In the higher education sector, web based facilities perform a vital aspect to offer success of an academic institution, due to the users depend on the universities websites to achieve different academic instructions. Simultaneously, users may face many usability difficulties while having access to the websites. For that reason, this research investigates user based testing and questionnaires methods from user perspective to evaluate three of lowermost university websites in KRG/Iraq according to Ranking Web of Universities (webometrics); university of Raparin, university of Garmian, and university of Halabja. Thirty participants contribute to implementing six tasks of user-based methods and ten questions of questionnaire approach. Based on the analysing process, the accuracy of universities websites are; 86.7%, 79.5%, and 61.1% for each University of Raparin, University of Halabja, and University of Garmian respectively. Moreover, user satisfaction for the University of Raparin is 3.59, while 3.24 and 3.01 are the rates of satisfaction for University of Halabja and University of Garmian.

Dialect recognition is one of the most attentive topics in the speech analysis area. Machine learning algorithms have been widely used to identify dialects. In this paper, a model that based on three different 1D Convolutional Neural Network (CNN) structures is developed for Kurdish dialect recognition. This model is evaluated, and CNN structures are compared to each other. The result shows that the proposed model has outperformed the state of the art. The model is evaluated on the experimental data that have been collected by the staff of department of computer science at the University of Halabja. Three dialects are involved in the dataset as the Kurdish language consists of three major dialects, namely Northern Kurdish (Badini variant), Central Kurdish (Sorani variant), and Hawrami. The advantage of the CNN model is not required to concern handcraft as the CNN model is featureless. According to the results, the 1 D CNN method can make predictions with an average accuracy of 95.53% on the Kurdish dialect classification. In this study, a new method is proposed to interpret the closeness of the Kurdish dialects by using a confusion matrix and a non-metric multi-dimensional visualization technique. The outcome demonstrates that it is straightforward to cluster given Kurdish dialects and linearly isolated from the neighboring dialects.

The exponential growth of deep learning networks has enabled us to handle difficult tasks, even in the complex field of medicine with small datasets. In the sphere of treatment, they are particularly significant. To identify brain tumors, this research examines how three deep learning networks are affected by conventional data augmentation methods, including MobileNetV2, VGG19, and DenseNet201. The findings showed that before and after utilizing approaches, picture augmentation schemes significantly affected the networks. The accuracy of MobileNetV2, which was originally 85.33%, was then enhanced to 96.88%. The accuracy of VGG19, which was 77.33%, was then enhanced to 95.31%, and DenseNet201, which was originally 82.66%, was then enhanced to 93.75%. The models' accuracy percentage engagement change is 13.53%, 23.25%, and 23.25%, respectively. Finally, the conclusion showed that applying data augmentation approaches improves performance, producing models far better than those trained from scratch.

The purpose of an educational website is to efficiently provide up-to-date services and resources to students, faculty, and staff. However, the needs of university students are not always prioritized throughout the design phase of websites; instead, technological considerations, organizational concerns, and commercial goals may all drive the process. When it comes to meeting the demands of the target audience, website designers often fall short. At the same time, visitors may encounter a wide variety of usability issues while visiting the sites. Thus, the purpose of this study is to examine the efficacy of different approaches to evaluating the usability of university websites from the viewpoint of their target audiences via user-based testing and questionnaire methods involving actual users. The outcomes, which are based on data from tests conducted in real time, outline the difficulties and advantages associated with each strategy. Both a user-based test and a questionnaire, which are both trustworthy evaluation methodologies, were used in the process of analyzing and evaluating the websites.