ABSTRACT
Online banking-OB is a new information system that uses the Internet's creative resources to allow users to access a growing variety of financial services. It offers that country an advantage over other countries in financial transactions. During the lockdown and implementation of social distancing, it was difficult for students to buy daily necessities in stores, and many Malaysians turned to the Internet to fulfil their purchasing requirements. This paper aims to explore the adoption of online banking and describe the factors affecting online banking adoption among undergraduate students at UUM. The data was collected through a quantitative approach using a random sampling technique. A total of 210 students were selected and filled out the questionnaire. The study found that most students adopt online banking, and perceived usefulness, perceived ease of use, and consumer attitude represent most factors influencing online banking adoption. © 2022 IEEE.
ABSTRACT
COVID-19 pandemic has affected more than 144 million people and spread to over 200 countries. The prediction of COVID-19 behaviour and trend is crucial to prevent its spreading. Kingdom of Saudi Arabia (KSA) is Asia’s fifth largest country, and it hosts the two holiest cities of the Islamic world. KSA hosts millions of pilgrims every year, and it is of great importance to predict the COV-ID-19 spread to organize these religious activities and bring life to normality in KSA. This study proposes four tree-based ensemble methods to predict the COV-ID-19 daily new cases in KSA. Tree-based ensemble methods are suggested to reduce the variance and/or bias of inconsistent models. The four models utilized in the study are Gradient Tree Boosting (GB), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Voting Regressor (VR). The study is conducted using “Our Data in World” (OWID) COVID-19 dataset from the first confirmed case in KSA, i.e., 2nd March 2020 to 14th April 2021. The results suggest that the tree-based ensemble models provide a good prediction of daily COVID-19 new cases and can follow the trend of COVID-19. Among the models, XGBoost and VR performed better than the other three models with the best evaluation metric scores (MAE:4.41, RMSE:7.11, MAPE:0.95%). The significant prediction power of the tree-based ensemble methods, especially XGBoost can provide the platform for policymakers to put strategic plans for the closure periods of the educational institutions and organize Hajj and Umrah. © 2022, Tech Science Press. All rights reserved.