ABSTRACT
The COVID-19 pandemic spread worldwide in the year 2020 and became a global health emergency. This pandemic has brought awareness that social distancing and quarantine are ideal ways to protect people in the community from infection. Therefore, Saudi Arabia used online learning instead of stopping it completely to continue the education process. This paper proposes to use machine-learning algorithms for Arabic sentiment analysis to find out what students and teaching staff thought about online learning during the COVID-19 outbreak. During the pandemic, a real-world data set was gathered that included about 100,000 Arabic tweets related to online learning. The overall goal is to use sentiment analysis of tweets to find patterns that help improve the quality of online learning. The data set that was collected has three classes: 'Positive,' 'Negative,' and 'Neutral.' Crossvalidation is used to run the experiments ten times. Precision, recall, and F-measure was used to measure how well the algorithms worked. Classifiers, such as Support Vector Machines, K nearest neighbors, and Random Forest, were used to classify the dataset. Moreover, a detailed analysis and comparison of the results are made in this research. Finally, a visual examination of the data is made using the word cloud technique. © 2022 IEEE.
ABSTRACT
The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks. Video surveillance and crowd management using video analysis techniques have significantly impacted today's research, and numerous applications have been developed in this domain. This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis. Managing the Kaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic. The Umrah videos are analyzed, and a system is devised that can track and monitor the crowd flow in Kaaba. The crowd in these videos is sparse due to the pandemic, and we have developed a technique to track the maximum crowd flow and detect any object (person) moving in the direction unlikely of the major flow. We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow. Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity to maintain a smooth crowd flow in Kaaba during the pandemic. © 2022 Tech Science Press. All rights reserved.
ABSTRACT
Covid-19 Was Declared A Pandemic By World Health Organization In March 2020. Since Then, It Has Attracted The Enormous Attention Of Researchers From Around The World. The World Has Gone Through Previous Instances Of Corona-Viruses Such As Severe Acute Respiratory Syndrome And Middle Eastern Respiratory Syndrome. Nevertheless, None Was Of These Were Of This Serious Nature As Covid-19. In This Research, We Carry Out A Bibliometric Analysis Of Coronavirus Research Using The Scopus Database. However, We Restricted Ourselves To The Gulf Cooperation Council Countries, Comprising Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, And The United Arab Emirates. The Analysis Was Performed Using Biblioshiny Software. We Analyzed 4288 Articles Written By 24226 Researchers From 1994 Till 2021, Published In 1429 Sources. The Number Of Authors Per Publication Is 5.65. A Bulk Of The Research (More Than 68%) Appeared In The Form Of Articles. More Than 43% Of The Publications Appeared In 2020 And More Than 44% In 2021. Saudi Arabia Appears The Most-Cited Country, Followed By Qatar. Journal Of Infection And Public Health Published The Most Number Of Papers, Whereas New England Journal Of Medicine Is The Most-Cited One. Memish, Z.A. Wrote The Maximum Number Of Papers. The Top Source, According To The H-Index, Is The Journal Of Virology. Furthermore, The Two Most Prolific Universities Are King Saud University And King Abdulaziz University, Both From Saudi Arabia. The Research Uncovered Deep Learning As A Niche Theme Used In Recent Publications. The Research Landscape Continues To Alter As The Pandemic Keeps On Evolving © 2021,International journal of online and biomedical engineering.All Rights Reserved
ABSTRACT
Coronavirus constitutes a family of RNA viruses causing respiratory tract infections in both humans and birds. A mild disease appears like the common cold, and in other cases, causes Severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), or COVID-19. As compared to COVID-19, SARS and MERS were limited to certain countries. On the other hand, COVID-19 was declared a pandemic by the World Health Organization on Mar. 11, 2020. In this research, we perform the bibliometric assessment of Coronavirus research using the Scopus database. We studied 27,824 articles written by 64,903 researchers from 1951 till June 20, 2020, published in 3,858 different sources. More than 65% of research appeared in the form of articles. More than 34% of publications appeared in 2020, coinciding with the appearance of COVID-19. This also resulted in a sharp increase in the average citation from 2.2 observed in 2019 to 14.5 seen in the year 2020. The USA is the most-cited country, followed by China. Nevertheless, Russia appears as the most-cited country per year. © 2021 WITPress. All rights reserved.