Remote sensing products are effectively used as a tool for decision making in various fields, especially in medical research and health care analyses. GIS is particularly well suited in this context because of its spatial analysis and display capabilities. The integration of RS techniques in public health has been categorised as continuous and discrete strategies where latter is preferred. We have investigated the integration of these approaches through linguistic interpretation of images. In this paper, we propose a framework for direct natural language interpretation of satellite images using probabilistic grammar rules in conjunction with evolutionary computing techniques. Spectral and spatial information has been dynamically combined using adaptive kernel strategy for effective representation of the contextual knowledge. The developed methodology has been evaluated in different querying contexts and investigations revealed that considerable success has been achieved with the procedure. The methodology has also demonstrated to be effective in intelligent interpolation, automatic interpretation as well as attribute, topology, proximity, and semantic analyses.

GeoComputation and Public Health is fundamentally a multi-disciplinary book, which presents an overview and case studies to exemplify numerous methods and solicitations in addressing vectors borne diseases (e.g, Visceral leishmaniasis, Malaria, Filaria). This book includes a practical coverage of the use of spatial analysis techniques in vector-borne disease using open source software solutions. Environmental factors (relief characters, climatology, ecology, vegetation, water bodies etc.) and socio-economic issues (housing type & pattern, education level, economic status, income level, domestics’ animals, census data, etc) are investigated at micro -level and large scale in addressing the various vector-borne disease. This book will also generate a framework for interdisciplinary discussion, latest innovations, and discoveries on public health. The first section of the book highlights the basic and principal aspects of advanced computational practices. Other sections of the book contain geo-simulation, agent-based modeling, spatio-temporal analysis, geospatial data mining, various geocomputational applications, accuracy and uncertainty of geospatial models, applications in environmental, ecological, and biological modeling and analysis in public health research. This book will be useful to the postgraduate students of geography, remote sensing, ecology, environmental sciences and research scholars, along with health professionals looking to solve grand challenges and management on public health.

Presenting current research on spatial epidemiology, this book covers topics such as exposure, chronic disease, infectious disease, accessibility to health care settings and new methods in Geographical Information Science and Systems. For epidemiologists, and for the management and administration of health care settings, it is critical to understand the spatial dynamics of disease. For instance, it is crucial that hospital administrators develop an understanding of the flow of patients over time, especially during an outbreak of a particular disease, so they can plan for appropriate levels of staffing and to carry out adaptive prevention measures. Furthermore, understanding where and why a disease occurs at a certain geographic location is vital for decision makers to formulate policy to increase the accessibility to health services (either by prevention, or adding new facilities). Spatial epidemiology relies increasingly on new methodologies, such as clustering algorithms, visualization and space-time modelling, the domain of Geographic Information Science. Implementation of those techniques appears at an increasing pace in commercial Geographic Information Systems, alongside more traditional techniques that are already part of such systems. This book provides the latest methods in GI Science and their use in health related problems.

Negation and speculation detection is an emerging topic that has attracted the attention of many researchers, and there is clearly a lack of relevant textbooks and survey texts. This book aims to define negation and speculation from a natural language processing perspective, to explain the need for processing these phenomena, to summarise existing research on processing negation and speculation, to provide a list of resources and tools, and to speculate about future developments in this research area. An advantage of this book is that it will not only provide an overview of the state of the art in negation and speculation detection, but will also introduce newly developed data sets and scripts. It will be useful for students of natural language processing subjects who are interested in understanding this task in more depth and for researchers with an interest in these phenomena in order to improve performance in other natural language processing tasks.

This book presents a comprehensive state-of the-art approach to digital health technologies and practices within the broad confines of healthcare practices. It provides a canvas to discuss emerging digital health solutions, propelled by the ubiquitous availability of miniaturized, personalized devices and affordable, easy to use wearable sensors, and innovative technologies like 3D printing, virtual and augmented reality and driverless robots and vehicles including drones. One of the most significant promises the digital health solutions hold is to keep us healthier for longer, even with limited resources, while truly scaling the delivery of healthcare. Digital Health: Scaling Healthcare to the World addresses the emerging trends and enabling technologies contributing to technological advances in healthcare practice in the 21st Century. These areas include generic topics such as mobile health and telemedicine, as well as specific concepts such as social media for health, wearables and quantified-self trends. Also covered are the psychological models leveraged in design of solutions to persuade us to follow some recommended actions, then the design and educational facets of the proposed innovations, as well as ethics, privacy, security, and liability aspects influencing its acceptance. Furthermore, sections on economic aspects of the proposed innovations are included, analyzing the potential business models and entrepreneurship opportunities in the domain.

Regionalization refers to the process of partitioning geographic space into regions that are internally similar yet distinct from each other. This process is fundamental for understanding complex spatial relationships and how areas interact with each other. Spatial networks provide a rich source of data (e.g., flows of people, goods or information) that can be used to generate regions. How to extract useful information from the spatial networks for better regionalization has been a longstanding topic of interest, and methods such as community detection and node clustering have been well studied. However, nowadays, many regionalization tasks remain confined to domain-specific datasets and traditional methods, while the rapid growth of the big data era and Artificial Intelligence (AI) methods has created more opportunities for improved regionalization. This dissertation focuses on how to utilize mobility-based spatial networks and Geospatial Artificial Intelligence (GeoAI) methods for better regionalization. This dissertation will start by demonstrating how spatial networks can be applied to delineate Rational Service Areas (RSAs) in the public health domain. Existing RSAs are usually developed based on the local knowledge of public health needs and are created through time-intensive manual work by health service officials. In this dissertation, a data-driven and spatially constrained community detection method based on aggregated human mobility flow data is proposed to automate the process of establishing the statewide RSAs in GIS software. The proposed method outperforms other baselines in the multiple metrics and shows the promising potential of mobility-based regionalization in the public health domain. Then, the dissertation improves the existing community detection method by proposing GeoAI-enhanced models based on Graph Convolutional Networks and Graph Attention Networks. The methods identify regions by considering attribute similarity, geographic adjacency and spatial interactions. The methods are compared with multiple baselines and perform best when one wants to maximize node attribute similarity and spatial interaction intensity simultaneously within the communities. It is applied to the shortage area delineation problem in public health and demonstrates its promise in solving regionalization problems. Lastly, the dissertation improves the interpretability of the proposed GeoAI methods. Most of the AI methods are considered black boxes, meaning that humans cannot understand the internal process, leading to low transparency and trustworthiness of the AI-generated results. However, geographic research often requires an explanation and discussion of why certain predictions are generated to understand the processes underlying the observed data. To promote the application of GeoAI methods to regionalization, the dissertation applies an explainable AI model to the proposed GeoAI-based community detection method. The explanations provide important node features and subgraph structures that contribute the most to each prediction. By examining individual and aggregated explanations, community activity spaces are compared, and potential regional patterns are discovered to support the reasoning of regionalization results. In summary, this dissertation aims to provide valuable insights into how human mobility data and GeoAI methods can be applied to regionalization tasks in spatial networks. It proposes a framework for automatic area delineation using mobility data that can be expanded to other use cases. Then, it improves the existing regionalization methods by introducing GeoAI models to combine node attributes and edge connections. Thirdly, it provides insights into how GeoAI models can be interpreted and explained to better understand the underlying geographic phenomena.

This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing, and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments, and applications of deep learning, and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists, biomedical engineers, and healthcare professionals researching and developing deep learning techniques. In short, the volume : Discusses the relationship between AI and healthcare, and how AI is changing the health care industry. Considers uses of deep learning in diagnosis and prediction of disease spread. Presents a comprehensive review of research applying deep learning in health informatics across multiple fields. Highlights challenges in applying deep learning in the field. Promotes research in ddeep llearning application in understanding the biomedical process. Dr.. M.A. Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India. Prof. (Dr.) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA. Dr.. Onur Dogan is an assistant professor at İzmir Bakırçay University, Turkey. Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP), Portugal. Dr.. Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas, Mexico.

This book discusses research in Artificial Intelligence for the Internet of Health Things. It investigates and explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in design, implementation, and optimization of challenging healthcare solutions. This book features a wide range of topics such as AI techniques, IoT, cloud, wearables, and secured data transmission. Written for a broad audience, this book will be useful for clinicians, health professionals, engineers, technology developers, IT consultants, researchers, and students interested in the AI-based healthcare applications. Provides a deeper understanding of key AI algorithms and their use and implementation within the wider healthcare sector Explores different disease diagnosis models using machine learning, deep learning, healthcare data analysis, including machine learning, and data mining and soft computing algorithms Discusses detailed IoT, wearables, and cloud-based disease diagnosis model for intelligent systems and healthcare Reviews different applications and challenges across the design, implementation, and management of intelligent systems and healthcare data networks Introduces a new applications and case studies across all areas of AI in healthcare data K. Shankar (Member, IEEE) is a Postdoctoral Fellow of the Department of Computer Applications, Alagappa University, Karaikudi, India. Eswaran Perumal is an Assistant Professor of the Department of Computer Applications, Alagappa University, Karaikudi, India. Dr. Deepak Gupta is an Assistant Professor of the Department Computer Science & Engineering, Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India.