Browsing by Author "Kolukisa, Burak"

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A Comparative Analysis on Medical Article Classification Using Text Mining & Machine Learning Algorithms
(Institute of Electrical and Electronics Engineers Inc., 2021) Kolukisa, Burak; Dedeturk, Bilge Kagan; Dedeturk, Beyhan Adanur; Gulşen, Abdulkadir; Bakal, Gokhan; 0000-0003-0423-4595; 0000-0002-4250-2880; 0000-0003-2897-3894; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Kolukisa, Burak; Gulşen, Abdulkadir; Bakal, Gokhan; Dedeturk, Beyhan Adanur
The document classification task is one of the widely studied research fields on multiple domains. The core motivation of the classification task is that the manual classification efforts are impractical due to the exponentially growing document volumes. Thus, we densely need to exploit automated computational approaches, such as machine learning models along with data & text mining techniques. In this study, we concentrated on the classification of medical articles specifically on common cancer types, due to the significance of the field and the decent number of available documents of interest. We deliberately targeted MEDLINE articles about common cancer types because most cancer types share a similar literature composition. Therefore, this situation makes the classification effort relatively more complicated. To this end, we built multiple machine learning models, including both traditional and deep learning architectures. We achieved the best performance (R¿82% F score) by the LSTM model. Overall, our results demonstrate a strong effect of exploiting both text mining and machine learning methods to distinguish medical articles on common cancer types.
Deep learning approaches for vehicle type classification with 3-D magnetic sensor
(ELSEVIER, 2022) Kolukisa, Burak; Yildirim, Veli Can; Elmas, Bahadir; Ayyildiz, Cem; Gungor, Vehbi Cagri; 0000-0003-0423-4595; 0000-0003-0803-8372; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Gungor, Vehbi Cagri; Kolukisa, Burak
In the Intelligent Transportation Systems, it is crucial to determine the type of vehicles to improve traffic management, increase human comfort, and enable future development of transport infrastructures. This paper presents a deep learning-based vehicle type classification approach for intermediate road traffic. Specifically, a low-cost, easy-to-install, battery-operated 3-D traffic sensor is designed and developed. In addition, a total of 376 vehicle samples are collected, and the vehicles are identified into three different classes according to their structures: light, medium, and heavy. Firstly, an oversampling method is applied to increase the number of samples in the training set. Then, the signals are converted into time series for LSTM and GRU and 2-D images for transfer learning models. Finally, soft voting is proposed using the LSTM, GRU, and VGG16, which is the best transfer learning method for vehicle type classification. The developed system is portable, power-limited, battery-operated, and reliable. Comparative performance results show that the soft voting ensemble method using a deep learning classifier improves the accuracy and f-measure performances by 92.92% and 93.42%, respectively. Additionally, the battery lifetime of the developed magnetic sensor node can work for up to 2 years.
A deep neural network approach with hyper-parameter optimization for vehicle type classification using 3-D magnetic sensor
(ELSEVIER, 2023) Kolukisa, Burak; Yildirim, Veli Can; Ayyildiz, Cem; Gungor, Vehbi Cagri; 0000-0003-0423-4595; 0000-0003-0803-8372; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Gungor, Vehbi Cagri; Kolukısa, Burak
The identification of vehicle types plays a critical role in Intelligent Transportation Systems. In this study, battery-operated, easy-to-install, low-cost 3-D magnetic traffic sensors have been developed for vehicle type classification problems. In addition, a new machine learning approach based on deep neural networks (DNN) with hyper-parameter optimization using feature selection and extraction methods has been proposed for vehicle type classification. A dataset is collected from the field, and vehicles are classified into three different classes, i.e., light: motorcycles, medium: passenger cars, and heavy: buses, based on vehicle structures and sizes. The proposed system is portable, energy-efficient, and reliable. The performance results show that the proposed method, which is based on a DNN classifier, has an accuracy of 91.15%, an f-measure of 91.50%, and a battery life of up to 2 years.
Ensemble Churn Prediction for Internet Service Provider with Machine Learning Techniques
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2020) Goy, Gokhan; Kolukisa, Burak; Bahcevan, Cenk; Gungor, Vehbi Cagri; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
With the developing technology in every fields, a competitive marketing environment has been arised In this competitive environment analyzing customer behavior has become vital In particular, the ability to easily change any service provider has become vet) , critical for the company to continue its existence At the same time, the amount of financial resources spent on retaining instituters much less than to obtain new clients. In this context, the traditional methods of examining vast amount of data obtained today for establishing decision support systems have lost their validities In this study. we used a dataset which is provided by TurkNet serving as an internet service provider in Turkey. Various preprocessing steps has performed on this dataset and then classification algorithms ran. Afterwards results have obtained and compared. The results of these experiments analyzed in terms of the area under the curve value In this context the aunt successful classifier algorithm has been determined as the Random Trees algorithm with a value of 0.936.
Ensemble feature selection and classification methods for machine learning-based coronary artery disease diagnosis
(ELSEVIER, 2023) Kolukisa, Burak; Bakir-Gungor, Burcu; 0000-0002-2272-6270; 0000-0003-0423-4595; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Kolukısa, Burak; Bakır Güngör, Burcu
Coronary artery disease (CAD) is a condition in which the heart is not fed sufficiently as a result of the accumulation of fatty matter. As reported by the World Health Organization, around 32% of the total deaths in the world are caused by CAD, and it is estimated that approximately 23.6 million people will die from this disease in 2030. CAD develops over time, and the diagnosis of this disease is difficult until a blockage or a heart attack occurs. In order to bypass the side effects and high costs of the current methods, researchers have proposed to diagnose CADs with computer-aided systems, which analyze some physical and biochemical values at a lower cost. In this study, for the CAD diagnosis, (i) seven different computational feature selection (FS) methods, one domain knowledge-based FS method, and different classification algorithms have been evaluated; (ii) an exhaustive ensemble FS method and a probabilistic ensemble FS method have been proposed. The proposed approach is tested on three publicly available CAD data sets using six different classification algorithms and four different variants of voting algorithms. The performance metrics have been comparatively evaluated with numerous combinations of classifiers and FS methods. The multi-layer perceptron classifier obtained satisfactory results on three data sets. Performance evaluations show that the proposed approach resulted in 91.78%, 85.55%, and 85.47% accuracy for the Z-Alizadeh Sani, Statlog, and Cleveland data sets, respectively.
Ensemble Feature Selection for Clustering Damage Modes in Carbon Fiber-Reinforced Polymer Sandwich Composites Using Acoustic Emission
(John Wiley and Sons Inc, 2024) Gulsen, Abdulkadir; Kolukisa, Burak; Caliskan, Umut; Bakir-Gungor, Burcu; Gungor, Vehbi Cagri; 0000-0002-4250-2880; 0000-0003-0423-4595; 0000-0002-2272-6270; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Gulsen, Abdulkadir; Kolukisa, Burak; Bakir-Gungor, Burcu
Acoustic emission (AE) serves as a noninvasive technique for real-time structural health monitoring, capturing the stress waves produced by the formation and growth of cracks within a material. This study presents a novel ensemble feature selection methodology to rank features highly relevant with damage modes in AE signals gathered from edgewise compression tests on honeycomb-core carbon fiber-reinforced polymer. Two distinct features, amplitude and peak frequency, are selected for labeling the AE signals. An ensemble-supervised feature selection method ranks feature importance according to these labels. Using the ranking list, unsupervised clustering models are then applied to identify damage modes. The comparative results reveal a robust correlation between the damage modes and the features of counts and energy when amplitude is selected. Similarly, when peak frequency is chosen, a significant association is observed between the damage modes and the features of partial powers 1 and 2. These findings demonstrate that, in addition to the commonly used features, other features, such as partial powers, exhibit a correlation with damage modes.
An Ensemble Feature Selection Methodology That Incorporates Domain Knowledge for Cardiovascular Disease Diagnosis
(IEEE, 2020) Kolukisa, Burak; Güngör, Vehbi Çağrı; Gungor, Burcu Bakir; 0000-0003-0423-4595; 0000-0002-2272-6270; 0000-0003-0803-8372; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Kolukisa, Burak; Güngör, Vehbi Çağrı; Gungor, Burcu Bakir
Koroner Arter Hastalığı (KAH), arterlerin duvarlarında aterom denilen yağlı madde birikiminin bir sonucu olarak kalbin yeterince beslenememesi durumudur. KAH, 2016 yılında dünyadaki toplam ölümlerin %31'ine (17,9 milyon) neden olmuştur ve teşhis edilmesi zordur. 2030 yılında, yaklaşık olarak 23,6 milyon insanın bu hastalıktan öleceği tahmin edilmektedir. Makine öğrenmesi ve veri madenciliği yöntemlerinin gelişmesiyle birlikte, bazı fiziksel ve biyokimyasal değerleri inceleyerek, KAH’nı ucuz ve zahmetsiz bir şekilde teşhis etmek mümkün olabilir. Bu çalışmada, KAH sınıflandırma problemi için, uzman bilgisini içine alan yeni bir topluluk öznitelik seçim yöntemi önerilmiştir. Önerilen çözüm, UCI Cleveland KAH veri kümesi üzerinde uygulanmış, farklı sınıflandırma algoritmaları kullanılarak, farklı performans ölçütleri karşılaştırılmıştır. Gerçekleştirdiğimiz deneylerde, önerdiğimiz çözümün, MLP sınıflandırıcısı ve seçilen 9 öznitelik kullanıldığında, %85.47 doğruluk, %82.96 hassasiyet ve 0.839 F-ölçüsüne ulaştığı gösterilmiştir. Bu çalışmanın devamında, hastanelerde gerçek zamanlı veriler üzerinde, hızlı bir şekilde KAH tahminlemesi yapabilecek bir makine öğrenmesi modeli oluşturabilmeyi amaçlıyoruz.
Evaluation of Classification Algorithms, Linear Discriminant Analysis and a New Hybrid Feature Selection Methodology for the Diagnosis of Coronary Artery Disease
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2018) Kolukisa, Burak; Hacilar, Hilal; Goy, Gokhan; Kus, Mustafa; Bakir-Gungor, Burcu; Aral, Atilla); Gungor, Vehbi Cagri; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
According to the World Health Organization (WHO), 31% of the world's total deaths in 2016 (17.9 million) was due to cardiovascular diseases (CVD). With the development of information technologies, it has become possible to predict whether people have heart diseases or not by checking certain physical and biochemical values at a lower cost. In this study, we have evalated a set of different classification algorithms, linear discriminant analysis and proposed a new hybrid feature selection methodology for the diagnosis of coronary heart diseases (CHD). Throughout this research effort, using three publicly available Heart Disease diagnosis datasets (UCI Machine Learning Repository), we have conducted comparative performance evaluations in terms of accuracy, sensitivity, specificity, F-measure, AUC and running time.