COVID -19
Page: 1-22 (22)
Author: Ambika Nagaraj*
DOI: 10.2174/9789815179453123010004
PDF Price: $15
Abstract
Corona is a single-stranded RNA virus that has been around since the late
1960s when it was first discovered. The Nidovirales order includes the Corona viridae
family of viruses. The crown-shaped spikes on the virus structure's outer surface inspire
the name Corona. The virus has affected chickens and pigs, but there hasn't been a
significant human-to-human transmission. The virus's mode of communication and
other related information are continually updated every few weeks, increasing
uncertainty. A Chinese study suggests that the COVID-19 pandemic had a significant
psychological impact on more than half of the participants. One more ongoing review
from Denmark revealed mental prosperity as adversely impacted. According to the
American Psychiatric Association's survey, nearly half of Americans were anxious.
The chapter details the disease, its symptoms and measures taken.
Supervised Learning Algorithms
Page: 23-75 (53)
Author: Ambika Nagaraj*
DOI: 10.2174/9789815179453123010005
PDF Price: $15
Abstract
Numerous domains now employ learning algorithms. It has distinct
performance metrics appropriate for them.. Based on a predetermined set of paired
input-output training samples, a machine learning paradigm known as “Supervised
Learning” is used to gather information about a system's input-output relationship. An
input-output training sample is also known as supervised or labeled training data
because the output is regarded as the input data or supervision label. Supervised
learning aims to build an artificial system that can learn the mapping between input and
output and predict the system's output, given new information. The learned mapping
results in the classification of the input data if the output takes a limited set of discrete
values representing the input's class labels. Regression of the information occurs if the
output takes continuous values. The chapter details the various algorithms,
technologies used and their applications.
Semi-Supervised Algorithms
Page: 76-108 (33)
Author: Ambika Nagaraj*
DOI: 10.2174/9789815179453123010006
PDF Price: $15
Abstract
Semi-supervised learning, or SSL, falls somewhere between supervised and
unsupervised learning. The algorithm is provided with some supervision data in
addition to unlabeled data. There are two primary learning paradigms in it.
Transductive education aims to use the trained classifier on unlabeled instances
observed during training. This kind of algorithm is mainly used for node embedding on
graphs, like random walks, where the goal is to label the graph's unlabeled nodes at the
training time. Inductive learning aims to develop a classifier that can generalize
unobserved situations during a test. This chapter details different semi-supervised
algorithms in healthcare.
Unsupervised Algorithms
Page: 109-128 (20)
Author: Ambika Nagaraj*
DOI: 10.2174/9789815179453123010007
PDF Price: $15
Abstract
The broad term “health care” refers to a system that focuses on improving
medical services to meet the needs of patients. Patients, doctors, vendors, health
companies, and IT companies all work to keep and restore health records in the
healthcare industry. It uses machine learning. Healthcare analysis addresses a variety of
diseases, including cancer, diabetes, stroke, and others. Both the labeled value and the
target value are known. Training the data for unsupervised learning is also involved.
Because the label value is either unknown or absent, it is impossible to evaluate the
model's performance in unsupervised learning. The chapter details different
unsupervised algorithms.
Role of Internet-of-Things During Covid-19
Page: 129-213 (85)
Author: Ambika Nagaraj*
DOI: 10.2174/9789815179453123010008
PDF Price: $15
Abstract
In December 2019, the severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) infection that caused pneumonia spread to Wuhan City, Hubei
Province, China. Fever, dry cough, and fatigue are typical clinical manifestations of
COVID-19, frequently accompanied by pulmonary involvement. SARS-CoV-2 is
highly contagious, making most people in the general population susceptible to
infection. One of the most popular technologies, the Internet of Things (IoT), has much
potential for combating the coronavirus outbreak. It has transformed real-world objects
into sophisticated virtual ones. The Internet of Things (IoT) aims to connect everything
in our world and assist users in controlling the objects in their immediate vicinity and
keeping them informed of their current state. IoT devices sense the environment
without human or machine interaction and send the gathered data to the Internet cloud.
Tens of millions of devices are connected via the Internet of Things (IoT), and the
number of connected devices is rapidly increasing.
The chapter aims to highlight the role of IoT devices in detecting Covid-19. It details
the different architectures of the system. Various domains, like the role of machines in
healthcare, transportation, entertainment, retailing, and education, are detailed. It
addresses challenges - awareness, accessibility, human power crisis, affordability, and
accountability. Some of the future directions managed including edge architecture,
cryptography, blockchain, machine learning, digital twin, unified network integration,
context-aware accessibility, edge and fog computing, and sensor and actuator
integration are summarized.
Introduction
In the battle against the COVID-19 pandemic, the integration of Internet of Things (IoT) technologies has played a pivotal role in reshaping public health and healthcare delivery. Interconnected devices have demonstrated their capacity to collect, transmit, and analyze data, significantly impacting various aspects of pandemic management. COVID-19 Monitoring with IoT Devices is a comprehensive guide to measuring the impact of COVID-19 infection and monitoring outbreak metrics. Beginning with an introduction to SARS-CoV-2 and its symptoms, the book presents chapters on machine learning (supervised and unsupervised algorithms) and techniques to predict COVID-19 outcomes. The book concludes with the role of IoT technology in detecting COVID-19 infections within a community, showcasing different computing models applicable to specific use-cases. Key Features: Explores the pivotal role of IoT technology in the fight against the COVID-19 pandemic. Covers a data-driven approach to COVID-19 monitoring by explaining methods for data collection, prediction, and analysis. Includes specific recommendations for machine learning algorithms designed for COVID-19 monitoring. Easy-to-read structured chapters suitable for novices in computer science and biomedical engineering. COVID-19 Monitoring with IoT Devices provides a valuable resource for understanding the role of IoT technology in managing and mitigating the impact of COVID-19, and developing adequate infection control policies. It also showcases the potential of IoT for future research and applications in the healthcare sector. This book is intended for a diverse readership, including academicians, industry professionals, researchers, and healthcare practitioners.