Arslan, İlker

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https://hdl.handle.net/20.500.11779/2691
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arslanil@mef.edu.tr
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02.03. Department of Mechanical Engineering
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Current Staff
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1728752412-academic-1701013836.avif (9.76 KB)

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International Journal of Statistics in Medical Research1
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  • Thumbnail Image
    Article
    Determination of Alzheimer's Disease Stages by Artificial Learning Algorithms
    (Lifescience Global, 2025) Bulut, Nurgül; Çakar, Tuna; Arslan, İlker; Akıncı, Zeynep Karaoğlu; Oner, Kevser Setenay
    Introduction: This study aims to determine the stages of Alzheimer's disease (AD) using different machine learning algorithms, and compares the performance of these models. Methods: Demographic, genetic, and neurocognitive inventory data from the National Alzheimer's Coordinating Center (NACC) database as well as brain volume/thickness data from magnetic resonance imaging (MRI) scans were used. Deep Neural Networks, Ordinal Logistic Regression, Random Forest, Gaussian Naive Bayes, XGBoost, and LightGBM models were used to identify four different ordinal stages of AD. Results: Although the performance measures of the developed models were similar, the highest classification rate of AD stages was achieved by the Random Forest model (accuracy: 0.86; F1 score: 0.86; AUC: 0.95). The outputs of the model with the best performance were explained by the SHapley Addictive exPlanations (SHAP) method. Conclusions: This indicates that non-invasive markers and machine learning models can be used effectively in early diagnosis and decision support systems to predict stages of AD. © 2025 Elsevier B.V., All rights reserved.
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    Master Thesis
    Convolutional Neural Network for Facial Emotion Recognition With Geometrical Features of Face
    (MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Arslan, İlker; Tuna Çakar
    One of the recent challenging machine learning problems is to make predictions on image datasets. The aim of the project is to construct a convolutional neural network to guess emotions for a face of a human given in an image file considering the face. After the geometrical features are extracted using pretrained models, we construct five models which are convolutional networks fed with handcrafted geometrical features extracted. The last model uses the outputs of other four models to predict more accurately.