Today the Genetic Algorithm (GA) tops all the standard algorithms in solving complex nonlinear equations based on the laws of nature. However, permute convergence is considered one of the most significant drawbacks of GA, which is known as increasing the number of iterations needed to achieve a global optimum. To address this shortcoming, this paper proposes a new GA based on chaotic systems. In GA processes, we use the logistic map and the Linear Feedback Shift Register (LFSR) to generate chaotic values to use instead of each step requiring random values. The Chaos Genetic Algorithm (CGA) avoids local convergence more frequently than the traditional GA due to its diversity. The concept is using chaotic sequences with LFSR to gene

Abstract

   Suffering the human because of pressure normal life of exposure to several types of heart disease as a result of due to different factors. Therefore, and in order to find out the case of a death whether or not, are to be modeled using binary logistic regression model

    In this research used, one of the most important models of nonlinear regression models extensive use in the modeling of applications statistical, in terms of heart disease which is the binary logistic regression model. and then estimating the parameters of this model using the statistical estimation methods, another problem will be appears in estimating its parameters, as well as when the numbe

Medical imaging is a technique that has been used for diagnosis and treatment of a large number of diseases. Therefore it has become necessary to conduct a good image processing to extract the finest desired result and information. In this study, genetic algorithm (GA)-based clustering technique (K-means and Fuzzy C Means (FCM)) were used to segment thyroid Computed Tomography (CT) images to an extraction thyroid tumor. Traditional GA, K-means and FCM algorithms were applied separately on the original images and on the enhanced image with Anisotropic Diffusion Filter (ADF). The resulting cluster centers from K-means and FCM were used as the initial population in GA for the implementation of GAK-Mean and GAFCM. Jaccard index was used to s

Nowadays, information systems constitute a crucial part of organizations; by losing security, these organizations will lose plenty of competitive advantages as well. The core point of information security (InfoSecu) is risk management. There are a great deal of research works and standards in security risk management (ISRM) including NIST 800-30 and ISO/IEC 27005. However, only few works of research focus on InfoSecu risk reduction, while the standards explain general principles and guidelines. They do not provide any implementation details regarding ISRM; as such reducing the InfoSecu risks in uncertain environments is painstaking. Thus, this paper applied a genetic algorithm (GA) for InfoSecu risk reduction in uncertainty. Finally, the ef

     In this paper, the botnet detection problem is defined as a feature selection problem and the genetic algorithm (GA) is used to search for the best significant combination of features from the entire search space of set of features. Furthermore, the Decision Tree (DT) classifier is used as an objective function to direct the ability of the proposed GA to locate the combination of features that can correctly classify the activities into normal traffics and botnet attacks. Two datasets  namely the UNSW-NB15 and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017), are used as evaluation datasets. The results reveal that the proposed DT-aware GA can effectively find the relevant features from

Background: TIDM is known to be polygenic disease that appears from the interaction of mutation in multiple genes including HLA. The autoimmune mediated destruction of
pancreatic β-cells is reflected by the presence of autoantibodies against prominent antigens in the pancreatic β-cells.
Objective: This study was designed to investigate the role of HLA-class I and class II antigens in the etiology of type 1 diabetes mellitus (T1DM) and also assessment of
glutamic acid decarboxylase (GAD65) autoantibodies in the patients at the onset of the disease.
Patients & Methods: Sixty T1DM patients who were newly onset of the disease (diagnosed less than five months) were selected. Eighty apparently healthy

      Samples of the root nodules were collected to isolate different species of the genus Rhizobium from several leguminous plants; Trigonella  foenum-graecum, Medicago sativa, Lens culinaris, Vigna mungo, Vicia faba,  Phaseolus vulgaris, and Cicer arietinum, and based on their morphological, cultural, and biochemical characteristics, in addition to the identification of each isolate at the species level by amplified polymerase chain reaction (PCR) and using the sequencing of the nitrogenous bases of the 16S rRNA gene, it was identified as Sinrhizobium meliloti, Sinrhizobium meliloti, Bradyrhizobium elkanii, Rhizobium leguminosarium biovar viciae, Rhizobium leguminosarium biovar phaseoli and Mesorh

    Ferns are considered as important medical herbs. They produce a wide range of secondary metabolites that could be useful in treating different diseases. However, they still remain underexplored in medical aspects in Iraq. Hence, the purpose of this study was to identify alkaloids components in the methanol extracts of four fern species (Asplenium ceterach L., A. scolopendrium L., Cheilanthes pteridioides (Richard) C. Chr. from Malakan region in Erbil Governorate and Equisetum ramosissimum Desf. from Chemi Rezan in Sulaimaniyah Governorate,  of northen Iraq during March - June 2018. The research was conducted by using High performance liquid chromatography (H