ABSTRACT
Predicting the COVID-19 outbreak has been studied by many researchers in recent years. Many machine learning models have been used for the prediction of the transmission in a country or region, but few studies aim to predict whether an individual has been infected by COVID-19. However, due to the gravity of this global pandemic, prediction at an individual level is critical. The objective of this paper is to predict if an individual has COVID-19 based on the symptoms and features. The prediction results can help the government better allocate the medical resources during this pandemic. Data of this study was taken on June 18th from the Israeli Ministry of Health on COVID-19. The purpose of this study is to compare and analyze different models, which are Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayesian (NB), Decision Tree (DT), Random Forest (RF) and Neural Network (NN). © 2021 ACM.
ABSTRACT
At present, personalized recommendation system has become an indispensable technology in the fields of e-commerce, social network and news recommendation. However, the development of personalized recommendation system in the field of education and teaching is relatively slow with lack of corresponding application.In the era of Internet Plus, many colleges have adopted online learning platforms amidst the coronavirus (COVID-19) epidemic. Overwhelmed with online learning tasks, many college students are overload by learning resources and unable to keep orientation in learning. It is difficult for them to access interested learning resources accurately and efficiently. Therefore, the personalized recommendation of learning resources has become a research hotspot. This paper focuses on how to develop an effective personalized recommendation system for teaching resources and improve the accuracy of recommendation. Based on the data on learning behaviors of the online learning platform of our university, the authors explored the classic cold start problem of the popular collaborative filtering algorithm, and improved the algorithm based on the data features of the platform. Specifically, the data on learning behaviors were extracted and screened by knowledge graph. The screened data were combined with the collaborative filtering algorithm to recommend learning resources. Experimental results show that the improved algorithm effectively solved the loss of orientation in learning, and the similarity and accuracy of recommended learning resources surpassed 90%. Our algorithm can fully satisfy the personalized needs of students, and provide a reference solution to the personalized education service of intelligent online learning platforms. © 2022, North Atlantic University Union NAUN. All rights reserved.
ABSTRACT
Severe acute respiratory syndrome coronavirus (SARS-CoV) was discovered as a novel pathogen in the 2002-2003 SARS epidemic. The emergence and disappearance of this pathogen have brought questions regarding its source and evolution. Within the genome sequences of 281 SARS-CoVs, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and SARS-related CoVs (SARSr-CoVs), a ~430 bp genomic region (from 27 701 bp to 28 131 bp in AY390556.1) with regular variations was investigated. This ~430 bp region overlaps with the ORF8 gene and is prone to deletions and nucleotide substitutions. Its complexity suggested the need for a new genotyping method for coronaviruses related to SARS-similar coronaviruses (SARS-CoV, SARSr-CoV, and SARS-CoV-2). Bat SARSr-CoV presented 3 genotypes, of which type 0 is only seen in bat SARSr-CoV, type I is present in SARS in the early phase, and type II is found in all SARS-CoV-2. This genotyping also shows potential usage in distinguishing the SARS-similar coronaviruses from different hosts and geographic areas. This genomic region has important implications for predicting the epidemic trend and studying the evolution of coronavirus.