This study investigates the extent to which the usability attributes, namely, effectiveness, efficiency; learnability and memorability, satisfaction, errors, and cognitive load of PSAU mobile application exist from students' point of view who were enrolling at the academic year 2019-2020 in College of Business Administration (CBA) at Prince Sattam Bin Abdulaziz University. The study employs the People at the Center of Mobile Application Development (PAMCAD) usability model to determine the extent to which the usability attributes are available of PSAU mobile application. A survey-based methodology is used to collect data from a random sample size of 137 enrolled students in the College of Business Administration (CBA) at Prince Sattam bin Abdulaziz University. The results demonstrate the state of usability attributes of PSAU mobile application is acceptable; the highest mean was 3.3 for the cognitive load dimension, after that, the learnability and memorability dimensions with mean 3.0. The lowest mean is 2.4 for the Efficiency dimension. The overall mean for usability is 2.8 which reflect the level of usability for the PSAU mobile application. The results of this study should be useful to IT deanships and related policymakers at the university level with empirical evidence about the issues and problems that faced users of mobile applications in higher educational institutions in KSA; and helping in developing high-quality mobile application.
The rule based categorization approaches such as associative classification have the capability to produce classifiers rival to those learned by traditional categorization approaches such as Naïve Bayes and K-nearest Neighbor. However, the lack of useful discovery and usage of categorization rules are the major challenges of rule based approaches and their performance is declined with large set of rules. Genetic Algorithm (GA) is effective to reduce the high dimensionality and improve categorization performance. However, the usage of GA in most researches is limited in the categorization preprocessing stage and its results is used to simplify the categorization process performed by other categorization algorithms. This paper proposed a hybrid GA rule based categorization method, named genetic algorithm rule based categorization (GARC), to enhance the accuracy of categorization rules and to produce accurate classifier for text mining. The GARC consists of three main stages; namely, search space determination, rule discovery with validation (rule generation), and categorization. The experimental results are carried out on three Arabic text datasets with multiple categories to evaluate the efficiency of GARC. The results show that a promising performance was achieved by using GARC for Arabic text categorization. The GARC achieves the best performance with small feature space in most situations. .
Abstract-In this paper, a new classification approach combining support vector machine with scatter search approach for hepatitis disease diagnosis is presented, called 3SVM. The scatter search approach is used to find near optimal values of SVM parameters and its kernel parameters. The hepatitis dataset is obtained from UCI. Experimental results and comparisons prove that the 3SVM gives better outcomes and has a competitive performance relative to other published methods found in literature, where the average accuracy rate obtained is 98.75%.
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