Browsing by Author "Ozupak, Yildirim"

Now showing 1 - 4 of 4

Citation - WoS: 1
Citation - Scopus: 1
Air Quality Forecasting Using Machine Learning: Comparative Analysis and Ensemble Strategies for Enhanced Prediction
(Springer Int Publ Ag, 2025) Aslan, Emrah; Alpsalaz, Feyyaz; Aslan, Emrah; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
Air pollution poses a critical challenge to environmental sustainability, public health, and urban planning. Accurate air quality prediction is essential for devising effective management strategies and early warning systems. This study utilized a dataset comprising hourly measurements of pollutants such as PM2.5, NOx, CO, and benzene, sourced from five metal oxide sensors and a certified analyzer in a polluted urban area, totaling 9,357 records collected over one year (March 2004-February 2005) from the Kaggle Air Quality Data Set. A comprehensive comparison of ten machine learning regression models XGBoost, LightGBM, Random Forest, Gradient Boosting, CatBoost, Support Vector Regression (SVR) with Bayesian Optimization, Decision Tree, K-Nearest Neighbors (KNN), Elastic Net, and Bayesian Ridge was conducted. Model performance was enhanced through Bayesian optimization and randomized cross-validation, with stacking employed to leverage the strengths of base models. Experimental results showed that hyperparameter optimization and ensemble strategies significantly improved accuracy, with the SVR model optimized via Bayesian optimization achieving the highest performance: an R2 score of 99.94%, MAE of 0.0120, and MSE of 0.0005. These findings underscore the methodology's efficacy in precisely capturing the spatial and temporal dynamics of air pollution.
Citation - WoS: 2
Citation - Scopus: 2
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; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
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.
Comparison of Machine Learning Algorithms for Automatic Prediction of Alzheimer Disease
(Lippincott Williams & Wilkins, 2025) Aslan, Emrah; Ozupak, Yildirim; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
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.
Hybrid Deep Learning Model for Maize Leaf Disease Classification With Explainable AI
(Taylor & Francis Ltd, 2025) Aslan, Emrah; Alpsalaz, Feyyaz; Aslan, Emrah; Uzel, Hasan; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
This study presents a hybrid learning model that integrates MobileNetV2 and Vision Transformer (ViT) with a stacking model to classify maize leaf diseases, addressing the critical need for early detection to improve agricultural productivity and sustainability. Utilising the 'Corn or Maize Leaf Disease Dataset' from Kaggle, comprising 4,062 high-resolution images across five classes (Common Rust, Grey Leaf Spot, Healthy, Northern Leaf Blight, Not Maize Leaf), the model achieves an impressive accuracy of 96.73%. Transfer learning from ImageNet, coupled with data augmentation (rotation, flipping, scaling, brightness adjustment), enhances generalisation, while a 20% dropout rate mitigates overfitting. The key advantage of the hybrid model lies in its ability to combine the strengths of MobileNetV2's localised feature extraction and ViT's global context understanding, enhanced by the stacking model's ability to reduce the weaknesses of either model. Explainable AI techniques, including SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Grad-CAM, provide transparent insights into model decisions, fostering trust among agricultural stakeholders. Comparative analysis demonstrates the model's superiority over prior works, with F1-scores ranging from 0.9276 to 1.0000. Despite minor misclassifications due to visual similarities, the model offers a robust, interpretable solution for precision agriculture.