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The COVID-19 pandemic has led to increased use of online resources in educational institutions, making e-learning a necessity. This study aimed to investigate how students from four colleges at Jazan University in Saudi Arabia perceived and accepted e-learning and e-evaluation. Methods: 236 students participated in a cross-sectional study conducted in October 2021. The students completed a well-constructed questionnaire with 22 closed-ended questions divided into five domains. The students rated their answers on a 5-point Likert scale from 1 to 5. To analyze the collected data, the researchers utilized SPSS (v26). Result: Out of the total 236 students who participated in the study, most (30.5%) from the College of Public Health and Tropical Medicine (59.8%) used laptops, while 78% of students had a favorable impression of e-learning. Students' perceptions were focused on whether e-learning and e-evaluation methods helped them understand the study material smoothly and clearly. Among the students, 27% strongly agreed, 25% agreed, 16% were neutral, 11% disagreed, and 11% strongly disagreed. Additionally, 28% of the students strongly agreed, and 34% agreed that exam questions during e-evaluation were appropriate and comprehensive. Although online learning may result in less social contact, a lack of social presence, and difficulties in communication harmonization, e-learning still has some positive effects on students. It is considered a powerful platform, especially during emergencies or for those unable to attend in-person classes to complete their studies. The study has significant implications for higher education institutions, especially during emergencies, where online learning is necessary. Future research can further explore the factors that affect student perceptions and acceptance of elearning and e-evaluation and how to improve them. © 2023, Pakistan Journal of Life and Social Sciences. All Rights Reserved.
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Coronavirus (COVID-19) is an ecumenical pandemic that has affected the whole world drastically by raising a global calamitous situation. Due to this pernicious disease, millions of people have lost their lives. The scientists are still far from knowing how to tackle the coronavirus due to its multiple mutations found around the globe. Standard testing technique called Polymerase Chain Reaction (PCR) for the clinical diagnosis of COVID-19 is expensive and time consuming. However, to assist specialists and radiologists in COVID-19 detection and diagnosis, deep learning plays an important role. Many research efforts have been done that leverage deep learning techniques and technologies for the identification or categorization of COVID-19 positive patients, and these techniques are proved to be a powerful tool that can automatically detect or diagnose COVID-19 cases. In this paper, we identify significant challenges regarding deep learning-based systems and techniques that use different medical imaging modalities, including Cough and Breadth, Chest X-ray, and Computer Tomography (CT) to combat COVID-19 outbreak. We also pinpoint important research questions for each category of challenges. IEEE
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Fake news is a major threat to democracy (e.g., influencing public opinion), and its impact cannot be understated particularly in our current socially and digitally connected society. Researchers from different disciplines (e.g., computer science, political science, information science, and linguistics) have also studied the dissemination, detection, and mitigation of fake news;however, it remains challenging to detect and prevent the dissemination of fake news in practice. In addition, we emphasize the importance of designing artificial intelligence (AI)-powered systems that are capable of providing detailed, yet user-friendly, explanations of the classification / detection of fake news. Hence, in this article, we systematically survey existing state-of-the-art approaches designed to detect and mitigate the dissemination of fake news, and based on the analysis, we discuss several key challenges and present a potential future research agenda, especially incorporating AI explainable fake news credibility system.
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Background: The outbreak of coronavirus 2019 (COVID-19) which emerged in December 2019 spread rapidly and created a public health emergency. Geospatial records of case data are needed in real time to monitor and anticipate the spread of infection. Methods: This study aimed to identify the emerging hotspots of COVID-19 using a geographic information system (GIS)-based approach. Data of laboratory-confirmed COVID-19 patients from March 15 to June 12, 2020, who visited the emergency department of a tertiary specialized academic hospital in Dubai were evaluated using ArcGIS Pro 2.5. Spatiotemporal analysis, including optimized hotspot analysis, was performed at the community level. Results: The cases were spatially concentrated mostly over the inner city of Dubai. Moreover, the optimized hotspot analysis showed statistically significant hotspots (p < 0.01) in the north of Dubai. Waxing and waning hotspots were also observed in the southern and central regions of Dubai. Finally, there were nonsustaining hotspots in communities with a very low population density. Conclusion: This study identified hotspots of COVID-19 using geospatial analysis. It is simple and can be easily reproduced to identify disease outbreaks. In the future, more attention is needed in creating a wider geodatabase and identifying hotspots with more intense transmission intensity.
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In the next few years, smart farming will reach each and every nook of the world. The prospects of using unmanned aerial vehicles (UAV) for smart farming are immense. However, the cost and the ease in controlling UAVs for smart farming might play an important role for motivating farmers to use UAVs in farming. Mostly, UAVs are controlled by remote controllers using radio waves. There are several technologies such as Wi-Fi or ZigBee that are also used for controlling UAVs. However, Smart Bluetooth (also referred to as Bluetooth Low Energy) is a wireless technology used to transfer data over short distances. Smart Bluetooth is cheaper than other technologies and has the advantage of being available on every smart phone. Farmers can use any smart phone to operate their respective UAVs along with Bluetooth Smart enabled agricultural sensors in the future. However, certain requirements and challenges need to be addressed before UAVs can be operated for smart agriculture-related applications. Hence, in this article, an attempt has been made to explore the types of sensors suitable for smart farming, potential requirements and challenges for operating UAVs in smart agriculture. We have also identified the future applications of using UAVs in smart farming.
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Fake news, particularly with the speed and reach of unverified/false information dissemination, is a troubling trend with potential political and societal consequences, as evidenced in the 2016 United States presidential election, the ongoing COVID-19 pandemic, and the ongoing protests. To mitigate such threats, a broad range of approaches have been designed to detect and mitigate online fake news. In this paper, we systematically review existing fake news mitigation and detection approaches, and identify a number of challenges and potential research opportunities (e.g., the importance of a data sharing platform that can also be used to facilitate machine/deep learning). We hope that the findings reported in this paper will motivate further research in this area. © 2020, Springer Nature Switzerland AG.