BACKGROUND: Montelukast (Singulair) is a cysteinyl leukotriene receptor antagonist, used for the maintenance treatment of asthma and to relieve symptoms of seasonal allergic rhinitis and asthma, also used for exercise-induced bronchospasm. AIM: This study was performed to determine the prevalence of Montelukast use as an add-on therapy among Iraqi asthmatic patients on treatment. Comparing the effectiveness of regimens with and without montelukast. METHODS: This descriptive cross-sectional study was carried out on 73 Iraqi asthmatic patients on treatment of both sexes with age range (18-60) years old, attending Al-Kindy Teaching Hospital and Al-Zahraa Centre of Asthma and Allergy, Baghdad, for the period between February

BACKGROUND: Montelukast (Singulair) is a cysteinyl leukotriene receptor antagonist, used for the maintenance treatment of asthma and to relieve symptoms of seasonal allergic rhinitis and asthma, also used for exercise-induced bronchospasm. AIM: This study was performed to determine the prevalence of Montelukast use as an add-on therapy among Iraqi asthmatic patients on treatment. Comparing the effectiveness of regimens with and without montelukast. METHODS: This descriptive cross-sectional study was carried out on 73 Iraqi asthmatic patients on treatment of both sexes with age range (18-60) years old, attending Al-Kindy Teaching Hospital and Al-Zahraa Centre of Asthma and Allergy, Baghdad, for the period between February

        Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and

Intrusion detection systems (IDS) are useful tools that help security administrators in the developing task to secure the network and alert in any possible harmful event. IDS can be classified either as misuse or anomaly, depending on the detection methodology. Where Misuse IDS can recognize the known attack based on their signatures, the main disadvantage of these systems is that they cannot detect new attacks. At the same time, the anomaly IDS depends on normal behaviour, where the main advantage of this system is its ability to discover new attacks. On the other hand, the main drawback of anomaly IDS is high false alarm rate results. Therefore, a hybrid IDS is a combination of misuse and anomaly and acts as a solution to overcome the dis

The rise of edge-cloud continuum computing is a result of the growing significance of edge computing, which has become a complementary or substitute option for traditional cloud services. The convergence of networking and computers presents a notable challenge due to their distinct historical development. Task scheduling is a major challenge in the context of edge-cloud continuum computing. The selection of the execution location of tasks, is crucial in meeting the quality-of-service (QoS) requirements of applications. An efficient scheduling strategy for distributing workloads among virtual machines in the edge-cloud continuum data center is mandatory to ensure the fulfilment of QoS requirements for both customer and service provider. E

In regression testing, Test case prioritization (TCP) is a technique to arrange all the available test cases. TCP techniques can improve fault detection performance which is measured by the average percentage of fault detection (APFD). History-based TCP is one of the TCP techniques that consider the history of past data to prioritize test cases. The issue of equal priority allocation to test cases is a common problem for most TCP techniques. However, this problem has not been explored in history-based TCP techniques. To solve this problem in regression testing, most of the researchers resort to random sorting of test cases. This study aims to investigate equal priority in history-based TCP techniques. The first objective is to implement

The objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.