For past several years, microarray technology has attracted tremendous interest for both scientific community and industry. Recently, the applications of microarrays include gene discovery, disease diagnosis and prognosis, drug discovery, etc. High dimensional data with small sample size is the main problem that generate the application of dimension reduction in microarray data analysis. It is seen that SVM, ANN and NB have recently gained wide popularity for cancer classification problems. An efficient and reliable method of dimension reduction plays an important role to improve the performance of SVM, ANN and NB, when applied for classification of high dimensional microarray data. In this book, we applied different combinations of feature selection / extraction methods, as a novel hybrid dimension reduction method for SVM, ANN and NB classifiers. The obtained results are compared with other popular published dimension reduction methods for SVM, NB and ANN classifiers.

Machine Learning Approach

Today world thinks about coronavirus disease that which means all even this pandemic disease is not unique. The purpose of this study is to detect the role of machine-learning applications and algorithms in investigating and various purposes that deals with COVID-19. Review of the studies that had been published during 2020 and were related to this topic by seeking in Science Direct, Springer, Hindawi, and MDPI using COVID-19, machine learning, supervised learning, and unsupervised learning as keywords. The total articles obtained were 16,306 overall but after limitation; only 14 researches of these articles were included in this study. Our findings show that machine learning can produce an important role in COVID-19 investigations, prediction, and discrimination. In conclusion, machine learning can be involved in the health provider programs and plans to assess and triage the COVID-19 cases. Supervised learning showed better results than other Unsupervised learning algorithms by having 92.9% testing accuracy. In the future recurrent supervised learning can be utilized for superior accuracy.

/pdf/coronavirus-disease-covid-19-cases-analysis-using-machine-43fti7dfvz.pdf

Coronavirus disease (COVID-19) cases analysis using machine-learning applications.

Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.

/pdf/applications-of-artificial-intelligence-in-battling-against-18cfgukc2t.pdf

Applications of artificial intelligence in battling against covid-19: A literature review.

Low digital health literacy affects large percentages of populations around the world and is a direct contributor to the spread of COVID-19-related online misinformation (together with bots). The ease and 'viral' nature of social media sharing further complicate the situation. This paper provides a quick overview of the magnitude of the problem of COVID-19 misinformation on social media, its devastating effects, and its intricate relation to digital health literacy. The main strategies, methods and services that can be used to detect and prevent the spread of COVID-19 misinformation, including machine learning-based approaches, health literacy guidelines, checklists, mythbusters and fact-checkers, are then briefly reviewed. Given the complexity of the COVID-19 infodemic, it is very unlikely that any of these approaches or tools will be fully effective alone in stopping the spread of COVID-19 misinformation. Instead, a mixed, synergistic approach, combining the best of these strategies, methods, and services together, is highly recommended in tackling online health misinformation, and mitigating its negative effects in COVID-19 and future pandemics. Furthermore, techniques and tools should ideally focus on evaluating both the message (information content) and the messenger (information author/source) and not just rely on assessing the latter as a quick and easy proxy for the trustworthiness and truthfulness of the former. Surveying and improving population digital health literacy levels are also essential for future infodemic preparedness.

https://www.mdpi.com/1660-4601/18/15/8091/pdf

COVID-19 Misinformation Online and Health Literacy: A Brief Overview.

While the world has experience with many different types of infectious diseases, the current crisis related to the spread of COVID-19 has challenged epidemiologists and public health experts alike, leading to a rapid search for, and development of, new and innovative solutions to combat its spread. The transmission of this virus has infected more than 18.92 million people as of August 6, 2020, with over half a million deaths across the globe; the World Health Organization (WHO) has declared this a global pandemic. A multidisciplinary approach needs to be followed for diagnosis, treatment and tracking, especially between medical and computer sciences, so, a common ground is available to facilitate the research work at a faster pace. With this in mind, this survey paper aimed to explore and understand how and which different technological tools and techniques have been used within the context of COVID-19. The primary contribution of this paper is in its collation of the current state-of-the-art technological approaches applied to the context of COVID-19, and doing this in a holistic way, covering multiple disciplines and different perspectives. The analysis is widened by investigating Artificial Intelligence (AI) approaches for the diagnosis, anticipate infection and mortality rate by tracing contacts and targeted drug designing. Moreover, the impact of different kinds of medical data used in diagnosis, prognosis and pandemic analysis is also provided. This review paper covers both medical and technological perspectives to facilitate the virologists, AI researchers and policymakers while in combating the COVID-19 outbreak.

A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic.

Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. COVID-19 has affected more than 100 countries in a matter of no time. People all over the world are vulnerable to its consequences in future. It is imperative to develop a control system that will detect the coronavirus. One of the solution to control the current havoc can be the diagnosis of disease with the help of various AI tools. In this paper, we classified textual clinical reports into four classes by using classical and ensemble machine learning algorithms. Feature engineering was performed using techniques like Term frequency/inverse document frequency (TF/IDF), Bag of words (BOW) and report length. These features were supplied to traditional and ensemble machine learning classifiers. Logistic regression and Multinomial Naive Bayes showed better results than other ML algorithms by having 96.2% testing accuracy. In future recurrent neural network can be used for better accuracy.

/pdf/machine-learning-based-approaches-for-detecting-covid-19-l9j0ajreyt.pdf

Masarat Mohi Ud Din

Papers

Machine learning based approaches for detecting COVID-19 using clinical text data.