@article{ozkurt_improving_2024,
	title = {Improving {Tuberculosis} {Diagnosis} using {Explainable} {Artificial} {Intelligence} in {Medical} {Imaging}},
	volume = {7},
	url = {https://dergipark.org.tr/en/pub/jmsm/issue/83125/1417160},
	number = {1},
	urldate = {2024-04-17},
	journal = {Journal of Mathematical Sciences and Modelling},
	author = {Özkurt, Cem},
	year = {2024},
	note = {Publisher: Mahmut AKYİĞİT},
	pages = {33--44},
}

@article{cetin_assessing_2024,
	title = {Assessing the performance of state-of-the-art machine learning algorithms for predicting electro-erosion wear in cryogenic treated electrodes of mold steels},
	copyright = {All rights reserved},
	doi = {https://doi.org/10.1016/j.aei.2024.102468},
	abstract = {In manufacturing, predicting and reducing electro-erosion wear during the electric discharge machining (EDM) process is critical to minimize delays, financial losses and product defects. Achieving this requires developing and evaluating accurate machine learning models. In our study, we focus on cryogenically treated mold steel elec­ trodes to investigate the potential of different machine learning algorithms to predict EDM wear. We considered five machine learning algorithms—artificial neural networks, ensemble learning, boosting algorithms, tree-based algorithms, and k-nearest neighbors—to evaluate their ability to predict wear patterns accurately. Each algo­ rithm was trained and tested using actual experimental data from EDM processes. Our results show that the machine learning models demonstrated exceptional accuracy, accurately predicting EDM wear in training and testing datasets with almost 99\% accuracy. In addition, we identified the most influential characteristics that affect wear patterns, including operating current, cryogenic process parameters, and electrode composition. Based on these findings, manufacturers can gain valuable insight into the factors that cause EDM wear and optimize their EDM processes accordingly to improve productivity, reduce wear-related costs, and increase production quality across multiple manufacturing industries. Furthermore, this research provides insights into the possibilities of implementing these models in real manufacturing contexts and motivates future research on this topic. Ultimately, integrating advanced computing techniques and prudent decision-making strategies will shape the future of manufacturing operations management and promote sustainable and profitable business growth.},
	language = {en},
	journal = {Advanced Engineering Informatics},
	author = {Cetin, Abdurrahman},
	year = {2024},
}

In manufacturing, predicting and reducing electro-erosion wear during the electric discharge machining (EDM) process is critical to minimize delays, financial losses and product defects. Achieving this requires developing and evaluating accurate machine learning models. In our study, we focus on cryogenically treated mold steel elec­ trodes to investigate the potential of different machine learning algorithms to predict EDM wear. We considered five machine learning algorithms—artificial neural networks, ensemble learning, boosting algorithms, tree-based algorithms, and k-nearest neighbors—to evaluate their ability to predict wear patterns accurately. Each algo­ rithm was trained and tested using actual experimental data from EDM processes. Our results show that the machine learning models demonstrated exceptional accuracy, accurately predicting EDM wear in training and testing datasets with almost 99% accuracy. In addition, we identified the most influential characteristics that affect wear patterns, including operating current, cryogenic process parameters, and electrode composition. Based on these findings, manufacturers can gain valuable insight into the factors that cause EDM wear and optimize their EDM processes accordingly to improve productivity, reduce wear-related costs, and increase production quality across multiple manufacturing industries. Furthermore, this research provides insights into the possibilities of implementing these models in real manufacturing contexts and motivates future research on this topic. Ultimately, integrating advanced computing techniques and prudent decision-making strategies will shape the future of manufacturing operations management and promote sustainable and profitable business growth.

@article{kurnaz_comparison_2024,
	title = {Comparison of machine learning algorithms for slope stability prediction using an automated machine learning approach},
	copyright = {All rights reserved},
	issn = {0921-030X, 1573-0840},
	url = {https://link.springer.com/10.1007/s11069-024-06490-8},
	doi = {10.1007/s11069-024-06490-8},
	language = {en},
	urldate = {2024-03-08},
	journal = {Natural Hazards},
	author = {Kurnaz, Talas Fikret and Erden, Caner and Dağdeviren, Uğur and Demir, Alparslan Serhat and Kökçam, Abdullah Hulusi},
	month = mar,
	year = {2024},
}

@article{erden_genetic_2023,
	title = {Genetic algorithm-based hyperparameter optimization of deep learning models for {PM2}.5 time-series prediction},
	volume = {20},
	copyright = {All rights reserved},
	issn = {1735-2630},
	url = {https://doi.org/10.1007/s13762-023-04763-6},
	doi = {https://doi.org/10.1007/s13762-023-04763-6},
	abstract = {Since air pollution negatively affects human health and causes serious diseases, accurate air pollution prediction is essential regarding environmental sustainability. Although conventional statistical and machine learning methods have been widely used for air quality forecasting, they have limitations in finding nonlinear relations and modeling sequential data. In recent years, deep learning methods such as long short-term memory, recurrent neural networks, and gated recurrent units have been successfully applied in several research areas, including time-series forecasting. In this study, deep learning algorithm is employed to predict the PM2.5 dataset, including air pollutants (NO, NO2, NOX, O3, PM2.5, SO, and SO2) and meteorological features (wind speed, wind direction, and air temperature) in Istanbul metropolitan. Deep learning algorithms have many hyperparameters such as learning and dropout rate, the number of hidden layers and units in each hidden layer, activation function, loss function, and optimizer that need to be optimized in order to achieve optimal training performance. Therefore, a genetic algorithm-based hyperparameter optimization approach is proposed to find the best parameter combination. The prediction results of deep learning algorithms are compared with default hyperparameters and random search algorithms to confirm the efficacy of the genetic algorithm approach. The proposed method outperforms the other configurations, with the MSE error reduced by 13.38\% and 55.30\% for testing performance, respectively. The experimental results revealed that genetic algorithms are promising and applicable in hyperparameter optimization of deep neural network models, especially in air quality forecasting.},
	language = {en},
	number = {3},
	urldate = {2023-03-07},
	journal = {International Journal of Environmental Science and Technology},
	author = {Erden, C.},
	month = mar,
	year = {2023},
	note = {WOS:000920619500001},
	pages = {2959--2982},
}

Since air pollution negatively affects human health and causes serious diseases, accurate air pollution prediction is essential regarding environmental sustainability. Although conventional statistical and machine learning methods have been widely used for air quality forecasting, they have limitations in finding nonlinear relations and modeling sequential data. In recent years, deep learning methods such as long short-term memory, recurrent neural networks, and gated recurrent units have been successfully applied in several research areas, including time-series forecasting. In this study, deep learning algorithm is employed to predict the PM2.5 dataset, including air pollutants (NO, NO2, NOX, O3, PM2.5, SO, and SO2) and meteorological features (wind speed, wind direction, and air temperature) in Istanbul metropolitan. Deep learning algorithms have many hyperparameters such as learning and dropout rate, the number of hidden layers and units in each hidden layer, activation function, loss function, and optimizer that need to be optimized in order to achieve optimal training performance. Therefore, a genetic algorithm-based hyperparameter optimization approach is proposed to find the best parameter combination. The prediction results of deep learning algorithms are compared with default hyperparameters and random search algorithms to confirm the efficacy of the genetic algorithm approach. The proposed method outperforms the other configurations, with the MSE error reduced by 13.38% and 55.30% for testing performance, respectively. The experimental results revealed that genetic algorithms are promising and applicable in hyperparameter optimization of deep neural network models, especially in air quality forecasting.

@article{eren_predicting_2023,
	title = {Predicting next hour fine particulate matter ({PM2}.5) in the {Istanbul} {Metropolitan} {City} using deep learning algorithms with time windowing strategy},
	volume = {48},
	copyright = {All rights reserved},
	issn = {2212-0955},
	url = {https://doi.org/10.1016/j.uclim.2023.101418},
	doi = {10.1016/j.uclim.2023.101418},
	abstract = {Poor air quality has various detrimental physical and mental effects on human health and quality of life. In particular, PM2.5 air pollution has been associated with cardiovascular and respiratory problems. Therefore, air quality management is an essential issue for densely populated cities to reduce or prevent the adverse effects of air pollution. Considering this, reliable models for predicting pollution levels for pollutants like PM2.5 are critical tools for decision-making. For this purpose, this study presents three kinds of deep learning (DL) algorithms (LSTM, RNN, and GRU) that utilize a time-windowing strategy to predict the hourly concentration of PM2.5 in the Istanbul metropolitan. The models were trained and tested using large data sets that envelope air quality parameters (PM2.5, SO2, NO, NO2, NOX, and O3) and meteorological factors (temperature, wind speed, relative humidity, and air pressure) for about five years. The experimental results demonstrate that the LSTM+LSTM model performs significantly better with an R2 of 0.98 and 0.97 at the significance level (p {\textless} 0.05) for training and test sets compared to other deep learning algorithms. In addition, data for one year from several stations located in nine different districts of Istanbul were used to evaluate the proposed model's generalization ability. As a result, the proposed LSTM+LSTM model has a good generalization ability with an R2 accuracy rate of 0.90 (p {\textless} 0.05) and above for all stations and can be used for non-linear, non-stationary multidimensional time series data. Furthermore, the results were compared to other studies in the literature; it was found that the proposed LSTM+LSTM model performed better in predicting PM2.5 concentrations.},
	journal = {Urban Climate},
	author = {Eren, Beytullah and Aksangür, İpek and Erden, Caner},
	month = mar,
	year = {2023},
	keywords = {Deep learning, Fine particulate matter (PM), Gated recurrent unit (GRU), Long-short term memory (LSTM), Recurrent neural network (RNN), Time windowing},
	pages = {101418},
}

Poor air quality has various detrimental physical and mental effects on human health and quality of life. In particular, PM2.5 air pollution has been associated with cardiovascular and respiratory problems. Therefore, air quality management is an essential issue for densely populated cities to reduce or prevent the adverse effects of air pollution. Considering this, reliable models for predicting pollution levels for pollutants like PM2.5 are critical tools for decision-making. For this purpose, this study presents three kinds of deep learning (DL) algorithms (LSTM, RNN, and GRU) that utilize a time-windowing strategy to predict the hourly concentration of PM2.5 in the Istanbul metropolitan. The models were trained and tested using large data sets that envelope air quality parameters (PM2.5, SO2, NO, NO2, NOX, and O3) and meteorological factors (temperature, wind speed, relative humidity, and air pressure) for about five years. The experimental results demonstrate that the LSTM+LSTM model performs significantly better with an R2 of 0.98 and 0.97 at the significance level (p \textless 0.05) for training and test sets compared to other deep learning algorithms. In addition, data for one year from several stations located in nine different districts of Istanbul were used to evaluate the proposed model's generalization ability. As a result, the proposed LSTM+LSTM model has a good generalization ability with an R2 accuracy rate of 0.90 (p \textless 0.05) and above for all stations and can be used for non-linear, non-stationary multidimensional time series data. Furthermore, the results were compared to other studies in the literature; it was found that the proposed LSTM+LSTM model performed better in predicting PM2.5 concentrations.

@article{erden_derin_2023,
	title = {Derin Öğrenme ve {ARIMA} {Yöntemlerinin} {Tahmin} {Performanslarının} {Kıyaslanması}: {Bir} {Borsa} İstanbul {Hissesi} Örneği},
	volume = {30},
	copyright = {All rights reserved},
	issn = {1302-0064},
	shorttitle = {Derin Öğrenme ve {ARIMA} {Yöntemlerinin} {Tahmin} {Performanslarının} {Kıyaslanması}},
	url = {https://dergipark.org.tr/tr/doi/10.18657/yonveek.1208807},
	doi = {https://doi.org/10.18657/yonveek.1208807},
	abstract = {Finansal zaman serisi verileri doğrusal olmayan, karmaşık, birçok ekonomik faktörden etkilenen ve tahmin edilmesi zor verilerdir. Çok boyutlu ilişkilerin tahminini gerektiren finansal zaman serisi modelleri için çeşitli istatistiksel yöntemler geliştirilmiştir. Ancak günümüzde büyük verilerin kaydedilmesi, analiz edilmesi ve anlamlı bilgiye dönüştürülmesi kolaylaştığından dolayı finansal tahmin geliştirmede makine öğrenmesi algoritmalarının kullanımı özellikle son yıllarda artmıştır. Bu çalışmada, Borsa İstanbul endeksinde metal ana pazarında işlem gören EREGL hissesine ait veriler zaman serisi yöntemleri ile analiz edilmiş ardından ARIMA ve derin öğrenme modelleri ile tahmin edilmiştir. Geliştirilen derin öğrenme yönteminde veri ön işleme aşamaları, özellik çıkarımı çalışmaları ve farklı zaman çerçeveleri ile tahmin performansı iyileştirilmiştir. Derin öğrenme algoritmalarının zaman serisi çalışmalarında kullanılabilmesi için zaman gecikmelerinden oluşan bir çerçeve kullanılmalıdır. Bu çalışmada, farklı zaman gecikmeleri için senaryolar denenmiş ve performans kıyaslaması ARIMA modelleri ve uzun-kısa vadeli bellek (LSTM), geçitli tekrarlayan ünite (GRU) ve özyineli sinir ağları (RNN) algoritmalarını kullanan derin öğrenme modelleri arasında gerçekleştirilmiştir. Deneysel çalıştırmalar ile RNN algoritmasının diğerlerine göre daha iyi tahmin performansına sahip olduğu ve ele alınan test veri seti üzerinde ortalama \%93’lük doğrulukla tahmin ettiği ortaya konulmuştur.  
Anahtar Kelimeler: ARIMA, BIST, Derin Öğrenme, GRU, Hisse Senedi Tahmini, LSTM, RNN 
JEL Sınıflandırması: E47, G17, E37
          , 
            Financial time-series data are nonlinear, complex, influenced by many economic factors, and are difficult to predict. Several traditional statistical methods have been developed for financial time series modeling. However, because it is now easier to record, analyze, and transform big data into meaningful information, the use of machine learning algorithms in financial forecast development has increased in recent years. In this study, the data of EREGL stocks, which are among the stocks traded in the main metal market in the Borsa İstanbul index, are analyzed using time series methods and then modeled using ARIMA and deep models. In the developed deep learning method, the prediction performance improved with data preprocessing stages, feature extraction studies, and different time windows. For deep learning algorithms to be used in time-series studies, a framework of time delays must be used. In this study, scenarios for different time delays and performance comparisons are performed between ARIMA models and deep learning models using long-short term memory (LSTM), gated repeating unit (GRU), and recursive neural network (RNN) algorithms. Experimental studies demonstrate that the RNN algorithm has a better prediction performance than the others and predicts with an average accuracy of 93\% on the test dataset.  
Key Words: ARIMA, BIST, Deep Learning, GRU, LSTM, RNN, Stock Price Prediction 
JEL Classification: E47, G17, E37},
	language = {tr},
	number = {3},
	urldate = {2023-09-14},
	journal = {Yönetim ve Ekonomi Dergisi},
	author = {Erden, Caner},
	month = apr,
	year = {2023},
	pages = {419--438},
}

Finansal zaman serisi verileri doğrusal olmayan, karmaşık, birçok ekonomik faktörden etkilenen ve tahmin edilmesi zor verilerdir. Çok boyutlu ilişkilerin tahminini gerektiren finansal zaman serisi modelleri için çeşitli istatistiksel yöntemler geliştirilmiştir. Ancak günümüzde büyük verilerin kaydedilmesi, analiz edilmesi ve anlamlı bilgiye dönüştürülmesi kolaylaştığından dolayı finansal tahmin geliştirmede makine öğrenmesi algoritmalarının kullanımı özellikle son yıllarda artmıştır. Bu çalışmada, Borsa İstanbul endeksinde metal ana pazarında işlem gören EREGL hissesine ait veriler zaman serisi yöntemleri ile analiz edilmiş ardından ARIMA ve derin öğrenme modelleri ile tahmin edilmiştir. Geliştirilen derin öğrenme yönteminde veri ön işleme aşamaları, özellik çıkarımı çalışmaları ve farklı zaman çerçeveleri ile tahmin performansı iyileştirilmiştir. Derin öğrenme algoritmalarının zaman serisi çalışmalarında kullanılabilmesi için zaman gecikmelerinden oluşan bir çerçeve kullanılmalıdır. Bu çalışmada, farklı zaman gecikmeleri için senaryolar denenmiş ve performans kıyaslaması ARIMA modelleri ve uzun-kısa vadeli bellek (LSTM), geçitli tekrarlayan ünite (GRU) ve özyineli sinir ağları (RNN) algoritmalarını kullanan derin öğrenme modelleri arasında gerçekleştirilmiştir. Deneysel çalıştırmalar ile RNN algoritmasının diğerlerine göre daha iyi tahmin performansına sahip olduğu ve ele alınan test veri seti üzerinde ortalama %93’lük doğrulukla tahmin ettiği ortaya konulmuştur. Anahtar Kelimeler: ARIMA, BIST, Derin Öğrenme, GRU, Hisse Senedi Tahmini, LSTM, RNN JEL Sınıflandırması: E47, G17, E37 , Financial time-series data are nonlinear, complex, influenced by many economic factors, and are difficult to predict. Several traditional statistical methods have been developed for financial time series modeling. However, because it is now easier to record, analyze, and transform big data into meaningful information, the use of machine learning algorithms in financial forecast development has increased in recent years. In this study, the data of EREGL stocks, which are among the stocks traded in the main metal market in the Borsa İstanbul index, are analyzed using time series methods and then modeled using ARIMA and deep models. In the developed deep learning method, the prediction performance improved with data preprocessing stages, feature extraction studies, and different time windows. For deep learning algorithms to be used in time-series studies, a framework of time delays must be used. In this study, scenarios for different time delays and performance comparisons are performed between ARIMA models and deep learning models using long-short term memory (LSTM), gated repeating unit (GRU), and recursive neural network (RNN) algorithms. Experimental studies demonstrate that the RNN algorithm has a better prediction performance than the others and predicts with an average accuracy of 93% on the test dataset. Key Words: ARIMA, BIST, Deep Learning, GRU, LSTM, RNN, Stock Price Prediction JEL Classification: E47, G17, E37

@article{erden_distribution_2023,
	title = {Distribution {Center} {Location} {Selection} in {Humanitarian} {Logistics} {Using} {Hybrid} {BWM}–{ARAS}: {A} {Case} {Study} in {Türkiye}},
	volume = {0},
	copyright = {All rights reserved},
	issn = {1547-7355},
	shorttitle = {Distribution {Center} {Location} {Selection} in {Humanitarian} {Logistics} {Using} {Hybrid} {BWM}–{ARAS}},
	url = {https://www.degruyter.com/document/doi/10.1515/jhsem-2022-0052/html},
	doi = {10.1515/jhsem-2022-0052},
	abstract = {This study investigates the criteria affecting the location of humanitarian logistics distribution centers in the Sakarya province of Turkey, an area prone to natural disasters. The study identifies potential distribution center locations and uses the BestWorst Method (BWM) to determine criteria such as population, distance to major highways and airports, public transportation availability, natural disaster risk, and suitable infrastructure. BWM is used to assign weights to each criterion and rank them based on their importance. The Additive Ratio Assessment (ARAS) method is then used to evaluate potential distribution center locations based on the established criteria. Disaster management experts and academicians provide their opinions through an online and face-to-face survey. Based on the results, Adapazarı is identified as the most suitable district for a humanitarian logistics distribution center. The study highlights the importance of considering multiple criteria when selecting distribution center locations and provides a framework for using multi-criteria decision-making methods in logistics planning. Disaster managers and policymakers can use the results to make informed decisions about the location of humanitarian logistics distribution centers.},
	language = {en},
	number = {0},
	urldate = {2023-06-27},
	journal = {Journal of Homeland Security and Emergency Management},
	author = {Erden, Caner and Ateş, Çağdaş and Esen, Sinan},
	month = jun,
	year = {2023},
}

This study investigates the criteria affecting the location of humanitarian logistics distribution centers in the Sakarya province of Turkey, an area prone to natural disasters. The study identifies potential distribution center locations and uses the BestWorst Method (BWM) to determine criteria such as population, distance to major highways and airports, public transportation availability, natural disaster risk, and suitable infrastructure. BWM is used to assign weights to each criterion and rank them based on their importance. The Additive Ratio Assessment (ARAS) method is then used to evaluate potential distribution center locations based on the established criteria. Disaster management experts and academicians provide their opinions through an online and face-to-face survey. Based on the results, Adapazarı is identified as the most suitable district for a humanitarian logistics distribution center. The study highlights the importance of considering multiple criteria when selecting distribution center locations and provides a framework for using multi-criteria decision-making methods in logistics planning. Disaster managers and policymakers can use the results to make informed decisions about the location of humanitarian logistics distribution centers.

@article{kilicaslan_ant_2023,
	title = {Ant {Colony} optimization application in bottleneck station scheduling},
	volume = {56},
	copyright = {All rights reserved},
	issn = {1474-0346},
	url = {https://www.sciencedirect.com/science/article/pii/S1474034623000976},
	doi = {10.1016/j.aei.2023.101969},
	abstract = {Finding optimal solutions to production planning and scheduling problems is crucial for surviving in a competitive environment and meeting customer expectations over time. Planning can become complicated in sectors with many different products such as tire production. This study focuses on the bottleneck problem caused by a machine called a Quadruplex Extruder in a tire factory. With this machine, rubber is extruded and transformed into a tread material product, which is critically important in some essential tire features, such as low rolling resistance and brake distance. This study aims to minimize the set-up times in production by optimizing the manufacturing order of the products produced in a quadruplex extruder machine using the Ant Colony Algorithm (ACA), a well-known metaheuristic method to solve polynomial optimization problems. In addition, the second version of the Lin–Kernighan–Helsgaun (LKH-2) algorithm was adapted to this problem. Manually prepared, LKH-2 and ACA-produced schedules were compared in terms of global efficiency. As a result, it has been shown that ACA can provide fast and suitable solutions for decision makers in production planning.},
	language = {en},
	urldate = {2023-04-14},
	journal = {Advanced Engineering Informatics},
	author = {Kılıçaslan, Emre and Demir, Halil Ibrahim and Kökçam, Abdullah Hulusi and Phanden, Rakesh Kumar and Erden, Caner},
	month = apr,
	year = {2023},
	keywords = {Ant Colony Algorithm, Bottleneck Station Scheduling, Lin–Kernighan–Helsgaun Algorithm, Optimization, Production Planning, Tire Production},
	pages = {101969},
}

Finding optimal solutions to production planning and scheduling problems is crucial for surviving in a competitive environment and meeting customer expectations over time. Planning can become complicated in sectors with many different products such as tire production. This study focuses on the bottleneck problem caused by a machine called a Quadruplex Extruder in a tire factory. With this machine, rubber is extruded and transformed into a tread material product, which is critically important in some essential tire features, such as low rolling resistance and brake distance. This study aims to minimize the set-up times in production by optimizing the manufacturing order of the products produced in a quadruplex extruder machine using the Ant Colony Algorithm (ACA), a well-known metaheuristic method to solve polynomial optimization problems. In addition, the second version of the Lin–Kernighan–Helsgaun (LKH-2) algorithm was adapted to this problem. Manually prepared, LKH-2 and ACA-produced schedules were compared in terms of global efficiency. As a result, it has been shown that ACA can provide fast and suitable solutions for decision makers in production planning.

@article{erden_modified_2023,
	title = {A modified integer and categorical {PSO} algorithm for solving integrated process planning, dynamic scheduling and due date assignment problem},
	volume = {30},
	copyright = {All rights reserved},
	issn = {2345-3605},
	url = {http://scientiairanica.sharif.edu/article_22245.html},
	doi = {10.24200/sci.2021.55250.4130},
	language = {en},
	number = {2},
	urldate = {2023-04-20},
	journal = {Scientia Iranica},
	author = {Erden, Caner and Demir, Halil Ibrahim and Canpolat, Onur},
	year = {2023},
	pages = {738--756},
}

@article{kurnaz_hyper_2023,
	title = {A hyper parameterized artificial neural network approach for prediction of the factor of safety against liquefaction},
	volume = {319},
	copyright = {All rights reserved},
	issn = {00137952},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0013795223001278},
	doi = {10.1016/j.enggeo.2023.107109},
	language = {en},
	urldate = {2023-04-07},
	journal = {Engineering Geology},
	author = {Kurnaz, Talas Fikret and Erden, Caner and Kökçam, Abdullah Hulusi and Dağdeviren, Uğur and Demir, Alparslan Serhat},
	month = jun,
	year = {2023},
	pages = {107109},
}

@article{aksangur_evaluation_2022,
	title = {Evaluation of data preprocessing and feature selection process for prediction of hourly {PM10} concentration using long short-term memory models},
	volume = {311},
	copyright = {All rights reserved},
	issn = {02697491},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0269749122011873},
	doi = {10.1016/j.envpol.2022.119973},
	language = {en},
	urldate = {2023-04-20},
	journal = {Environmental Pollution},
	author = {Aksangür, İpek and Eren, Beytullah and Erden, Caner},
	month = oct,
	year = {2022},
	pages = {119973},
}

@article{ozsagir_machine_2022,
	title = {Machine learning approaches for prediction of fine-grained soils liquefaction},
	volume = {152},
	copyright = {All rights reserved},
	issn = {0266352X},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0266352X22003512},
	doi = {10.1016/j.compgeo.2022.105014},
	language = {en},
	urldate = {2023-04-20},
	journal = {Computers and Geotechnics},
	author = {Ozsagir, Mustafa and Erden, Caner and Bol, Ertan and Sert, Sedat and Özocak, Aşkın},
	month = dec,
	year = {2022},
	pages = {105014},
}