COVID-19 was declared a global pandemic by the World Health Organization in March 2020, and has infected more than 4 million people worldwide with over 300,000 deaths by early May 2020. Many researchers around the world incorporated various prediction techniques such as Susceptible–Infected–Recovered model, Susceptible–Exposed–Infected–Recovered model, and Auto Regressive Integrated Moving Average model (ARIMA) to forecast the spread of this pandemic. The ARIMA technique was not heavily used in forecasting COVID-19 by researchers due to the claim that it is not suitable for use in complex and dynamic contexts. The aim of this study is to test how accurate the ARIMA best-fit model predictions were with the actual values reported after the entire time of the prediction had elapsed. We investigate and validate the accuracy of an ARIMA model over a relatively long period of time using Kuwait as a case study. We started by optimizing the parameters of our model to find a best-fit through examining auto-correlation function and partial auto correlation function charts, as well as different accuracy measures. We then used the best-fit model to forecast confirmed and recovered cases of COVID-19 throughout the different phases of Kuwait’s gradual preventive plan. The results show that despite the dynamic nature of the disease and constant revisions made by the Kuwaiti government, the actual values for most of the time period observed were well within bounds of our selected ARIMA model prediction at 95% confidence interval. Pearson’s correlation coefficient for the forecast points with the actual recorded data was found to be 0.996. This indicates that the two sets are highly correlated. The accuracy of the prediction provided by our ARIMA model is both appropriate and satisfactory.

This research aims to customize the System Usability Scale (SUS) so it can be used as a standard usability measure on native Arabic-language speakers. A process of rigor translation followed by psychometric evaluation has been administered through a questionnaire in the Arabic-language that reflects a reasonable level of reliability, validity and sensitivity when compared to the original English-language SUS questionnaire. The final result of the Arabic-System Usability Scale (A-SUS) is then applied on students majoring in Communication Disorders Sciences (Kuwait University) to measure the usability of a mobile application. A-SUS encapsulates the essence of the original SUS and provides an adequate tool for professionals to use with Arabic native speaking users to evaluate systems usability.

Chronic kidney disease (CKD) is a medical condition characterized by impaired kidney function, which leads to inadequate blood filtration. To reduce mortality rates, recent advancements in early diagnosis and treatment have been made. However, as diagnosis is time-consuming, an automated system is necessary. Researchers have been employing various machine learning approaches to analyze extensive and complex medical data, aiding clinicians in predicting CKD and enabling early intervention. Identifying the most crucial attributes for CKD diagnosis is this paper's primary objective. To address this gap, six nature-inspired algorithms and nine machine learning classifiers were compared to evaluate their combined effectiveness in detecting CKD. A benchmark CKD dataset from the UCI repository was utilized for this analysis. The proposed model outperforms other classifiers with a remarkable 99.5% accuracy rate; it also achieves a 58% reduction in feature dimensionality. By providing a reliable, cost-effective tool for early CKD detection, the authors aim to revolutionize patient care.

Nowadays, the rapid growth of Internet applications open the doors for people to communicate and do business around the world and thus saving time, efforts and money. However, the success of these applications is based on protecting the data from hackers. It is well known that preventing sensitive data from hackers while they are sent through the global network is a big challenge. There are many techniques used for securing data. Some of these techniques are based on encrypting a readable text into an unreadable text using mathematical operations, while other techniques are based on encrypting a readable text into an image (e.g., the Text-to-Image Encryption algorithm – TTIE) or into musical notes using an encryption key. The encryption key must be secure and should not be sent through the Internet. This paper proposes adding a new security level to the TTIE algorithm, and demonstrates how the encryption key produced by the TTIE algorithm is exchanged with the other party using the Diffie Hellman technique. Thus, this paper proposes a modified TTIE algorithm called the Diffie Hellman Text-to-Image Encryption algorithm (DHTTIE), and tests and analyses the proposed algorithm.

Educational systems worldwide have been forced into shifting to online learning during COVID-19 pandemic. This decision faced diverse challenges, especially in underdeveloped countries that still use traditional teaching methods, with minimal or no integrated technology, and no guidance in the literature. This study explores factors, challenges, and adaptation initiatives that might underlie the success and failure of abrupt shifting and accepting online learning systems. To explore the acceptance of online learning under these extreme circumstances, the reactions of Kuwait educational institutions to COVID-19 were collected and analyzed. A framework was utilized, and a questionnaire developed to enable quantitative analysis of these data. In total, 4,024 responses were gathered from instructors and students with acceptable reliability. Findings from the statistical analysis unveiled specific acceptance facts relevant to the crisis within its environment. This study establishes the utility of this framework for researchers to synthesize users’ acceptance of online learning systems.

This study adapts an Abbreviated Technology Anxiety Scale (ATAS) into Arabic to serve as a measurement tool for assessing technology anxiety among native Arabic speakers. The adapted tool is called the Arabic-Abbreviated Technology Anxiety Scale (A-ATAS), which was psychometrically validated in this research. Using Google Maps as an example of a technology tool, a psychometric evaluation was conducted with 342 participants, assessing the validity and reliability of the A-ATAS tool. The results indicated that A-ATAS has an acceptable value of reliability with a Cronbach's alpha value of 0.866, in addition to acceptable values for both the scale-level content validity index (0.883), the face validity index (0.864) and factor analysis were also conducted. The findings indicated that abbreviated versions of the adapted A-ATAS suggested acceptable psychometrically evaluated properties for the abbreviated version of the adapted A-ATAS. The implications of these findings are substantial for both academic research and professional practice in software development and design.