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.

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.

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.

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 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 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.

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.