Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/203
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Conference Object Citation - Scopus: 1Man-Hour Prediction for Complex Industrial Products(Institute of Electrical and Electronics Engineers Inc., 2023) Unal, Ahmet Emin; Boyar, Halit; Kuleli Pak, Burcu Kuleli; Cem Yildiz, Mehmet; Erten, Ali Erman; Güngör, Vehbi Çağrı; Pak, Burcu Kuleli; Cagri Gungor, VehbiAccurately predicting the cost is crucial for the success of complex industrial projects. There can be several sources contributing to the cost. Traditional methods for cost estimation may not provide the required accuracy and speed to ensure the success of the project. Recently, machine learning techniques have shown promising results in improving cost estimation in various industrial products. This study investigates the performance of gradient-boosting machine learning models and feature engineering techniques on a private dataset of metal sheet project man-hour costs. A comparison of distinct models is conducted, key aspects influencing cost are identified, and the implications of incorporating domain-specific knowledge, including its advantages and disadvantages, are assessed based on performance outcomes. Experimental results demonstrate that LightGBM and XGBoost outperform other models, and feature selection and synthetic data generation techniques improve the performance. Overall, this study highlights the potential of machine learning in metal sheet sampling projects and emphasizes the importance of feature engineering and domain expertise for better model performance. © 2024 Elsevier B.V., All rights reserved.Article Citation - WoS: 15Citation - Scopus: 16Stacking Ensemble Learning-Based Wireless Sensor Network Deployment Parameter Estimation(Springer Heidelberg, 2023) Akbas, Ayhan; Buyrukoglu, SelimIn wireless sensor network projects, it is generally desired to cover the area to be monitored at a given cost and to achieve the maximum useful network lifetime. In the deployment of the wireless sensors, it is necessary to know in advance how many sensor nodes will be required, how much the distance between the nodes should be, etc., or what the transmit power level should be, etc. depending on the channel parameters of the area. This necessitates accurate calculation of variables such as maximum network lifetime, communication channel parameters, number of nodes to be used, and distance between nodes. As numbers reach to the order of hundreds, calculation tends to a NP hard problem to solve. At this point, we employed both single-based and stacked ensemble-based machine learning models to speed up the parameter estimations with highly accurate outcomes. Adaboost was superior over other models (Elastic Net, SVR) in single-based models. Stacked ensemble models achieved best results for the WSN parameter prediction compared to single-based models.