One of the significant stages in computer vision is image segmentation which is fundamental for different applications, for example, robot control and military target recognition, as well as image analysis of remote sensing applications. Studies have dealt with the process of improving the classification of all types of data, whether text or audio or images, one of the latest studies in which researchers have worked to build a simple, effective, and high-accuracy model capable of classifying emotions from speech data, while several studies dealt with improving textual grouping. In this study, we seek to improve the classification of image division using a novel approach depending on two methods used to segment the images. The first

Background: Low back pain is the most common health problem in men and women between the ages of 20 and 50 years. The lumbar disc prolapse has a major role in this condition. Treatment is either conservative or surgical. The most common surgical interventions are either laminectomy or interlaminar approach.

Objective: To determine which is the best surgical approach for the patient according to his/her type of disc herniation.

Patients and methods: A comparative clinical study conducted in the Neurosciences Hospital, Baghdad, Iraq from January 2016 to January 2018. In this paper we evaluated the clinical outcome following both approaches

Results:

     Face detection systems are based on the assumption that each individual has a unique face structure and that computerized face matching is possible using facial symmetry. Face recognition technology has been employed for security purposes in many organizations and businesses throughout the world. This research examines the classifications in machine learning approaches using feature extraction for the facial image detection system. Due to its high level of accuracy and speed, the Viola-Jones method is utilized for facial detection using the MUCT database. The LDA feature extraction method is applied as an input to three algorithms of machine learning approaches, which are the J48, OneR, and JRip classifiers.  The experiment’s

HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F

The research aims to analysis the future profits of companies listed on the Iraq Stock Exchange through analyzing the relationship between Actual Growth and Sustainable Growth, The study was applied to the sample of companies included in the Iraq Stock Exchange for the period (2010- 2014), The Internal Growth Rate has been used as a measure for the Actual Return Rate but the Sustainable Growth Rate has been measured under the Rose model, The research showed group of conclusions, the most important are:1. From the analysis of sustainable growth for the banks sample and the variables involved in its measure shows that investment in the investors amounts have achieved a significant return, which demonstrates the company's efficiency at gene