Person: İNKAYA, TÜLİN
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İNKAYA
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TÜLİN
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Publication Extracting the boundaries of clusters: A post-clustering tool for spatial datasets(World Scientific Publ Co Pte Ltd, 2020-04-01) Kayalıgil, Sinan; Özdemirel, Nur Evin; İnkaya, Tulin; İNKAYA, TÜLİN; 0000-0002-6260-0162; AAH-2155-2021; AAZ-8000-2020Boundary extraction is a fundamental post-clustering problem. It facilitates interpretability and usability of clustering results. Also, it provides visualization and dataset reduction. However, it has not attracted much attention compared to the clustering problem itself. In this work, we address the boundary extraction of clusters in 2- and 3-dimensional spatial datasets. We propose two algorithms based on Delaunay Triangulation (DT). Numerical experiments show that the proposed algorithms generate the cluster boundaries effectively. Also, they yield significant amounts of dataset reduction.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 A density and connectivity based decision rule for pattern classification(Elsevier, 2015-02-01) İnkaya, Tülin; İNKAYA, TÜLİN; Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü; 0000-0002-6260-0162; AAH-2155-2021In this paper we propose a novel neighborhood classifier, Surrounding Influence Region (SIR) decision rule. Traditional Nearest Neighbor (NN) classifier is a distance-based method, and it classifies a sample using a predefined number of neighbors. In this study neighbors of a sample are determined using not only the distance, but also the connectivity and density information. One of the well-known proximity graphs, Gabriel Graph, is used for this purpose. The neighborhood is unique for each sample. SIR decision rule is a parameter-free approach. Our experiments with artificial and real data sets show that the performance of the SIR decision rule is superior to the k-NN and Gabriel Graph neighbor (GGN) classifiers in most of the data sets.Publication A novel LOF-based ensemble regression tree methodology(Springer, 2023-06-27) Öngelen, Gözde; İnkaya, Tülin; Öngelen, Gözde; İNKAYA, TÜLİN; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü; 0000-0002-6260-0162; AAH-2155-2021; IWT-8849-2023With the emergence of digitilization, numeric prediction has become a prominent problem in various fields including finance, engineering, industry, and medicine. Among several machine learning methods, regression tree is a widely preferred method due to its simplicity, interpretability and robustness. Motivated by this, we introduce a novel ensemble regression tree based methodology, namely LOF-BRT+OR. The proposed methodology is an integrated solution approach with outlier removal, regression tree and ensemble learning. First, irregular data points are removed using local outlier factor (LOF), which measures the degree of being an outlier for each point. Next, a novel regression tree with LOF weighted node model is introduced. In the proposed node model, the weights of the points in the nodes are determined according to their surrounding neighborhood, as a function of LOF values and neighbor ranks. Finally, in order to increase the prediction performance, ensemble learning is adopted. In particular, bootstrap aggregation is used to generate multiple regression trees with LOF weighted node model. The experimental study shows that the proposed methodology yields the best root mean squared error (RMSE) values in five out of nine data sets. Also, the non-parametric tests demonstrate the statistical significance of the proposed approach over the benchmark methods. The proposed methodology can be applicable to various prediction problems.Publication LOF weighted knn regression ensemble and its application to a die manufacturing company(Springer, 2023-11-04) Öngelen, Gözde; İnkaya, Tülin; Öngelen, Gözde; İNKAYA, TÜLİN; Bursa Uludağ Üniversitesi/Endüstri Mühendisliği Bölümü; 0000-0002-6260-0162; AAH-2155-2021; IWT-8849-2023K-nearest neighbor (KNN) algorithm is a widely used machine learning technique for prediction problems due its simplicity, flexibility and interpretability. When predicting the output variable of a data point, it basically averages the output values of its k closest neighbors. However, the impact of the neighboring points on the estimation may differ. Even though there are weighted versions of KNN, the effect of outliers and density differences within the neighborhoods are not considered. In order to fill this gap, we propose a novel weighting scheme for KNN regression based on local outlier factor (LOF). In particular, we combine the inverse of the Euclidean distance and LOF value so that the weights of the neighbors are determined using not only distance and connectivity but also outlier and density information around the neighborhood. Also, bootstrap aggregation is used to leverage the stability and accuracy of the LOF weighted KNN regression. Using real-life benchmark datasets, extensive experiments and statistical tests were performed for evaluating the performance of the proposed approach. The experimental results indicate the superior performance of the proposed approach in small neighborhood sizes. Moreover, the proposed approach was implemented in a make-to-order manufacturing company, and die production times were estimated successfully.Publication Remaining useful life estimation of turbofan engines with deep learning using change-point detection based labeling and feature engineering(Mdpi, 2023-11-01) Ensarioğlu, Kıymet; Emel, Erdal; EMEL, ERDAL; İnkaya, Tülin; İNKAYA, TÜLİN; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi.; 0000-0002-6260-0162; 0000-0002-9220-7353; JNT-1214-2023; N-8691-2014Accurate remaining useful life (RUL) prediction is one of the most challenging problems in the prognostics of turbofan engines. Recently, RUL prediction methods for turbofan engines mainly involve data-driven models. Preprocessing the sensor data is essential for the performance of the prognostic models. Most studies on turbofan engines use piecewise linear (PwL) labeling, which starts with a constant initial RUL value in normal/healthy operating time. In this study, we designed a prognostic procedure that includes difference-based feature construction, change-point-detection-based PwL labeling, and a 1D-CNN-LSTM (one-dimensional-convolutional neural network-long short-term memory) hybrid neural network model for RUL prediction. The procedure was evaluated on the subset FD001 of the C-MAPSS dataset. The proposed procedure was compared with machine learning and deep learning models with and without the new difference feature. Also, the results were compared with the studies that used similar labeling approaches. Our analysis of the numerical results underscores the clear superiority of the proposed 1D-CNN-LSTM model with the difference feature in RUL prediction, with a score of 437.2 and an RMSE value of 16.1. This result illustrates the superior predictive capability of the 1D-CNN-LSTM model, which outperformed traditional machine learning methods and one of the earliest deep learning methods. These findings emphasize the superior predictive capability of the 1D-CNN-LSTM model and underline the potential of the feature engineering process for more accurate and robust RUL prediction in the context of turbofan engine prognostics.Publication Identifying green citizen typologies by mining household-level survey data(Elsevier, 2023-10-24) Petriçli, Gülcan; İnkaya, Tülin; Emel, Gül Gökay; PETRİÇLİ, GÜLCAN; İNKAYA, TÜLİN; Emel, Gül Gökay; 0000-0002-6260-0162; AAH-5172-2021; JCN-8103-2023; AAH-2155-2021Some impactful but unfavorable results of rapid urbanization are human-nature disconnection, waste of energy/ water resources, and increased greenhouse gas emissions. To save the future of our planet, a transition to a more sustainable urban life is a must. However, there is no single sustainable city model because cities differ in terms of their assets. Hence, locally customized sustainable actions linked to global sustainability should be developed, such as a change in individual behaviors leads to a sustainable society, city, and country. This research investigated green citizen profiles and variables affecting the profiles in the context of environmental behavior and sustainability. For this purpose, survey research was done at the household level in a metropolis in Turkey. Measurement scales about environmental concern, human-nature connections, and sustainable consumption behavior were used for collecting data. A data analysis approach was proposed as the survey dataset contains mixed-type variables. It amalgamates statistics with machine learning algorithms, namely two-stage clustering with multilayered self-organizing maps, k-medoid clustering algorithm, factor analysis, permutational multivariate analysis of variance, principal component analysis and classification and regression trees algorithm. The results reveal that (i) five distinct profiles, namely unconscious greens, risky greens, economic greens, potential greens, and wasters are identified, none of which is entirely green; (ii) district, family life-cycle, household size, number of rooms, altruistic and biocentric environmental concerns are the most critical variables in distinguishing profiles; (iii) the proposed approach enables processing socio-demographic, psychographic, behavioral and consumption variables together.Publication Demand forecasting with deep learning: Case study in a third-party logistics company for the COVID-19 period(Pamukkale Üniversitesi, 2023-01-01) Zeybel Peköz, Ayşe; İnkaya, Tülin; Zeybel Peköz, Ayşe; İNKAYA, TÜLİN; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.; 0000-0002-6260-0162; JDL-0934-2023; AAH-2155-2021The restrictions and closures experienced during the COVID-19 pandemic period have affected the global supply chains greatly. The logistics sector is among the most affected sectors from this process. For this reason, accurate and fast estimation of logistics demand is important for effective resource planning. In this study, the aim is to predict the demand accurately in a third-party logistics company during the COVID-19 pandemic period. The shipment data of a logistics company between June 2020 and December 2020 were examined, and the prediction problem was considered as univariate time series. In the scope of the study, a deep learning-based demand forecasting model is proposed. In the proposed prediction model, convolutional neural network (CNN) and long short-term memory (LSTM) network are integrated. CNN provides feature extraction, LSTM captures long-term dependencies, and the proposed model is called hybrid CNN-LSTM. The prediction performance of the hybrid CNN-LSTM was evaluated by comparing it with the classical prediction approaches as well as machine learning and deep learning approaches. The parameter values of all forecasting methods were determined by experimental studies. According to the experimental results, the proposed hybrid CNN-LSTM method showed higher performance than the other methods. The proposed approach generates input to workforce and resource planning activities by providing accurate estimation of logistics demand.