This research Sought to identify the correlation relationships and the impact of each of the job description and perceived organizational support, Excellent Job performance of the heads of academic departments in the faculties of the University of Sulaymaniyah Iraqi Kurdistan Region, totaling (89) as President, and to achieve this was Default plan includes research variables as well as the formulation of a number of preparation fundamental assumptions, and researchers used a questionnaire for this purpose as a tool head of the collection of data and information, as it was distributed (80) copies, and the number of retrieved them (76) a copy of a valid statistical analysis, as well as conducting personal i

Predicting peterophysical parameters and doing accurate geological modeling which are an active research area in petroleum industry cannot be done accurately unless the reservoir formations are classified into sub-groups. Also, getting core samples from all wells and characterize them by geologists are very expensive way; therefore, we used the Electro-Facies characterization which is a simple and cost-effective approach to classify one of Iraqi heterogeneous carbonate reservoirs using commonly available well logs.

The main goal of this work is to identify the optimum E-Facies units based on principal components analysis (PCA) and model based cluster analysis(MC

Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

In the current study, two sites were selected from the city of Adhamiya, central Baghdad. The first site is the Adhamiya Corniche, which includes a sample of river water and the second includes domestic sewage in the same area. The total density of benthic invertebrates was 775 ind/m2, which is divided into 15 taxa. Biological indices were found, such as the stability index, the abundance index, the biodiversity index (Shannon’s index), the homogeneity index, and the invader’s guide. The result showed an increase in the density of benthic invertebrates, as well as an increase in the diversity of these organisms.