Browsing by Author "Buyrukoğlu, Selim"

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A Comparison of Ensemble and Base Learner Algorithms for the Prediction of Machining Induced Residual Stresses in the Turning of Aerospace Materials
(Bitlis Eren Üniversitesi, 2022) Buyrukoğlu, Selim; Kesriklioglu, Sinan; 0000-0002-2914-808X; AGÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümü; Kesriklioglu, Sinan
The estimation of residual stresses is essential to prevent the catastrophic failures of the components used in the aerospace industry. The objective of this work is to predict the machining induced residual stresses with bagging, boosting, and single-based machine learning models based on the design and cutting parameters used in the turning of Inconel 718 and Ti6Al4V alloys. Experimentally measured residual stress data of these two materials was compiled from the literature, including the surface material of the cutting tools, cooling conditions, rake angles, as well as the cutting speed, feed, and width of cut to show the robustness of the models. These variables were also grouped into different combinations to clearly show the contribution and necessity of each element. Various predictive models in machine learning (AdaBoost, Random Forest, Artificial Neural Network, K-Neighbors Regressor, Linear Regressor) were then applied to estimate the residual stresses on the machined surfaces for the classified groups using the generated data. It was found that the AdaBoost algorithm was able to predict the machining induced residual stresses with a mean absolute error of 18.1 MPa for the IN718 alloy and 31.3 MPa for Ti6Al4V by taking into account all the variables, while the artificial neural network provides the lowest mean absolute errors for the Ti6Al4V alloy. On the other hand, the linear regression model gives poor agreement with the experimental data. All the analyses showed that AdaBoost (boosting) ensemble learning and artificial neural network models can be used for the prediction of the machining induced residual stresses with the small datasets of the IN718 and Ti6Al4V materials.
Machine Learning based Early Prediction of Type 2 Diabetes: A New Hybrid Feature Selection Approach using Correlation Matrix with Heatmap and SFS
(İstanbul Teknik Üniversitesi, 2022) Akbas, Ayhan; Buyrukoğlu, Selim; 0000-0002-6425-104X; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Akbas, Ayhan
A new hybrid machine learning method for the prediction of type 2 diabetes is introduced and explained in detail. Also outcomes are compared with the similar researches. Early prediction of diabetes is crucial to take necessary measures (i.e. changing eating habits, patient weight control etc.), to defer the emergence of diabetes and to reduce the death rate to some extent and ease medical care professionals’ decision making in preventing and managing diabetes mellitus.The purpose of this study is the creation of a new hybrid feature selection approach combination of Correlation Matrix with Heatmap and Sequential forward selection (SFS) to reveal the most effective features in the detection of diabetes. A diabetes data set with 520 instances and seven features were studied with the application of the proposed hybrid feature selection approach. The evaluation of the selected optimal features was measured by applying Support Vector Machines(SVM), Random Forest(RF), and Artificial Neural Networks(ANN) classifiers. Five evaluation metrics, namely, Accuracy, F-measure, Precision, Recall, and AUC showed the best performance with ANN (99.1%), F-measure (99.1%), Precision (99.3%), Recall (99.1%), and AUC (99.8%). Our proposed hybrid feature selection model provided a more promising performance with ANN compared to other machine learning algorithms.