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
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3GOOD HEALTH AND WELL-BEING
4
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4QUALITY EDUCATION
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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
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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
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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
19
Articles
19
Views / Downloads
81/0
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
69
Scopus Citation Count
93
Patents
0
Projects
0
WoS Citations per Publication
3.63
Scopus Citations per Publication
4.89
Open Access Source
13
Supervised Theses
0
| Journal | Count |
|---|---|
| Scientific Reports | 4 |
| 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
Scopus Quartile Distribution
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19 results
Scholarly Output Search Results
Now showing 1 - 10 of 19
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 - 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 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 Explainable LSTM-AdamW Based Fault Diagnosis of Aircraft Rotating Components Using Airborne Acoustic Signals under Dynamic Operating Conditions(Nature Portfolio, 2026) Özüpak, Yıldırım; Aslan, Emrah; Zaitsev, IevgenAcoustic signal (AS) has emerged as a powerful non-contact technique for early detection of incipient faults in aircraft rotating components due to its high sensitivity to transient damage mechanisms. However, the strong non-stationarity and noise susceptibility of acoustic signals under dynamically varying operating conditions present significant challenges for reliable fault diagnosis. In this study, an explainable deep learning framework based on a Long Short-Term Memory (LSTM) network optimized with the AdamW algorithm is proposed for fault diagnosis of aircraft-related rotating components using acoustic signals. The framework leverages sequential learning to capture the temporal evolution of acoustic signals and is systematically compared with conventional recurrent architectures, including Recurrent Neural Networks (RNNs) and Gated Recurrent Units (GRUs). Experimental results demonstrate that the proposed LSTM-AdamW model achieves superior diagnostic performance, reaching a test accuracy and macro-F1 score of 99.26% under dynamic operating conditions. The enhanced performance is attributed to the LSTM's ability to model long-term temporal dependencies and the regularization benefits of the AdamW optimizer through decoupled weight decay. To improve transparency and physical interpretability, explainable artificial intelligence techniques based on Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are employed. The explainability analysis reveals that classification decisions are driven by localized, physically meaningful transient acoustic patterns associated with fault-induced events. In addition, a Taylor diagram-based statistical assessment confirms strong agreement between model predictions and reference signals, indicating robust preservation of temporal signal characteristics. The results suggest that the proposed explainable LSTM-AdamW framework provides a reliable, computationally efficient, and interpretable solution for acoustic signal-based fault diagnosis in aerospace applications, with strong potential for real-time condition monitoring and predictive maintenance.Article Hybrid Machine Learning Approach for Enhanced Fault Detection and Power Estimation in Photovoltaic Systems(John Wiley and Sons Inc, 2026) Alpsalaz, F.; Özüpak, Y.; Aslan, E.; Uzel, H.Accurate power prediction and fault detection in photovoltaic (PV) systems are essential for improving energy efficiency and enabling predictive maintenance. This study proposes a novel hybrid regression model based on a stacking ensemble architecture, which integrates multiple machine learning algorithms: histogram-based gradient boosting (HGB), k-nearest neighbors (k-NN), decision tree (DT), random forest (RF), and LightGBM as base learners and employs Ridge regression as the meta-learner. The model was designed to detect complex fault conditions such as partial shading and module-level failures using SCADA-type input features. The performance of the proposed model was evaluated using standard regression metrics (R2, RMSE, MAE), achieving superior results with an R2 of 0.9939, RMSE of 12.0184, and MAE of 8.0544. Paired t-tests confirmed the statistical significance of performance improvements over baseline models (p < 0.05). To ensure transparency, explainability analyses were conducted using SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME), which revealed that fault-related features had the greatest influence on model predictions. Comparative evaluation with recent state-of-the-art approaches demonstrated that the proposed hybrid model is scalable, computationally efficient, and robust under varying environmental and operational conditions. The findings suggest that the model can serve as a reliable and interpretable solution for real-time power forecasting and fault detection in PV systems. © 2026 The Author(s). IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.Article Citation - WoS: 2Citation - Scopus: 2Advanced Fault Classification in Induction Motors for Electric Vehicles Using a Stacking Ensemble Learning Approach(MDPI, 2025) Benkaihoul, Said; Khadar, Saad; Ozupak, Yildirim; Aslan, Emrah; Almalki, Mishari Metab; Mossa, Mahmoud A.This study proposes an innovative stacking ensemble learning framework for classifying faults in induction motors utilized in Electric Vehicles (EVs). Employing a comprehensive dataset comprising motor data, such as speed, torque, current, and voltage, the analysis encompasses six distinct conditions: normal operating mode, over-voltage fault, under-voltage fault, overloading fault, phase-to-phase fault, and phase-to-ground fault. The proposed model integrates Gradient Boosting (GB), K-Nearest Neighbors (KNN), Gradient Boosting (XGBoost), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) algorithms in a synergistic manner. The findings reveal that the RF-GB-DT-XGBoost combination achieves a remarkable accuracy of 98.53%, significantly surpassing other methods reported in the literature. Performance is evaluated through metrics including accuracy, precision, sensitivity, and F1-score, with results analyzed in comparison to practical applications and existing studies. Validated with real-world data, this study demonstrates that the proposed model offers a groundbreaking solution for predictive maintenance systems in the EV industry, exhibiting high generalization capacity despite complex operating conditions. This approach holds transformative potential for both academic research and industrial applications. The dataset used in this study was generated using a MATLAB 2018/Simulink-based Variable Frequency Drive (VFD) model that emulates real-world EV operating conditions rather than relying solely on laboratory data. This ensures that the developed model accurately reflects practical electric vehicle environments.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 Citation - WoS: 2Citation - Scopus: 2Comparison and Optimization of Machine Learning Methods for Fault Detection in District Heating and Cooling Systems(Polska Akad Nauk, Polish Acad Sci, Div Iv Technical Sciences Pas, 2025) Cinar, Mehmet; Aslan, Emrah; Ozupak, YildirimIn this study, the methods used for the detection of sub-station pollution failures in district heating and cooling (DHC) systems are analyzed. In the study, high, medium, and low-level pollution situations are considered and machine learning methods are applied for the detection of these failures. Random forest, decision tree, logistic regression, and CatBoost regression algorithms are compared within the scope of the analysis. The models are trained to perform fault detection at different pollution levels. To improve the model performance, hyper parameter optimization was performed with random search optimization, and the most appropriate values were selected. The results show that the CatBoost regression algorithm provides the highest accuracy and overall performance compared to other methods. The CatBoost model stood out with an accuracy of 0.9832 and a superior performance. These findings reveal that CatBoost-based approaches provide an effective solution in situations requiring high accuracy, such as contamination detection in DHC systems. The study makes an important contribution as a reliable fault detection solution in industrial applications.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 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.
