Person: ÇAĞLAR GENÇOSMAN, BURCU
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ÇAĞLAR GENÇOSMAN
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Publication Characterization of syrian refugees with work permit applications in Turkey: A data mining based methodology(Elsevier, 2021-05-15) Gençosman, Burcu Çağlar; İnkaya, Tülin; ÇAĞLAR GENÇOSMAN, BURCU; İNKAYA, TÜLİN; Bursa Uludağ Üniversitesi/Endüstri Mühendisliği Bölümü; 0000-0003-0159-8529; 0000-0002-6260-0162; AAH-2155-2021; AAG-8600-2021With the technological advancements in data collection systems, data-driven approaches become a necessity for understanding and managing the socioeconomic systems. Motivated by this, we focus on the formal employment of Syrian refugees in Turkey, and propose a data mining based methodology in order to understand their profiles. In this context, Syrian refugees with work permit applications are examined between years 2010 and 2018. The dataset includes demographic properties of the applicants and characteristics of their workplaces. The proposed methodology aims to extract the hidden, interesting and useful characteristics of the Syrian refugees having formal employment potential. The proposed approach integrates several data mining tasks, i.e. clustering, classification, and association rule mining, and it has four phases. In the first phase, data pre-processing and visualization operations are performed. In the second phase, the profiles of the Syrian refugee workers are determined using clustering. Self-organizing map and hierarchical clustering are implemented for this purpose. In the third phase, decision tree is used to specify the distinguishing characteristics of the clusters. In the fourth phase, the association rules are generated to reveal the interesting and frequent properties of each cluster. The results reveal the profiles of Syrian refugees with work permit applications. The findings obtained from this study can be a basis for developing policies and strategies that facilitate the labor market integration of the immigrants. The proposed methodology can be used to analyze time-dependent patterns and other immigration data for different countries as well.Publication Prediction of polycyclic aromatic hydrocarbons (PAHs) removal from wastewater treatment sludge using machine learning methods(Springer, 2021-02-10) Cağlar Gencosman, Burcu; Eker Şanlı, Gizem; ÇAĞLAR GENÇOSMAN, BURCU; EKER ŞANLI, GİZEM; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.; 0000-0003-0159-8529; AAG-8600-2021; FVM-6329-2022Removal of polycyclic aromatic hydrocarbons (PAHs) from wastewater treatment sludge with appropriate technologies is of great importance for nature and public health. UV technology is one of the most frequently used methods for the removal of PAHs. While various photodegradation applications with UV-C (ultraviolet-C) light and photocatalysts can be performed to remove these compounds, a large number of tests should be implemented to determine optimum removal conditions, which increase time and cost. It is possible to make predictions for the removal efficiency of PAHs by using data mining classification and reveal the hidden knowledge from data. This study aims to determine appropriate machine learning (ML) methods for the prediction of the PAH removal efficiency from wastewater treatment sludges regarding the initial PAH levels. The samples have multi-class imbalanced outputs; thus, random over-sampling and Synthetic Minority Over-sampling TEchniques (SMOTE) are used to improve the prediction results. Well-known data mining classification/machine learning methods, artificial neural network (multi-layer perceptron-MLP), k-means (k-NN), support vector machine (SVM), decision tree (C4.5), random forest (RF), and Bagging, are proposed for the prediction of removal efficiencies. Different evaluation metrics, Accuracy, multi-class AUC (MAUC-multi-class area under ROC curve), F-measure, Precision, Recall, and Specificity are used for the performance comparisons. RF and k-NN perform better with 92.35% and 92.36% average prediction accuracies, respectively. Besides, RF outperforms other methods with 0.97 MAUC value. RF and k-NN can be used for the removal efficiency prediction on the multi-class imbalanced datasets successfully, and removal efficiencies can be highly predicted considering input components with less cost and effort.Publication Hybrid radial basis function neural networks for urban traffic signal control(Academic Publication Council, 2020-12-01) Gençosman, Burcu Çağlar; ÇAĞLAR GENÇOSMAN, BURCU; Bursa Uludağ Üniversitesi/Endüstri Mühendisliği Bölümü; 0000-0003-0159-8529; AAG-8600-2021In this study, a real-world isolated signalized intersection with a fixed-time signal control system is considered. The signal timing plans are arranged regardless of the traffic density, and these plans cause delays in vehicle queues. To increase the efficiency of the intersection, an adaptive traffic signal control system is proposed to manage the intersection. To find the appropriate adaptive green times for each lane, simulations are performed by traffic simulation software using vehicle arrivals and other information about vehicle movements gathered from the real-world intersection. Then, a hybrid radial basis function neural network is developed to forecast the adaptive green times, which is trained and tested with historical arrivals and simulation results. The performance of the proposed network is compared with well-known data mining classification methods, such as support vector regression, k-nearest neighbors, decision tree, random forest, and multilayer perceptron methods, by different evaluation parameters. The comparison results provide that the developed radial basis function neural network outperforms other classification methods and can be successfully used for forecasting adaptive green times as an alternative to complex unsupervised classification methods.