex values of diversity, multilingualism,
and plurality that can help to reconceive the multilingual society
and education for postmodern times.
This book provides an insight into ways of inculcating the need for
applying mobile edge data analytics in bioinformatics and medicine.
The book is a comprehensive reference that provides an overview of
the current state of medical treatments and systems and offers
emerging solutions for a more personalized approach to the
healthcare field. Topics include deep learning methods for
applications in object detection and identification, object
tracking, human action recognition, and cross-modal and multimodal
data analysis. High performance computing systems for applications
in healthcare are also discussed. The contributors also include
information on microarray data analysis, sequence analysis,
genomics based analytics, disease network analysis, and techniques
for big data Analytics and health information technology.
Discover detailed insights into the methods, algorithms, and
techniques for deep learning in sensor data analysis Sensor Data
Analysis and Management: The Role of Deep Learning delivers an
insightful and practical overview of the applications of deep
learning techniques to the analysis of sensor data. The book
collects cutting-edge resources into a single collection designed
to enlighten the reader on topics as varied as recent techniques
for fault detection and classification in sensor data, the
application of deep learning to Internet of Things sensors, and a
case study on high-performance computer gathering and processing of
sensor data. The editors have curated a distinguished group of
perceptive and concise papers that show the potential of deep
learning as a powerful tool for solving complex modelling problems
across a broad range of industries, including predictive
maintenance, health monitoring, financial portfolio forecasting,
and driver assistance. The book contains real-time examples of
analyzing sensor data using deep learning algorithms and a
step-by-step approach for installing and training deep learning
using the Python keras library. Readers will also benefit from the
inclusion of: A thorough introduction to the Internet of Things for
human activity recognition, based on wearable sensor data An
exploration of the benefits of neural networks in real-time
environmental sensor data analysis Practical discussions of
supervised learning data representation, neural networks for
predicting physical activity based on smartphone sensor data, and
deep-learning analysis of location sensor data for human activity
recognition An analysis of boosting with XGBoost for sensor data
analysis Perfect for industry practitioners and academics involved
in deep learning and the analysis of sensor data, Sensor Data
Analysis and Management: The Role of Deep Learning will also earn a
place in the libraries of undergraduate and graduate students in
data science and computer science programs.
This volume inserts the place of the local in theorizing about
language policies and practices in applied linguistics. While the
effects of globalization around the world are being discussed in
such diverse circles as corporations, law firms, and education, and
while the spread of English has come to largely benefit those in
positions of power, relatively little has been said about the
impact of globalization at the local level, directly or indirectly.
Reclaiming the Local in Language Policy and Practice is unique in
focusing specifically on the outcomes of globalization in and among
the communities affected by these changes. The authors make a case
for why it is important for local social practices, communicative
conventions, linguistic realities, and knowledge paradigms to
actively inform language policies and practices for classrooms and
communities in specific contexts, and to critically inform those
pertaining to other communities. Engaging with the dominant
paradigms in the discipline of applied linguistics, the chapters
include research relating to second language acquisition,
sociolinguistics, literacy, and language planning. The majority of
chapters are case studies of specific contexts and communities,
focused on situations of language teaching. Beyond their local
contexts these studies are important for initiating discussion of
their relevance for other, different communities and contexts.
Taken together, the chapters in this book approach the task of
reclaiming and making space for the local by means of negotiating
with the present and the global. They illuminate the paradox that
the local contains complex values of diversity, multilingualism,
and plurality that can help to reconceive the multilingual society
and education for postmodern times.
BIOINFORMATICS AND MEDICAL APPLICATIONS The main topics addressed
in this book are big data analytics problems in bioinformatics
research such as microarray data analysis, sequence analysis,
genomics-based analytics, disease network analysis, techniques for
big data analytics, and health information technology.
Bioinformatics and Medical Applications: Big Data Using Deep
Learning Algorithms analyses massive biological datasets using
computational approaches and the latest cutting-edge technologies
to capture and interpret biological data. The book delivers various
bioinformatics computational methods used to identify diseases at
an early stage by assembling cutting-edge resources into a single
collection designed to enlighten the reader on topics focusing on
computer science, mathematics, and biology. In modern biology and
medicine, bioinformatics is critical for data management. This book
explains the bioinformatician's important tools and examines how
they are used to evaluate biological data and advance disease
knowledge. The editors have curated a distinguished group of
perceptive and concise chapters that presents the current state of
medical treatments and systems and offers emerging solutions for a
more personalized approach to healthcare. Applying deep learning
techniques for data-driven solutions in health information allows
automated analysis whose method can be more advantageous in
supporting the problems arising from medical and health-related
information. Audience The primary audience for the book includes
specialists, researchers, postgraduates, designers, experts, and
engineers, who are occupied with biometric research and
security-related issues.
A Geopolitics of Academic Writing critiques current scholarly
publishing practices, exposing the inequalities in the way academic
knowledge is constructed and legitimized. As a periphery scholar
now working in (and writing from) the center, Suresh Canagarajah is
uniquely situated to demonstrate how and why contributions from
Third World scholars are too often relegated to the perimeter of
academic discourse. He examines three broad conventions governing
academic writing: textual concerns (matters of languages, style,
tone, and structure), social customs (the rituals governing the
interactions of members of the academic community), and publishing
practices (from submission protocols to photocopying and postage
requirements). Canagarajah argues that the dominance of Western
conventions in scholarly communication leads directly to the
marginalization or appropriation of the knowledge of Third World
communities.
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