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Access to approved scientific medical literature is vital in the context of providing standard, evidence-based health care. Despite the abundance of medical information, ensuring that it is available to health care professionals in the field remains a challenge. Remote areas in economically disadvantaged sub-Saharan Africa often lack internet facilities or reference libraries. At the same time, the long-term storage of books and other documents is a problem due to climatic factors such as humidity and heat, the presence of pests like insects and rodents, as well as environmental hazards and the lack of trained staff to adequately manage, maintain and distribute literary material. Against this background and based on practical experience, this book compiles a series of common disease management protocols adapted for minimal-resource facilities, formulas for national healthcare schemes (vaccination, antenatal, child care) and other useful documents that can facilitate effective hospital management in low-resource countries. Primarily intended for healthcare workers in The Gambia and other nations with a similar socio-economical background, the book is also a valuable resource for international students and healthcare workers from regions like Europe and the Americas intending to do internships in economically underprivileged geographic areas.
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This book includes high-quality research on various aspects of intelligent interactive multimedia technologies in healthcare services. The topics covered in the book focus on state-of-the-art approaches, methodologies, and systems in the design, development, deployment, and innovative use of multimedia systems, tools, and technologies in healthcare. The volume provides insights into smart healthcare service demands. It presents all information about multimedia uses in e-healthcare applications. The book also includes case studies and self-assessment problems for readers and future researchers. This book proves to be a valuable resource to know how AI can be an alternative tool for automated and intelligent analytics for e-healthcare applications.
Internet of Things (IoTs) are now being integrated at a large scale in fast-developing applications such as healthcare, transportation, education, finance, insurance and retail. The next generation of automated applications will command machines to do tasks better and more efficiently. Both industry and academic researchers are looking at transforming applications using machine learning and deep learning to build better models and by taking advantage of the decentralized nature of Blockchain. But the advent of these new technologies also brings very high expectations to industries, organisations and users. The decrease of computing costs, the improvement of data integrity in Blockchain, and the verification of transactions using Machine Learning are becoming essential goals. This edited book covers the challenges, opportunities, innovations, new concepts and emerging trends related to the use of machine learning, Blockchain and Big Data analytics for IoTs. The book is aimed at a broad audience of ICTs, data science, machine learning and cybersecurity researchers interested in the integration of these disruptive technologies and their applications for IoTs.
Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured (labelled) and unstructured (unlabelled) data. It is the future of Artificial Intelligence (AI) and the necessity of future to make things easier and more productive. In simple terms, Data science is the discovery of data or uncovering hidden patterns (like complex behaviors, trends, and inferences) from data. Moreover, Big Data Analytics/Data Analytics are the analysis mechanism used in Data Science by Data Scientist. Several tools like Hadoop, R, etc., are being used to analyse this large amount of data that can be used in predicting the valuable information/ making decisions. Note that structured data can be easily analysed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in unstructured form that requires advanced analytics tools. But while analysing, we face several concerns like complexity, scalability, privacy leaking and trust issues. Data science helps us in extracting meaningful information (or insights) from the unstructured or complex or large amount of data (available or stored around us virtually at cloud).
This book discuss how deep learning can help healthcare images or text data in making useful decisions”. For that, the need of reliable deep learning models like Neural networks, Convolutional neural network, Backpropagation, Recurrent neural network is increasing in medical image processing, i.e., in Colorization of Black and white images of X-Ray, automatic machine translation, object classification in photographs / images (CT-SCAN), character or useful generation (ECG), image caption generation, etc. Hence, Reliable Deep Learning methods for perception or producing belter results are highly effective for e-healthcare applications, which is the challenge of today. For that, this book provides some reliable deep leaning or deep neural networks models for healthcare applications via receiving chapters from around the world. In summary, this book will cover introduction, requirement, importance, issues and challenges, etc., faced in available current deep learning models (also include innovative deep learning algorithms/ models for curing disease in Medicare) and provide opportunities for several research communities with including several research gaps in deep learning models (for healthcare applications).
This book includes high-quality research on various aspects of intelligent interactive multimedia technologies in healthcare services. The topics covered in the book focus on state-of-the-art approaches, methodologies, and systems in the design, development, deployment, and innovative use of multimedia systems, tools, and technologies in healthcare. The volume provides insights into smart healthcare service demands. It presents all information about multimedia uses in e-healthcare applications. The book also includes case studies and self-assessment problems for readers and future researchers. This book proves to be a valuable resource to know how AI can be an alternative tool for automated and intelligent analytics for e-healthcare applications.