Sürdürülebilir Kalkınma Amaçları
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- MakaleYalnız künye2026Scopus SJRQ1Modeling stack voltage and remaining useful life of proton exchange membrane fuel cells using deep neural networks
Dogan, Gurkan; Imak, Andaç
Currently, various energy conversion systems are being developed to reduce environmental impacts. Proton exchange membrane fuel cells (PEMFCs), which stand out with their high efficiency characteristics, have emerged as an important alternative. However, these advantages, along with high costs and uncertainties regarding remaining useful life (RUL) estimation, restrict their further commercialization, especially in the automotive industry. This study presents a forecasting framework using a long short-term memory (LSTM) architecture enhanced with the Bahdanau attention mechanism for predicting RUL and long-term degradation trends. The model thus exhibits a performance that can more effectively capture dependencies in time series and adaptively weight significant time steps. Furthermore, the tree-structured parzen estimator (TPE) was chosen for the appropriate selection of hyperparameters in the developed forecasting framework. The validity, stability, and performance of the proposed model under different operating conditions were examined on the FC1 and FC2 datasets. Experimental studies have shown that the proposed method is the most successful among the best methods, achieving RE metric values of 1.628 × 10−3, 0.003 × 10−1, 99.16 × 10−2, and 0.044 × 10−1 respectively in estimating the best RMSE, MAPE, R2, and RUL.
- MakaleYayıncıda açık erişim2026Scopus SJRQ1Explainable AI-based deep learning architectures for hydrogen prediction in biomass syngas and photocatalysis processes: Towards a sustainable and efficient energy economy
Imak, Andaç
This study, focusing on sustainable energy production and particularly the hydrogen (H2) economy, presents an advanced artificial intelligence (AI) framework for accurately predicting H2 production from sucrose photocatalysis catalyzed by LaFeO3 (GLFO) and the H2 concentration in syngas derived from biomass conversion. The research was conducted along two main directions: (i) deep learning (DL) architectures comprising nine specially designed recurrent neural network-based models and (ii) six conventional machine learning (ML) algorithms. For both H2 production systems, the best-performing models were identified and further interpreted using explainable AI (XAI) based on SHapley Additive exPlanations (SHAP). Among all investigated algorithms, a hybrid One-dimensional Convolutional Neural Network–Bidirectional Long Short-Term Memory–Bidirectional Gated Recurrent Unit (1D-CNN–BiLSTM–BiGRU) model demonstrated the highest predictive performance. Using 10-fold cross-validation, the proposed model achieved outstanding accuracy, with an R2 value of 0.9987 ± 0.0011 for photocatalytic H2 production prediction and 0.9980 ± 0.0006 for syngas H2 concentration prediction. These results highlight the potential of the proposed framework as a precise, reliable, and practical methodological approach for modeling and optimizing complex thermochemical energy systems.
- MakaleAçık Erişim2025Scopus SJRQ2Hybrid CNN-Transformer Model for Accurate Impacted Tooth Detection in Panoramic Radiographs
Küçük, Deniz Bora; Imak, Andaç; Özçelik, Salih Taha Alperen; Çelebi, Adalet; Türkoğlu, Muammer; Sengur, Abdulkadir; Koundal, Deepika
Background/Objectives: The integration of digital imaging technologies in dentistry has revolutionized diagnostic and treatment practices, with panoramic radiographs playing a crucial role in detecting impacted teeth. Manual interpretation of these images is time consuming and error prone, highlighting the need for automated, accurate solutions. This study proposes an artificial intelligence (AI)-based model for detecting impacted teeth in panoramic radiographs, aiming to enhance accuracy and reliability. Methods: The proposed model combines YOLO (You Only Look Once) and RT-DETR (Real-Time Detection Transformer) models to leverage their strengths in real-time object detection and learning long-range dependencies, respectively. The integration is further optimized with the Weighted Boxes Fusion (WBF) algorithm, where WBF parameters are tuned using Bayesian optimization. A dataset of 407 labeled panoramic radiographs was used to evaluate the model’s performance. Results: The model achieved a mean average precision (mAP) of 98.3% and an F1 score of 96%, significantly outperforming individual models and other combinations. The results were expressed through key performance metrics, such as mAP and F1 scores, which highlight the model’s balance between precision and recall. Visual and numerical analyses demonstrated superior performance, with enhanced sensitivity and minimized false positive rates. Conclusions: This study presents a scalable and reliable AI-based solution for detecting impacted teeth in panoramic radiographs, offering substantial improvements in diagnostic accuracy and efficiency. The proposed model has potential for widespread application in clinical dentistry, reducing manual workload and error rates. Future research will focus on expanding the dataset and further refining the model’s generalizability.
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İncele - MakaleAçık Erişim2025Scopus SJRQ2HASK-Net: new hybrid attention selective kernel network for automatic colon cancer detection from colonoscopy images
Imak, Andaç; Doğan, Gürkan; Sengur, Abdulkadir; Ergen, Burhan
Colorectal cancer is a frequently analyzed cancer type seen today. It is the third leading of cancer in terms of incidence. It is among the second leading cases resulting in death. However, when detecting pre-cancerous polyps using traditional methods, the rate of being overlooked is high. Therefore, the rate of missed polyps can be greatly reduced with deep learning-based computer-aided diagnosis (CADx) methods. In this study, a novel convolutional neural network based on CADx, called HASK-Net, is proposed for automatic polyp detection. While HASK-Net uses ResNet50 as a feature extractor in the encoder network, the novely designed hybrid attention selective kernel convolution uses it in the decoder network to increase the representation power in feature maps and learn more complex features. In experimental results on the publicly available Hiper-Kvasir and EndoTech 2020 benchmark datasets, HASK-Net showed extremely promising performance with a dice similarity score of 97.02% and 84.83% and an mIoU score of 95.38% and 84.32%, respectively.
- MakaleAçık Erişim2024Scopus SJRQ1A new hybrid approach for grapevine leaves recognition based on ESRGAN data augmentation and GASVM feature selection
Doğan, Gürkan; Imak, Andaç; Ergen, Burhan; Sengur, Abdulkadir
Grapevine leaf is a commodity that is collected only once a year and has a high return on investment due to its export. However, only certain types of grapevine leaves are consumed. Therefore, it is extremely important to distinguish the types of grapevine leaves. In particular, performing this process automatically on industrial machines will reduce human errors, workload, and thus cost. In this study, a new hybrid approach based on a convolutional neural network is proposed that can automatically distinguish the types of grapevine leaves. In the proposed approach, firstly, the overfitting of network models is prevented by applying data augmentation techniques. Second, new synthetic images were created with the ESRGAN technique to obtain detailed texture information. Third, the top blocks of the MobileNetV2 and VGG19 CNN models were replaced with the newly designed top block, effectively extracting features with the data. Fourthly, the GASVM algorithm was adapted and used to create a subset of the features to eliminate the ineffective and unimportant ones from the obtained features. Finally, SVM classification was performed with the feature subset consisting of 314 features, and approximately 2% higher accuracy and MCC score were obtained compared to the approaches in the literature.
AtıfScopus8WoS6OpenAlex9İncele - MakaleYalnız künye2024Scopus SJRQ2Maxillary sinus detection on cone beam computed tomography images using ResNet and Swin Transformer-based UNet
Çelebi, Adalet; Imak, Andaç; Üzen, Hüseyin; Budak, Ümit; Türkoğlu, Muammer; Hanbay, Davut; Şengür, Abdulkadir
Objectives: This study, which uses artificial intelligence-based methods, aimed to determine the limits of pathologic conditions and infections related to the maxillary sinus in cone beam computed tomography (CBCT) images to facilitate the work of dentists. Methods: A new UNet architecture based on a state-of-the-art Swin transformer called Res-Swin-UNet was developed to detect the sinus. The encoder part of the proposed network model consists of a pre-trained ResNet architecture, and the decoder part consists of Swin transformer blocks. Swin transformers achieve powerful global context properties with self-attention mechanisms. Because the output of the Swin transformer generates sectorized features, the patch expanding layer was used in this section instead of the traditional upsampling layer. In the last layer of the decoder, sinus diagnosis was conducted through classical convolution and sigmoid function. In experimental works, we used a data set including 298 CBCT images. Results: The Res-Swin-UNet model achieved more success, with a 91.72% F1-score, 99% accuracy, and 84.71% IoU, outperforming the state-of-the-art models. Conclusions: The deep learning-based model proposed in the present study can assist dentists in automatically detecting the boundaries of pathologic conditions and infections within the maxillary sinus based on CBCT images.
AtıfScopus21OpenAlex20İncele - MakaleAçık Erişim2023Scopus SJRQ2ResMIBCU-Net: an encoder–decoder network with residual blocks, modified inverted residual block, and bi-directional ConvLSTM for impacted tooth segmentation in panoramic X-ray images
Imak, Andaç; Çelebi, Adalet; Polat, Onur; Türkoğlu, Muammer; Şengür, Abdulkadir
Objective: Impacted tooth is a common problem that can occur at any age, causing tooth decay, root resorption, and pain in the later stages. In recent years, major advances have been made in medical imaging segmentation using deep convolutional neural network-based networks. In this study, we report on the development of an artificial intelligence system for the automatic identification of impacted tooth from panoramic dental X-ray images. Methods: Among existing networks, in medical imaging segmentation, U-Net architectures are widely implemented. In this article, for dental X-ray image segmentation, blocks and convolutional block structures using inverted residual blocks are upgraded by taking advantage of U-Net’s network capacity-intensive connections. At the same time, we propose a method for jumping connections in which bi-directional convolution long short-term memory is used instead of a simple connection. Assessment of the proposed artificial intelligence model performance was evaluated with accuracy, F1-score, intersection over union, and recall. Results: In the proposed method, experimental results are obtained with 99.82% accuracy, 91.59% F1-score, 84.48% intersection over union, and 90.71% recall. Conclusion: Our findings show that our artificial intelligence system could help with future diagnostic support in clinical practice.
AtıfScopus21OpenAlex23İncele - MakaleYayıncıda açık erişim2022Scopus SJRQ2Dental Material Detection based on Faster Regional Convolutional Neural Networks and Shape Features
Imak, Andaç; Çelebi, Adalet; Türkoğlu, Muammer; Şengür, Abdulkadir
In this paper, a novel approach is proposed to detect the dental materials in dental Panoramic images. In general, dental Panoramic images are examined by the dentists to detect the dental materials of the patients. Examining the dental Panoramic images is a time-consuming procedure and necessitates highly skilled dentists. Thus, computer imaging and decision support systems based on machine learning are in demand for dental applications. In this study, a semi-automatic, image processing and machine learning based approach is proposed for detection of dental materials in dental Panoramic images. More specifically, the dental materials namely dental filling, dental bridge and tooth crown are targeted by the proposed approach. Within the approach developed, the dental Panoramic images used as input are initially pre-processed for noise removal and tooth region is determined using a deep learning approach. To this end, a faster Regional Convolutional Neural Networks (RCNN) method is employed. A tooth region correction procedure is applied after application of faster RCNN to determine the exact location of the tooth region as faster RCNN may produce more tooth regions than one correct tooth region. Graph cut (GC) based image segmentation is applied to segment the tooth region into foreground and background regions. Shape based feature extraction and classification are applied to determine the tooth problem. A dataset has been established in dental faculty hospital of private dental clinics. The proposed approach is simple yet quite effective. In all classification methods, the average accuracy scores were around 90%.
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