Electrical and Electronic Engineering, Artificial Intelligence, Signal Processing
19
Scopus Publications
256
Scholar Citations
9
Scholar h-index
9
Scholar i10-index
Scopus Publications
Optimized ANN–RF hybrid model with optuna for fault detection and classification in power transmission systems Hasan Uzel, Yıldırım Özüpak, Feyyaz Alpsalaz, Emrah Aslan Scientific Reports, 2026 This 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.
Acoustic-based fault diagnosis of electric motors using Mel spectrograms and convolutional neural networks Hasan Uzel, Yıldırım Özüpak, Feyyaz Alpsalaz, Emrah Aslan, Ievgen Zaitsev Scientific Reports, 2026 This 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.
A hybrid machine learning approach for reliably predicting surface roughness in CNC turning operations Hakan Yurtkuran, Güven Demirtaş, Feyyaz Alpsalaz, Hasan Uzel, Ievgen Zaitsev Scientific Reports, 2026 Surface roughness in CNC turning is a pivotal quality metric shaping functional performance, service life and production cost. This study investigates data-driven prediction of arithmetic mean surface roughness (Ra) during the turning of AISI H13 steel under both new-tool and progressively worn-tool conditions. Several machine learning models including k-Nearest neighbors (KNN), random forest (RF) and extra trees (ExT) are evaluated and compared with a stacking ensemble model that integrates these base learners using a linear regression meta-learner. The input variables consist of cutting speed, feed rate, depth of cut and triaxial cutting force components. The results show that the KNN model exhibits limited predictive accuracy whereas the RF and ExT models achieve competitive performance. The proposed stacking ensemble consistently outperforms all individual models achieving a coefficient of determination (R²) exceeding 0.98 along with substantial reductions in root mean square error (RMSE) and mean absolute error (MAE) under tool-wear conditions indicating strong generalization capability. To enhance model transparency SHapley additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) are employed. The interpretability analyses identify feed rate as the dominant factor influencing surface roughness while the importance of cutting forces and the interaction between depth of cut and feed rate increases as tool wear progresses. Overall the findings demonstrate that the proposed stacking-based hybrid model provides an accurate, robust and explainable framework for surface roughness prediction in CNC turning offering practical potential for in-process quality monitoring and decision support applications.
High Accuracy Dissolved Oxygen Prediction in Water Quality Analysis with A-XG-Q Hybrid Model Yıldırım Özüpak, Feyyaz Alpsalaz, Emrah Aslan, Hasan Uzel New Zealand Journal of Marine and Freshwater Research, 2026 This study presents A‐XG‐Q, a groundbreaking hybrid model for predicting dissolved oxygen (DO) levels in water quality analysis that integrates ARIMA, XGBoost, and QAOA. ARIMA captures linear trends and seasonal patterns, XGBoost models complex nonlinear relationships, and QAOA optimizes hyperparameters such as learning rate and tree depth for computational efficiency. Using a Kaggle dataset spanning 1989–2019, the model achieved an R 2 of 0.990 and an RMSE of 0.050 despite missing data (36% DO, 96% air temperature), demonstrating exceptional accuracy. Temporal analyses revealed seasonal variations in DO and temperature, while Secchi depth and water depth remained stable. Correlation analysis identified a negative DO‐water temperature relationship, providing ecological insights. QAOA optimization reduced training time, enabling real‐time monitoring applications. By combining classical statistical methods, advanced machine learning, and quantum optimization, A‐XG‐Q outperforms many hybrid models and effectively handles data variability and missing values. This work advances environmental data science by providing a robust framework for sustainable water resource management and informed policy making, with potential for broader applications in ecosystem monitoring and environmental forecasting. The model's high performance underscores its value in addressing complex environmental challenges and supporting sustainable development goals.
Hybrid Machine Learning Approach for Enhanced Fault Detection and Power Estimation in Photovoltaic Systems Feyyaz Alpsalaz, Yıldırım Özüpak, Emrah Aslan, Hasan Uzel Iet Renewable Power Generation, 2026 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 ( R 2 , RMSE, MAE), achieving superior results with an R 2 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.
Explainable DL Based Classification for Power Quality Disturbances in Renewable-Energy-Integrated Distribution Networks Bekir Emre Altun, Feyyaz Alpsalaz, Hasan Uzel, Yavuz Türkay Iet Renewable Power Generation, 2026 The increasing penetration of renewable energy sources and converter‐based distributed generation has significantly intensified power quality disturbance (PQD) challenges in modern distribution systems. Accurate and reliable multi‐class classification of disturbances is therefore essential to ensure grid stability and protect sensitive equipment. In this study, an explainable deep learning framework is proposed for multi‐class PQD classification using electrical structured descriptors (ESD), which provide physically interpretable representations of electrical signals. Three representative architectures—MLP, GRU and BiLSTM—are systematically evaluated under a unified preprocessing and stratified cross‐validation scheme. Model performance is assessed using imbalance‐aware metrics, particularly macro‐averaged measures. Experimental results demonstrate near‐saturated classification performance across all models. The GRU model achieves the best overall accuracy (0.9979) and macro‐F1 score (0.9980), while all models reach an identical macro‐AUC of 0.9998, indicating excellent class separability. These findings suggest that increasing architectural complexity does not necessarily yield significant performance gains when structured and physically meaningful features are employed. To enhance transparency, explainability analyses based on SHAP and LIME are integrated into the framework. The results reveal that RMS voltage, peak voltage and total harmonic distortion (THD) are the most influential features, aligning with established power system knowledge. The proposed framework provides a balanced solution in terms of accuracy, computational efficiency and interpretability, making it suitable for real‐time PQ monitoring in renewable‐integrated smart grid environments.
Explainability-Aligned Reliability-Weighted Fuzzy Ensemble for Automated Cervical Cancer Classification Süheyla Demirtaş Alpsalaz, Emrah Aslan, Yıldırım Özüpak, Feyyaz Alpsalaz, Hasan Uzel, Ievgen Zaitsev International Journal of Intelligent Systems, 2026 Cervical cancer remains a major global health concern, highlighting the need for computer‐aided diagnostic systems that are both reliable and interpretable. Despite advances in deep learning–based cytology image classification, a gap persists in aligning model predictions with biologically meaningful explanations. This study aims to develop an explainability‐aligned, sample‐wise reliability‐weighted fuzzy ensemble framework for cervical cytology image classification to enhance both performance and interpretability. Methods The proposed framework integrates three pretrained convolutional neural network backbones—InceptionV3, MobileNetV2, and Inception‐ResNetV2—within a fuzzy ensemble structure. A novel explainability metric, termed Explainable Artificial Intelligence Alignment (XAIHit), is introduced to quantitatively assess the spatial correspondence between Grad‐CAM activation maps and annotated cytoplasmic and nuclear regions. The model combines calibrated confidence estimates with XAIHit to produce a per‐sample reliability score that guides fuzzy aggregation, ensuring anatomically informed and statistically robust decision‐making. Experiments were conducted on the SIPaKMeD dataset. Results The proposed ensemble achieved strong predictive performance, with accuracy ≈ 0.94, F1‐score ≈ 0.94, and area under the curve (AUC) ≈ 0.99. Calibration metrics further confirmed model reliability, with an expected calibration error (ECE) of 0.030, a Brier score of 0.078, and a negative log‐likelihood (NLL) of 0.198. The approach consistently outperformed conventional deep learning and fuzzy ensemble baselines. Conclusions This study presents an interpretable and reliability‐aware fuzzy ensemble framework that advances AI‐assisted cervical cancer screening. By integrating explainability alignment and calibrated confidence into a unified reliability measure, the method fosters both diagnostic accuracy and clinical trust, marking a significant step toward safe, transparent medical AI systems. Comparable performance was also observed on an independent external validation dataset, confirming the cross‐dataset generalization capability of the proposed framework.
Hybrid deep learning with attention fusion for enhanced colon cancer detection Süheyla Demirtaş Alpsalaz, Emrah Aslan, Yıldırım Özüpak, Feyyaz Alpsalaz, Hasan Uzel, Viktoria Bereznychenko Scientific Reports, 2025 This 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.
Air Quality Forecasting Using Machine Learning: Comparative Analysis and Ensemble Strategies for Enhanced Prediction Yıldırım Özüpak, Feyyaz Alpsalaz, Emrah Aslan Water Air and Soil Pollution, 2025 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.
A hybrid machine learning approach for reliably predicting surface roughness in CNC turning operations H Yurtkuran, G Demirtaş, F Alpsalaz, H Uzel, I Zaitsev Scientific Reports , 2026 2026
High Accuracy Dissolved Oxygen Prediction in Water Quality Analysis with A‐XG‐Q Hybrid Model Y Özüpak, F Alpsalaz, E Aslan, H Uzel New Zealand Journal of Marine and Freshwater Research 60 (1), e70032 , 2026 2026
Accurate Short-Horizon Multi-Target Prediction of PMSM Operational Parameters via Residual Dilated 1D Convolutional Neural Networks E Aslan, Y Özüpak, F Alpsalaz, H Uzel Computational Systems and Artificial Intelligence 2 (1), 7-14 , 2026 2026
Explainable DL Based Classification for Power Quality Disturbances in Renewable‐Energy‐Integrated Distribution Networks BE Altun, F Alpsalaz, H Uzel, Y Türkay IET Renewable Power Generation 20 (1), e70269 , 2026 2026
Explainability‐Aligned Reliability‐Weighted Fuzzy Ensemble for Automated Cervical Cancer Classification SD Alpsalaz, E Aslan, Y Özüpak, F Alpsalaz, H Uzel, I Zaitsev International Journal of Intelligent Systems 2026 (1), 2931556 , 2026 2026
Hybrid Machine Learning Approach for Enhanced Fault Detection and Power Estimation in Photovoltaic Systems F Alpsalaz, Y Özüpak, E Aslan, H Uzel IET Renewable Power Generation 20 (1), e70153 , 2026 2026 Citations: 2
Acoustic-based fault diagnosis of electric motors using Mel spectrograms and convolutional neural networks H Uzel, Y Özüpak, F Alpsalaz, E Aslan, I Zaitsev Scientific Reports , 2025 2025 Citations: 5
A COMPREHENSIVE BENCHMARK OF LINEAR AND ENSEMBLE MACHINE LEARNING MODELS FOR GLOBAL CO₂ EMISSION FORECASTING H Uzel, F Alpsalaz, E Aslan, Y Özüpak Middle East Journal of Science 11 (2), 247-262 , 2025 2025 Citations: 1
Optimized ANN–RF hybrid model with optuna for fault detection and classification in power transmission systems H Uzel, Y Özüpak, F Alpsalaz, E Aslan Scientific Reports , 2025 2025 Citations: 15
Hybrid deep learning model for maize leaf disease classification with explainable AI Y Özüpak, F Alpsalaz, E Aslan, H Uzel New Zealand Journal of Crop and Horticultural Science 53 (5), 2942-2964 , 2025 2025 Citations: 24
Seri Hibrit Elektrikli Araçlarda Süperkapasitör & Lityum İyon Batarya Yönetimi. F ALPSALAZ, Y TÜRKAY Journal of the Institute of Science & Technology/Iğdır Üniversitesi Fen … , 2025 2025
Hybrid deep learning with attention fusion for enhanced colon cancer detection SD Alpsalaz, E Aslan, Y Özüpak, F Alpsalaz, H Uzel, V Bereznychenko Scientific Reports , 2025 2025 Citations: 14
Alzheimer’s classification with a MaxViT-based deep learning model using magnetic resonance imaging E Aslan, SD Alpsalaz, F Alpsalaz, H Uzel Journal of Applied Science and Technology Trends 6 (2) , 2025 2025 Citations: 13
Boiler efficiency and performance optimization in district heating and cooling systems with machine learning models E Aslan, Y Özüpak, F Alpsalaz Journal of the Chinese Institute of Engineers 48 (7), 1115-1130 , 2025 2025 Citations: 9
Fault Detection in Power Transmission Lines: Comparison of Chirp-Z Algorithm and Machine Learning Based Prediction Models. F Alpsalaz Maintenance & Reliability/Eksploatacja i Niezawodność 27 (4) , 2025 2025 Citations: 18
Detection of imbalance faults in industrial machines by means of frequency-based feature extraction using machine learning and deep learning approaches F Alpsalaz Çukurova Üniversitesi Mühendislik Fakültesi Dergisi 40 (3), 581-592 , 2025 2025 Citations: 1
Classification of maize leaf diseases with deep learning: Performance evaluation of the proposed model and use of explicable artificial intelligence F Alpsalaz, Y Özüpak, E Aslan, H Uzel Chemometrics and Intelligent Laboratory Systems 262, 105412 , 2025 2025 Citations: 35
Air quality forecasting using machine learning: comparative analysis and ensemble strategies for enhanced prediction Y Özüpak, F Alpsalaz, E Aslan Water, Air, & Soil Pollution 236 (7), 464 , 2025 2025 Citations: 62
Journal of Science F ALPSALAZ 2025
A hybrid machine learning approach for predicting power transformer failures using internet of things based monitoring and explainable artificial intelligence E Aslan, Y Ozupak, F Alpsalaz, ZMS Elbarbary IEEE Access , 2025 2025 Citations: 39
MOST CITED SCHOLAR PUBLICATIONS
Air quality forecasting using machine learning: comparative analysis and ensemble strategies for enhanced prediction Y Özüpak, F Alpsalaz, E Aslan Water, Air, & Soil Pollution 236 (7), 464 , 2025 2025 Citations: 62
A hybrid machine learning approach for predicting power transformer failures using internet of things based monitoring and explainable artificial intelligence E Aslan, Y Ozupak, F Alpsalaz, ZMS Elbarbary IEEE Access , 2025 2025 Citations: 39
Classification of maize leaf diseases with deep learning: Performance evaluation of the proposed model and use of explicable artificial intelligence F Alpsalaz, Y Özüpak, E Aslan, H Uzel Chemometrics and Intelligent Laboratory Systems 262, 105412 , 2025 2025 Citations: 35
Hybrid deep learning model for maize leaf disease classification with explainable AI Y Özüpak, F Alpsalaz, E Aslan, H Uzel New Zealand Journal of Crop and Horticultural Science 53 (5), 2942-2964 , 2025 2025 Citations: 24
Fault Detection in Power Transmission Lines: Comparison of Chirp-Z Algorithm and Machine Learning Based Prediction Models. F Alpsalaz Maintenance & Reliability/Eksploatacja i Niezawodność 27 (4) , 2025 2025 Citations: 18
Optimized ANN–RF hybrid model with optuna for fault detection and classification in power transmission systems H Uzel, Y Özüpak, F Alpsalaz, E Aslan Scientific Reports , 2025 2025 Citations: 15
Hybrid deep learning with attention fusion for enhanced colon cancer detection SD Alpsalaz, E Aslan, Y Özüpak, F Alpsalaz, H Uzel, V Bereznychenko Scientific Reports , 2025 2025 Citations: 14
Alzheimer’s classification with a MaxViT-based deep learning model using magnetic resonance imaging E Aslan, SD Alpsalaz, F Alpsalaz, H Uzel Journal of Applied Science and Technology Trends 6 (2) , 2025 2025 Citations: 13
Detection of arc faults in transformer windings via transient signal analysis F Alpsalaz, MS Mamiş Applied Sciences 14 (20), 9335 , 2024 2024 Citations: 11
Boiler efficiency and performance optimization in district heating and cooling systems with machine learning models E Aslan, Y Özüpak, F Alpsalaz Journal of the Chinese Institute of Engineers 48 (7), 1115-1130 , 2025 2025 Citations: 9
Acoustic-based fault diagnosis of electric motors using Mel spectrograms and convolutional neural networks H Uzel, Y Özüpak, F Alpsalaz, E Aslan, I Zaitsev Scientific Reports , 2025 2025 Citations: 5
Fault Location Prediction in Power Transmission Lines Using an Artificial Neural Network Model F Alpsalaz, Z Yalçinöz, A Kaygusuz, MS Mamiş 2024 8th International Artificial Intelligence and Data Processing Symposium … , 2024 2024 Citations: 5
Hybrid Machine Learning Approach for Enhanced Fault Detection and Power Estimation in Photovoltaic Systems F Alpsalaz, Y Özüpak, E Aslan, H Uzel IET Renewable Power Generation 20 (1), e70153 , 2026 2026 Citations: 2
A COMPREHENSIVE BENCHMARK OF LINEAR AND ENSEMBLE MACHINE LEARNING MODELS FOR GLOBAL CO₂ EMISSION FORECASTING H Uzel, F Alpsalaz, E Aslan, Y Özüpak Middle East Journal of Science 11 (2), 247-262 , 2025 2025 Citations: 1
Detection of imbalance faults in industrial machines by means of frequency-based feature extraction using machine learning and deep learning approaches F Alpsalaz Çukurova Üniversitesi Mühendislik Fakültesi Dergisi 40 (3), 581-592 , 2025 2025 Citations: 1
Deep Learning-Based Lung Cancer Diagnosis: Data Balancing, Model Optimisation and Performance Analysis F Alpsalaz Gazi University Journal of Science Part A: Engineering and Innovation 12 (2 … , 2025 2025 Citations: 1
Using Ansys 3D Electromagnetic Analysis for Investigation the Effect of Harmonics on Power Transformers F Alpsalaz, MS Mamiş Sivas Cumhuriyet Üniversitesi Mühendislik Fakültesi Dergisi 1 (2), 89-93 , 2023 2023 Citations: 1
A hybrid machine learning approach for reliably predicting surface roughness in CNC turning operations H Yurtkuran, G Demirtaş, F Alpsalaz, H Uzel, I Zaitsev Scientific Reports , 2026 2026
High Accuracy Dissolved Oxygen Prediction in Water Quality Analysis with A‐XG‐Q Hybrid Model Y Özüpak, F Alpsalaz, E Aslan, H Uzel New Zealand Journal of Marine and Freshwater Research 60 (1), e70032 , 2026 2026
Accurate Short-Horizon Multi-Target Prediction of PMSM Operational Parameters via Residual Dilated 1D Convolutional Neural Networks E Aslan, Y Özüpak, F Alpsalaz, H Uzel Computational Systems and Artificial Intelligence 2 (1), 7-14 , 2026 2026
