Research
Research Areas
- Veri Madenciliği (Data Science) link
- Yöneylem Araştırması (Operations Research)
- Benzetim (Simulation)
Profiles
- 👉 ORCID page:
orcid.org/0000-0002-7311-862X
- 👉 Google Scholar page:
user=W4EALHAAAAAJ&hl
Selected Papers
2023
Erden, C., Demir, H., Kökçam, A. (2023). Enhancing Machine Learning Model Performance with Hyper Parameter Optimization: A Comparative Study. arXiv. arxiv.org/abs/2302.11406
Erden, C. (2023). Genetic algorithm-based hyperparameter optimization of deep learning models for PM2.5 time-series prediction. Springer Science and Business Media LLC. dx.doi.org/10.1007/S13762-023-04763-6
Eren, B., Aksangür, İ., Erden, C. (2023). Predicting next hour fine particulate matter (PM2.5) in the Istanbul Metropolitan City using deep learning algorithms with time windowing strategy. Elsevier BV. dx.doi.org/10.1016/J.UCLIM.2023.101418
2022
Erden, C. (2022). Machine Learning Experiment Management With MLFlow. IGI Global. dx.doi.org/10.4018/978-1-7998-9220-5.ch071
Aksangür, İ., Eren, B., Erden, C. (2022). Evaluation of data preprocessing and feature selection process for prediction of hourly PM10 concentration using long short-term memory models. Elsevier BV. dx.doi.org/10.1016/J.ENVPOL.2022.119973
Ozsagir, M., Erden, C., Bol, E., Sert, S., Özocak, A. (2022). Machine learning approaches for prediction of fine-grained soils liquefaction. Elsevier BV. dx.doi.org/10.1016/J.COMPGEO.2022.105014
2021
Erden, C., Demir, H., Canpolat, O. (2021). A modified integer and categorical PSO algorithm for solving integrated process planning, dynamic scheduling and due date assignment problem. SCI AND TECH UNIVERSAL INC. dx.doi.org/10.24200/SCI.2021.55250.4130
2020
Demir, H., Erden, C. (2020). Dynamic integrated process planning, scheduling and due-date assignment using ant colony optimization. Elsevier BV. dx.doi.org/10.1016/J.CIE.2020.106799
2019
Erden, C., Demir, H., Kökçam, A. (2019). Solving Integrated Process Planning, Dynamic Scheduling, and Due Date Assignment Using Metaheuristic Algorithms. Hindawi Limited. dx.doi.org/10.1155/2019/1572614