Yazar "Kaya Keles, Mumine" seçeneğine göre listele

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    AN OVERVIEW: THE IMPACT OF DATA MINING APPLICATIONS ON VARIOUS SECTORS
    (Univ North, 2017) Kaya Keles, Mumine
    In recent years, it has become difficult to reach to the reliable information with increasing complicated, non-significant, unclear, large and raw data. The need for accurate analysis of reliable information from large data has also increased in direct proportion to the rate of data growth. The Data Mining Method, which is a statistical application, is used in any desired area to be accessed to the reliable and meaningful information. In this study, the areas where data mining methods are used were explained, a literature review about banking and finance, education, telecommunication, health, public, construction, engineering and science sectors was made, and the impact of the data mining was discussed. This study is aimed to provide a contribution to the literature eliminating the gap in the mentioned area and to bring an innovation to the applications and work in these areas.
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    Binary Anarchic Society Optimization for Feature Selection
    (Editura Acad Romane, 2023) Kılıç, Umit; Sarac Essiz, Esra; Kaya Keles, Mumine
    Datasets comprise a collection of features; however, not all of these features may be necessary. Feature selection is the process of identifying the most relevant features while eliminating redundant or irrelevant ones. To be effective, feature selection should improve classification performance while reducing the number of features. Existing algorithms can be adapted and modified into feature selectors. In this study, we introduce the implementation of the Anarchic Society Optimization algorithm, a human-inspired algorithm, as a feature selector. This is the first study that utilizes the binary version of the algorithm for feature selection. The proposed Binary Anarchic Society Algorithm is evaluated on nine datasets and compared to three known algorithms: Binary Genetic Algorithm, Binary Particle Swarm Optimization, and Binary Gray Wolf Optimization. Additionally, four traditional feature selection techniques (Info Gain, Gain Ratio, Chi-square, and ReliefF) are incorporated for performance comparison. Our experiments highlight the competitive nature of the proposed method, suggesting its potential as a valuable addition to existing feature selection techniques.
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    Breast Cancer Prediction and Detection Using Data Mining Classification Algorithms: A Comparative Study
    (Univ Osijek, Tech Fac, 2019) Kaya Keles, Mumine
    Today, cancer has become a common disease that can afflict the life of one of every three people. Breast cancer is also one of the cancer types for which early diagnosis and detection is especially important. The earlier breast cancer is detected, the higher the chances of the patient being treated. Therefore, many early detection or prediction methods are being investigated and used in the fight against breast cancer. In this paper, the aim was to predict and detect breast cancer early with non-invasive and painless methods that use data mining algorithms. All the data mining classification algorithms in Weka were run and compared against a data set obtained from the measurements of an antenna consisting of frequency bandwidth, dielectric constant of the antenna's substrate, electric field and tumor information for breast cancer detection and prediction. Results indicate that Bagging, IBk, Random Committee, Random Forest, and SimpleCART algorithms were the most successful algorithms, with over 90% accuracy in detection. This comparative study of several classification algorithms for breast cancer diagnosis using a data set from the measurements of an antenna with a 10-fold cross-validation method provided a perspective into the data mining methods' ability of relative prediction. From data obtained in this study it can be said that if a patient has a breast cancer tumor, detection of the tumor is possible.
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    DISTANCE EDUCATION WITH MOODLE IN ENGINEERING EDUCATION: ONLINE PROGRAMMING ASSIGNMENTS COMPILATION
    (Univ North, 2018) Kaya Keles, Mumine; Keles, Abdullah Emre
    The concept of distance education systems is a concept that applies to all levels of education, including universities. The use of distance education systems has increased considerably in universities today. Many faculties in many universities use distance education systems for their courses. The purpose of this paper is to design and develop a system that can be used to upload lecture notes and assignments online via the Internet, to do online exams, to provide a compilation control of all the assignments written, especially in the C programming language, by instructors who are primarily in the Engineering Department, then all instructors in the universities using the Moodle platform. Moreover, the aim of this paper is to design and develop a system in which the students primarily in the Engineering Department using the Moodle platform and then students in all the universities can follow the course contents, upload the assignments, and discuss their questions about the course with their instructors and their friends. As a result of this paper, a scheme is provided to easily compile, run and grade the programming assignments (source codes) given in the Programming courses using the Moodle website collected in a single place.
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    Feature Selection with Artificial Bee Colony Algorithm on Z-Alizadeh Sani Dataset
    (IEEE, 2018) Kılıç, Umit; Kaya Keles, Mumine
    Relevant and irrelevant features compose data. Evaluation of these features is the fundamental task for classification and clustering processes and during this processes, irrelevant features induce obtaining false results. Likewise, due to the relevant features' direct effect on the processes, results can be more correct and stable. This also represents the aim of the feature selection process that tries to achieve as high as possible results with as small as possible feature selection subset. In this study, Artificial Bee Colony (ABC) algorithm based feature selection method is updated and employed on Z-Alizadeh Sani data set that consists of 56 features including the class attribute collected from 303 patients. 16 of the 56 features are selected by ABC based updated feature selection method. Also, accuracy and F-measure values are measured as 89.4% and 0.894 respectively, which are higher than the values produced by the raw dataset.
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    PREDICTION OF CONCRETE STRENGTH WITH DATA MINING METHODS USING ARTIFICIAL BEE COLONY AS FEATURE SELECTOR
    (IEEE, 2018) Kaya Keles, Mumine; Keles, Abdullah Emre; Kılıç, Umit
    Concrete which is a highly complex material is the most basic input of the construction industry. Because of its strength, concrete is one of the most preferred structural building materials. In the ready-mixed concrete sector, there is an increasing need for earthquake resistant structures due to the fact that some producers produce out of control and poor quality. Ready-mixed concrete is a product whose quality can only be understood at the end of the 28th day if it is only controlled by taking the sample by the user. In this study, a data mining study was conducted on the factors affecting the 28-day compressive strength of concrete using the Concrete Slump Test Data Set from UCI Machine Learning Repository. The Artificial Bee Colony Algorithm is used as a feature selection method in order to determine the important ones of the concrete components, which are cement, slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate, affecting concrete strength and tried to predict the strength with data mining algorithms. As a result of the study, it was observed that Random Forest Algorithm gave the highest success rate with 91.2621% accuracy using only 3 features, which are cement, fly ash, and water. This means that it is possible to predict the compressive strength of concrete with a ratio above 90% by using a smaller number of concrete components.
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    Similarity Detection between Turkish Text Documents with Distance Metrics
    (IEEE, 2017) Kaya Keles, Mumine; Ozel, Selma Ayse
    The aim of this study is to compare the successes of various distance metrics and to determine the most appropriate methods in order to detect similarities among textual documents written in Turkish. Computing similarities between text documents is the basic step of plagiarism detection, and text mining methods like author detection, text classification and clustering. Therefore, plagiarism detection and text mining applications will be more successful by using the distance metrics that are determined according to the results obtained in this study. For this purpose, chunks of texts in different lengths are selected as the experimental dataset in this study. After that, preprocessing methods are applied to the dataset that is used; therefore new and different experimental scenarios are created by removing stopwords and Turkish characters, and stemming words with Zemberek. According to the experimental results, it is observed that the preprocessing phase increases the accuracy of similarity detection. Especially, stemming using Zemberek increases the success rate. In all cases, the Cosine Similarity method has been observed as more successful than other distance metrics, because of producing more realistic results.