Amy Neustein – författare

Advances in Ubiquitous Computing: Cyber-Physical Systems, Smart Cities and Ecological Monitoring debuts some of the newest methods and approaches to multimodal user-interface design, safety compliance, formal code verification and deployment requirements, as they pertain to cyber-physical systems, smart homes and smart cities, and biodiversity monitoring. In this anthology, the authors assiduously examine a panoply of topics related to wireless sensor networks. These topics include interacting with smart-home appliances and biomedical devices, designing multilingual speech recognition systems that are robust to vehicular, mechanical and other noises common to large metropolises, and an examination of new methods of speaker recognition to control for the emotion-state of the speaker, which can easily impede speaker verification over a wireless medium.

This volume recognizes that any discussion of pervasive computing in smart cities must not end there, as the perilous effects of climate change proves that our lives are not circumscribed by the geographically sculpted boundaries of cities, counties, countries, or continents. Contributors address present and emerging technologies of scalable biodiversity monitoring: pest control, disease transmission, environmental monitoring, and habitat preservation. The need to collect, store, process, and interpret vast amounts of data originating from sources spread over large areas and for prolonged periods of time requires immediate data storage and processing, reliable networking, and solid communication infrastructure, along with intelligent data analysis and interpretation methods that can resolve contradictions and uncertainty in the data-all of which can be bolstered by modern advances in ubiquitous computing.

In recent years, scientific research and translation medicine have placed increased emphasis on computational methodology and data curation across many disciplines, both to advance underlying science and to instantiate precision-medicine protocols in the lab and in clinical practice. The nexus of concerns related to oncology, cardiology, and virology (SARS-CoV-2) presents a fortuitous context within which to examine the theory and practice of biomedical data curation.

Innovative Data Integration and Conceptual Space Modeling for COVID, Cancer, and Cardiac Care argues that a well-rounded approach to data modeling should optimally embrace multiple perspectives inasmuch as data-modeling is neither a purely formal nor a purely conceptual discipline, but rather a hybrid of both. On the one hand, data models are designed for use by computer software components, and are, consequently, constrained by the mechanistic demands of software environments; data modeling strategies must accept the formal rigors imposed by unambiguous data-sharing and query-evaluation logic. In particular, data models are not well-suited for software-level deployment if such models do not translate seamlessly to clear strategies for querying data and ensuring data integrity as information is moved across multiple points. On the other hand, data modeling is, likewise, constrained by human conceptual tendencies, because the information which is managed by databases and data networks is ultimately intended to be visualized/utilized by humans as the end-user.

Thus, at the intersection of both formal and humanistic methodology, data modeling takes on elements of both logico-mathematical frameworks (e.g., type systems and graph theory) and conceptual/philosophical paradigms (e.g., linguistics and cognitive science). The authors embrace this two-sided aspect of data models by seeking non-reductionistic points of convergence between formal and humanistic/conceptual viewpoints, and by leveraging biomedical contexts (viz., COVID, Cancer, and Cardiac Care) so as to provide motivating examples and case-studies in this volume.

This book provides the reader with empirical findings on innovative signal processing approaches to detecting pathologies in infant cries, by comparing new technological approaches to standard ones. The contributors examine novel approaches to machine adaptation to dysarthric speech.

This book provides the reader with empirical findings on innovative signal processing approaches to detecting pathologies in infant cries, by comparing new technological approaches to standard ones. The contributors examine novel approaches to machine adaptation to dysarthric speech.

• Includes Text Mining and Natural Language Processing Methods for extracting information from electronic health records and biomedical literature.• Analyzes text analytic tools for new media such as online forums, social media posts, tweets and video sharing.• Demonstrates how to use speech and audio technologies for improving access to online content for the visually impaired.

Text Mining of Web-Based Medical Content examines various approaches to deriving high quality information from online biomedical literature, electronic health records, query search terms, social media posts and tweets. Using some of the latest empirical methods of knowledge extraction, the authors show how online content, generated by both professionals and laypersons, can be mined for valuable information about disease processes, adverse drug reactions not captured during clinical trials, and tropical fever outbreaks. Additionally, the authors show how to perform infromation extraction on a hospital intranet, how to build a social media search engine to glean information about patients'' own experiences interacting with healthcare professionals, and how to improve access to online health information.

This volume provides a wealth of timely material for health informatic professionals and machine learning, data mining, and natural language researchers.

Topics in this book include:• Mining Biomedical Literature and Clinical Narratives • Medication Information Extraction • Machine Learning Techniques for Mining Medical Search Queries • Detecting the Level of Personal Health Information Revealed in Social Media • Curating Layperson’s Personal Experiences with Health Care from Social Media and Twitter • Health Dialogue Systems for Improving Access to Online Content • Crowd-based Audio Clips to Improve Online Video Access for the Visually Impaired • Semantic-based Visual Information Retrieval for Mining Radiographic Image Data • Evaluating the Importance of Medical Terminology in YouTube Video Titles and Descriptions