Profile URL: https://hdl.handle.net/20.500.13091/11854
Job Title:Prof. Dr.
Email Address:eulker@ktun.edu.tr
Main Affiliation:10.01. Department of Computer Engineering
Status: Current Staff
ORCID:
0000-0003-4393-9870
0000-0003-4393-9870Scopus ID:
23393979800
23393979800YÖK Akademik: 9653507F0C718D4D
Google Scholar:
wKdHsP0AAAAJ
wKdHsP0AAAAJWeb of Science ID:
ABA-5846-2020
ABA-5846-2020Name Variants:
Ulker, E. Erkan, U.
45 results
Scholarly Output Search Results
Now showing 1 - 10 of 45
Article Citation - WoS: 2B-Spline Curve Approximation by Utilizing Big Bang-Big Crunch Method(TECH SCIENCE PRESS, 2020) İnik, Özkan; Ülker, Erkan; Koç, İsmailThe location of knot points and estimation of the number of knots are undoubtedly known as one of the most difficult problems in B-Spline curve approximation. In the literature, different researchers have been seen to use more than one optimization algorithm in order to solve this problem. In this paper, Big Bang-Big Crunch method (BB-BC) which is one of the evolutionary based optimization algorithms was introduced and then the approximation of B-Spline curve knots was conducted by this method. The technique of reverse engineering was implemented for the curve knot approximation. The detection of knot locations and the number of knots were randomly selected in the curve approximation which was performed by using BB-BC method. The experimental results were carried out by utilizing seven different test functions for the curve approximation. The performance of BB-BC algorithm was examined on these functions and their results were compared with the earlier studies performed by the researchers. In comparison with the other studies, it was observed that though the number of the knot in BB-BC algorithm was high, this algorithm approximated the B-Spline curves at the rate of minor error.Article Citation - WoS: 3Citation - Scopus: 6A Hierarchical Approach Based on Aco and Pso by Neighborhood Operators for Tsps Solution(WORLD SCIENTIFIC PUBL CO PTE LTD, 2020-04-17) Eldem, Hüseyin; Ülker, ErkanIt is known that some of the algorithms in optimization field have originated from inspiration from animal behaviors in nature. Natural phenomena such as searching behavior of ants for food in a collective way, movements of birds and fish groups as swarms provided the inspiration for solutions of optimization problems. Traveling Salesman Problem (TSP), a classical problem of combinatorial optimization, has implementations in planning, scheduling and various scientific and engineering fields. Ant colony optimization (ACO) and Particle swarm optimization (PSO) techniques have been commonly used for TSP solutions. The aim of this paper is to propose a new hierarchical ACO- and PSO-based method for TSP solutions. Enhancing neighboring operators were used to achieve better results by hierarchical method. The performance of the proposed system was tested in experiments for selected TSPLIB benchmarks. It was shown that usage of ACO and PSO methods in hierarchical structure with neighboring operators resulted in better results than standard algorithms of ACO and PSO and hierarchical methods in literature.Publication Study about Block Size Distribution Curves and Commercial Boundaries of the Marble Quarries(2024) Ülker, Erkan; Asimopolos, Adrian-aristide; Turanboy, Alparslan; Asimopolos, Natalia-silvia; Asimopolos, LaurențiuSummary Within the EraMin 3 project “Artificial Intelligence Characterization Ornamental Stones Quarry Optimization (AI-COSTSQO)” we completed the following work stages: field measurements, identification of discrete fracture network (DFN) in marble quarries and determination of Block Size Distribution Curves (BSDC). In this paper we present examples from each stage of work. Thus, we started with non-destructive geophysical methods (GPR -Ground penetration radar, electrometry and seismic) in the Ruschita, Pietroasa, Carpinis, Botticino and Finike quarries. These contain fractures with various characteristics that negatively influence the quality of the extracted blocks. The Discrete Fracture Network (DFN) was the next step, after interpreting the results of the geophysical measurements, and simulates the distribution of natural fractures in a 3D model. Finally, we presented BSDC and Commercial Boundaries, which represents the preliminary result for the input data in the AI-based software program for the optimization of quarry exploitation.Other Classification of Pressure and Diabetic Chronic Wound Tissue Images with Deep Learning Methods(2022) Ülker, Erkan; Işıklı, Osman Yaşar; Eldem, HüseyinAbstract Pressure wounds are skin injuries that occur due to prolonged inactivity of the patient. They reduce blood flow. They cause muscle and skin ischemia, which leads to necrosis and cell death. The automatic classification of pressure sores according to international principles allows rapid recommendation of appropriate treatment methods. In this way, life quality of patients will be increased by ensuring that the treatments to be applied are efficient. Diabetes foot wounds are among the current serious and chronic diseases and are among the wound types that threaten life and reduce the quality of life. In the literature, there are mostly studies that make binary classification (ulcer-nonulcer) showing which type pressure and diabetic foot ulcers belong to. In this study, the impact of recently popular deep learning methods in image processing for tissue classification of pressure and diabetic wound images is discussed. Deep Convolutional Neural Network (CNN) architectures were used to determine which of the granule, necrotic and slough classes wound images belong to, according to their characteristics on the tissue. In this study, in which 19 CNN architectures were discussed, CNN architectures that give successful results on the dataset were observed. In addition, the effect of Maximum Epoch Number (ME), Initial learning rate (LR) and Minibatch size (MBS) parameter values used in the architectures were also researched. In the experiments performed on 1045 pressure and 1045 diabetic wound images collected from different patients from the Wound Care Unit of Karaman Education and Research Hospital, the best results were obtained with the AlexNet architecture with an Accuracy value of 95.83%. Optimum values of ME, LR and MBS parameter values were also found in parameter optimization tests. Comparisons were made with the results of studies on similar wound datasets in the literature, and it was shown that AlexNet and VGG16 architectures achieved competitive results.Article A New Approach for Estimating Discontinuity Network in Rock Mass(Mining Revue, 2025-06-01) Turanboy, Alparslan; Ülker, Erkan; Arık, Görkem Rıdvan; Uysal, İbrahimIt is essential to reveal a rock mass network as realistic as possible for efficient production in natural stone quarries. To create an in-situ rock mass network model, data must first be obtained using one or more robust survey techniques. In addition, it is necessary to use an ideal network model that can reach as many intact blocks as possible that may be encountered in the production sector and even in the next cutting periods with their locations and dimensions. Due to the inhomogeneous and anisotropic structure of the rock mass, numerical and stochastic processes and methods are frequently used to simulate the discontinuity network. These require complex, computationally intensive operations, and the ability to represent the rock mass largely depends on the number and quality of data. In this paper, a practical approach that includes analytical and geometric solution methods for discontinuity network modelling, in which all survey techniques can be used, has been presented. The strategy aims to reflect discontinuities outside the relevant production sector to the rock mass too. Thus, an attempt is made to achieve a realistic rock mass network. The generated model has been tested on samples of highway slope and marble quarry bench, and the results have been presented.Article Citation - WoS: 31Citation - Scopus: 34A New Method for Automatic Counting of Ovarian Follicles on Whole Slide Histological Images Based on Convolutional Neural Network(PERGAMON-ELSEVIER SCIENCE LTD, 2019-09-01) İnik, Özkan; Ceyhan, Ayşe; Balcıoğlu, Esra; Ülker, ErkanThe ovary is a complex endocrine organ that shows significant structural and functional changes in the female reproductive system over recurrent cycles. There are different types of follicles in the ovarian tissue. The reproductive potential of each individual depends on the numbers of these follicles. However, genetic mutations, toxins, and some specific drugs have an effect on follicles. To determine these effects, it is of great importance to count the follicles. The number of follicles in the ovary is usually counted manually by experts, which is a tedious, time-consuming and intense process. In some cases, the experts count the follicles in a subjective way due to their knowledge. In this study, for the first time, a method has been proposed for automatically counting the follicles of ovarian tissue. Our method primarily involves filter-based segmentation applied to whole slide histological images, based on a convolutional neural network (CNN). A new method is also proposed to eliminate the noise that occurs after the segmentation process and to determine the boundaries of the follicles. Finally, the follicles whose boundaries are determined are classified. To evaluate its performance, the results of the proposed method were compared with those obtained by two different experts and the results of the Faster R-CNN model. The number of follicles obtained by the proposed method was very close to the number of follicles counted by the experts. It was also found that the proposed method was much more successful than the Faster R-CNN model.Article Citation - WoS: 32Citation - Scopus: 35A Binary Social Spider Algorithm for Uncapacitated Facility Location Problem(PERGAMON-ELSEVIER SCIENCE LTD, 2020-12-01) Baş, Emine; Ülker, ErkanIn order to find efficient solutions to real complex world problems, computer sciences and especially heuristic algorithms are often used. Heuristic algorithms can give optimal solutions for large scale optimization problems in an acceptable period. Social Spider Algorithm (SSA), which is a heuristic algorithm created on spider behaviors are studied. The original study of this algorithm was proposed to solve continuous problems. In this paper, the binary version of the Social Spider Algorithm called Binary Social Spider Algorithm (BinSSA) is proposed for binary optimization problems. BinSSA is obtained from SSA, by transforming constant search space to binary search space with four transfer functions. Thus, BinSSA variations are created as BinSSA1, BinSSA2, BinSSA3, and BinSSA4. The study steps of the original SSA are re-updated for BinSSA. A random walking schema in SSA is replaced by a candidate solution schema in BinSSA. Two new methods (similarity measure and logic gate) are used in candidate solution production schema for increasing the exploration and exploitation capacity of BinSSA. The performance of both techniques on BinSSA is examined. BinSSA is named as BinSSA(Sim&Logic). Local search and global search performance of BinSSA is increased by these two methods. Three different studies are performed with BinSSA. In the first study, the performance of BinSSA is tested on the classic eighteen unimodal and multimodal benchmark functions. Thus, the best variation of BinSSA and BinSSA (Sim&Logic) is determined as BinSSA4(Sim&Logic). BinSSA4(Sim&Logic) has been compared with other heuristic algorithms on CEC2005 and CEC2015 functions. In the second study, the uncapacitated facility location problems (UFLPs) are solved with BinSSA(Sim&Logic). UFL problems are one of the pure binary optimization problems. BinSSA is tested on low-scaled, middle-scaled, and large-scaled fifteen UFLP samples and obtained results are compared with eighteen state-of-art algorithms. In the third study, we solved UFL problems on a different dataset named M* with BinSSA(Sim&Logic). The results of BinSSA (Sim&Logic) are compared with the Local Search (LS), Tabu Search (TS), and Improved Scatter Search (ISS) algorithms. Obtained results have shown that BinSSA offers quality and stable solutions. (c) 2020 Elsevier Ltd. All rights reserved.Article Citation - WoS: 1Citation - Scopus: 3A New Stability Approach Using Probabilistic Profile Along Direction of Excavation(SHAHROOD UNIV TECHNOLOGY, 2020) Turanboy, A.; Ülker, E.; Küçüksütçü, C. B.Estimation of the possible instability that may be encountered in the excavation slope(s) during the planning and application steps of the rock excavation processes is an important issue in geoengineering. In this paper, a modelling method is presented for assessing the probability of wedge failure involving new permanent or temporary slope(s) along the planned excavation direction. The geostructural rock slopes including wedge blocks are determined geometrically in the first step. Here, a structural data analysis system that includes a series of filterings, sortings, and linear equations used to reveal the necessary geometric conditions for the wedge form is developed and used. The second step involves the 3D visualization and Factor of Safety (FS) using the limit equilibrium analysis of wedges on both the actual and planned new excavation surfaces. The last step is the Monte Carlo simulation, which is used in assessing the instabilities on the actual and planned new excavation surfaces. These new slope surfaces that have not yet been excavated are called the virtual structures. As a result of this work, the mean and probabilistic FS variations in the planned excavation direction are obtained as profiles. We suggest the preliminary guidelines for the mean and probability of the wedge failure in the excavation direction. The model is tested on a motorway cut slope. The FS results obtained from the Monte Carlo simulation calculations are compared with the mean results and the changes are revealed with the reasons.Article Citation - WoS: 24Citation - Scopus: 30Classification of Physiological Disorders in Apples Fruit Using a Hybrid Model Based on Convolutional Neural Network and Machine Learning Methods(Springer London Ltd, 2022-05-25) Büyükarıkan, Birkan; Ülker, ErkanPhysiological disorders in apples are due to post-harvest conditions. For this reason, automatic identification of physiological disorders is important in obtaining agricultural information. Image processing is one of the techniques that can help achieve the features of physiological disorders. Physiological disorders during image acquisition can be affected by the changes in brightness values created by different lighting conditions. This changes the results of the classification. In recent years, the convolutional neural network (CNN) has been a successful approach in automatically obtaining deep features from raw images in image classification problems. The study aims to classify physiological disorders using machine learning (ML) methods according to extracted deep features of the images under different lighting conditions. The data sets were created by acquired images (1080 images) and augmentation images (4320 images). Deep features were extracted using five popular pre-trained CNN models in these data sets, and these features were classified using five ML methods. The highest average accuracy was obtained with the VGG19(fc6) + SVM method in the data set-1 and data set-2 and were 96.11 and 96.09%, respectively. With this study, physiological disorders can be determined early, and needed precautions can be taken before and after harvest, not too late.Article Citation - WoS: 29Citation - Scopus: 54Alexnet Architecture Variations With Transfer Learning for Classification of Wound Images(Elsevier B.V., 2023-09-01) Eldem, H.; Ülker, E.; Işıklı, O.Y.In medical world, wound care and follow-up is one of the issues that are gaining importance to work on day by day. Accurate and early recognition of wounds can reduce treatment costs. In the field of computer vision, deep learning architectures have received great attention recently. The achievements of existing pre-trained architectures for describing (classifying) data belonging to many image sets in the real world are primarily addressed. However, to increase the success of these architectures in a certain area, some improvements and enhancements can be made on the architecture. In this paper, the classification of pressure and diabetic wound images was performed with high accuracy. The six different new AlexNet architecture variations (3Conv_Softmax, 3Conv_SVM, 4Conv_Softmax, 4Conv_SVM, 6Conv_Softmax, 6Conv_SVM) were created with a different number of implementations of Convolution, Pooling, and Rectified Linear Activation (ReLU) layers. Classification performances of the proposed models are investigated by using Softmax classifier and SVM classifier separately. A new original Wound Image Database are created for performance measures. According to the experimental results obtained for the Database, the model with 6 Convolution layers (6Conv_SVM) was the most successful method among the proposed methods with 98.85% accuracy, 98.86% sensitivity, and 99.42% specificity. The 6Conv_SVM model was also tested on diabetic and pressure wound images in the public medetec dataset, and 95.33% accuracy, 95.33% sensitivity, and 97.66% specificity values were obtained. The proposed method provides high performance compared to the pre-trained AlexNet architecture and other state-of-the-art models in the literature. The results showed that the proposed 6Conv_SVM architecture can be used by the relevant departments in the medical world with good performance in medical tasks such as examining and classifying wound images and following up the wound process. © 2023 Karabuk University
Research Topics
Domains
Physical Sciences
Fields
Computer ScienceEngineering
Subfields
Artificial IntelligenceComputational MechanicsIndustrial and Manufacturing EngineeringComputational Theory and MathematicsMechanics of Materials
Specific Research Areas
Metaheuristic Optimization Algorithms Research
Advanced Numerical Analysis Techniques
Vehicle Routing Optimization Methods
Advanced Multi-Objective Optimization Algorithms
Rock Mechanics and Modeling
AI in cancer detection
Sustainable Development Goals
7AFFORDABLE AND CLEAN ENERGY
1
Research Products
16PEACE, JUSTICE AND STRONG INSTITUTIONS
1
Research Products
9INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
Documents
60
Citations
1057
h-index
19
Documents
56
Citations
837
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Selçuk University | 48 |
| Konya Technical University | 32 |
| Necmettin Erbakan University | 16 |
| Karamanoğlu Mehmetbey University | 8 |
| Tokat Gaziosmanpaşa Üniversitesi | 3 |
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Data obtained from OpenAlex
| Journal | Count |
|---|---|
| ARTIFICIAL INTELLIGENCE REVIEW | 3 |
| EXPERT SYSTEMS WITH APPLICATIONS | 2 |
| Applied Sciences | 1 |
| Applied Sciences (Switzerland) | 1 |
| APPLIED SOFT COMPUTING | 1 |
Current Page: 1 / 7
Scholarly Output
45
Articles
36
Views / Downloads
152/210
Supervised MSc Theses
3
Supervised PhD Theses
4
WoS Citation Count
457
Scopus Citation Count
569
Patents
0
Projects
0
WoS Citations per Publication
10.16
Scopus Citations per Publication
12.64
Open Access Source
19
Supervised Theses
7
Scopus Quartile Distribution
Competency Cloud
