Aslan, Emrah
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Aslan, E.
Aslan, Emrah
Aslan, Emrah
Job Title
Dr. Öğr. Üyesi
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Main Affiliation
Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü
Status
Current Staff
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WoS Researcher ID
Sustainable Development Goals
1NO POVERTY
0
Research Products
2ZERO HUNGER
2
Research Products
3GOOD HEALTH AND WELL-BEING
4
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4QUALITY EDUCATION
0
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5GENDER EQUALITY
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6CLEAN WATER AND SANITATION
0
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7AFFORDABLE AND CLEAN ENERGY
3
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8DECENT WORK AND ECONOMIC GROWTH
2
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
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10REDUCED INEQUALITIES
0
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11SUSTAINABLE CITIES AND COMMUNITIES
1
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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13CLIMATE ACTION
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14LIFE BELOW WATER
1
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15LIFE ON LAND
0
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
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17PARTNERSHIPS FOR THE GOALS
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This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output
17
Articles
17
Views / Downloads
81/0
Supervised MSc Theses
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Supervised PhD Theses
0
WoS Citation Count
69
Scopus Citation Count
93
Patents
0
Projects
0
WoS Citations per Publication
4.06
Scopus Citations per Publication
5.47
Open Access Source
11
Supervised Theses
0
| Journal | Count |
|---|---|
| Scientific Reports | 3 |
| IEEE Access | 2 |
| Gazi University Journal of Science Part A: Engineering and Innovation | 1 |
| IET Renewable Power Generation | 1 |
| International Journal of Integrated Engineering | 1 |
Current Page: 1 / 3
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17 results
Scholarly Output Search Results
Now showing 1 - 10 of 17
Article Acoustic-Based Fault Diagnosis of Electric Motors Using Mel Spectrograms and Convolutional Neural Networks(Nature Portfolio, 2025) Uzel, Hasan; Ozupak, Yildirim; Alpsalaz, Feyyaz; Aslan, Emrah; Zaitsev, IevgenThis study presents a comprehensive deep learning framework for diagnosing acoustic faults in electric motors. The framework uses Mel spectrograms and a lightweight convolutional neural network (CNN). The method classifies three motor states, engine_good, engine_broken, and engine_heavyload, based on audio recordings from the IDMT-ISA-ELECTRIC-ENGINE dataset. To prevent data leakage and ensure a robust evaluation, the study employed file-level splitting, session separation, 5-fold cross-validation, and repeated trials. The raw audio signals were transformed into Mel spectrograms and processed through a CNN architecture that integrates convolutional, pooling, normalization, and dropout layers. Quantitative metrics, including THD, spectral entropy, and SNR, further characterize the acoustic distinctions between motor states. The proposed model achieved a test accuracy of 99.7%, outperforming or matching state-of-the-art approaches, such as ResNet-18, CRNN, and Transformer classifiers, as well as traditional MFCC-based baselines. Noise robustness and sensitivity analyses demonstrated stable performance under varying SNR conditions and preprocessing settings. Feature-importance maps revealed that low-frequency regions (0-40 Mel bins) were key discriminative components linked to physical fault mechanisms. Computational evaluation confirmed the model's real-time feasibility on embedded hardware with low latency and a modest parameter count. Though primarily validated on one motor type, external-domain testing revealed strong adaptability. Future work may incorporate transfer learning or multimodal fusion. Overall, the proposed framework provides a highly accurate, interpretable, and efficient solution for real-time motor fault diagnosis and predictive maintenance in industrial environments.Article Citation - WoS: 2Citation - Scopus: 4Hybrid Deep Learning Model for Maize Leaf Disease Classification With Explainable AI(Taylor & Francis Ltd, 2025) Ozupak, Yildirim; Alpsalaz, Feyyaz; Aslan, Emrah; Uzel, HasanThis 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.Article Citation - Scopus: 1Alzheimer’s Classification with a MaxViT-Based Deep Learning Model Using Magnetic Resonance Imaging(Interdisciplinary Publishing Academia, 2025) Demirtaş Alpsalaz, S.; Aslan, E.; Özüpak, Y.; Alpsalaz, F.; Uzel, H.Alzheimer’s disease (AD), a progressive neurodegenerative disorder, poses significant challenges for early diagnosis due to subtle symptom onset and overlap with normal aging. This study aims to develop an effective deep learning model for classifying four AD stages (Non-Demented, Very Mild Demented, Mild Demented, Moderate Demented) using brain MRI scans. We propose a Multi-Axis Vision Transformer (MaxViT)-based framework, leveraging transfer learning and robust data augmentation on the Kaggle Alzheimer’s MRI Dataset to address class imbalance and enhance generalization. The model employs MaxViT’s multi-axis attention mechanisms to capture both local and global patterns in MRI images. Our approach achieved a classification accuracy of 99.60%, with precision of 99.0%, recall of 98.1%, and F1-score of 98.51%. These results highlight MaxViT’s superior ability to differentiate AD stages, particularly in distinguishing challenging early stages. The proposed model offers a reliable tool for early AD diagnosis, laying a strong foundation for future clinical applications and interdisciplinary research in neurodegenerative disease detection. Future work should explore larger, more diverse datasets and additional biomarkers to further validate and enhance model performance. © 2025 Elsevier B.V., All rights reserved.Article Citation - Scopus: 1Development of Malaria Diagnosis With Convolutional Neural Network Architectures: a Cnn-Based Software for Accurate Cell Image Analysis(Galileo Institute of Technology and Education of the Amazon (ITEGAM), 2025) Aslan, E.This study emphasizes that early diagnosis and treatment of malaria is critical in reducing health problems and mortality from the disease, especially in developing countries where the disease is prevalent. Malaria is a potentially fatal disease transmitted to humans by mosquitoes infected by a blood parasite called Plasmodium. The traditional method of diagnosis relies on experts examining red blood cells under a microscope and is inefficient as it is dependent on expert knowledge and experience. Nowadays, machine learning methods that provide high accuracy are increasingly used in disease detection. In this paper, a Convolutional Neural Network (CNN) architecture is proposed to distinguish between parasitized and non-parasitized cells. In addition, the performance of the proposed CNN architecture is compared to pre-trained CNN models such as VGG-19 and EfficientNetB3. The studies were carried out using the Malaria Dataset supplied by the National Institute of Health (NIH), and our proposed architecture was shown to function with 99.12% accuracy. The results of the study reveal that it is effective in improving the accuracy of cell images containing Plasmodium. In addition, a software that predicts whether cell images are noisy or not has been developed. © 2025 by authors and Galileo Institute of Technology and Education of the Amazon (ITEGAM).Article Citation - WoS: 12Citation - Scopus: 14Air Quality Forecasting Using Machine Learning: Comparative Analysis and Ensemble Strategies for Enhanced Prediction(Springer Int Publ Ag, 2025) Ozupak, Yildirim; Alpsalaz, Feyyaz; Aslan, EmrahAir 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.Article Citation - WoS: 2Citation - Scopus: 1Boiler Efficiency and Performance Optimization in District Heating and Cooling Systems With Machine Learning Models(Taylor & Francis Ltd, 2025) Aslan, Emrah; Oezuepak, Yildirim; Alpsalaz, Feyyaz; Özüpak, YıldırımThis study focuses on the detection and analysis of boiler efficiency degradation in District Heating and Cooling (DHC) substations. The research presents an innovative approach to optimize boiler efficiency under different scenarios. Although DHC systems provide both heating and cooling services, this study focuses specifically on heating substations. In this context, various machine learning algorithms have been applied to effectively detect boiler efficiency degradation, and hyper-parameter adjustments have been performed using Bayesian optimization to improve the performance of the models. As a result of the analyses, the Gradient Boosting Regressor model showed significantly higher performance compared to other machine learning algorithms. The model successfully predicted the decline in boiler efficiency with an accuracy of 97.8%, and the Matthews Correlation Coefficient (MCC) value was recorded as 0.952. These results show that Gradient Boosting Regressor based approaches provide an effective solution for fault detection and diagnosis in district heating systems. In conclusion, this study provides both theoretical and practical contributions to the optimization of boiler efficiency, fault detection and diagnosis in DHC systems. The solutions offered by the study have the potential to increase the reliability and efficiency of the systems.Article Citation - WoS: 6Citation - Scopus: 9Classification of Maize Leaf Diseases With Deep Learning: Performance Evaluation of the Proposed Model and Use of Explicable Artificial Intelligence(Elsevier, 2025) Alpsalaz, Feyyaz; Ozupak, Yildirim; Aslan, Emrah; Uzel, HasanMaize 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.Article Hybrid Deep Learning with Attention Fusion for Enhanced Colon Cancer Detection(Nature Portfolio, 2025) Alpsalaz, Suheyla Demirtas; Aslan, Emrah; Ozupak, Yildirim; Alpsalaz, Feyyaz; Uzel, Hasan; Bereznychenko, ViktoriaThis study introduces a hybrid deep learning model integrating EfficientNet-B3 and Vision Transformer with an Attention Fusion mechanism for automated colon cancer detection using the Kvasir endoscopic dataset. The model leverages EfficientNet-B3's strength in capturing fine-grained local textures and Vision Transformer's ability to model global contextual relationships. A multi-head attention-based fusion block harmonizes these features, achieving comprehensive representations and enhanced classification stability. Model optimization was guided by the Matthews Correlation Coefficient (MCC), alongside evaluations of accuracy, F1-score, and Brier Score. Experimental results demonstrate a 96.2% accuracy and an MCC of 0.961, surpassing standalone baselines and existing benchmark architectures. Cross-validation confirmed robust generalization, while Grad-CAM analyses improved interpretability by visualizing salient histopathological regions influencing predictions. Despite slight overfitting tendencies, the model maintained strong performance across all eight image classes. These findings highlight the model's ability to address limitations of single-architecture approaches by combining local and global feature extraction, offering rapid, objective, and reliable diagnostic support. The proposed framework shows significant promise for integration into computer-aided colonoscopy systems, paving the way for enhanced clinical diagnostics and reduced pathologist workload through AI-driven precision medicine.Article Citation - WoS: 5Citation - Scopus: 8Comparison of Machine Learning Algorithms for Automatic Prediction of Alzheimer Disease(Lippincott Williams & Wilkins, 2025) Aslan, Emrah; Ozupak, YildirimBackground: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.Article Optimized ANN-RF Hybrid Model With Optuna for Fault Detection and Classification in Power Transmission Systems(Nature Portfolio, 2025) Uzel, Hasan; Ozupak, Yildirim; Alpsalaz, Feyyaz; Aslan, EmrahThis study proposes a hybrid machine learning approach that integrates Artificial Neural Networks (ANN) and Random Forest (RF) classifiers, enhanced by Optuna hyperparameter optimization, for fault detection and classification in power transmission networks. The model is trained on a synthetic dataset generated from MATLAB/Simulink simulations of an 11 kV multi-generator system, incorporating three-phase current (Ia, Ib, Ic) and voltage (Va, Vb, Vc) signals under fault scenarios such as line-to-ground (LG), double line-to-ground (LLG), and three-phase symmetrical (LLLG) faults. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is applied, ensuring balanced representation of rare fault categories. The ANN-RF model achieves superior performance, with 99.8% accuracy, 99.5% precision, and 99.4% recall, consistently outperforming traditional classifiers including K-Nearest Neighbors, Bagging, AdaBoost, and Gradient Boosting. Its effectiveness arises from ANN's non-linear feature extraction, RF's ensemble robustness, and Optuna's hyperparameter tuning, with SMOTE improving detection of rare fault types. Compared with advanced models such as Modified InceptionV3 (98.93% accuracy) and Extreme Learning Machines (99.60% accuracy), the proposed approach provides a balanced trade-off between sensitivity and specificity, offering a reliable solution for fault identification. Nonetheless, challenges in computational efficiency and reliance on simulated data highlight the need for validation with real-world measurements and further optimization for real-time smart grid applications.
