The COVID-19 pandemic at its peak compelled students to stay home and adapt to the distance learning system. The world has gone through phases of fear and respite in the recent years. There have been a number of studies related to student learning via online teaching during the pandemic. Now, as the vaccination coverage picks up and the pandemic appears to have achieved a plateau, it is time to take a view of students’ perceptions of online learning and its effectiveness in skill development during the COVID-19 pandemic. This study assesses the students’ preferences and perceptions regarding offline and online learning post-COVID-19 lockdown and with the resumption of offline classes. A cross-sectional study was conducted at King Khalid University, Aseer region, from the period of 1 January 2022 to 30 January 2022. A convenience sampling technique was utilized to collect data from female students. Data analysis was conducted by using SPSS version 22.0. A total of 480 students participated in the study, their mean age was 19.79 ± 1.48. More than half of the students (64%) still fear getting COVID-19 as they continue with the in-person or offline classes despite having completed their doses of vaccination. Almost half of the students have difficulty in waking up after the recommencement of offline classes. The majority (77%) felt tired after starting offline classes, and 63% felt unhappy after starting offline classes again. The majority of students believe that, with offline classes, they have difficulty in time management and concentration. The majority of students believed that, with online classes, they are more comfortable in gaining knowledge and learning, more alert, more satisfied, and gain higher scores in exams. The majority of students preferred the online mode of learning, with about 72% of students wishing to continue online learning in the future. This research underlines the influence of fear of getting COVID-19 after the commencement of face-to-face learning amongst the students. Students were more inclined to continue with online classes because of fear of getting COVID-19, despite having full doses of the vaccination in Saudi Arabia. There is a need for a better understanding of students’ motivations and coping mechanisms during the pandemic.

COVID-19, continually developing and raising increasingly significant issues, has impacted human health and caused countless deaths. It is an infectious disease with a high incidence and mortality rate. The spread of the disease is also a significant threat to human health, especially in the developing world. This study suggests a method called shuffle shepherd optimization-based generalized deep convolutional fuzzy network (SSO-GDCFN) to diagnose the COVID-19 disease state, types, and recovered categories. The results show that the accuracy of the proposed method is as high as 99.99%; similarly, precision is 99.98%; sensitivity/recall is 100%; specificity is 95%; kappa is 0.965%; AUC is 0.88%; and MSE is less than 0.07% as well as 25 s. Moreover, the performance of the suggested method has been confirmed by comparison of the simulation results from the proposed approach with those from several traditional techniques. The experimental findings demonstrate strong performance and high accuracy for categorizing COVID-19 stages with minimal reclassifications over the conventional methods.