COVID-19 and Black Fungus: Analysis of the Public Perceptions through Machine Learning

Islam, Muhammad Nazrul; Khan, Nafiz Imtiaz; Mahmud, Tahasin

doi:10.1101/2021.07.08.21260188

Cited by 2 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, this study examined the public attitudes of several crucial topics (such as education, lockdown, hospital, oxygen, quarantine, and vaccine) and discovered that public perceptions varied. For instance, while the majority of people reported anxiety on social media regarding education, hospitals, and vaccines, some expressed joy regarding education, hospitals, vaccines, and oxygen [ 13 ].…”

Section: Related Studymentioning

confidence: 99%

A Novel Deep Learning‐Based Black Fungus Disease Identification Using Modified Hybrid Learning Methodology

Karthikeyan

Ramkumar

Sekar³

et al. 2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

Currently, countries across the world are suffering from a prominent viral infection called COVID-19. Most countries are still facing several issues due to this disease, which has resulted in several fatalities. The first COVID-19 wave caused devastation across the world owing to its virulence and led to a massive loss in human lives, impacting the country’s economy drastically. A dangerous disease called mucormycosis was discovered worldwide during the second COVID-19 wave, in 2021, which lasted from April to July. The mucormycosis disease is commonly known as “black fungus,” which belongs to the fungus family Mucorales. It is usually a rare disease, but the level of destruction caused by the disease is vast and unpredictable. This disease mainly targets people already suffering from other diseases and consuming heavy medication to counter the disease they are suffering from. This is because of the reduction in antibodies in the affected people. Therefore, the patient’s body does not have the ability to act against fungus-oriented infections. This black fungus is more commonly identified in patients with coronavirus disease in certain country. The condition frequently manifests on skin, but it can also harm organs such as eyes and brain. This study intends to design a modified neural network logic for an artificial intelligence (AI) strategy with learning principles, called a hybrid learning-based neural network classifier (HLNNC). The proposed method is based on well-known techniques such as convolutional neural network (CNN) and support vector machine (SVM). This article discusses a dataset containing several eye photographs of patients with and without black fungus infection. These images were collected from the real-time records of people afflicted with COVID followed by the black fungus. This proposed HLNNC scheme identifies the black fungus disease based on the following image processing procedures: image acquisition, preprocessing, feature extraction, and classification; these procedures were performed considering the dataset training and testing principles with proper performance analysis. The results of the procedure are provided in a graphical format with the precise specification, and the efficacy of the proposed method is established.

show abstract

Section: Related Studymentioning

confidence: 99%

A Novel Deep Learning‐Based Black Fungus Disease Identification Using Modified Hybrid Learning Methodology

Karthikeyan

Ramkumar

Sekar³

et al. 2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

show abstract

“…The exchange of huge data related to COVID-19 in social media created the concerns of researchers, and some studies are carried out focusing the sentiment analysis using Twitter data. 6,7 Few have conducted sentiment analyses during the COVID-19 pandemic; for example, Islam et al 8 studied the public perceptions toward COVID-19 and Black Fungus, Chandra and Krishna 9 analyzed public sentiment regarding COVID-19 by deep learning methods. However, the reasons behind the change in public perceptions during the COVID-19 pandemic are yet to be explored.…”

Section: Introductionmentioning

confidence: 99%

Leveraging machine learning to analyze sentiment from COVID‐19 tweets: A global perspective

Rahman¹,

Khan²,

Sarker

et al. 2022

Engineering Reports

Self Cite

View full text Add to dashboard Cite

Since the advent of the worldwide COVID‐19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision‐makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state‐of‐the‐art technologies has been focused on during the COVID‐19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID‐19 pandemic from a cross‐country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective machine learning (ML) technique for classifying public sentiments, to analyze the variations of public sentiment across the globe, and to find the critical contributing factors to sentiment variations. To attain the objectives, 12,000 tweets, 3000 each from the USA, UK, and Bangladesh, were rigorously annotated by three independent reviewers. Based on the labeled tweets, four different boosting ML models, namely, CatBoost, gradient boost, AdaBoost, and XGBoost, are investigated. Next, the top performed ML model predicted sentiment of 300,000 data (100,000 from each country). The public perceptions have been analyzed based on the labeled data. As an outcome, the CatBoost model showed the highest (85.8%) F1‐score, followed by gradient boost (84.3%), AdaBoost (78.9%), and XGBoost (83.1%). Second, it was revealed that during the time of the COVID‐19 pandemic, the sentiments of the people of the three countries mainly were negative, followed by positive and neutral. Finally, this study identified a few critical concerns that impact primarily varying public sentiment around the globe: lockdown, quarantine, hospital, mask, vaccine, and the like.

show abstract

COVID-19 and Black Fungus: Analysis of the Public Perceptions through Machine Learning

Cited by 2 publications

References 21 publications

A Novel Deep Learning‐Based Black Fungus Disease Identification Using Modified Hybrid Learning Methodology

A Novel Deep Learning‐Based Black Fungus Disease Identification Using Modified Hybrid Learning Methodology

Leveraging machine learning to analyze sentiment from COVID‐19 tweets: A global perspective

Contact Info

Product

Resources

About