Yazar "Hacibeyoglu, Mehmet" seçeneğine göre listele

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    Advancing competitive position in healthcare: a hybrid metaheuristic nutrition decision support system
    (Springer Heidelberg, 2019) Ileri, Yusuf Yalcin; Hacibeyoglu, Mehmet
    In the highly competitive environment of healthcare industry, managers incorporate patient centered care as a major component in the healthcare mission. Management information systems have gained increasing attention to develop effective plans for patient centered care, and quality improvement in healthcare organizations. Consumers are becoming actively involved in their health, which is great for patient engagement, but to meet this demand, healthcare providers need to make significant changes. Information technology and artificial intelligence can significantly improve workflows for healthcare professionals, and support the development of new services to meet changing consumer demands while maintaining costs. In this study, we aimed to build a decision support system including Genetic algorithm and Simulated Annealing metaheuristic machine learning methods for nutritionists to regulate a hospitals' nutrition cycle and provide the best possible diet menu solutions while decreasing costs. Our main reason of using hybrid metaheuristic machine learning methods is to obtain a better quality solution on the problem dealt with study. Experimental results showed that; the proposed system is a novel, smart, cost-effective, flexible and machine learning based decision support system for nutritionist and healthcare managers.
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    A Comparative Analysis of Metaheuristic Approaches for Multidimensional Two-Way Number Partitioning Problem
    (Springer Heidelberg, 2018) Hacibeyoglu, Mehmet; Alaykiran, Kemal; Acilar, Ayse Merve; Tongur, Vahit; Ulker, Erkan
    In this study, a novel usage of four metaheuristic approaches Genetic algorithm (GA), Simulated annealing (SA), migrating bird optimization algorithm (MBO) and clonal selection algorithm (CSA) are applied to multidimensional two-way number partitioning problem (MDTWNPP). MDTWNPP is a classical combinatorial NP-hard optimization problem where a set of vectors have more than one coordinate is partitioned into two subsets. The main objective function of MDTWNPP is to minimize the maximum absolute difference between the sums per coordinate of elements. In order to solve this problem, GA is applied with greedy crossover and mutation operators. SA is improved with dual local search mechanism. MBO is specialized as multiple flock migrating birds optimization algorithms. CSA is applied with problem specific hyper mutation process. Furthermore, all instances are solved using an integer linear programming model which was previously presented in the literature. In the experiments, four metaheuristic approaches and integer linear programming model are used to solve 126 datasets with different sizes and coordinates. As a brief result, the GA and SA approaches designed for this problem outperformed all other heuristics and the integer programming model. Both the performance of GA and SA approaches are in a competitive manner where GA and SA yielded the best solution for 56 and 65 out of 125 datasets, respectively.
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    Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System
    (Ieice-Inst Electronics Information Communications Eng, 2016) Akyol, Aslihan; Hacibeyoglu, Mehmet; Karlik, Bekir
    With the increase of network components connected to the Internet, the need to ensure secure connectivity is becoming increasingly vital. Intrusion Detection Systems (IDSs) are one of the common security components that identify security violations. This paper proposes a novel multilevel hybrid classifier that uses different feature sets on each classifier. It presents the Discernibility Function based Feature Selection method and two classifiers involving multilayer perceptron (MLP) and decision tree (C4.5). Experiments are conducted on the KDD'99 Cup and ISCX datasets, and the proposal demonstrates better performance than individual classifiers and other proposed hybrid classifiers. The proposed method provides significant improvement in the detection rates of attack classes and Cost Per Example (CPE) which was the primary evaluation method in the KDD'99 Cup competition.
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    Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System
    (Ieice-Inst Electronics Information Communications Eng, 2016) Akyol, Aslihan; Hacibeyoglu, Mehmet; Karlik, Bekir
    With the increase of network components connected to the Internet, the need to ensure secure connectivity is becoming increasingly vital. Intrusion Detection Systems (IDSs) are one of the common security components that identify security violations. This paper proposes a novel multilevel hybrid classifier that uses different feature sets on each classifier. It presents the Discernibility Function based Feature Selection method and two classifiers involving multilayer perceptron (MLP) and decision tree (C4.5). Experiments are conducted on the KDD'99 Cup and ISCX datasets, and the proposal demonstrates better performance than individual classifiers and other proposed hybrid classifiers. The proposed method provides significant improvement in the detection rates of attack classes and Cost Per Example (CPE) which was the primary evaluation method in the KDD'99 Cup competition.
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    EF_Unique: An Improved Version of Unsupervised Equal Frequency Discretization Method
    (Springer Heidelberg, 2018) Hacibeyoglu, Mehmet; Ibrahim, Mohammed H.
    Discretization is an important data preprocessing technique used in data mining and knowledge discovery processes. The purpose of discretization is to transform or partition continuous values into discrete ones. In this manner, many data mining classification algorithms can be applied the discrete data more concisely and meaningfully than continuous ones, resulting in better performance. In this study, an improved version of the unsupervised equal frequency (EF) discretization method, EF_Unique, is proposed for enhancing the performance of discretization. The proposed EF_Unique discretization method is based on the unique values of the attribute to be discretized. In order to test the success of the proposed method, 17 benchmark datasets from the UCI repository and four data mining classification algorithms were used, namely Naive Bayes, C.45, k-nearest neighbor, and support vector machine. The experimental results of the proposed EF_Unique discretization method were compared with those obtained using well-known discretization methods; unsupervised equal width (EW), EF, and supervised entropy-based ID3 (EB-ID3). The results show that the proposed EF_Unique discretization method outperformed EW, EF, and EB-ID3 discretization methods in 43, 41, and 27 out of the 68 benchmark tests, respectively.
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    Glaucoma disease diagnosis with an artificial algae-based deep learning algorithm
    (Springer Heidelberg, 2022) Ibrahim, Mohammed H.; Hacibeyoglu, Mehmet; Agaoglu, Afsin; Ucar, Fikret
    Glaucoma disease is optic neuropathy; in glaucoma, the optic nerve is damaged because the long duration of intraocular pressure can be caused blindness. Nowadays, deep learning classification algorithms are widely used to diagnose various diseases. However, in general, the training of deep learning algorithms is carried out by traditional gradient-based learning techniques that converge slowly and are highly likely to fall to the local minimum. In this study, we proposed a novel decision support system based on deep learning to diagnose glaucoma. The proposed system has two stages. In the first stage, the preprocessing of glaucoma disease data is performed by normalization and mean absolute deviation method, and in the second stage, the training of the deep learning is made by the artificial algae optimization algorithm. The proposed system is compared to traditional gradient-based deep learning and deep learning trained with other optimization algorithms like genetic algorithm, particle swarm optimization, bat algorithm, salp swarm algorithm, and equilibrium optimizer. Furthermore, the proposed system is compared to the state-of-the-art algorithms proposed for the glaucoma detection. The proposed system has outperformed other algorithms in terms of classification accuracy, recall, precision, false positive rate, and F1-measure by 0.9815, 0.9795, 0.9835, 0.0165, and 0.9815, respectively.
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    A Hybrid Method for Fast Finding the Reduct with the Best Classification Accuracy
    (Univ Suceava, Fac Electrical Eng, 2013) Hacibeyoglu, Mehmet; Arslan, Ahmet; Kahramanli, Sirzat
    Usually a dataset has a lot of reducts finding all of which is known to be an NP hard problem. On the other hand, different reducts of a dataset may provide different classification accuracies. Usually, for every dataset, there is only a reduct with the best classification accuracy to obtain this best one, firstly we obtain the group of attributes that are dominant for the given dataset by using the decision tree algorithm. Secondly we complete this group up to reducts by using discernibility function techniques. Finally, we select only one reduct with the best classification accuracy by using data mining classification algorithms. The experimental results for datasets indicate that the classification accuracy is improved by removing the irrelevant features and using the simplified attribute set which is derived from proposed method.
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    The logic transformations for reducing the complexity of the discernibility function-based attribute reduction problem
    (Springer London Ltd, 2016) Hacibeyoglu, Mehmet; Salman, Mohammad Shukri; Selek, Murat; Kahramanli, Sirzat
    The basic solution for locating an optimal reduct is to generate all possible reducts and select the one that best meets the given criterion. Since this problem is NP-hard, most attribute reduction algorithms use heuristics to find a single reduct with the risk to overlook for the best ones. There is a discernibility function (DF)-based approach that generates all reducts but may fail due to memory overflows even for datasets with dimensionality much below the medium. In this study, we show that the main shortcoming of this approach is its excessively high space complexity. To overcome this, we first represent a DF of attributes by a bit-matrix (BM). Second, we partition the BM into no more than sub-BMs (SBMs). Third, we convert each SBM into a subset of reducts by preventing the generation of redundant products, and finally, we unite the subsets into a complete set of reducts. Among the SBMs of a BM, the most complex one is the first SBM with a space complexity not greater than the square root of that of the original BM. The proposed algorithm converts such a SBM with attributes into the subset of reducts with the worst case space complexity of .
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    A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training
    (Hindawi Ltd, 2018) Hacibeyoglu, Mehmet; Ibrahim, Mohammed H.
    Multilayer feed-forward artificial neural networks are one of the most frequently used data mining methods for classification, recognition, and prediction problems. The classification accuracy of a multilayer feed-forward artificial neural networks is proportional to training. A well-trained multilayer feed-forward artificial neural networks can predict the class value of an unseen sample correctly if provided with the optimum weights. Determining the optimum weights is a nonlinear continuous optimization problem that can be solved with metaheuristic algorithms. In this paper, we propose a novelmultimean particle swarmoptimization algorithm for multilayer feed-forward artificial neural networks training. The proposed multimean particle swarm optimization algorithm searches the solution space more efficiently with multiple swarms and finds better solutions than particle swarm optimization. To evaluate the performance of the proposed multimean particle swarm optimization algorithm, experiments are conducted on ten benchmark datasets from the UCI repository and the obtained results are compared to the results of particle swarm optimization and other previous research in the literature. The analysis of the results demonstrated that the proposed multimean particle swarm optimization algorithm performed well and it can be adopted as a novel algorithm for multilayer feedforward artificial neural networks training.
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    A novel switching function approach for data mining classification problems
    (Springer, 2020) Ibrahim, Mohammed Hussein; Hacibeyoglu, Mehmet
    Rule induction (RI) is one of the known classification approaches in data mining. RI extracts hidden patterns from instances in terms of rules. This paper proposes a logic-based rule induction (LBRI) classifier based on a switching function approach. LBRI generates binary rules by using a novel minimization function, which depends on simple and powerful bitwise operations. Initially, LBRI generates instance codes by encoding the dataset with standard binary code and then generates prime cubes (PC) for all classes from the instance codes by the proposed reduced offset method. Finally, LBRI selects the most effective PC of the current classes and adds them into the binary rule set that belongs to the current class. Each binary rule represents an If-Then rule for the rule induction classifiers. The proposed LBRI classifier is based on basic logic functions. It is a simple and effective method, and it can be used by intelligent systems to solve real-life classification/ prediction problems in areas such as health care, online/financial banking, image/voice recognition, and bioinformatics. The performance of the proposed algorithm is compared to six rule induction algorithms; decision table, Ripper, C4.5, REPTree, OneR, and ICRM by using nineteen different datasets. The experimental results show that the proposed algorithm yields better classification accuracy than the other rule induction algorithms on ten out of nineteen datasets.
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    Solving a big-scaled hospital facility layout problem with meta-heuristics algorithms
    (Elsevier - Division Reed Elsevier India Pvt Ltd, 2020) Tongur, Vahit; Hacibeyoglu, Mehmet; Ulker, Erkan
    The main objective of the hospital facility layout problem is to place the polyclinics, laboratories and radiology units within the predefined boundaries in such way that minimize the movement cost of patients and healthcare staff. Especially in big-scaled hospitals including several different specialized departments, it is important in terms of hospital efficiency that interacting units are placed closely. Nowadays meta-heuristic algorithms are often used to solve optimization problems such as facility layout. In this study; polyclinic, laboratory and radiology units' layout of a big-scaled university hospital was organized using three meta-heuristic algorithms which are migrating bird optimization (MBO), tabu search (TS) and simulated annealing (SA). The results were compared with the existing clinic layout. Consequently MBO and SA meta-heuristic algorithms have given the same best results improving the existing clinic layout efficiency approximately by 58%. (C) 2019 Karabuk University. Publishing services by Elsevier B.V.
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    SOLVING THE BI-DIMENSIONAL TWO-WAY NUMBER PARTITIONING PROBLEM WITH HEURISTIC ALGORITHMS
    (IEEE, 2014) Hacibeyoglu, Mehmet; Tongur, Vahit; Alaykiran, Kemal
    The two-way number partitioning problem is to divide set of numbers into two subsets. As a result of the dividing process the sums of numbers in subsets must be as nearly equal as possible. The two-way number partitioning problem problem is NP-complete. The bi-dimensional two-way number partitioning problem is a kind of number partitioning problem. The sets have only two coordinates and the aim is minimized the differences of the sum of the numbers for both coordinates. This work presents two heuristic algorithm for solving bi-dimensional two-way number partitioning problem. Fist algorithm is best known and most used greedy algorithm. The other one is a novel genetic algorithm approach. These algorithms are analyzed, implemented and tested on randomly different 20 datasets.