Association rules mining (ARM) is a fundamental and widely used data mining technique to achieve useful information about data. The traditional ARM algorithms are degrading computation efficiency by mining too many association rules which are not appropriate for a given user. Recent research in (ARM) is investigating the use of metaheuristic algorithms which are looking for only a subset of high-quality rules. In this paper, a modified discrete cuckoo search algorithm for association rules mining DCS-ARM is proposed for this purpose. The effectiveness of our algorithm is tested against a set of well-known transactional databases. Results indicate that the proposed algorithm outperforms the existing metaheuristic methods.
Evolution in the modern era Which led to the rapid change in the forms of industrial products For many reasons, So put current research into question the view (What are the design requirements that define the formal change in the Iron clothes)? To reach the aim of In the design cornerstonesUnderlying the formal changethe Iron of the clothes, In the first section shed light on the development stages of systems design lists the historic stages of development and energy operator devices irons and mechanism of action and internal components, while in the second part, which was entitled (The role of technology and the factors influencing the change formality of Iron) touched on the three topics which technology modern industrial and receiver,
... Show MoreThe present study was set to investigate the potential association between the level of Interleukin-6 (IL-6), as a key component of the pro-inflammatory response, with different thalassemia’s biological and clinical features. For this purpose, one hundred fifty blood samples were collected from 100 beta-thalassemia patients, who attended the Genetic Hematology Centre at Ibn Al- Baladi Hospital in Baghdad, Iraq, and 50 healthy subjects who were employed as a control group. IL-6 levels were estimated using an ELISA Kit, whereas other thalassemia-related clinical features (such as HbA, HbF, ferritin, blood transfusions, splenectomy status, and the history of frequent infection) were additionally assessed. The results of the present s
... Show MoreDiabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att
... Show MoreThis paper presents a study of the application of gas lift (GL) to improve oil production in a Middle East field. The field has been experiencing a rapid decline in production due to a drop in reservoir pressure. GL is a widely used artificial lift technique that can be used to increase oil production by reducing the hydrostatic pressure in the wellbore. The study used a full field model to simulate the effects of GL on production. The model was run under different production scenarios, including different water cut and reservoir pressure values. The results showed that GL can significantly increase oil production under all scenarios. The study also found that most wells in the field will soon be closed due to high water cuts. Howev
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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
... Show Moreplanning is among the most significant in the field of robotics research. As it is linked to finding a safe and efficient route in a cluttered environment for wheeled mobile robots and is considered a significant prerequisite for any such mobile robot project to be a success. This paper proposes the optimal path planning of the wheeled mobile robot with collision avoidance by using an algorithm called grey wolf optimization (GWO) as a method for finding the shortest and safe. The research goals in this study for identify the best path while taking into account the effect of the number of obstacles and design parameters on performance for the algorithm to find the best path. The simulations are run in the MATLAB environment to test the
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