Studying password-cracking techniques is essential in the information security discipline as it highlights the vulnerability of weak passwords and the need for stronger security measures to protect sensitive information. While both methods aim to uncover passwords, both approach the task in different ways. A brute force algorithm generates all possible combinations of characters in a specified range and length, while the dictionary attack checks against a predefined word list. This study compares the efficiency of these methods using parallel versions of Python, C++, and Hashcat. The results show that the NVIDIA GeForce GTX 1050 Ti with CUDA is significantly faster than the Intel(R) HD Graphics 630 GPU for cracking passwords, with a speedup of 11.5× and 10.4× for passwords with and without special characters, respectively. Special characters increase password-cracking time, making the process more challenging. The results of our implementation indicate that parallel processing greatly improves the speed of password-cracking techniques. The brute force algorithm achieved a speedup of 1.9× with six cores, while the dictionary attack showed a speedup of 4.4× with eight-core static scheduling. Studying password-cracking techniques highlights the need for stronger security measures to protect sensitive information and the vulnerability of weak passwords.

In the healthcare field, diagnosing disease is the most concerning issue. Various diseases including cardiovascular diseases (CVDs) significantly influence illness or death. On the other hand, early and precise diagnosis of CVDs can decrease chances of death, resulting in a better and healthier life for patients. Researchers have used traditional machine learning (ML) techniques for CVD prediction and classification. However, many of them are inaccurate and time-consuming due to the unavailability of quality data including imbalanced samples, inefficient data preprocessing, and the existing selection criteria. These factors lead to an overfitting or bias issue towards a certain class label in the prediction model. Therefore, an intelligent system is needed which can accurately diagnose CVDs. We proposed an automated ML model for various kinds of CVD prediction and classification. Our prediction model consists of multiple steps. Firstly, a benchmark dataset is preprocessed using filter techniques. Secondly, a novel arithmetic optimization algorithm is implemented as a feature selection technique to select the best subset of features that influence the accuracy of the prediction model. Thirdly, a classification task is implemented using a multilayer perceptron neural network to classify the instances of the dataset into two class labels, determining whether they have a CVD or not. The proposed ML model is trained on the preprocessed data and then tested and validated. Furthermore, for the comparative analysis of the model, various performance evaluation metrics are calculated including overall accuracy, precision, recall, and F1-score. As a result, it has been observed that the proposed prediction model can achieve 88.89% accuracy, which is the highest in a comparison with the traditional ML techniques.

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.

Accreditation is widely considered to be a vital tool for quality assurance in higher education; however, there is disagreement in the academic community on the intended benefits of accreditation. Preparing for accreditation requires extensive financial and human resources to complete the required documentation. All accreditation agencies require improvements in institutional infrastructure, enhanced student support, appropriate learning environments, and faculty development, which can directly improve students’ learning experiences. In this paper, we explore the impact of accreditation on students’ learning by using a case study-based approach. We selected four degree programs from a University in Saudi Arabia and compared the performances of students in different courses before and after acquiring local program accreditation (NCAAA). The results highlight that although there is no direct relationship between increased student performance and acquiring accreditation, there is a significant impact on the performance of student learning. However, there is a need for sustained efforts to continuously adopt accreditation-aligned practices to gain a sustained advantage. We have presented a model that can enable academic institutions to continuously adhere to best practices even if no accreditation visit has been scheduled in the near future. This way, academic programs can consistently improve their processes and enhance student learning.

In the present article an attempt has been made to predict the occurrences of customers leaving or ‘churning’ a business enterprise and explain the possible causes for the customer churning. Three different algorithms are used to predict churn, viz. decision tree, support vector machine and rough set theory. While two are rule-based learning methods which lead to more interpretable results that might help the marketing division to retain or hasten cross-sell of customers, one of them is a kernel-based classification that separates the customers on a feature hyperplane. The nature of predictions and rules obtained from them are able to provide a choice between a more focused or more extensive program the company may wish to implement as part of its customer retention program.

Background: A traffic and speed enforcement system in the Kingdom of Saudi Arabia known as Saher employs traffic monitoring and speed enforcement around the clock using surveillance cameras. Vehicle drivers' attitudes towards this system vary due to several variables. Objective: The study investigates the impact on the prediction of male vehicle drivers’ orientation towards the application of the Saher system and whether there is a difference in the vehicle drivers’ orientation toward the system over time (five years later). Methods: The study sample consisted of 761 participants from Imam Abdulrahman bin Faisal University. The quantitative approach was a questionnaire titled “Vehicle Drivers’ Orientation towards the Application of the Traffic and Speed Enforcement Surveillance System (Saher)”, which was applied in 2016 and 2021. Results: The psychometric characteristics of the study tool were ascertained, the data was quantitatively analyzed, and the results showed that vehicle drivers’ orientations toward the Saher system were positive, the nationality and number and type of violation contribute to predicting their orientation toward the system at varying rates, and the orientation of vehicle drivers toward the system improved five years later. Conclusion: The study recommends conducting a study with wider societal segments, including women who started driving in Saudi Arabia in 2018, while also focusing on the qualitative aspect of the analysis of the study findings, taking testimonials of the groups that have been involved in accidents and families of groups who have suffered from deaths in order to determine their orientations towards the system.