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1.
Big Data and Cognitive Computing ; 7(1), 2023.
Article in English | Scopus | ID: covidwho-2264364

ABSTRACT

COVID-19 infection has been a major topic of discussion on social media platforms since its pandemic outbreak in the year 2020. From daily activities to direct health consequences, COVID-19 has undeniably affected lives significantly. In this paper, we especially analyze the effect of COVID-19 on education by examining social media statements made via Twitter. We first propose a lexicon related to education. Then, based on the proposed dictionary, we automatically extract the education-related tweets and also the educational parameters of learning and assessment. Afterwards, by analyzing the content of the tweets, we determine the location of each tweet. Then the sentiments of the tweets are analyzed and examined to extract the frequency trends of positive and negative tweets for the whole world, and especially for countries with a significant share of COVID-19 cases. According to the analysis of the trends, individuals were globally concerned about education after the COVID-19 outbreak. By comparing between the years 2020 and 2021, we discovered that due to the sudden shift from traditional to electronic education, people were significantly more concerned about education within the first year of the pandemic. However, these concerns decreased in 2021. The proposed methodology was evaluated using quantitative performance metrics, such as the F1-score, precision, and recall. © 2023 by the authors. Licensee MDPI, Basel, Switzerland.

2.
5th International Conference on Signal Processing and Information Security, ICSPIS 2022 ; : 103-106, 2022.
Article in English | Scopus | ID: covidwho-2226980

ABSTRACT

Numerous comments from various world regions have been posted during the COVID-19 outbreak regarding the impact of drug use on the COVID-19 disease. Alongside this, this paper proposes a method for extracting drug-related tweets from the COVID-19 tweets dataset. Initially, using the Addiction Center and Oxford databases, a lexicon of drug-related words and phrases is proposed. Then, incremental revisions are made to this lexicon to enhance the accuracy, recall, and F1 score evaluation metrics. The final results demonstrate that the proposed lexicon is precise and accurate. © 2022 IEEE.

3.
Intelligent Decision Technologies ; 16(3):557-574, 2022.
Article in English | Scopus | ID: covidwho-2109696

ABSTRACT

The pandemic COVID-19 is already in its third year and there is no sign of ebbing. The world continues to be in a never-ending cycle of disease outbreaks. Since the introduction of Omicron-the most mutated and transmissible of the five variants of COVID-19-fear and instability have grown. Many papers have been written on this topic, as early detection of COVID-19 infection is crucial. Most studies have used X-rays and CT images as these are highly sensitive to detect early lung changes. However, for privacy reasons, large databases of these images are not publicly available, making it difficult to obtain very accurate AI Deep Learning models. To address this shortcoming, transfer learning (pre-trained) models are used. The current study aims to provide a thorough comparison of known AI Deep Transfer Learning models for classifying lung radiographs into COVID-19, non COVID pneumonia and normal (healthy). The VGG-19, Inception-ResNet, EfficientNet-B0, ResNet-50, Xception and Inception models were trained and tested on 3568 radiographs. The performance of the models was evaluated using accuracy, sensitivity, precision and F1 score. High detection accuracy scores of 98% and 97% were found for the VGG-19 and Inception-ResNet models, respectively. © 2022-IOS Press. All rights reserved.

4.
2021 4th International Conference on Signal Processing and Information Security (Icspis) ; 2021.
Article in English | Web of Science | ID: covidwho-2042775

ABSTRACT

The modernization of advanced healthcare infrastructure in the early 21st century is still failing to cope up as the whole world is struggling to get rid of the deadly disease named COVID-19. The scarcity of clinical resources is one of the most fundamental as well as critical reasons behind this calamity. The entire healthcare system faces severe challenges to re-stabilize the system. Digitization of technology which is primarily driven by the next-generation communication networks has given an exclusive paradigm shift to resolving the issues. 5th generation of mobile communication-(5G) introduces classical techniques which play crucial roles in e-healthcare transformations. Software-Defined Networking(SDN), Network Function Virtualization (NFV), Network Slicing (NS), and the concept of programmable networks introduce URLLC communication and time-critical service delivery in a resource-restricted environment. Leveraging the concept of programmable slicing approach, in this work, we have framed a flexible e-healthcare slicing model for dedicated and optimized resource provisioning. We have introduced a vSDN server that can significantly balance the healthcare slice and classify the complex medical databases into simplified segments for quick data processing, management, and orchestration. Considering the reformation of global healthcare customization, our proposed approach will play a vital role in the field of the e-healthcare domain.

5.
2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021 ; : 947-952, 2021.
Article in English | Scopus | ID: covidwho-1948731

ABSTRACT

Student mental health in higher education has been an increasing concern. The COVID-19 pandemic situation has brought this vulnerable population into renewed focus. Governments had to close several sections, including educational institutes and universities, around the world suddenly in March 2020. Hence, emergency remote learning was adopted as alternative and as an immediate response to the ongoing situation using whatever available online tools. As a result, both students and instructors were forced to adapt to this new situation. While there were several studies that addressed several issues related to preparation, contents, course delivery, readiness, etc., there are few ones that were intended to address the mental challenges resulted from the shift to emergency remote learning. The main motivation behind this study is to understand the mental challenges and effects of the sudden transformation into emergency remote learning considering engineering students as case study and improve the delivery and experience of learning for both the instructors and the students. © 2021 IEEE.

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