Comparative Analysis of Classification Techniques on Olympic Games Datasets

Olympic game is a prestigious ceremony that occurs after every four years. However, due to the spread of coronavirus in 2020, the game was held in 2021, which is post-Covid. The main aim of this research is to find out if there was a difference in the performance of nations in Rio 2016 Olympics (pre-Covid) and Tokyo 2020 Olympics (post-Covid). Statistical analysis is carried out to find the correlation between the different variables. One of the highly correlated variables (Gold Tally) is removed while performing the classification analysis. The idea is to see if the classifiers are able to do the comparative analysis without it or not. The classification algorithms utilized in this research are Decision Table, Decision Tree, Naïve Bayes, and Random Forest. The datasets used in this research are imbalanced sets, which were later transformed to balance sets through under-sampling. Random Forest was able to give 100% accuracy in both datasets whereas the True Positive Rate (TPR) was also 100%. After doing the comparative analysis it was found that irrespective of pre and post-Covid, the performance of athletes did not change. This paves the way for other researchers to investigate if Covid had any impact on the performance of the athletes or not. In the future, more vast variables will be investigated to do a more detailed comparative analysis. © 2022 IEEE.

First-Generation SARS-CoV-2 Vaccines: A Comparative Analysis between Vaccinated and Unvaccinated Hospitalized Patients Infected with SARS-CoV-2.

Background Severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) variants, which have emerged due to several mutations in spike protein, have a potential to escape immune protection provided by the first-generation vaccines, thereby resulting in breakthrough infections. Objective To identify the socio-demographic factors, clinical features, and outcomes in both vaccinated and unvaccinated hospitalized patients infected with SARS-CoV-2. Method Socio-demographic details, clinical features, and the outcomes among fully vaccinated (double for Covishield/AstraZeneca and BBIBP-CorV and single for Janssen), partially vaccinated, and unvaccinated hospitalized patients with coronavirus disease of 2019 (COVID-19) were collected and analyzed using SPSS version 17. Result Among the hospitalized COVID-19 patients (n=299), 175 (58.5%) patients received a single-dose, 82 (27.4%) double-dose, and 124 (41.5%) did not receive any dose of the COVID-19 vaccines. The risk of SARS-CoV-2 infection when compared between vaccinated and unvaccinated patients was found to be associated among professional degree holders (23.4% versus 9.7%) (p<0.05), professional workers (43.4% vs. 25.0%) (p<0.05), hospitalization to general ward (76.6% vs. 72.6%) (p<0.05), and presence of multiple symptoms (> or equel 3) (86.8% vs. 75.0%) (p>0.05) and comorbidities (> or equal 2) (15.5% vs. 13.7%) (p>0.05). Despite such approximate incidences, the risk of in-hospital mortality among the vaccinated patients was reduced (0.6% vs. 3.2%) (p>0.05), when compared to the unvaccinated patients. The risk of in-hospital mortality was associated with the older age and the presence of multiple comorbidities including bronchial asthma, diabetes, and hypertension. Conclusion Full or partial vaccination against the SARS-CoV-2 variants of concerns might be effective in preventing in-hospital mortality among COVID-19 patients.

Policing the COVID-19 pandemic: Police and public perceptions of enforcement of health protocols in the Fiji Islands

The COVID-19 pandemic has become a global health security concern (World Health Organization, 2020), and governments have called upon police agencies to assist control the spread of the COVID-19 virus. This study looks at the new roles performed by police agencies in the context of the Fiji Islands. This study addresses two main research questions. Firstly, what are the issues and challenges faced by police officers when carrying out their COVID-19 duties? Secondly, what is the public perceptions of police officers' effectiveness in enforcing COVID-19 health protocols? This study has utilized a mixed-method approach based on qualitative interviews with police officers and a quantitative survey of the public. The police officers' interviews reveal that police performed new health duties and ground-level police faced several challenges. The public survey findings reveal that most people were happy with police performance. We conclude by discussing the policy implications of our findings on police practice and the agenda for future comparative research in small island countries so that SIDs can learn from each other.

First-Generation SARS-CoV-2 Vaccines: A Comparative Analysis between Vaccinated and Unvaccinated Hospitalized Patients Infected with SARS-CoV-2

Background Severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) variants, which have emerged due to several mutations in spike protein, have a potential to escape immune protection provided by the first-generation vaccines, thereby resulting in breakthrough infections. Objective To identify the socio-demographic factors, clinical features, and outcomes in both vaccinated and unvaccinated hospitalized patients infected with SARS-CoV-2. Method Socio-demographic details, clinical features, and the outcomes among fully vaccinated (double for Covishield/AstraZeneca and BBIBP-CorV and single for Janssen), partially vaccinated, and unvaccinated hospitalized patients with coronavirus disease of 2019 (COVID-19) were collected and analyzed using SPSS version 17. Result Among the hospitalized COVID-19 patients (n=299), 175 (58.5%) patients received a single-dose, 82 (27.4%) double-dose, and 124 (41.5%) did not receive any dose of the COVID-19 vaccines. The risk of SARS-CoV-2 infection when compared between vaccinated and unvaccinated patients was found to be associated among professional degree holders (23.4% versus 9.7%) (p<0.05), professional workers (43.4% vs. 25.0%) (p<0.05), hospitalization to general ward (76.6% vs. 72.6%) (p<0.05), and presence of multiple symptoms (>=3) (86.8% vs. 75.0%) (p>0.05) and comorbidities (>=2) (15.5% vs. 13.7%) (p>0.05). Despite such approximate incidences, the risk of in-hospital mortality among the vaccinated patients was reduced (0.6% vs. 3.2%) (p>0.05), when compared to the unvaccinated patients. The risk of in-hospital mortality was associated with the older age and the presence of multiple comorbidities including bronchial asthma, diabetes, and hypertension. Conclusion Full or partial vaccination against the SARS-CoV-2 variants of concerns might be effective in preventing in-hospital mortality among COVID-19 patients. Copyright © 2022, Kathmandu University. All rights reserved.

e–talanoa as an online research method: extending vā–relations across spaces

Talanoa (Moana-centred orality) is a widely used Indigenous Pacific discursive approach within research contexts across the diaspora. In a globalised and technologically enhanced era, the online space continues to shape Moana (Oceania) peoples’ talanoa engagement and communication. e–talanoa in this article is an extension of talanoa research engagement and practice. We unpack the contexts in which e–talanoa is negotiated and made sense, and employ talanoa–vā (relational sense-making and meaning-making) as a critical analytical framework for interrogating and unpacking the complexities associated with e–talanoa as a Moana–Pacific research praxis. e–talanoa considers our current post–covid research space and how Pacific researchers navigate their ethical vā–relations within the temporal–spatial and physical–online boundaries that govern meaningful research undertakings. Being open about the challenges enables further understanding of the dynamic and fluid, yet contextually grounded spaces in which e–talanoa as a method can be realised. © The Author(s) 2022.

Effectiveness of Planned Teaching Programme on Knowledge of Lifestyle Modification among the in Selected Areas of Pune City

The most recently discovered coronavirus (COVID-19), generates a contagious illness. The COVID-19 pandemic has ushered in a new war for a safe and fearless living in human history.Although millions of people are recovered from COVID-19 most of the recovered patients had no idea that after recovery they also needed good lifestyle modification. On the other side, many are not following the proper diet plan, sleep pattern, physical and mental exercise, etc. We all know how dangerous and infectious coronavirus is and its infection breaks our immunity system, so for fighting coronavirus infection and reducing complications for all COVID-19 survivors, good life modification knowledge plays a very important role. Out of 60 COVID-19 survivors, revealed that 24(40%) had poor, 27(45%)had an average, and 9(15%) had good knowledge about lifestyle modification.but after implementing the planned teaching program the post-test results were 0(0%) had poor, 16(26.66%) had an average, and 44(73.33%) had good knowledge about lifestyle modification.The entire pre-test mean of COVID-19 survivors was11.15while the post-test mean was 18.28.The study used a quantitative design with a one-group pre-test and post-test adopting pre-experimental and purposive sampling techniques.The effectiveness of a planned teaching programknowledge questionnaire was tested usingby self-structured questionnaire.According to the conclusions of the study, COVID-19 survivors lack adequate knowledge.The findings showedthat theplanned teaching program was very effective in increasing the knowledge level of COVID-19survivors. This study helps COVID-19 survivors to adopt good lifestyle changes and increases their level of knowledge regarding the benefits of choosing good lifestyle changes after recovering from this deadly coronavirus.

Transfer Learning-Based Detection of Covid-19 Using Chest CT Scan Images

The global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus has had historical impact on the world. The virus causes severe respiratory problems and with an R0 of 5.7, spreads at a rapid rate. At the time of writing, there were over 85 million cases and 1.8 million deaths caused by COVID-19. In the proposed methodology, Deep Convolutional Neural Networks (DCNNs) have been trained, with the help of transfer learning, to learn to identify whether a suspected patient is suffering from this disease using their chest CT scan image. Transfer learning technique enables the transfer of knowledge from pre-trained models which have been previously trained on extremely large datasets. Various DCNN models have been applied such as AlexNet, ResNet-18, ResNet-34, ResNet-50, VGG-16, and VGG-19. The DCNNs were evaluated on a set of 2,481 chest CT scan images. Various performance metrics (Accuracy, MCC, Kappa, F1 score, etc.) were calculated for all DCNN models to enable their comparative evaluation. After extensive testing, ResNet50 was found to give the best results in this binary classification task. The highest accuracy achieved was 97.37% and highest kappa was 0.947. Identification of presence of COVID-19 using this method would provide great benefit to society and mankind. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Analyzing students' online presence in undergraduate courses using Clustering

The Higher Education Institute (HEI) are experiencing a major paradigm shift due to recent global pandemic. A sudden shift from face-to-face (F2F) and blended modes of study to completely online mode of delivery has introduced hidden challenges to facilitators and students alike. Student's online engagement has become even more important for their academic success as F2F component is not there in most cases. Therefore, there is a need to investigate the effects of the various indicators of students' online presence towards their academic performance. This paper explores the effectiveness of online presence in HEI where Covid-19 has shifted the course deliveries to fully online mode. Previously, Online Measurable Presence Model (OMPM) was used to find students effectiveness in a blended learning environment where two indicators used were Frequency and Duration. The chosen indicator in this research is frequency, which will be adequately used to quantify the effectiveness of the online presence in two mathematics courses in the Pacific. Clustering technique is used to create clusters of Frequency and see their relation to OMPM model. Prediction is made using neural network to see the accuracy based on model. The clusters would allow to build predictive models to predict future outcomes or occurrences and student performances, with a major focus on mathematics courses. © 2020 IEEE.