Publications | Caner Erden

2026

Frequency Ratio and Machine Learning-Based Classification of Landslide Susceptibility in the West of Mersin Province, Türkiye: An Integrated Approach Using SMOTE

Çağan, Çağrı, Kurnaz, Talas Fikret, and Erden, Caner

Journal of African Earth Sciences Jun 2026

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Liquefaction-Induced Settlement Prediction Using Ensemble Machine Learning and LIME-Based Explainable AI

Dağdeviren, Uğur, Kökçam, Abdullah Hulusi, Erden, Caner and 2 more authors

Environmental Earth Sciences Jun 2026

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Adaptive Learning Rate Optimization in Deep Recurrent Architectures for Precision PM2.5 Forecasting under Climate Variability

Erden, Caner

Chemosphere Jun 2026

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Prediction of soil compaction parameters using AutoML with multidataset validation

Erden, Caner, Demir, Alparslan Serhat, Kökçam, Abdullah Hulusi and 2 more authors

Computers & Industrial Engineering Apr 2026

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An Integrated Techno-Economic and Schedule Optimization Framework for Industrial Solar PV Projects

Günhan, Atakan, Demir, Halil Ibrahim, Erden, Caner and 1 more author

Computers & Industrial Engineering May 2026

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Sustainable Resilience in Critical Mineral Supply Chains: An ESG and Circular Economy Perspective

Tutar, Hasan, and Erden, Caner

Business Strategy and the Environment Apr 2026

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Targeted cash transfers vs universal price subsidies: policy persistence and cost-effectiveness in EU energy poverty (2015–2024)

Tutar, Hasan, and Erden, Caner

International Journal of Sociology and Social Policy Apr 2026

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Beyond onboarding: social media, microlearning, and psychological safety in hybrid talent integration

Tutar, Hasan, Mutlu, Hakan Tahiri, and Erden, Caner

Employee Relations Mar 2026

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2025

Estimation of soil liquefaction using artificial intelligence techniques: an extended comparison between machine and deep learning approaches

Şehmusoğlu, Eyyüp Hakan, Kurnaz, Talas Fikret, and Erden, Caner

Environmental Earth Sciences Feb 2025

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This study investigates the effectiveness of various deep learning (DL) algorithms in predicting soil liquefaction susceptibility. We explore a spectrum of algorithms, including machine learning models such as Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), and Logistic Regression (LR), alongside DL architectures like Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Bidirectional LSTMs (BiLSTMs), and Gated Recurrent Units (GRUs). The performance of these algorithms is assessed using comprehensive metrics, including accuracy, precision, recall, F1-score, receiver operating characteristic (ROC) curve analysis, and area under the curve (AUC). Cross-entropy loss is employed as the loss function during model training to optimize the differentiation between liquefiable and non-liquefiable soil samples. Our findings reveal that the GRU model achieved the highest overall accuracy of 0.98, followed by the BiLSTM model with an accuracy of 0.95. Notably, the BiLSTM model excelled in precision for class 1, attaining a score of 0.96 on the test dataset. These results underscore the potential of both GRU and BiLSTM models in predicting soil liquefaction susceptibility, with the BiLSTM model’s simpler architecture proving particularly effective in certain metrics and datasets. The findings of this study could assist practitioners in seismic risk assessment by providing more accurate and reliable tools for evaluating soil liquefaction potential, thereby enhancing mitigation strategies and informing decision-making in earthquake-prone areas. This study contributes to developing robust tools for liquefaction hazard assessment, ultimately supporting improved seismic risk mitigation.
Explainable AI using ensemble machine learning with integrated SHapley additive explanations (SHAP)-Borda approach for estimation of the safety factor against soil liquefaction

Dağdeviren, Uğur, Demir, Alparslan Serhat, Erden, Caner and 2 more authors

Environmental Earth Sciences Sep 2025

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In most of the studies on soil liquefaction prediction based on Machine Learning (ML), the models presented are presented in a closed box structure. In the studies where the effect of the features on the model performance is analyzed with Interpretability methods, it is seen that the order of effect of the features changes for each ML algorithm. This situation makes the results of the studies conducted on the same subject inconsistent. In this study, we propose an integrated SHapley Additive exPlanations (SHAP)-Borda approach to overcome this problem. With this study, we provide decision makers with ease in explaining ML models by combining SHAP analysis results with the Borda method for the first time. In the study, ensemble ML algorithms were used for soil liquefaction prediction using data collected from the literature. The performances of the model predictions obtained by hyper parameterization were compared, and correlation results ranging from 0.91 to 0.93 were obtained. Ensemble ML algorithms that were found to be successful as a result of evaluating other performance criteria were analyzed with the SHAP-Borda approach in the study. It has been observed that with the proposed SHAP-Borda approach, the interpretability results of different ML algorithms can be brought together, and a final result can be presented, providing ease of evaluation for decision makers. The study also shows that (N1)60 and amax are the most effective features in predicting soil liquefaction.
An integrated SHAP-MCDM approach for slope stability prediction based on machine learning algorithms

Demir, Alparslan Serhat, Dağdeviren, Uğur, Kurnaz, Talas Fikret and 2 more authors

Natural Hazards Sep 2025

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In machine learning (ML)-based slope stability prediction studies, feature importance results often vary across different algorithms, leading to inconsistent interpretations. This issue arises because the importance of features differs depending on the algorithm applied within the same study. To address this challenge, this study proposes a novel methodology for obtaining a final, unified ranking of features by combining the feature importance rankings of various ML algorithms using a Multi-Criteria Decision-Making (MCDM) technique. This approach ensures a consistent and reliable feature ranking derived from the results of successful ML models. Furthermore, the study demonstrates how performance indicators of ML algorithms can be translated into criterion weights within the MCDM framework. Hyperparameter optimization was applied to the ML models, achieving accuracy rates between 70% and 92.5%. Successful algorithms were analyzed using SHapley Additive Explanations (SHAP) to evaluate feature importance, and the results were integrated into the proposed SHAP-MCDM methodology. The MULTIMOORA method, a well-established MCDM technique, was employed to combine the SHAP rankings. The results confirmed that a final feature ranking could be derived by merging different SHAP rankings of ML algorithms using the proposed SHAP-MULTIMOORA approach. The study also identified key features like cohesion, internal friction angle, and slope height, which significantly influence slope stability prediction. This methodology both contributes to the integration of SHAP rankings and advances prediction accuracy by calculating criterion weights of ML algorithms based on multiple performance metrics. The proposed approach has a broad applicability, improving both classification and regression-based prediction tasks in various domains beyond slope stability.
Türkiye’de Orman Yangınları ile Güneş Lekeleri Arasındaki Olası İlişkinin Araştırılması

Anğınoğlu, Neziha, Utkucu, Murat, and Erden, Caner

Afet ve Risk Dergisi Jul 2025

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Ormanlar, bazı temel kaynakları sağlar, dünya çapında milyonlarca insanın geçimini destekler, iklim değişiklikleri ve afetlerin etkilerini azaltırlar. Bununla birlikte, kötü arazi yönetimi ve iklim değişiklikleriyle ilişkilendirilen daha sıcak ve kuru hava nedeniyle ormanlardaki yangınlar aşırı ve yaygın hale gelerek ormanları bir afet kaynağı haline dönüştürmektedir. Bu çalışmada, 1944 ile 2021 yılları arasında Türkiye’de belgelenen orman yangınlarının sayıları ve etki alanları, gözlemlenen Güneş Lekelerinin günlük sayıları ile olası korelasyonu araştırılmıştır. İlk önce yıl bazında karşılaştırma yapılmış ve korelasyon bulunmamıştır. Nisan 1954- Kasım 2019 zaman aralığında Güneş Lekesi çevrim-süresi uzunluğu temelinde yapılan karşılaştırmada güçlü bir korelasyon (+ 0,955) elde edilmiştir. Yıllık sayıları yerine, bir Güneş Lekesi çevrimi içindeki Güneş Lekelerinin günlük sayılarının toplamlarının orman yangınları ile ilişkili olduğu sonucuna varılmıştır. Şubat 1944 ile Mart 1954 yılları arasındaki ve Aralık 2019’da başlayan çevrimlerdeki azalan korelasyonlar insan-kaynaklı unsurların işaretleri olarak ele alınmıştır. Güneş Lekelerinin döngüsel davranışının orman yangınlarının etki alanını etkilediği ve aralarındaki korelasyonun doğal ve insan-kaynaklı unsurlar arasında ayrım yapmak için kullanılabileceği ileri sürülmüştür.
Welding strength prediction in nuts to sheets joints: machine learning and ANFIS comparative analysis

Albak, Bircan, Erden, Caner, Ünal, Osman and 2 more authors

International Journal on Interactive Design and Manufacturing (IJIDeM) Apr 2025

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This study uses machine learning algorithms and the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict welding strength in DD13 sheet metal joints with AISI 1010 nuts. The objective is to optimize industrial welding processes and improve quality control. The study investigates weld current, time, and hold time as critical input variables for joint integrity. The performance of different ML algorithms, including linear regression, random forest regression, ridge regression, Bayesian regression, K-Nearest Neighbors regression, decision tree regression, and ANFIS, are evaluated. Training and testing data consist of welding parameters and corresponding strength measurements. Performance metrics such as R2 score, mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) are used to assess the predictive capabilities. Random forest regression is the most efficient algorithm, with a high R2 score of 0.992 and minimal errors. ANFIS also exhibits comparable performance, highlighting its efficacy in this context. These findings can be useful for optimizing welding parameters in industrial settings, potentially leading to improved quality control and weld strength, particularly in automotive applications. Using ML and ANFIS, industries can make informed decisions to optimize welding processes and ensure joint integrity, ultimately meeting the rigorous demands of demanding applications.
A comparative analysis of hyperparameter optimization using LSTM-based deep learning models for urban air quality predictions

Eren, Beytullah, Erden, Caner, Atalı, Gökhan and 1 more author

Ain Shams Engineering Journal Apr 2025

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Air pollution poses significant threats to human health and the environment, necessitating accurate prediction models for effective management and mitigation strategies. This study presents a comprehensive analysis of hyperparameter optimization techniques for Long Short-Term Memory (LSTM) based deep learning models in urban air quality forecasting. We focus on predicting concentrations of four key pollutants: carbon monoxide (CO), nitrogen oxides (NOX), nitrogen dioxide (NO2), and particulate matter (PM10). The study employs and compares three prominent hyperparameter optimization methods: Random Search, Bayesian Optimization, and Hyperband. Using air quality data from Sakarya, Turkey, collected between January 2020 and September 2022, we first addressed missing data through comparative analysis of mean imputation and k-Nearest Neighbors (kNN) imputation methods. Our results demonstrate that kNN imputation generally outperforms mean imputation, except for NOX predictions. The hyperparameter-optimized LSTM models consistently outperformed baseline models across all pollutants. Notably, the Hyperband Search algorithm excelled in NOX prediction, while Bayesian Optimization showed superior performance for other pollutants. Our analysis also revealed temporal trends in pollutant concentrations during the COVID-19 pandemic, including significant reductions in PM10 and CO levels. This study contributes to AI-driven environmental monitoring by comparing hyperparameter optimization techniques in urban air quality modeling. The improved prediction accuracy offered by our optimized models has significant implications for public health protection, environmental policymaking, and smart city initiatives. Our findings underscore the importance of tailored optimization approaches for different pollutants and highlight the potential of advanced machine learning techniques in addressing environmental challenges.

2024

Comparison of machine learning algorithms for slope stability prediction using an automated machine learning approach

Kurnaz, Talas Fikret, Erden, Caner, Dağdeviren, Uğur and 2 more authors

Natural Hazards Mar 2024

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Evaluation of slope failures, which cause significant loss of life and property comparable to natural disasters such as earthquakes, floods and hurricanes, is one of the main areas of interest in geotechnical engineering. Although traditional and modern methods have been developed for slope stability analysis, the importance given to computer-based approaches has increased in recent years. In this study, we investigated the effectiveness of advanced machine learning (ML) algorithms in classification-based slope stability assessment. In this context, examining the impact of input parameters, such as slope height, slope angle, unit volume weight, internal friction angle of the soil, cohesion of the slope material, and water pressure ratio on the slope stability potential and a comparative analysis was performed on the ML algorithms. On the other hand, automated machine learning (AutoML) approaches were used to make rapid and comprehensive comparisons of ensemble, boosting, bagging and traditional ML algorithms to simplifying application development. The weighted ensemble learning algorithm provided by the AutoGluon package outperformed other algorithms in both testing and training accuracy, achieving an impressive rate of 97.5%, according to the obtained results. All algorithms included in the study performed well, with NeuralNetTorch and CatBoost securing the second position with an accuracy rate of 95%. Furthermore, when evaluating the importance of features using the best algorithm, it can be seen that unit volume weight and internal friction angle of soil had the highest weights, 0.225 and 0.200, respectively, indicating their importance in classifying slope stability. In conclusion, our research significantly advanced slope stability assessment, achieving one of the highest accuracy of 0.975 among various classification-based studies.
Welding strength prediction in nuts to sheets joints: machine learning and ANFIS comparative analysis

Albak, Bircan, Erden, Caner, Ünal, Osman and 2 more authors

International Journal on Interactive Design and Manufacturing (IJIDeM) May 2024

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This study uses machine learning algorithms and the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict welding strength in DD13 sheet metal joints with AISI 1010 nuts. The objective is to optimize industrial welding processes and improve quality control. The study investigates weld current, time, and hold time as critical input variables for joint integrity. The performance of different ML algorithms, including linear regression, random forest regression, ridge regression, Bayesian regression, K-Nearest Neighbors regression, decision tree regression, and ANFIS, are evaluated. Training and testing data consist of welding parameters and corresponding strength measurements. Performance metrics such as R2 score, mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) are used to assess the predictive capabilities. Random forest regression is the most efficient algorithm, with a high R2 score of 0.992 and minimal errors. ANFIS also exhibits comparable performance, highlighting its efficacy in this context. These findings can be useful for optimizing welding parameters in industrial settings, potentially leading to improved quality control and weld strength, particularly in automotive applications. Using ML and ANFIS, industries can make informed decisions to optimize welding processes and ensure joint integrity, ultimately meeting the rigorous demands of demanding applications.
Assessing the performance of state-of-the-art machine learning algorithms for predicting electro-erosion wear in cryogenic treated electrodes of mold steels

Cetin, Abdurrahman

Advanced Engineering Informatics May 2024

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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 electrodes 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 algorithm 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.
A comparative analysis of ensemble learning algorithms with hyperparameter optimization for soil liquefaction prediction

Demir, Alparslan Serhat, Kurnaz, Talas Fikret, Kökçam, Abdullah Hulusi and 2 more authors

Environmental Earth Sciences May 2024

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Accurate prediction of soil liquefaction potential is crucial for evaluating the stability of structures in earthquake regions. This study focuses on predicting soil liquefaction using a dataset that included historical liquefaction cases from the 1999 Turkey and Taiwan earthquakes. The dataset was divided into three subsets: Dataset A (fine-grained), Dataset B (coarse-grained), and Dataset C (all samples). Through the analysis of these subsets, the study aims to assess the performance of machine learning algorithms in predicting soil liquefaction potential. This study applied ensemble machine learning algorithms, including extreme gradient boosting, adaptive boosting, extra trees, bagging classifiers, light gradient boosting machine, and random forest, to accurately classify the liquefaction potential of fine-grained and coarse-grained soils. A comparison between the genetic algorithm approach for hyperparameter optimization and traditional methods such as grid search and random search revealed that genetic algorithms outperformed both in terms of average test and train accuracy. Specifically, the light gradient boosting machine yielded the best predictions of soil liquefaction potential among the algorithms tested. The study demonstrated that Dataset B achieved the highest learning performance with accuracy of 0.92 on both the test and training sets. Furthermore, Dataset A showed a training accuracy of 0.88 and a test accuracy of 0.84, while Dataset C exhibited a training accuracy of 0.87 and a test accuracy of 0.87. Future studies could build on these findings by evaluating the performance of genetic algorithms on a wider range of machine learning algorithms and datasets, thus advancing our understanding of soil liquefaction prediction and its implications for geotechnical engineering.

2023

Distribution Center Location Selection in Humanitarian Logistics Using Hybrid BWM-ARAS: A Case Study in Türkiye

Erden, Caner, Ateş, Çağdaş, and Esen, Sinan

Journal of Homeland Security and Emergency Management Jun 2023

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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.
Integrated Process Planning, Scheduling, and Due-Date Assignment

Demir, Halil Ibrahim, Kökçam, Abdullah Hulusi, and Erden, Caner

Jun 2023

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Traditionally, the three most important manufacturing functions are process planning, scheduling, and due-date assignment, which are handled sequentially and separately.This book integrates these manufacturing processes and functions to increase global performance along with manufacturing and production cost savings. Integrated Process Planning, Scheduling, and Due-Date Assignment combines the most important manufacturing functions to use manufacturing resources better, reduce production costs, and eliminate bottlenecks with increased production efficiency. The book covers how the integration will help eliminate scheduling conflicts and how to adapt to irregular shop floor disturbances. It also explains how other elements, such as tardiness and earliness, are penalized and how prioritizing helps improve weight performance function. This book will draw the interest of professionals, students, and academicians in process planning, scheduling, and due-date assignment. It could also be supplemental material for manufacturing courses in industrial engineering and manufacturing engineering departments.
Enhancing Machine Learning Model Performance with Hyper Parameter Optimization: A Comparative Study

Erden, Caner, Demir, Halil Ibrahim, and Kökçam, Abdullah Hulusi

Feb 2023

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One of the most critical issues in machine learning is the selection of appropriate hyper parameters for training models. Machine learning models may be able to reach the best training performance and may increase the ability to generalize using hyper parameter optimization (HPO) techniques. HPO is a popular topic that artificial intelligence studies have focused on recently and has attracted increasing interest. While the traditional methods developed for HPO include exhaustive search, grid search, random search, and Bayesian optimization; meta-heuristic algorithms are also employed as more advanced methods. Meta-heuristic algorithms search for the solution space where the solutions converge to the best combination to solve a specific problem. These algorithms test various scenarios and evaluate the results to select the best-performing combinations. In this study, classical methods, such as grid, random search and Bayesian optimization, and population-based algorithms, such as genetic algorithms and particle swarm optimization, are discussed in terms of the HPO. The use of related search algorithms is explained together with Python programming codes developed on packages such as Scikit-learn, Sklearn Genetic, and Optuna. The performance of the search algorithms is compared on a sample data set, and according to the results, the particle swarm optimization algorithm has outperformed the other algorithms.
Genetic Algorithm-Based Hyperparameter Optimization of Deep Learning Models for PM2.5 Time-Series Prediction

Erden, C.

Int. J. Environ. Sci. Technol. Mar 2023

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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.
A Modified Integer and Categorical PSO Algorithm for Solving Integrated Process Planning, Dynamic Scheduling and Due Date Assignment Problem

Erden, Caner, Demir, Halil Ibrahim, and Canpolat, Onur

Scientia Iranica Mar 2023

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Predicting next Hour Fine Particulate Matter (PM2.5) in the Istanbul Metropolitan City Using Deep Learning Algorithms with Time Windowing Strategy

Eren, Beytullah, Aksangür, İpek, and Erden, Caner

Urban Climate Mar 2023

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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 < 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 < 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.
Ant Colony Optimization Application in Bottleneck Station Scheduling

Kılıçaslan, Emre, Demir, Halil Ibrahim, Kökçam, Abdullah Hulusi and 2 more authors

Advanced Engineering Informatics Apr 2023

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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.
A Hyper Parameterized Artificial Neural Network Approach for Prediction of the Factor of Safety against Liquefaction

Kurnaz, Talas Fikret, Erden, Caner, Kökçam, Abdullah Hulusi and 2 more authors

Engineering Geology Jun 2023

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Machine Learning Experiment Management with MLFlow

Erden, Caner

Jun 2023

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Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data...
Derin Öğrenme ve ARIMA Yöntemlerinin Tahmin Performanslarının Kıyaslanması: Bir Borsa İstanbul Hissesi Örneği

Erden, Caner

Yönetim ve Ekonomi Dergisi Sep 2023

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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.

2022

Evaluation of Data Preprocessing and Feature Selection Process for Prediction of Hourly PM10 Concentration Using Long Short-Term Memory Models

Aksangür, İpek, Eren, Beytullah, and Erden, Caner

Environmental Pollution Oct 2022

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The Transition to 5G Project In The Telecommunications Industry Using CPM and PERT Techniques

Arslan, Faruk, Demir, Halil Ibrahim, Erden, Caner and 1 more author

In 4th International Conference on Applied Engineering and Natural Sciences Nov 2022
Taşı ma Modları ve Ulaştı rma Modelleri

Erden, Caner

Nov 2022
Makine Öğrenmesinde Hiper Parametre Optimizasyonu

Erden, Caner

Nov 2022

Abs

Yapay zeka ve makine öğrenmesi uygulamaları iş dünyası ndan akademiye, sağlı k alanı ndan eğitim sektörüne kadar hemen hemen tüm alanlardaki etkisini hı zlı bir şekilde artı rmaktadı r. Makine öğrenmesi kullanan organizasyonlar kullanmayan organizasyonlara göre daha başarı lı uygulamalar gerçekleştirdiğinden dolayı makine öğrenmesi uygulamaları geçmişe kı yasla daha kolay ve küçük işletmeler tarafı ndan bile uygulanabilir hale gelmiştir. Ancak verimli çalışan ve sürekliliği olan makine öğrenmesi uygulamaları üretmek zor ve karmaşı k bir süreçtir. Geçmişteki verilerden faydalanarak çalışan makine öğrenmesi uygulamaları nı n yeni veri girişine adapte edilmesi, öğrenmenin sürekliliğinin sağlanması gibi zorluklar aynı zamanda günümüz makine öğrenmesi uygulamaları nı öne çı karan özelliklerdendir. Bu anlamda, makine öğrenmesi alanı ndaki en önemli konulardan birisi de Hiper Parametre Optimizasyonu(HPO) konusudur. İyi gerçekleştirilmiş bir HPO ile gerçeğe daha uygun performansa sahip makine öğrenmesi çalışmaları geliştirilebilir. Bu bölümde ilk olarak HPO konusunda yapı lan çalışmalar özetlenerek HPO konusuna duyulan ihtiyaçtan bahsedilecektir. İkinci kı sı mda, HPO çözümü için geliştirilmiş yöntemler ve çözüm araçları anlatı lacaktı r. Son olarak, uygulamalarda sı klı kla kullanı lan ve güçlü bir HPO aracı olan Hyperopt aracı ndan bahsedilecek ve bir uygulama üzerinde aracı n kullanı mı gösterilecektir.
Machine Learning Approaches for Prediction of Fine-Grained Soils Liquefaction

Ozsagir, Mustafa, Erden, Caner, Bol, Ertan and 2 more authors

Computers and Geotechnics Dec 2022

DOI
Recent Advances in Intelligent Manufacturing and Service Systems: Select Proceedings of IMSS 2021

Dec 2022
Economic Analysis and Determination of Profitability of an Automotive Company’s Building Construction Investment

Yuvalı, Enes, Demir, Halil Ibrahim, Kökçam, Abdullah and 1 more author

In 4th International Conference on Applied Engineering and Natural Sciences Nov 2022

2021

A Multi-Criteria Decision Making and Goal Programming Model for Fire Station Location Selection

Alkan, Melek, Demir, Halil, Erden, Caner and 1 more author

In Proceedings of 11th International Symposium on Intelligent Manufacturing and Service Systems May 2021
Alternative Cleaning Company Selection Method With Fuzzy Topsis

Bayındır, Sümeyye, Demir, Halil, Erden, Caner and 1 more author

In Proceedings of 11th International Symposium on Intelligent Manufacturing and Service Systems May 2021
Hybrid Evolutionary Strategy and Simulated Annealing Algorithms for Integrated Process Planning, Scheduling and Due-Date Assignment Problem

Demir, Halil I\.ḃrahim, Phanden, Rakesh, Kökçam, Abdullah and 2 more authors

Academic Platform Journal of Engineering and Science Jan 2021

DOI
A Tabu Search and Hybrid Evolutionary Strategies Algorithms for the Integrated Process Planning and Scheduling with Due-date Agreement

Demi̇R, Halil İbrahim, Erden, Caner, Kökçam, Abdullah and 1 more author

Journal of Intelligent Systems: Theory and Applications Mar 2021

DOI
Python İle Veri Madenciliği

Erden, Caner

Mar 2021
Green Supply Chain Management in the Context of Sustainability

Koç, Serkan, and Erden, Caner

Journal of Business and Trade Mar 2021

Abs

For centuries, humanity has seen nature as an unlimited resource, despised it, they polluted it. Hence, that caused environmental problems. On the one hand, the rapidly increasing population phenomenon, the existence of natural resources that are running out has made it necessary to seek new solutions for humanity. The solution put forward in this framework is the understanding of sustainable development, which can be summarized as ensuring that natural resources are transferred to future generations without completely consuming them. The priority in sustainable development approach has been the sustainability of the natural environment. With the increasing awareness of the international community and states, production systems and processes are revised and transition to green practices that will minimize environmental damage has rapidly gained importance. A traditional supply chain is a one-way process that involves productionrelated activities from raw material procurement to delivery of the final product. Due to environmental requirements affecting production operations today, an increasing emphasis is placed on the development of environmental management strategies for the supply chain. In this study, green approaches, and green supply chain management (GSCM) are considered conceptually, and the green activities implemented and the obstacles in front of them are evaluated. A general perspective is put forward to ensure and maintain the green supply chain.

2020

Dynamic Integrated Process Planning, Scheduling and Due-Date Assignment Using Ant Colony Optimization

Demir, Halil Ibrahim, and Erden, Caner

Computers & Industrial Engineering Nov 2020

DOI

2019

Concurrent Solution of WATC Scheduling with WPPW Due Date Assignment for Environmentally Weighted Customers, Jobs and Services Using SA and Its Hybrid

Demir, Halil Ibrahim, Erden, Caner, Kökçam, Abdullah Hulusi and 1 more author

PEN Jan 2019

DOI
Solving Integrated Process Planning, Dynamic Scheduling, and Due Date Assignment Using Metaheuristic Algorithms

Erden, Caner, Demir, Halil Ibrahim, and Kökçam, Abdullah Hulusi

Mathematical Problems in Engineering May 2019

Abs DOI

Because the alternative process plans have significant contributions to the production efficiency of a manufacturing system, researchers have studied the integration of manufacturing functions, which can be divided into two groups, namely, integrated process planning and scheduling (IPPS) and scheduling with due date assignment (SWDDA). Although IPPS and SWDDA are well-known and solved problems in the literature, there are limited works on integration of process planning, scheduling, and due date assignment (IPPSDDA). In this study, due date assignment function was added to IPPS in a dynamic manufacturing environment. And the studied problem was introduced as dynamic integrated process planning, scheduling, and due date assignment (DIPPSDDA). The objective function of DIPPSDDA is to minimize earliness and tardiness (E/T) and determine due dates for each job. Furthermore, four different pure metaheuristic algorithms which are genetic algorithm (GA), tabu algorithm (TA), simulated annealing (SA), and their hybrid (combination) algorithms GA/SA and GA/TA have been developed to facilitate and optimize DIPPSDDA on the 8 different sized shop floors. The performance comparisons of the algorithms for each shop floor have been given to show the efficiency and effectiveness of the algorithms used. In conclusion, computational results show that the proposed combination algorithms are competitive, give better results than pure metaheuristics, and can effectively generate good solutions for DIPPSDDA problems.

2018

Process Planning and Scheduling with WNOPPT Weighted Due-Date Assignment Where Earliness, Tardiness and Due-Dates Are Penalized

Demi̇r, Halil İbrahim, Canpolat, Onur, Erden, Caner and 1 more author

Journal of Intelligent Systems: Theory and Applications Sep 2018

DOI
Solving Process Planning, ATC Scheduling and Due-date Assignment Problems Concurrently Using Genetic Algorithm for Weighted Customers

Erden, Caner, Demir, Halil İbrahim, Göksu, Alper and 1 more author

Academic Platform-Journal of Engineering and Science Jan 2018

DOI

2017

Integrating Process Planning, WATC Weighted Scheduling, and WPPW Weighted Due-Date Assignment Using Pure and Hybrid Metaheuristics for Weighted Jobs

Demir, Halil Ibrahim, Erden, Caner, Demiriz, Ayhan and 2 more authors

International Journal of Computational and Experimental Science and Engineering Jun 2017

DOI
Proses Planlama ve Ağı rlı klı Teslim Tarihi Atama Ile Birlikte Ağı rlı klı Çizelgeleme Probleminin Bazı Saf ve Melez Meta-Sezgisel Yöntemler Ile Çözümü

Demir, Halil İbrahim, and Erden, Caner

SAÜ Fen Bilimleri Enstitüsü Dergisi Apr 2017

DOI

2016

Accident Causation Factor Analysis of Traffic Accidents using Rough Relational Analysis:

Erden, Caner, and Çelebi, Numan

International Journal of Rough Sets and Data Analysis Jul 2016

Abs DOI

The aim of this study is to show that the decision rules generated from Rough Sets Theory can be used for a new relational analysis. Rough Sets Theory generally works with small datasets more than big data. If we can deal with the decision rules and its complexities, it is still possible to analyze big data with Rough Set Theory. That is why in this study the authors offer a statistical method to overdue problems which belongs to big data. According statistical methods, a lots of decision rules generated from rough sets theory become useful information. Using a real case data on the traffic accident which were taken place in USA in 2013, this paper finds the relationships between accident causation factors which may be referred to decision makers in the field of traffic.
İplik Sürtünme Özelliklerinin İncelemesinde Kaba Kümeler Yaklaşı mı

Erden, Caner, and Nazarov, Muhammed

Erzincan University Journal of Science and Technology Jun 2016

DOI

2015

Gri Sistem Teorisi Kullanı larak Türkiye’nin Büyüme Oranı Faktörlerinin Analizi

Ceviz, Emre, and Erden, Caner

SAUFenBilDer Aug 2015

DOI