An aircraft's landing stage involves inherent hazards and problems associated with many factors, such as weather, runway conditions, pilot experiences, etc. The pilot is responsible for selecting the proper landing procedure based on information provided by the landing console operator (LCO). Given the likelihood of human decisions due to errors and biases, creating an intelligent system becomes important to predict accurate decisions. This paper proposes the fuzzy logic method, which intends to handle the uncertainty and ambiguity inherent in the landing phase, providing intelligent decision support to the pilot while reducing the workload of the LCO. The fuzzy system, built using the Mamdani approach in MATLAB software, considers critical

Background: Oral Lichen Planus is a chronic inflammatory mucosal disease, presenting in various clinical forms .Both antigen-specific and non-specific mechanisms involved in the pathogenesis of OLP. Apoptosis or programmed-cell death is a physiological process essential for the normal development and maintenance of homeostasis in many organisms. Fas is a cell-surface glycoprotein, 40-kDa, that belongs to the nerve growth factor / tumor necrosis factor (TNF) receptor family. Fas is expressed in several tissues including blood, where its expression is upregulated on activated T and B lymphocytes and natural killer cells. Fas ligand is a type II transmembrane protein that belongs to the tumor necrosis factor family. The proto-oncogene c-Myc is

Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the

Celiac disease (CD) is an autoimmune disorder characterized by chronic inflammation that essentially affects the small intestine and is caused by eating gluten-containing foods. This study sought to determine gene expression of NLRP3 Inflammasome in peripheral blood of Iraqi CD children using quantitative real-time PCR (qRT-PCR) assay. Thirty children with CD (12 males and 18 females) were enrolled in the study and their age range was 3-15 years. The diagnosis of the disease was confirmed by serological examinations and intestinal endoscopy. A control sample of 20 age-matched healthy children was also included. The children were stratified for age, gender, body max index (BMI), histological findings, and marsh classification. Furthe

Background: Chronic myelogenous leukemia is a malignant hematological disease of hematopoietic stem cells. It is difficult to adapt treatment to each patient's risk level because there are currently few clinical tests and no molecular diagnostics that may predict a patient's clock for the advancement of CML at the time of chronic phase diagnosis. Biomarkers that can differentiate people based on the outcome at diagnosis are needed for blast crisis prevention and response improvement. Objective: This study is an effort to exploit the SLC25A3 gene as a potential biomarker for CML. Methods: RT-qPCR was applied to assess the expression levels of the SLC25A3 gene. Results: In comparison to the mean ΔCt of the control group, which was found to b

miRNAs regulate protein abundance and control diverse aspects of cellular processes and biological functions in metabolic diseases, such as obesity and diabetes. Lethal-7(Let-7) miRNAs specifically target genes associated with diabetes and have a role in the regulation of peripheral glucose metabolism. The present study aimed to describe the gene expressions of the let-7a gene with the development of diabetes in Iraq and the difference in the expression of this gene in patients with diabetes and healthy individuals. The association between age and gender with the development of diabetes was studied in this study and the results were compared with those of healthy individuals in the group of control. Based on the obtained results, there was

The Machine learning methods, which are one of the most important branches of promising artificial intelligence, have great importance in all sciences such as engineering, medical, and also recently involved widely in statistical sciences and its various branches, including analysis of survival, as it can be considered a new branch used to estimate the survival and was parallel with parametric, nonparametric and semi-parametric methods that are widely used to estimate survival in statistical research. In this paper, the estimate of survival based on medical images of patients with breast cancer who receive their treatment in Iraqi hospitals was discussed. Three algorithms for feature extraction were explained: The first principal compone

The emphasis of Master Production Scheduling (MPS) or tactic planning is on time and spatial disintegration of the cumulative planning targets and forecasts, along with the provision and forecast of the required resources. This procedure eventually becomes considerably difficult and slow as the number of resources, products and periods considered increases. A number of studies have been carried out to understand these impediments and formulate algorithms to optimise the production planning problem, or more specifically the master production scheduling (MPS) problem. These algorithms include an Evolutionary Algorithm called Genetic Algorithm, a Swarm Intelligence methodology called Gravitational Search Algorithm (GSA), Bat Algorithm (BAT), T

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne