Browsing by Author "Ozupak, Yildirim"

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Classification of Maize Leaf Diseases With Deep Learning: Performance Evaluation of the Proposed Model and Use of Explicable Artificial Intelligence
(Elsevier, 2025) Aslan, Emrah; Ozupak, Yildirim; Aslan, Emrah; Uzel, Hasan; Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü
Maize leaf diseases pose significant threats to global agricultural productivity, yet traditional diagnostic methods are slow, subjective, and resource-intensive. This study proposes a lightweight and interpretable convolutional neural network (CNN) model for accurate and efficient classification of maize leaf diseases. Using the 'Corn or Maize Leaf Disease Dataset', the model classifies four disease categories Healthy, Gray Leaf Spot, Common Rust, and Northern Leaf Blight with 94.97 % accuracy and a micro-average AUC of 0.99. With only 1.22 million parameters, the model supports real-time inference on mobile devices, making it ideal for field applications. Data augmentation and transfer learning techniques were applied to ensure robust generalization. To enhance transparency and user trust, Explainable Artificial Intelligence (XAI) methods, including LIME and SHAP, were employed to identify disease-relevant features such as lesions and pustules, with SHAP achieving an IoU of 0.82. The proposed model outperformed benchmark models like ResNet50, MobileNetV2, and EfficientNetB0 in both accuracy and computational efficiency. Robustness tests under simulated environmental challenges confirmed its adaptability, with only a 2.82 % performance drop under extreme conditions. Comparative analyses validated its statistical significance and practical superiority. This model represents a reliable, fast, and explainable solution for precision agriculture, especially in resource-constrained environments. Future enhancements will include multi-angle imaging, multimodal inputs, and extended datasets to improve adaptability and scalability in realworld conditions.
Citation - WoS: 0
Citation - Scopus: 0
Comparison of Machine Learning Algorithms for Automatic Prediction of Alzheimer Disease
(Lippincott Williams & Wilkins, 2025) Aslan, Emrah; Ozupak, Yildirim; Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü
Background:Alzheimer disease is a progressive neurological disorder marked by irreversible memory loss and cognitive decline. Traditional diagnostic tools, such as intracranial volume assessments, electroencephalography (EEG) signals, and brain magnetic resonance imaging (MRI), have shown utility in detecting the disease. However, artificial intelligence (AI) offers promise for automating this process, potentially enhancing diagnostic accuracy and accessibility.Methods:In this study, various machine learning models were used to detect Alzheimer disease, including K-nearest neighbor regression, support vector machines (SVM), AdaBoost regression, and logistic regression. A neural network was constructed and validated using data from 150 participants in the University of Washington's Alzheimer's Disease Research Center (Open Access Imaging Studies Series [OASIS] dataset). Cross-validation was also performed on the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset to assess the robustness of the models.Results:Among the models tested, K-nearest neighbor regression achieved the highest accuracy, reaching 97.33%. The cross-validation on the ADNI dataset further confirmed the effectiveness of the models, demonstrating satisfactory results in screening and diagnosing Alzheimer disease in a community-based sample.Conclusion:The findings indicate that AI-based models, particularly K-nearest neighbor regression, provide promising accuracy for the early detection of Alzheimer disease. This approach has potential for further development into practical diagnostic tools that could be applied in clinical and community settings.