Profile URL: https://hdl.handle.net/20.500.13091/11754
Job Title:Doç. Dr.
Email Address:hkoyuncu@ktun.edu.tr
Main Affiliation:02.04. Department of Electrical and Electronics Engineering
Status: Current Staff
ORCID:
0000-0003-4541-8833
0000-0003-4541-8833Scopus ID:
55884277600
55884277600YÖK Akademik: BE425B6BF0D33EAC
Google Scholar:
htJHuNUAAAAJ
htJHuNUAAAAJWeb of Science ID:
C-2203-2019
C-2203-2019Name Variants:
Koyuncu, H.
27 results
Scholarly Output Search Results
Now showing 1 - 10 of 27
Article Citation - WoS: 42Citation - Scopus: 49A Pso Based Approach: Scout Particle Swarm Algorithm for Continuous Global Optimization Problems(OXFORD UNIV PRESS, 2018) Koyuncu, Hasan; Ceylan, RahimeIn the literature, most studies focus on designing new methods inspired by biological processes, however hybridization of methods and hybridization way should be examined carefully to generate more suitable optimization methods. In this study, we handle Particle Swarm Optimization (PSO) and an efficient operator of Artificial Bee Colony Optimization (ABC) to design an efficient technique for continuous function optimization. In PSO, velocity and position concepts guide particles to achieve convergence. At this point, variable and stable parameters are ineffective for regenerating awkward particles that cannot improve their personal best position (P-best). Thus, the need for external intervention is inevitable once a useful particle becomes an awkward one. In ABC, the scout bee phase acts as external intervention by sustaining the resurgence of incapable individuals. With the addition of a scout bee phase to standard PSO, Scout Particle Swarm Optimization (ScPSO) is formed which eliminates the most important handicap of PSO. Consequently, a robust optimization algorithm is obtained. ScPSO is tested on constrained optimization problems and optimum parameter values are obtained for the general use of ScPSO. To evaluate the performance, ScPSO is compared with Genetic Algorithm (GA), with variants of the PSO and ABC methods, and with hybrid approaches based on PSO and ABC algorithms on numerical function optimization. As seen in the results, ScPSO results in better optimal solutions than other approaches. In addition, its convergence is superior to a basic optimization method, to the variants of PSO and ABC algorithms, and to the hybrid approaches on different numerical benchmark functions. According to the results, the Total Statistical Success (TSS) value of ScPSO ranks first (5) in comparison with PSO variants; the second best TSS (2) belongs to CLPSO and SP-PSO techniques. In a comparison with ABC variants, the best TSS value (6) is obtained by ScPSO, while TSS of BitABC is 2. In comparison with hybrid techniques, ScPSO obtains the best Total Average Rank (TAR) as 1.375, and TSS of ScPSO ranks first (6) again. The fitness values obtained by ScPSO are generally more satisfactory than the values obtained by other methods. Consequently, ScPSO achieve promising gains over other optimization methods; in parallel with this result, its usage can be extended to different working disciplines. (C) 2018 Society for Computational Design and Engineering. Publishing Services by Elsevier.Article Citation - WoS: 18Citation - Scopus: 25Gm-Cpso: a New Viewpoint To Chaotic Particle Swarm Optimization Via Gauss Map(SPRINGER, 2020-05-02) Koyuncu, HasanChaos concept has been appealed in the recent optimization methods to achieve a convenient tradeoff between exploration and exploitation. Different chaotic maps have been considered to find out the appropriate one for the system dynamics. However, on particle swarm optimization (PSO), the usage of these maps has not been handled in an extensive manner, and the best fit one has not known yet. In this paper, ten chaotic maps are handled to reveal the best fit one for PSO, and to explore whether chaotic maps are necessary for PSO or not. Thirteen benchmark functions are used to perform a detailed evaluation at the first experiment. Chaotic PSO (CPSO) methods including different maps are tested on global function optimization. Concerning this, Gauss map based CPSO (GM-CPSO) has come to the forefront by achieving promising fitness values in all function evaluations and in comparison with the state-of-the-art methods. To test the efficiency of GM-CPSO on a different task, GM-CPSO is hybridized with neural network (NN) at the second experiment, and the epileptic seizure recognition is handled. Discrete wavelet transform (DWT) based features, GM-CPSO and NN are considered to design an efficient framework and to specify the type of electroencephalography signals. GM-CPSO-NN is compared with hybrid NNs including two state-of-the-art optimization methods so as to examine the efficiency of GM-CPSO. To accurately test the performances, twofold cross validation is realized on 11,500 instances, and four metrics [accuracy, area under ROC curve (AUC), sensitivity, specificity] are consulted for a detailed assessment beside of computational complexity analysis. In experiments, GM-CPSO including the necessary map, has provided remarkable fitness scores over the state-of-the-art optimization methods on optimization of various functions defined in different dimensions. Besides, the proposed framework including GM-CPSO-NN, has achieved remarkable performance by obtaining reliable accuracy (97.24%), AUC (95.67%), sensitivity (93.04%) and specificity (98.29%) scores, and by including less computational complexity than other algorithms. According to the results, GM-CPSO has arisen as the most convenient optimization method to be preferred in the formation of hybrid NNs. In addition to optimization and classification results, it's seen that the detail sub-bands of DWT comprise necessary information for seizure recognition. Consequently, it's revealed that GM-CPSO can be preferred on global function optimization for reliable convergence, and its usage can be extended to different disciplines like signal classification, pattern recognition or hybrid system design.Conference Object Citation - WoS: 3Citation - Scopus: 5Loss Function Selection in Nn Based Classifiers: Try-Outs With a Novel Method(IEEE, 2020) Koyuncu, HasanNeural Network (NN) based classifiers are frequently used in different disciplines like pattern recognition, image classification, regression, etc. The optimized NNs are produced to achieve more robust classifiers in terms of preventing the fluctuations at error by yielding better convergence. However, in the literature, an extensive study is not available that examines one of the most important dynamics of NNs: loss function (error metric) of algorithm. In this study, ten different loss functions are evaluated to find out which one stays more coherent to utilize in NN based classifiers. For this purpose, a novel optimized classifier is handled using Gauss map based chaotic particle swarm optimization (GM-CPSO) with NN. GM-CPSO-NN classifiers including ten different loss functions are compared on two challenging tasks (Parkinson's disease recognition and epileptic seizure detection). Four metric based comparisons and 2-fold cross validation method are processed to objectively test the performance of classifiers. As a result, it's concluded that GM-CPSO-NN comprising mean square error (MSE) attains remarkable performance for both tasks, and MSE is revealed as the most appropriate error metric to be considered for the datasets including low or high pattern numbers.Conference Object Citation - Scopus: 3Optimum Design of Flapping Wing Flying Robot by Modified Social Group Optimization(Institute of Electrical and Electronics Engineers Inc., 2022-10-03) Öcal, A.; Koyuncu, H.Constrained optimization is very often appealed to handle challenging design problems in engineering area. Herein, heuristic methods are frequently preferred to solve these design problems. For the best design of an engineering problem, the robustness of optimization algorithm occupies an important place.In this paper, a recent engineering problem is handled which evaluates the optimum design of a flapping wing flying robot / ornithopter. Concerning the issue, main function and constrained functions are combined using penalty function to define the problem encountered as a single objective optimization problem. The design problem is evaluated by four recent and promising algorithms that are chaotic dynamic weight particle swarm optimization (CDW-PSO), crystal structure algorithm (CryStAl), adaptive strategy particle swarm optimization (ASPSO), and modified social group optimization (MSGO). The best fitness, processing time and average best fitness evaluations are considered to objectively reveal the most appropriate method for optimum design. Consequently, MSGO and ASPSO achieve the optimum results and outperforms CDW-PSO and CryStAl algorithms for the best fitness-based experiments. Moreover, MSGO yields a remarkable performance than ASPSO by presenting a more robust behavior for average best fitness-based comparisons. © 2022 IEEE.Conference Object Citation - Scopus: 1Feature Selection Via Gm-Cpso and Binary Conversion: Analyses on a Binary-Class Dataset(Institute of Electrical and Electronics Engineers Inc., 2022) Çelik, S.; Koyuncu, H.Feature selection is oft-used to upgrade the system performance in classification-based applications. For this purpose, wrapper-based methods reserve an important place and are designed with efficient optimization methods so as to observe the highest performance. In this paper, a state-of-the-art optimization method named Gauss map-based chaotic particle swarm optimization (GM-CPSO) is handled. Binary conversion is considered to adapt the GM-CPSO to the feature selection. In classification part of the proposed method, k-nearest neighborhood (k-NN) is operated due to its fast and robust performance on classification-based implementations. In experiments, seven metrics (accuracy, sensitivity, specificity, g-mean, precision, f-measure, AUC) are utilized to objectively evaluate the performances, and 80%/20% training-test split is fulfilled to effectively assign the necessary features. Our wrapper-based method is tested on a balanced dataset that is based on Parkinson's disease (PD). As a result, our method presents promising scores by means of seven metrics, and especially, it improves the classification performance about 14.59% concerning the accuracy and AUC rates in comparison with the k-NN method. © 2022 IEEE.Conference Object Citation - WoS: 3Loss Function Selection in NN Based Classifiers: Try-Outs with a Novel Method(IEEE, 2020-06-01) Koyuncu, HasanNeural Network (NN) based classifiers are frequently used in different disciplines like pattern recognition, image classification, regression, etc. The optimized NNs are produced to achieve more robust classifiers in terms of preventing the fluctuations at error by yielding better convergence. However, in the literature, an extensive study is not available that examines one of the most important dynamics of NNs: loss function (error metric) of algorithm. In this study, ten different loss functions are evaluated to find out which one stays more coherent to utilize in NN based classifiers. For this purpose, a novel optimized classifier is handled using Gauss map based chaotic particle swarm optimization (GM-CPSO) with NN. GM-CPSO-NN classifiers including ten different loss functions are compared on two challenging tasks (Parkinson's disease recognition and epileptic seizure detection). Four metric based comparisons and 2-fold cross validation method are processed to objectively test the performance of classifiers. As a result, it's concluded that GM-CPSO-NN comprising mean square error (MSE) attains remarkable performance for both tasks, and MSE is revealed as the most appropriate error metric to be considered for the datasets including low or high pattern numbers.Master Thesis Termal Görüntülerde Derin Ögrenme Yaklasimlari ile Elektrik Arizalarinin Siniflandirilmasi(Konya Teknik Üniversitesi, 2023) Sakallı, Gönül; Koyuncu, HasanAsenkron motorlar, endüstriyel uygulamalarda diğer motor tiplerine olan avantajları sebebiyle sıklıkla tercih edilirler. Transformatörler ise elektrik sistemine beslenecek gerilimin ayarlanmasında kullanılan, vazgeçilemez bir diğer kategoriyi teşkil ederler. Bu ekipmanlara dair arıza teşhisi ise geleneksel elektrik bazlı ölçümleri gerektiren analizlerle (stator akım sinyallerinin, manyetik akı dağılımlarının vb. derinlemesine incelenmesiyle) yerine getirilir. Termal görüntü analizleri; yapıya doğrudan müdahale gerektirmeyen, elektrikli ekipmanların durumlarını belirlemenin kolay bir yolu olarak karşımıza çıkmaktadır. Gerçeklenen tez çalışmasında, asenkron motorların ve transformatörlerin durumlarını ayırt etmek için, termal görüntü temelli analizler ele alınmaktadır. Bu amaçla her iki ekipman koşulları birleştirilerek, yirmi farklı durumun eldesi sağlanmıştır. Bu durumlar; soğutma fanı arızası, rotor arızası, motor içinde farklı fazlarda ve çeşitli oranlarda kısa devre arızaları, trafo içinde farklı oranlarda kısa devre arızaları, yüksüz motor ve yüksüz trafo olarak tanımlanmıştır. Sınıflama problemi bir ön işleme olmaksızın, etkin derin öğrenme mimarileri (DenseNet201, MobileNetV2, ResNet50, ShuffleNet, Xception) üzerinden gerçekleştirilmiştir. Deneylerde mimarilere dair en yüksek performansların gözlenmesi için, sistem hiperparametreleri kapsamlı bir şekilde incelenmiştir. Burada transfer öğrenme görevini gerçekleştirmek, modellerin ana kısmını bozmamak ve termal görüntü sınıflaması için uygun modeli ortaya çıkarmak için dört olgu (mini parça boyutu, öğrenme oranı, LRDF değeri, optimize edici tipi) değerlendirilmiştir. Deneylerde mimarileri veri artırma olmadan karşılaştırmak için, %80-%20 eğitim-test yöntemi üzerinden sonuçlar analiz edilmiştir. Sonuç olarak, üç fazlı motora ve bir fazlı trafoya ait termal görüntülerin sınıflandırılmasında %100 doğruluk elde edilerek, tüm derin öğrenme mimarilerinde en yüksek performanslar gözlemlenmiştir. Doğruluk tabanlı analizlere ek olarak, termal görüntü sınıflamasında en uygun mimariyi ortaya çıkarmak için derinlemesine bir analiz sunulmuştur. Çalışma neticesinde, derinlemesine analizlere göre ShuffleNet mimarisinin diğer yapılardan daha üstün olduğu tespit edilmiştir.Article Citation - WoS: 3Citation - Scopus: 63t2fts: a Novel Feature Transform Strategy To Classify 3d Mri Voxels and Its Application on Hgg/Lgg Classification(MDPI, 2023-04-06) Hajmohamad, Abdulsalam; Koyuncu, HasanThe distinction between high-grade glioma (HGG) and low-grade glioma (LGG) is generally performed with two-dimensional (2D) image analyses that constitute semi-automated tumor classification. However, a fully automated computer-aided diagnosis (CAD) can only be realized using an adaptive classification framework based on three-dimensional (3D) segmented tumors. In this paper, we handle the classification section of a fully automated CAD related to the aforementioned requirement. For this purpose, a 3D to 2D feature transform strategy (3t2FTS) is presented operating first-order statistics (FOS) in order to form the input data by considering every phase (T1, T2, T1c, and FLAIR) of information on 3D magnetic resonance imaging (3D MRI). Herein, the main aim is the transformation of 3D data analyses into 2D data analyses so as to applicate the information to be fed to the efficient deep learning methods. In other words, 2D identification (2D-ID) of 3D voxels is produced. In our experiments, eight transfer learning models (DenseNet201, InceptionResNetV2, InceptionV3, ResNet50, ResNet101, SqueezeNet, VGG19, and Xception) were evaluated to reveal the appropriate one for the output of 3t2FTS and to design the proposed framework categorizing the 210 HGG-75 LGG instances in the BraTS 2017/2018 challenge dataset. The hyperparameters of the models were examined in a comprehensive manner to reveal the highest performance of the models to be reached. In our trails, two-fold cross-validation was considered as the test method to assess system performance. Consequently, the highest performance was observed with the framework including the 3t2FTS and ResNet50 models by achieving 80% classification accuracy for the 3D-based classification of brain tumors.Article Lupsix: a Cascade Framework for Lung Parenchyma Segmentation in Axial Ct Images(2018) Koyuncu, HasanLung imaging and computer aided diagnosis (CAD) play a critical role in detection of lung diseases. The most significant partof a lung based CAD is to fulfil the parenchyma segmentation, since disease information is kept in the parenchyma texture. For this purpose,parenchyma segmentation should be accurately performed to find the necessary diagnosis to be used in the treatment. Besides, lungparenchyma segmentation remains as a challenging task in computed tomography (CT) owing to the handicaps oriented with the imagingand nature of parenchyma. In this paper, a cascade framework involving histogram analysis, morphological operations, mean shiftsegmentation (MSS) and region growing (RG) is proposed to perform an accurate segmentation in thorax CT images. In training data, 20axial CT images are utilized to define the optimum parameter values, and 150 images are considered as test data to objectively evaluatethe performance of system. Five statistical metrics are handled to carry out the performance assessment, and a literature comparison isrealized with the state-of-the-art techniques. As a result, parenchyma tissues are segmented with success rates as 98.07% (sensitivity),99.72% (specificity), 99.3% (accuracy), 98.59% (Dice similarity coefficient) and 97.23% (Jaccard) on test dataset.Article Citation - WoS: 9Citation - Scopus: 14Covid-19 Discrimination Framework for X-Ray Images by Considering Radiomics, Selective Information, Feature Ranking, and a Novel Hybrid Classifier(ELSEVIER, 2021-09-01) Koyuncu, Hasan; Barstuğan, MücahidIn medical imaging procedures for the detection of coronavirus, apart from medical tests, approval of diagnosis has special significance. Imaging procedures are also useful for detecting the damage caused by COVID-19. Chest X-ray imaging is frequently used to diagnose COVID-19 and different pneumonias. This paper presents a task-specific framework to detect coronavirus in X-ray images. Binary classification of three different labels (healthy, bacterial pneumonia, and COVID-19) was performed on two differentiated data sets in which corona is stated as positive. First-order statistics, gray level co-occurrence matrix, gray level run length matrix, and gray level size zone matrix were analyzed to form fifteen sub-data sets and to ascertain the necessary radiomics. Two normalization methods are compared to make the data meaningful. Furthermore, five feature ranking approaches (Bhattacharyya, entropy, Roc, t-test, and Wilcoxon) are mentioned to provide necessary information to a state-of-the-art classifier based on Gauss-map-based chaotic particle swarm optimization and neural networks. The proposed framework was designed according to the analyses about radiomics, normalization approaches, and filter-based feature ranking methods. In experiments, seven metrics were evaluated to objectively determine the results: accuracy, area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, g-mean, precision, and f-measure. The proposed framework showed promising scores on two X-ray-based data sets, especially with the accuracy and area under the ROC curve rates exceeding 99% for the classification of coronavirus vs. others.
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Research Topics
Domains
Physical SciencesHealth SciencesLife Sciences
Fields
Computer ScienceMedicineNeuroscience
Subfields
Artificial IntelligenceRadiology, Nuclear Medicine and ImagingNeurologyComputer Vision and Pattern Recognition
Specific Research Areas
Metaheuristic Optimization Algorithms Research
Radiomics and Machine Learning in Medical Imaging
Brain Tumor Detection and Classification
Medical Image Segmentation Techniques
Advanced Neural Network Applications
Sustainable Development Goals
7AFFORDABLE AND CLEAN ENERGY
2
Research Products
3GOOD HEALTH AND WELL-BEING
1
Research Products
Documents
31
Citations
324
h-index
10
Documents
25
Citations
228
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Konya Technical University | 21 |
| Selçuk University | 14 |
| Ministry of Health | 2 |
| Konya Food and Agriculture University | 2 |
| Sağlık Bilimleri Üniversitesi | 2 |
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Data obtained from OpenAlex
| Journal | Count |
|---|---|
| MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING | 2 |
| 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) | 1 |
| 2018 IEEE 13TH INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE ON COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES (CSIT), VOL 1 | 1 |
| 2019 International Conference on Engineering and Telecommunication, EnT 2019 | 1 |
| 2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022 | 1 |
Current Page: 1 / 5
Scholarly Output
27
Articles
13
Views / Downloads
42/117
Supervised MSc Theses
3
Supervised PhD Theses
0
WoS Citation Count
148
Scopus Citation Count
205
Patents
0
Projects
0
WoS Citations per Publication
5.48
Scopus Citations per Publication
7.59
Open Access Source
9
Supervised Theses
3
Scopus Quartile Distribution
Competency Cloud
