Yazar "Bakir, H." seçeneğine göre listele

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    Fuzzy Logic and Correlation-Based Hybrid Classification on Hepatitis Disease Data Set
    (Springer International Publishing Ag, 2020) Basarslan, M. Sinan; Bakir, H.; Yucedag, I
    Developments in the health field are closely affecting humanity. The development of information technologies increases this effect. In this study, it was aimed to help the decision makers by increasing the accuracy rate in the detection of hepatitis disease. The data set was obtained from UCI machine learning source. Data preprocessing, attribute selection and classifier models were established on this data set, respectively. After the deficiency in the data of the patients with hepatitis was normalized, correlation-based and fuzzy-based rough force attribute selection methods were applied and the attributes that contributed to the classification were selected. The hepatitis dataset and the data set formed by the attributes determined by the correlation-based and the fuzzy-based rough-attribute selection methods were classified using the k-nearest neighbor, Random Forest, Naive Bayes, and Logistic Regression algorithms and the results were compared. Accuracy, sensitivity precision, ROC curve and F-measure values were used in the comparison of classification algorithms. In the process of separating the data set as a test and training set, a 5-fold cross-validation method was applied. It has been observed that the fuzzy rough clustering algorithm is more successful than the k-nearest neighbor, Random Forest, Naive Bayes, and Logistic Regression classification methods in the detection of hepatitis disease.
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    Optimal Coordination of Directional Overcurrent Relays Using Artificial Ecosystem-Based Optimization
    (Springer Science and Business Media Deutschland GmbH, 2021) Guvenc, U.; Bakir, H.; Duman, S.
    Optimal directional overcurrent relays (DOCRs) coordination aims to find the optimal relay settings in order to protect the system, where, the primary relays are operated in the first to clear the faults, then the corresponding backup relays should be operated in case of failing the primary relays. DOCRs coordination problem is a non-convex and high dimensional optimization problem and it should be solved subject to operating constraints. The objective function for optimal coordination of DOCRs aims to minimize total operation time for all primary relays without violation in constraints to maintain reliability and security of the electric power system. This paper proposes the artificial ecosystem-based optimization (AEO) algorithm is for the solution of the DOCRs coordination problem. Simulation studies were carried out in IEEE 3-bus and IEEE 4-bus test systems to evaluate the performance of the proposed algorithm. The simulation results are compared with differential evolution algorithm (DE), opposition based chaotic differential evolution algorithm (OCDE1and OCDE2), and three real coded genetic algorithms (RCGAs) namely: Laplace crossover power mutation (LX-PM), Laplace crossover polynomial mutation (LX-POL), bounded exponential crossover power mutation (BEX-PM). The results clearly showed that the proposed algorithm is a powerful and effective method to solve the DOCRs coordination problem. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Optimal Power Flow Using Manta Ray Foraging Optimization
    (Springer Science and Business Media Deutschland GmbH, 2021) Guvenc, U.; Bakir, H.; Duman, S.; Ozkaya, B.
    The optimal power flow (OPF) stands for the problem of specifying the best-operating levels for electric power plants in order to meet demands given throughout a transmission network, usually with the objective of minimizing operating cost. Recently, the OPF has become one of the most important problems for the economic operation of modern electrical power plants. The OPF problem is a non-convex, high-dimensional optimization problem, and powerful metaheuristic optimization algorithms are needed to solve it. In this paper, manta ray foraging optimization (MRFO) was used to solve the OPF problem which takes into account the prohibited operating zones (POZs). The performance of the MRFO was tested on IEEE 30-bus test system. The results obtained from the simulations were compared with well-known optimization algorithms in the literature. The comparative results showed that the MRFO method ensures high-quality solutions for the OPF problem. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.