Higher education institutions (HEIs) shifted from in-person attendance to blended and online learning due to the COVID-19 lockdowns. This study investigated the students' perception of satisfaction, convenience, engagement, and learning towards blended and online courses conducted before, during, and after the COVID-19 lockdowns. A longitudinal study design was adopted to examine the students' perception of online and blended learning courses before, during, and after the COVID-19 lockdowns. The subjects consist of Health science students (N = 130) belonging to two different colleges of a public university and the study period includes three academic years (i.e., six semesters) (2018–2021). A survey tool was developed to collect data from students studying the computer fundamentals course through blended and online learning modes from 2018 to 2021. Over 95% of students have been satisfied with the course offered through various blended learning formats since 2018. The blended 0.50-course format is the most preferred one for the students; however, the Blended 0.75-course format is highly rated by the students regarding their satisfaction and engagement than other learning formats during the COVID-19 lockdowns. Following adaption after COVID-19, the students reported a high perception of learning towards the course when delivered through 100% online learning mode. Students' achievement is significantly associated with the learning modes, exam modes, and various student batches. The blended 0.75-course format group shows a higher achievement than the other three-course format groups. Likewise, those students who appeared in their exams online showed higher achievement than those who appeared physically. Further, the students felt equally convenient with Blended 0.75 and 100% online learning course formats. These findings would also help HEIs choose appropriate learning and examination modes while designing courses.

Several academicians have been actively contributing to establishing a practical solution to storing and distributing medical images and test reports in the research domain of health care in recent years. Current procedures mainly rely on cloud-assisted centralized data centers, which raise maintenance expenditure, necessitate a large amount of storage space, and raise privacy concerns when exchanging data across a network. As a result, it is critically essential to provide a framework that allows for the efficient exchange and storage of large amounts of medical data in a secure setting. In this research, we describe a unique proof-of-concept architecture for a distributed patient-centric test report and image management (PCRIM) system that aims to facilitate patient privacy and control without the need for a centralized infrastructure. We used an Ethereum blockchain and a distributed file system technology called the Inter-Planetary File System in this system (IPFS). Then, to secure a distributed and trustworthy access control policy, we designed an Ethereum smart contract termed the patient-centric access control protocol. The IPFS allows for the decentralized storage of medical metadata, such as images, with worldwide accessibility. We demonstrate how the PCRIM system design enables hospitals, patients, and image requestors to obtain patient-centric data in a distributed and secure manner. Finally, we tested the proposed framework in the Windows environment by deploying a smart contract prototype on an Ethereum TESTNET blockchain. The findings of the study indicate that the proposed strategy is both efficient and practicable.

COVID-19 has become a very transmissible disease that has had a worldwide impact, resulting in a huge number of infections and fatalities. Testing is critical to the pandemic’s successful response because it helps detect illnesses and so attenuate (isolate/cure) them and now vaccination is a life-safer innovation against the pandemic which helps to make the immunity system stronger and fight against this infection. Patient-sensitive information, on the other hand, is now held in a centralized or third-party storage paradigm, according to COVID-19. One of the most difficult aspects of using a centralized storage strategy is maintaining patient privacy and system transparency. The application of blockchain technology to support health initiatives that can minimize the spread of COVID-19 infections in the context of accessibility of the system and for verification of digital passports. Only by combining blockchain technology with advanced cryptographic algorithms can a secure and privacy-preserving solution to COVID-19 be provided. In this article, we investigate the issue and propose a blockchain-based solution incorporating conscience identity, encryption, and decentralized storage via interplanetary file systems (IPFS). For COVID-19 test takers and vaccination takers, our solution includes digital health passports (DHP) as a certification of test or vaccination. We explain smart contracts constructed and tested with Ethereum to preserve a DHP for test and vaccine takers, allowing for a prompt and trustworthy response from the necessary medical authorities. We use an immutable trustworthy blockchain to minimize medical facility response times, relieve the transmission of incorrect information, and stop the illness from spreading via DHP. We give a detailed explanation of the proposed solution’s system model, development, and assessment in terms of cost and security. Finally, we put the suggested framework to the test by deploying a smart contract prototype on the Ethereum TESTNET network in a Windows environment. The study’s findings revealed that the suggested method is effective and feasible.

As the COVID-19 pandemic pushed universities worldwide to shift from traditional to online learning, there is a need to capture the students' perception of online learning using an appropriate tool. Hence, this study explores the appropriateness of the online learning assessment survey (OLAS) model for assessing the students' perception of online learning during the COVID-19 pandemic. It included the undergraduate students (N = 2523) of the selected four health science colleges at Imam Abdulrahman Bin Faisal University (IAU) during 2020–2021. The data was obtained through OLAS using “Google Docs” from 728 students. The structural equation modeling (SEM) analysis revealed that each item showed a significant positive relationship with its respective variable of OLAS. The proposed OLAS model with five variables showed a good fit to assess the students' perception of online learning during the COVID-19 pandemic. Those variables enable the university policy planners to evaluate the students' perception of online learning during the pandemic, thereby supporting them in framing appropriate strategies to improve the quality and success of online learning. Further research is necessary to include all students of various programs offered at Saudi universities to generalize the outcomes. OLAS can include a global item assessing overall students' satisfaction with online learning, and the influence of OLAS variables on the overall students' satisfaction can be evaluated in future studies.

The task of analyzing sentiment has been extensively researched for a variety of languages. However, due to a dearth of readily available Natural Language Processing methods, Urdu sentiment analysis still necessitates additional study by academics. When it comes to text processing, Urdu has a lot to offer because of its rich morphological structure. The most difficult aspect is determining the optimal classifier. Several studies have incorporated ensemble learning into their methodology to boost performance by decreasing error rates and preventing overfitting. However, the baseline classifiers and the fusion procedure limit the performance of the ensemble approaches. This research made several contributions to incorporate the symmetries concept into the deep learning model and architecture: firstly, it presents a new meta-learning ensemble method for fusing basic machine learning and deep learning models utilizing two tiers of meta-classifiers for Urdu. The proposed ensemble technique combines the predictions of both the inter- and intra-committee classifiers on two separate levels. Secondly, a comparison is made between the performance of various committees of deep baseline classifiers and the performance of the suggested ensemble Model. Finally, the study’s findings are expanded upon by contrasting the proposed ensemble approach efficiency with that of other, more advanced ensemble techniques. Additionally, the proposed model reduces complexity, and overfitting in the training process. The results show that the classification accuracy of the baseline deep models is greatly enhanced by the proposed MLE approach.