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1.
NeuroQuantology ; 20(11):3306-3318, 2022.
Article in English | EMBASE | ID: covidwho-2067339

ABSTRACT

Online learning is the solution chosen to avoid crowds, which are feared to lead to the new cluster of the Covid-19 pandemic. It's just that online learning is often followed by a different process of teacher interaction in online learning.This study is intended to describe teacher interactions in online learning during the pandemic between teacher and student perspectives.The research approach is quantitative with the type of comparative research, through random sampling, namely on respondents who are in teacher and student social media forums, from January to March in 2021.The results of data collection obtained 789 teachers and 910 student respondents. The data collection instrument used a closed questionnaire.The results showed interaction of teachers providing online learning during the pandemic at all levels of education is significantly different andthere is a significant difference between a teacher and student learning interactions, at the level of achievement of teacher and student respondents at certain times/times both have had online learning interactions during the pandemic. For teachers the average value is 2.55 with a percentage of 56.1%, for students, the average value is 2.55 with a percentage of 63.9%. The researcher recommends that it is necessary to reflect and evaluate teacher interactions in online learning. Copyright © 2022, Anka Publishers. All rights reserved.

2.
NeuroQuantology ; 20(11):2126-2139, 2022.
Article in English | EMBASE | ID: covidwho-2067335

ABSTRACT

Online learning is the solution chosen to avoid crowds, which are feared to lead to the new cluster of the Covid-19 pandemic. It's just that online learning is often followed by a different process of teacher interaction in online learning.This study is intended to describe teacher interactions in online learning during the pandemic between teacher and student perspectives.The research approach is quantitative with the type of comparative research, through random sampling, namely on respondents who are in teacher and student social media forums, from January to March in 2021.The results of data collection obtained 789 teachers and 910 student respondents. The data collection instrument used a closed questionnaire.The results showed interaction of teachers providing online learning during the pandemic at all levels of education is significantly different andthere is a significant difference between a teacher and student learning interactions, at the level of achievement of teacher and student respondents at certain times/times both have had online learning interactions during the pandemic. For teachers the average value is 2.55 with a percentage of 56.1%, for students, the average value is 2.55 with a percentage of 63.9%. The researcher recommends that it is necessary to reflect and evaluate teacher interactions in online learning.

4.
Malaysian Construction Research Journal ; 36(1):101-110, 2022.
Article in English | Scopus | ID: covidwho-2012167

ABSTRACT

COVID-19 pandemic has affected real estate sectors, including Real Estate Investment Trust (REIT), as the total return of a REIT is subject to the property market’s performance. This research assesses the risk and return of Malaysian REITs (M-REITs) and All-REIT portfolios during the COVID-19 pandemic. Based on a desktop study, the researchers analysed M-REITs’ historical monthly closing price, FBM KLCI and the yield of 10-year MGS using the Capital Asset Pricing Model (CAPM). The number of observations is 24, six (6) months before and 18 months after COVID-19 hits Malaysia. The result shows that the estimated return of M-REITs using CAPM ranged from-0.1905 to 0.2391. The researchers also develop an equally weighted portfolio where all 17 M-REITs have an equal weight allocation of 0.0588 to assess the fair estimated return of the All-REIT portfolio. The findings also suggest that despite the pandemic and implementation of Movement Control Order (MCO), the average monthly log-return of M-REITs outperformed the monthly log-return of the market portfolio in April and November of 2020 as well as March and June 2021. The remaining period recorded slightly at par with FBM KLCI. Beta at less than 0.1 also indicates that M-REITs is less volatile than the market portfolio. In conclusion, CAPM suggests that M-REITs show a low-performance deviation with market portfolio during the pandemic signifying that it is a low-risk investment and shall be included in any investment portfolios. The findings of this research are vital for investors in considering M-REITs for their investment portfolio. © 2022, Construction Research Institute of Malaysia. All rights reserved.

5.
NeuroQuantology ; 20(6):9291-9303, 2022.
Article in English | EMBASE | ID: covidwho-1988592

ABSTRACT

Various teaching and learning approaches have been applied among educators to the students during the Movement and Control Orders (MCO) employed due to COVID-19. Educators especially those who are teaching the mathematics subject require comprehensive and effective tools to help their students understand the concept and able to do exercises with less face-to-face guidance or a normal conventional teaching approach. Teaching the mathematics subject especially to the Pre-Diploma students is very challenging as they are quite weak in mathematics fundamentals and coincidentally, they have to face the obstacles of teaching approach with Online learning during MCO. Hence, a mathematics teaching model with an online learning approach was created to make the teaching delivery effective, thus increasing the learning curve or performance of mathematics among students. The ongoing and final assessment results of the students were analysed using an independent sample t-test to measure the difference between those experiencing and without experiencing the suggested model.

6.
International Journal of Advanced Computer Science and Applications ; 12(12):667-677, 2021.
Article in English | Scopus | ID: covidwho-1626524

ABSTRACT

In 2020 World Health Organization (WHO) has declared that the Coronaviruses (COVID-19) pandemic is causing a worldwide health disaster. One of the most effective protections for reducing the spread of COVID-19 is by wearing a face mask in densely and close populated areas. In various countries, it has become mandatory to wear a face mask in public areas. The process of monitoring large numbers of individuals to comply with the new rule can be a challenging task. A costeffective method to monitor a large number of individuals to comply with this new law is through computer vision and Convolution Neural Network (CNN). This paper demonstrates the application of transfer learning on pre-trained CNN architectures namely;AlexNet, GoogleNet ResNet-18, ResNet-50, ResNet-101, to classify whether or not a person in the image is wearing a facemask. The number of training images are varied in order to compare the performance of these networks. It is found that AlexNet performed the worst and requires 400 training images to achieve Specificity, Accuracy, Precision, and F-score of more than 95%. Whereas, GoogleNet and Resnet can achieve the same level of performance with 10 times fewer number of training images. © 2021. All Rights Reserved.

7.
International Journal of Advanced Computer Science and Applications ; 12(12):667-677, 2021.
Article in English | Web of Science | ID: covidwho-1619404

ABSTRACT

In 2020 World Health Organization (WHO) has declared that the Coronaviruses (COVID-19) pandemic is causing a worldwide health disaster. One of the most effective protections for reducing the spread of COVID-19 is by wearing a face mask in densely and close populated areas. In various countries, it has become mandatory to wear a face mask in public areas. The process of monitoring large numbers of individuals to comply with the new rule can be a challenging task. A cost-effective method to monitor a large number of individuals to comply with this new law is through computer vision and Convolution Neural Network (CNN). This paper demonstrates the application of transfer learning on pre-trained CNN architectures namely;AlexNet, GoogleNet ResNet-18, ResNet-50, ResNet-101, to classify whether or not a person in the image is wearing a facemask. The number of training images are varied in order to compare the performance of these networks. It is found that AlexNet performed the worst and requires 400 training images to achieve Specificity, Accuracy, Precision, and F-score of more than 95%. Whereas, GoogleNet and Resnet can achieve the same level of performance with 10 times fewer number of training images.

8.
Review of International Geographical Education Online ; 11(7):1225-1230, 2021.
Article in English | Scopus | ID: covidwho-1518966

ABSTRACT

Covid-19 is a pandemic that has been declared by the World Health Organization (WHO) as a pandemic that has claimed many lives around the world. As of January 1, 2021, the world has recorded at least two million infected victims. Implementing the movement control order is one of the best ways implemented by the government today in curbing the Covid-19 epidemic until a vaccine is available. The method of discussion found in this article is based on document analysis by referring to authoritative books, journals, articles, and websites. The study’s findings found that the Movement Control Order implemented by the Malaysian government is based on ‘Siyasah Syar’iyyah’ in maintaining and curbing the spread of Covid-19 in Malaysia. © 2021. RIGEO • 11(7), SPRING. All Rights Reserved.

9.
24th ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2021 ; : 79-83, 2021.
Article in English | Scopus | ID: covidwho-1515335

ABSTRACT

The covid-19 pandemic has severely limited the possibility for people to meet physically, which forced many individuals and organizations to employ online meetings as their predominant mode of communication. A potential problem with the unprecedentedly central role of online meetings in a wide range of everyday activities is the disruption it may cause to intersubjective experiences, an intuitive mutual understanding of the participants and their thinking of themselves as a group, a "we". To address this problem, about half a year into the pandemic we conducted an exploratory study, in which the informants (N=36) completed a survey, comprising a set of Likert scales and open-ended questions focusing on "team spirit", moment-to-moment coordination, emotions, and the sense of presence in online and physical meetings. The results indicate that online meetings may present particular challenges regarding the experience of "we-ness", and different types of online meetings can be experienced differently. Implications of the results for further research are discussed. © 2021 ACM.

10.
International Journal of Advanced Technology and Engineering Exploration ; 8(74):149-160, 2021.
Article in English | Scopus | ID: covidwho-1134595

ABSTRACT

A viral infection which is named as Coronavirus disease 2019 (COVID-19) is triggered by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). To date, almost two million cases and over 100,000 deaths from the disease caused by this virus were reported worldwide. The environmental and meteorological factors are claimed to stimulate the spread of the virus in which the transmissibility in terms of climatic fluctuations increases exponentially with high humidity and low temperature. In an attempt to understand this epidemic, there is a need to investigate the factors that could impact the spread and death of COVID-19. We, therefore, proposed to investigate global geographical climate impacts on the COVID-19 spread and death in Asia and America. The Artificial Neural Network (ANN) is a network that seeks to replicate neuronal functionality in the human brain. It is the preferred instrument for several predictive applications of data mining, due to its strength, versatility, and simplicity. A dataset of COVID-19 cases and deaths revealed from 49 states in America and 41 countries in Asia during April 2020 were tested. Nine covariates were used in the networks which are Cases, Death, High Temperature, Low Temperature, Average Temperature, Population, and Percentage of Cases over Population, Percentage of Death over Population, and Total Cases. Based on the analysis conducted, the global geographic climate is observed to have the least impacts on the COVID-19 spread and death in Asia and America particularly. However, different results could be reflected by different datasets used in the future. © 2021 Shafaf Ibrahim et al.

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