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Öğe A Review of Distance Learning and Learning Management Systems(Intech Europe, 2016) Keles, Mumine Kaya; Ozel, Selma AyseIn recent years, rapid developments in technology and the web have led to many changes in education. One of the most important changes in education is in the form of distance learning. Distance learning, which is used to define education where educators and learners are physically separated, is not a new concept; however, emerging technologies and the web allow web-based distance learning and therefore increase its popularity. As a result of these developments, many universities have started to use web-based distance learning systems to provide flexible education that is independent of time and place. In this chapter, we review all popular, widely used, and well-known learning management systems and include detailed comparison of some of these systems to allow institutions to choose the right system for their distance education activities.Öğe Artificial Bee Colony Algorithm for Feature Selection on SCADI Dataset(IEEE, 2018) Keles, Mumine Kaya; Kılıç, UmitData consists of relevant and irrelevant features. These irrelevant features may mislead classification or clustering algorithms. Feature selection algorithms are used to avoid that misleading and to obtain better results using fewer number of features than dataset has. The purpose of feature selection is to choose as small number of relevant features as possible to enhance the performance of the classification. In this paper, Artificial Bee Colony algorithm (ABC), which is proposed by karaboaa in 2005, is implemented as feature selection algorithm (ABC-FS) and used on Self-Care Activities Dataset based on ICF-CY (SCAM). SCADI that contains 206 attributes of 70 children with physical and motor disability is a dataset for self-care problems. Feature selection operation is carried out using Gain Ratio, Info Gain, and Chi Square. These selected features are utilized to obtain classification results. Then, features are selected by ABC-FS and results are compared. Seven of 206 features are selected by ABC-FS, and 88.5714% accuracy rate and 0.871 F-Measure value are obtained while best of Info Gain, Gain Ratio and Chi-Square is 84.2857 (1/0 and 0.824, respectively. The experimental results show that the selected features by ABC-FS generally have higher accuracy than the raw dataset and Info Gain, Gain Ratio and Chi-Square.Öğe Binary Black Widow Optimization Approach for Feature Selection(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Keles, Mumine Kaya; Kılıç, UmitFeature selection is a process of reduction of irrelevant, negligible, noisy features from data sets so as to obtain better performance measurements with fewer features. Throughout the literature, various methods are presented that use different approaches to get through this difficult problem, prevalently. In this study, a binary variant of the Black Widow Optimization (BWO) is proposed in a wrapper mode for the purpose of feature selection. The BWO algorithm has early convergence ability on continuous problems and that characteristic is also effective for finding an optimum solution in feature selection problem. The proposed approach compared with state-of-the-art and widely used approaches such as Binary Particle Swarm Optimization (BPSO and VPSO), Binary Grey Wolf Optimization (BGWO1 and BGWO2). The performance of these algorithms is assessed over 20 benchmark data sets from the UCI repository. The results show that the proposed binary method can be utilized effectively in discrete problems such as feature selection.Öğe Breast cancer detection using K-nearest neighbors data mining method obtained from the bow-tie antenna dataset(Wiley, 2017) Aydin, Emine Avsar; Keles, Mumine KayaBreast cancer, has been a significant cancer type for women on the society. Early diagnosis and timely medical treatment are important key factors spreading to the other tissues and permitting long-time survival of patients. Since the existing methods have several serious shortfalls, microwave imaging method for the diagnosis of early stage tumors has been interested by different scientific research groups in terms of moderating endogenous the electrical property difference between healthy tissue and malignancies. In this article, both an ultra-wideband bow-tie antenna with enhanced bandwidth and a 3D breast model which has different electrical properties which are permittivity and conductivity is created in simulation tool to solve electromagnetic field values. Return loss, VSWR, and radiation pattern characteristics, which are significant antenna parameters, are simulated and obtained whether the antenna possess an efficient characteristic or not. Electric field values over the breast tissue in which there is a tumor or not tumor are evaluated. In this article, above-mentioned values of frequency bandwidth, dielectric constant of antenna's substrate, electric field, and tumor information were consisted in their dataset. This dataset obtained from the Bow-Tie Antenna was used to detect the breast cancer with one of the data mining method, which is K-Nearest Neighbor Algorithm.Öğe Classification of Brain Volumetric Data to Determine Alzheimer's Disease Using Artificial Bee Colony Algorithm as Feature Selector(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Keles, Mumine Kaya; Kılıç, UmitAlzheimer's disease is a degenerative disease that affects the age progression and causes the brain to be unable to fulfill its expected functions. Depending on the stage, the effects of Alzheimer's disease (AD) vary from forgetting the names of the surrounding people to not being able to continue daily life without assistance. To the best of our knowledge, there are currently no generally accepted diagnostic or treatment methods. In this study, a binary version of the artificial bee colony algorithm (BABC) is proposed as a feature selector for classifying AD from volumetric and statistical data of brain magnetic resonance images (MRIs). MRIs were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Volumetric and statistical data from the collected MRIs were obtained from an online system called volBrain. Then, for comparison, binary particle swarm optimization (BPSO), binary grey wolf optimization (BGWO), and binary differential evolution (BDE) were employed. For a comprehensive comparison, three algorithms, K-nearest Neighborhood (KNN), Random Forest (RF), and Support Vector Machine (SVM), are used as classifiers in feature selection progress. The results of this comparison demonstrate that BGWO outperforms BABC, which is a competitive method for this purpose. The outputs of the experiments show that all methods achieve their personal best by using RF as the classifier. Additionally, traditional data mining methods such as the Info Gain (IG), Gain Ratio (GR), Chi-square (CHI), and ReliefF methods were utilized for comparison. The results also demonstrate the superiority of the BABC over traditional methods. Another research point that this study focused on was to explore which parts of the brain are more relevant for AD diagnosis. The novelty of this study lies in the output of this point. Alongside the hippocampus and amygdala, the globus pallidus can also help in AD diagnosis.Öğe Proposed Artificial Bee Colony Algorithm as Feature Selector to Predict the Leadership Perception of Site Managers(Oxford Univ Press, 2021) Keles, Mumine Kaya; Kılıç, Umit; Keles, Abdullah EmreDatasets have relevant and irrelevant features whose evaluations are fundamental for classification or clustering processes. The effects of these relevant features make classification accuracy more accurate and stable. At this point, optimization methods are used for feature selection process. This process is a feature reduction process finding the most relevant feature subset without decrement of the accuracy rate obtained by original feature sets. Varied nature inspiration-based optimization algorithms have been proposed as feature selector. The density of data in construction projects and the inability of extracting these data cause various losses in field studies. In this respect, the behaviors of leaders are important in the selection and efficient use of these data. The objective of this study is implementing Artificial Bee Colony (ABC) algorithm as a feature selection method to predict the leadership perception of the construction employees. When Random Forest, Sequential Minimal Optimization and K-Nearest Neighborhood (KNN) are used as classifier, 84.1584% as highest accuracy result and 0.805 as highest F-Measure result were obtained by using KNN and Random Forest classifier with proposed ABC Algorithm as feature selector. The results show that a nature inspiration-based optimization algorithm like ABC algorithm as feature selector is satisfactory in prediction of the Construction Employee's Leadership Perception.Öğe The Investigation of the Applicability of Data-Driven Techniques in Hydrological Modeling: The Case of Seyhan Basin(Middle Pomeranian Sci Soc Env Prot, 2019) Turhan, Evren; Keles, Mumine Kaya; Tantekin, Atakan; Keles, Abdullah EmreProper water resources planning and management is based on reliable hydrological data. Missing rainfall and runoff observation data, in particular, can cause serious risks in the planning of hydraulics structures. Hydrological modeling process is quitely complex. Therefore, using alternative estimation techniques to forecast missing data is reasonable. In this study, two data-driven techniques such as Artificial Neural Networks (ANN) and Data Mining were investigated in terms of availability in hydrology works. Feed Forward Back Propagation (FFBPNN) and Generalized Regression Neural Networks (GRNN) methods were performed on rainfall-runoff modeling for ANN. Besides, Hydrological drought analysis were examined using data mining technique. The Seyhan Basin was preferred to carry out these techniques. It is thought that the application of different techniques in the same basin could make a great contribute to the present work. Consequently, it is seen that FFBPNN is the best model for ANN in terms of giving the highest R2 and lowest MSE values. Multilayer Perceptron (MLP) algorithm was used to predict the drought type according to limit values. This system has been applied to show the relationship between hydrological data and measure the prediction accuracy of the drought analysis. According to the obtained data mining results, MLP algorithm gives the best accuracy results as flow observation stations using SRI-3 month data.