Performance Evaluation of CNN-based Transfer Learning for COVID-19 Pneumonia Identification with Various Levels of Layer Partial Freezing

Pneumonia is a serious complication of coronavirus that can be fatal, especially among the elderly. Early diagnosis of COVID-19 pneumonia increases the likelihood of recovery and prevents the further spread of the virus. Chest X-ray (CXR) images can be utilized to detect specific signs associated with COVID-19, but this needs well-trained radiologists. Alternatively, deep Convolutional Neural Network (CNN)-based models have been successfully applied to diagnose COVID-19 and the associated pneumonia from CXR using transfer learning. This study explores various levels combining layer fine-tuning and freezing in two popular pretrained CNN-based models, VGG16 and ResNET50, and how these combinations influence the learning transferability of pretrained models to improve the identification of COVID-19 pneumonia from CXR images. We found that robust models can be learned with less labeled data in a shorter training time by applying partial freezing instead of the full network fine-tuning without sacrificing a significant portion of their diagnostic performance. © 2022 IEEE.

Sentiment Analysis of COVID-19 Vaccination Responses From Twitter Using Ensemble Learning

Despite the evidence that shows the benefits and safety of immunizations, the widespread vaccine-related misinformation and conspiracy theories online have fueled a general vaccine hesitancy, and coronavirus disease (COVID-19) vaccinations are no exception. COVID-19 vaccine hesitancy is considered a global threat to public health that undermines the efforts to control the COVID-19 pandemic. Twitter and other social media platforms allow people to exchange information and express concerns and emotions on COVID-19-related issues. This research aims to understand people's sentiment on COVID-19 vaccines from data collected from Twitter. Analyzing the public attitude toward the vaccines helps the authorities to make better decisions and reach the intended herd immunity. In this paper, we utilize the state-of-the-art transformer-based classification models, RoBERTa and BERT, along with multiple task-specific versions, to classify people's opinions about COVID-19 vaccinations into positive, negative, and neutral. A Twitter dataset that consists of people's opinions about vaccines is used to train and evaluate the presented models. Two ensemble learning techniques that aggregate the individual classifiers are presented for further performance improvement: majority voting and stacking with Support Vector Machine (SVM) as meta-learner. The results also show that applying ensemble learning significantly outperforms the individual classifiers using all evaluation measures. We also found that ensembling with stacking has an advantage over simple majority voting. © 2022 IEEE.

Datasets and Approaches of COVID-19 Misinformation Detection: A Survey

Social media has become a primary source of news, providing a fertile environment for spreading misinformation. Since the outbreak of the COVID-19 pandemic, misleading information related to COVID-19 has been spreading rapidly and widely on social media. Several conspiracy theories have emerged regarding the origin of the COVID-19, potential treatments, and vaccines posing a real threat to the public health of people. Fake news that promotes vaccine hesitancy might jeopardize achieving the levels of vaccination needed to reach herd immunity and end the pandemic. The need for automatic tools that detect COVID-19 related misinformation has encouraged researchers to propose several Machine learning (ML) and Deep Learning (DL). Many datasets have been released since the start of the pandemic, aiming to assess the performance of misinformation detection methods. This survey reviews the datasets that have been released to analyze the related to COVID-19 in general and COVID-19 misinformation detection in particular released in Arabic, English, and other languages. We also provide an overview of the different methods used to detect COVID-19 fake news. In this paper, the terms 'misleading information', 'misinformation', and 'fake news' are used interchangeably. © 2022 IEEE.

The Effectiveness of Adopting e-Learning during COVID-19 at Hashemite University

e-Learning is the utilization of the electronic technologies and the media to deliver the educational content to the learners, enabling them to interact actively with the content, the teachers, and their peers. Students’ interaction can be either synchronous or asynchronous or a combination of both. One advantage of the e-learning is that learners can access the educational content at any place and time saving them effort, time, and cost. To deal with the unprecedented crisis of COVID-19 and the risk of virus transmission in the public, the vast majority of higher learning institutions globally were locked out and the delivery of the educational content moved from the traditional classroom teaching to the internet. The purpose of this study was to assess students’ perceptions of the effectiveness of the e-learning during COVID-19 pandemic at the Hashemite University, Jordan. A total of 399 students completed the online survey of the study. Study results showed that students’ overall evaluation of their e-learning experiences were generally positive. However, students reported that they faced problems in the e-learning experiences of which most were related to technical issues (e.g., lack of a viable internet network, lack of laptops, etc.). Microsoft Teams was the platform most preferred by students for e-learning and the majority of students accessed the educational content using smart phones. Only gender and student’s academic specialty had significant associations with their perceptions of the effectiveness of the e-learning. © 2020, International Journal of Advanced Computer Science and Applications, All Rights Reserved