Название: Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics: Techniques and Applications
Автор: Sujata Dash, Subhendu Kumar Pani, Joel Rodrigues
Издательство: CRC Press
Год: 2022
Страниц: 382
Язык: английский
Формат: pdf (true)
Размер: 72.2 MB

The novel applications of Internet of Things (IoT) in health care data analytics can be regarded as an emerging field in Computer Science, medicine, biology application, natural environmental engineering, and pattern recognition. The use of various techniques of IOT systems for health care data analytics are nowadays successfully implemented in many domestic, commercial, and industrial applications due to the low- cost and high performance of various tools. Biomedical and Health Informatics is a new era that brings tremendous opportunities and challenges because of the easily available abundance of biomedical data. Deep Learning (DL) has been showing tremendous improvement in accuracy, robustness, and cross- language generalizability over conventional approaches. The aim of healthcare informatics is to ensure high quality, efficient healthcare, with better treatment and quality of life, by efficiently analysing this abundant biomedical and healthcare data. Earlier, a common requirement was to have a domain expert develop a model for biomedical or healthcare; but now the patterns for prediction are learned automatically. Due to the rapid advances in wearable sensors and actuators, the IoT and intelligent algorithms have growing significance in healthcare data analytics. The IoT focuses on the common idea of things that are recognizable, readable, locatable, controllable, and addressable via the Internet. It covers devices, sensors, people, data, and machines.

Machine Learning (ML) can provide computational methods for accumulating, updating, and changing knowledge in the intelligent systems, especially learning mechanisms that help us induce knowledge from the data. ML is helpful in the cases where direct algorithmic solutions are unavailable, lack formal models, or require knowledge about the application domain that is skimpy. IoT is anticipated to have the capability to change the way we work and live. These computing methods also play a significant role in the design and optimization of diverse engineering disciplines. With the influence and development of the IoT concept, the need for Artificial Intelligence (AI) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties, and approximations to arrive at a rapid solution. However, recent advancements in the representation of Intelligent IoT systems suggest a more intelligent and robust system that could provide a human-interpretable, low- cost, rough solution. Intelligent IoT systems have demonstrated great performance in a variety of areas, including Big Data analytics, time series, biomedical and health informatics, etc.

This book will play a vital role in improving human life. Researchers and practitioners will be benefited by those who are working in the fields of biomedical, health informatics, IoT, and Machine Learning. This book contains a collection of state- of- the- art approaches for Machine Learning, Deep Learning, and IoT- based biomedical and health related applications. Here, researchers and practitioners working in the field can quickly find the most functional models. They can compare different approaches and carry forward their own research in this area to directly impact human life and health.

Discusses Deep Learning, IOT, Machine Learning, and biomedical data analysis with broad coverage of basic scientific applications
Presents deep learning and the tremendous improvement in accuracy, robustness, and cross-language generalizability it has over conventional approaches
Discusses various techniques of IOT systems for healthcare data analytics
Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics
Focuses more on the application of algorithms in various real life biomedical and engineering problems