http://localhost:8080/xmlui/handle/123456789/612 2051-04-01T21:50:32Z http://localhost:8080/xmlui/handle/123456789/630 The Difficulties Semi-Linear in Low dimensions between Brezis and Nirenberg Dr. BUSHARA EISA HAMAD ABDALLA This study discusses existence results for a quasi-linear elliptic equation of critical Sobolev growth [ ] in the low-dimensional case, where the problem has a global character which is encoded in sign properties of the “regular” part for the corresponding Green’s function as in [ ]. Keywords: Brezis- Nirenberg problem, Low dimensions, (1+ -Laplace operator, Blow up. 2025-03-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/629 Deep Learning Techniques in Acute Lymphoblastic Leukemia Osama M. A. Yousif; Eltyeb Elsamani Abdelgabar Elsamani; Adil Mahmoud Mohamed Mahmoud; Zakariya M. S. Mohammed Acute Lymphoblastic Leukemia (ALL) is a serious hematologic malignancy that requires prompt and accurate diagnosis for effective treatment. Traditional diagnostic approaches, such as microscopic inspection and flow cytometry, while reliable, are labor intensive and time-consuming. Deep learning (DL), particularly Convolutional Neural Networks (CNNs), has revolutionized ALL diagnosis by automatically extracting features from medical images with an accuracy comparable to experienced hematologists. This automation enhances diagnostic precision and significantly reduces diagnosis time, ultimately improving clinical decision-making and patient outcomes. This study uses deep learning through rigorous bibliometric analysis to examine research productivity indicators related to the classification of Acute Lymphoblastic Leukemia. The dataset comprises 152 publications on ALL classifications using deep learning from 2009 to 2023. The analysis is conducted using the R Bibliometrix library. These publications, authored by 654 researchers from 38 countries, are distributed across 99 sources, including journals and books. Collectively, the corpus has received 1,770 citations, with an average of 11.8 citations per document. The most-cited paper, Leukemia Diagnosis in Blood Slides Using Transfer Learning in CNNs and SVM for Classification by Luis H.S. Vogado et al., has been cited 143 times. In terms of global scientific output, India leads with 26 publications (17.3%), followed by China with 22 (14.7%) and the United States with 16 (10.7%). Keywords: bibliometric analysis; R Bibliometrix; Acute Lymphoblastic Leukemia; Deep Learning; Web of Science. 2025-03-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/628 Evaluation of Software Effort Estimation Methods for Machine learning techniques منتصر فضل الله ادم; هارون عبد الله عيسى Software development is a complicated process and includes a unique effort that Include many activities, resources, skills, and people to build a quality product. Thus, effort estimation is very important activity in scheduling of software project in order to deliver project on time and better effectively evaluate predictions. There are many models used in software effort estimation, including algorithmic, non-algorithmic, and machine learning models. This paper present a review of deferent machine learning methods that are using for effort estimation like regression models(liner regression ,decision trees, random forest ), neural networks , and then evaluate this models based on performance criteria such as MAE (Mean Absolute Error) and R2 Score. These models were tested on desharnias data using Python, The results were compared for the different models and gives notes on each model. It was found that the Random forest model is the best with deshanias data among the four models and achieved the highest score on the R SCORE scale, which amounted to 0.75 Keywords: Effort, Estimation ,Machine Learning , Neural Networks, Regression Models 2025-03-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/625 The Difficulties that Face Sudanese University Students in Speaking English Language. د. حسن محمد عبد العال المكاشقي This study aimed at investigating the problems of speaking English skills among Sudanese university students. The study adopted the descriptive analytical method to analyze the data. The researchers used a questionnaire for collecting data. Twenty-five students were chosen to represent the sample of population of this study. The study tackles the problems that face Sudanese university students when they speak English. The main factors that caused this problem either, psychologically or, linguistically. The study has some findings, the most important ones are: Teachers and students do not pay considerable attention for creating a new style to teach and learn speaking English skills. In addition, teachers do not motivate students to speak English appropriately. The study concluded with some recommendations, one of them is that, practicing speaking English can help students to master speaking skill effectively. 2025-03-01T00:00:00Z