Object tracking is one of the most important topics in the fields of image processing and computer vision. Object tracking is the process of finding interesting moving objects and following them from frame to frame. In this research, Active models–based object tracking algorithm is introduced. Active models are curves placed in an image domain and can evolve to segment the object of interest. Adaptive Diffusion Flow Active Model (ADFAM) is one the most famous types of Active Models. It overcomes the drawbacks of all previous versions of the Active Models specially the leakage problem, noise sensitivity, and long narrow hols or concavities. The ADFAM is well known for its very good capabilities in the segmentation process. In this

Background: Migraine is common in systemic lupus erythematosus.It is a significant source of patient disability.

Objective: To determine the rate of migraine in patients with systemic lupus erythematosus, to assess migraine type, severity, and the association between migraine and patient’s characteristics.

Type of the study: Cross-sectional study.

Methods: 100 subjected were recruited and divided into two groups; fifty patients with the diagnosis of systemic lupus erythematosus were recruited from the Rheumatologic department of medicine,and another 50 normal subjects, then complete medical and drugs history were taken from them.

Abstract

                  The research seeks to shed light on green accounting information systems, analyze them, identify sustainability reporting and how to improve it, as well as study the importance of the Iraqi oil sector, analyze it, and work on applying green accounting information systems in order to improve the quality of sustainability reporting. Oil as a branch of the General Corporation for the Distribution of Oil and Gas Products to apply the practical aspect and prove the hypothesis of the research. Explaining the company's role in improving environmental conditions

In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking de

Abstract

This research presents a on-line cognitive tuning control algorithm for the nonlinear controller of path-tracking for dynamic wheeled mobile robot to stabilize and follow a continuous reference path with minimum tracking pose error. The goal of the proposed structure of a hybrid (Bees-PSO) algorithm is to find and tune the values of the control gains of the nonlinear (neural and back-stepping method) controllers as a simple on-line with fast tuning techniques in order to obtain the best torques actions of the wheels for the cart mobile robot from the proposed two controllers. Simulation results (Matlab Package 2012a) show that the nonlinear neural controller with hybrid Bees-PSO cognitive algorithm is m

In the recent years, some of the newly constructed asphalt concrete pavements in Baghdad as well as other cities across Iraq showed premature failures with consequential negative impact on both roadway safety and economy. Frequently, load associated mode of failure (rutting and fatigue) as well as, occasionally, moisture damage in some poorly drained sections are the main failure types found in those newly constructed road. In this research, hydrated lime was introduced into asphalt concrete mixtures of wearing course in two methods. The first one was the addition of dry lime on dry aggregate and the second one was the addition of dry lime on saturated surface dry aggregate moisturized by 2.0 to 3.0 percent of water. For each

In the recent years, some of the newly constructed asphalt concrete pavements in Baghdad as well as other cities across Iraq showed premature failures with consequential negative impact on both roadway safety and economy. Frequently, load associated mode of failure (rutting and fatigue) as well as, occasionally, moisture damage in some poorly drained sections are the main failure types found in those newly constructed road.

In this research, hydrated lime was introduced into asphalt concrete mixtures of wearing course in two methods. The first one was the addition of dry lime on dry aggregate and the second one was the addition of dry lime on saturated surface dry aggregate moisturized by 2.0 to 3.0 percent of wa

The rapid and uncontrolled expansion of urban sprawl in Baghdad, particularly after 2003, has significantly transformed the city's landscape. This expansion stems from socio-political instability, a lack of affordable housing, and inadequate urban planning frameworks. As informal settlements encroach on agricultural lands, the city's infrastructure—including water, sanitation, and transportation systems—faces mounting pressure. This situation presents critical challenges to the sustainability of Baghdad’s public services and the quality of life for its residents. This study aims to evaluate the impact of unregulated urban sprawl on Baghdad’s public services and infrastructure, focusing on how informal growth has undermined the city'

Heart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based technique. The work study assesses the efficac