In the era of globalization, the region of Kurdistan, is participation in several ongoing projects where IT-enable systems are intended to be implemented in various sectors as part of the national IT policy. However, because of the establish system complex structure and the outdated system poor design, implementing IT enable systems locally is a significant issue. Particularly in the section dedicated to Hajj services, where files are still manually stored overcrowding happens ate registration offices since pilgrims cannot get any fresh instruction or announcements at the time they must got to the office or call them and insufficient security and safety measure for pilgrims’ data. The purpose of developing this file filing system was to facilitate and ease the management of current files by staff. As part of the research process, issues with Hajj file management were identified and information was gathered via literature research, interview and observation. Laravel (PHP), Bootstrap and HTML5 are programming language used in system development, along MySQL, Xampp. System Usability Scale (SUS) approach was used to test the system, and 10 participations sign up for it. The highest overall satisfaction rate, at roughly 80.1%

Appropriate selection of features may lead to the specificity of classification methods and identify the most critical features from all sparse or dense impact data using a filter based on the recognition selection method characterized. Filtration is used to reduce sample complexity, improve the clarity of viscous samples, and reduce background signals, resulting in increased signal-to-noise ratios in analytical tests. Depending on the filtration method applied, particles are separated based on properties such as size. This study assessed the impact of filter selection and the variation in the number of projections on the final reconstructed artificial phantom images. Utilizing image reconstruction techniques, it delves into the application of mathematical transforms, including Radon and Fourier, to improve image quality and resolution, particularly in medical imaging modalities such as CT and MRI. The research predominantly focuses on the application of the Filtered Back Projection (FBP) algorithm to reconstruct images from changing numbers of projections. The results underscore the main role of filter choice in removing noise, with the Ramp filter presenting the most promising results. The investigation concludes that reducing the number of projections results in a decline in image contrast and an increase in image noise.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak that is causing coronavirus disease 2019 is being deemed a pandemic because of its quick spread around the globe. Because chest X-ray pictures have shown to be beneficial in monitoring a variety of lung disorders, they have recently been utilized to monitor COVID-19 disease. It takes time to manually analyze a lot of chest X-ray pictures. Several previous studies have suggested machine-learning (ML)-based techniques for COVID-19 detection from chest X-ray pictures as a solution to this issue. Though little effort has been made to use traditional machine learning (ML) methods, the majority of these investigations use deep learning (DL) based techniques. Conventional ML-based algorithms will be favored for implementation if they can yield identical outcomes as DL-based methods. In this effort, we constructed four classic ML-based models for COVID-19 identification, driven by the need to close the gap in the literature. The accuracy rates for the various classification models were as follows, according to the results: 93.4% for Support Vector Machine (SVM), 93.3% for Random Forest (RF), 90.5% for K-Nearest Neighbors (KNN), and 87.9% for Decision Tree (DT). The results of the study showed that machine learning-based algorithms can produce great results for COVID-19 identification by being refined and improved using several well-known data preparation approaches

This article presents a preliminary report that uses minuscule images of blood tests to develop a diagnosis of leukemia. Examining through images is crucial since illnesses can be recognized and examined at an earlier stage using the images. The framework will be centered on leukemia and white blood cell illness. In fact, even the hematologist has trouble organizing the leukemic cells, and manually arranging the platelets takes a long time and is quite loose. In this way, early detection of leukemia recurrence allows the patient to receive the appropriate treatment. In order to address this problem, the framework will make use of the capabilities in small images and examine surface, geometry, shading, and quantifiable investigation modifications. These features' variations will be utilized as the classifier input. has transformed the use of images K proposes that (NN) and agglomeration. Examining a wide range of failure measures and increasing the intricacy of every system, the findings are examined. Utilizing feedforward (NN), image division is accomplished with less noise and a very sluggish conjunction rate. K-means agglomeration and (ANN) are intentionally used in this analysis to create a collection of processes that will work together to produce a much better presentation in (IS). An analysis has been conducted to determine the best rule for (IS).

This study analyzes the use of Computer-Supported Collaborative Learning (CSCL) in computer laboratory settings for secondary school science education. The literature review evaluates the impact of collaborative methods using computer simulations, virtual experiments, and other digital technologies. The search yielded 33 relevant studies, indicating that collaborative conditions outperformed individual laboratory work in computer environments across various measures. Collaborative work in pairs or small teams has been shown to lead to better learning outcomes and expert-like reasoning patterns. Additionally, students have reported finding collaborative computer laboratory work more enjoyable, engaging, and preferable to independent work. The integration of computer-supported collaborative learning (CSCL) into science education presents opportunities to enhance students' learning experiences through interactive and collaborative approaches. This study highlights the importance of student-centered learning design, effective group dynamics, and reliable technology infrastructure for successful CSCL implementation. Additional research is required to identify the best group composition and task design, as well as the implications for effectively implementing computer-supported collaborative learning in science laboratories.

The Information and Communication Technology (ICT) has witnessed great development in the recent years. Therefore, the design of Students and Alumni Web Portal (SAWP) involves the analysis of the internal and external environment of the three universities. For this آ آ purpose, SWOT technique has been used to detect the deep effect of environment factors on the strategic plan to discover the strengths, weaknesses, opportunities and threats facing the design of the proposed system. SAWP was designed using (MySQL, HTML, CSS, Java Script, jQuery, PHP, AJAX) techniques to provide robust portal system addressing two subsystems: student and alumni portal system. Testing of the SAWP was administered through two main stages: the first, to identify the student’ s views and their preferences. The second to measure the usability of the system through using System Usability Scale (SUS) method with subscription of 22 potential آ آ system users. The best results of SUS testing are: the rate of overall satisfaction was high nearly 80%. While the implementation outcomes found very compatibility and reasonable in wide extents between available data and system requirements.