The deaf society supports Sign Language Recognition (SLR) since it is used to educate individuals in communication, education, and socialization. In this study, the results of using the modified Convolutional Neural Network (CNN) technique to develop a model for real-time Kurdish sign recognition are presented. Recognizing the Kurdish alphabet is the primary focus of this investigation. Using a variety of activation functions over several iterations, the model was trained and then used to make predictions on the KuSL2023 dataset. There are a total of 71,400 pictures in the dataset, drawn from two separate sources, representing the 34 sign languages and alphabets used by the Kurds. A large collection of real user images is used to evaluate the accuracy of the suggested strategy. A novel Kurdish Sign Language (KuSL) model for classification is presented in this research. Furthermore, the hand region must be identified in a picture with a complex backdrop, including lighting, ambience, and image color changes of varying intensities. Using a genuine public dataset, real-time classification, and personal independence while maintaining high classification accuracy, the proposed technique is an improvement over previous research on KuSL detection. The collected findings demonstrate that the performance of the proposed system offers improvements, with an average training accuracy of 99.05% for both classification and prediction models. Compared to earlier research on KuSL, these outcomes indicate very strong performance.

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.

Keyboard acoustic recognition is a pivotal area within cybersecurity and human-computer interaction, where the identification and analysis of keyboard sounds are used to enhance security measures. The performance of acoustic-based security systems can be influenced by factors such as the platform used, typing style, and environmental noise. To address these variations and provide a comprehensive resource, we present the Multi-Keyboard Acoustic (MKA) Datasets. These extensive datasets, meticulously gathered by a team in the Computer Science Department at the University of Halabja, include recordings from six widely-used platforms: HP, Lenovo, MSI, Mac, Messenger, and Zoom. The MKA datasets have structured data for each platform, including raw recordings, segmented sound files, and matrices derived from these sounds. They can be used by researchers in keylogging detection, cybersecurity, and other fields related to acoustic emanation attacks on keyboards. Moreover, the datasets capture the intricacies of typing behaviour with both hands and all ten fingers by carefully segmenting and pre-processing the data using the Praat tool, thus ensuring high-quality and dependable data. This comprehensive approach allows researchers to explore various aspects of keyboard sound recognition, contributing to the development of robust recognition algorithms and enhanced security measures. The MKA Datasets stand as one of the largest and most detailed datasets in this domain, offering significant potential for advancing research and improving defences against acoustic-based threats.

Dialect recognition System (DRS) is a highly significant subject within the field of speech analysis. The performance of speech recognition systems is adversely impacted by factors such as the age, gender, and dialect features of the speaker. In order to address variations in dialect, it is possible to incorporate DRS into speech recognition systems. The system can be configured to utilize the appropriate speech recognition model based on the identification of the spoken dialect. Currently, there is a lack of available datasets suitable for the development of automatic dialect recognition systems specifically tailored for the Kurdish language. The proposed dataset under consideration is assessed using experimental data that has been gathered by personnel associated with the Computer Science Department at the University of Halabja. As the Kurdish language has three main dialects: Northern Kurdish (Badini variation), Central Kurdish (Sorani variant), and Hawrami, three dialects are included in the dataset.

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.

A smart clock is any digital clock that has at least one intelligent feature. Moreover, it provides time with synchronizing automatically base on the standard measurement, which is determined during the implementation software on the hardware architecture design. This study presents an efficient cost-effective smartwatch for disable people based on the Atmega328p microcontroller (Arduino Uno) that is programmed in “Arduino” (C based) programming language. Moreover, the system uses DS1302 real time clock, SD card memory, push button, voice recognition module, liquid crystal display (LCD), and sound speaker. The clock sounds time in Kurdish language when a related switch is pressed or asked via microphone, it also shows the time on LCD. Finally, the system is applied successfully with a satisfactory cost and performance for the proposed application according to the achieved test results.