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- MakaleAçık Erişim2025Scopus SJRQ2Hybrid ConvViT model for classification of dental radiography images; [Dental radyografi görüntülerinin sınıflandırılmasına yönelik hibrit ConvViT modeli]
Utku, Anıl
Dental radiography is used as an important diagnostic tool in the field of dentistry. Dental radiography provides detailed visualization of teeth, jaws and surrounding structures. Dental radiographs are manually interpreted by dentists for diagnosis and treatment purposes. Artificial intelligence methods are successfully applied to automate various image-related processes such as diagnosis and classification. In this study, a hybrid ConvViT model was developed to detect caries, fillings, impacted teeth, implants and normal teeth from dental radiography images. ConvViT combined the advantages of CNN and ViT architectures and provided more successful classification of dental radiography images. ConvViT was compared with popular pre-trained deep learning models such as ResNet-50, VGG-16, EfficientNetB0, and DenseNet201. Experimental results showed that ConvViT outperformed the compared models with 95% accuracy, 95% precision, 94% recall and 94% F-score. ConvViT makes a significant contribution to the development of artificial intelligence-based automatic diagnosis systems in the field of dentistry by effectively modeling both local and long-range features thanks to its hybrid structure.
AtıfScopus1
İncele - MakaleAçık Erişim2025Scopus SJRQ2Investigation of anticorrosive and antibacterial properties of electrochemically modified AISI430 steel surfaces; [Elektrokimyasal yöntem ile modifiye edilmiş AISI430 çelik yüzeylerin antikorozif ve antibakteriyel özelliklerinin incelenmesi]
Açar, Gülistan; Cihangir, Salih; Omaç, Basri; Aydoğdu, Hacı Mehmet; Oflaz, Mücahit
Both pharmacological and material-based approaches aim to slow bacterial evolution toward resistance. In this context, imparting specific antibacterial properties to materials is essential. Traditionally, alloying was a method to equip entire metals with antibacterial features, such as incorporating small amounts of copper (Cu) and/or silver (Ag). However, due to the high cost and inherent limitations of alloying, surface modification processes have gained prominence. Recent antibacterial studies on copper have led to the application of Cu(Ag) electrochemical coatings on AISI430 steel surfaces. This study aims to combine copper’s antibacterial properties with enhanced corrosion resistance and surface hardness. The coatings were deposited using deep eutectic solvents, known for their environmentally friendly properties, followed by potentiodynamic analysis. Microstructural and phase distribution characteristics were examined through SEM-EDS analysis, while surface roughness was analyzed using profilometry. Transitioning from micro-disc electrodes to macro-scale AISI430 substrates, Cu(Ag) films containing 16% Ag demonstrated nanoscale roughness, approximately double the hardness, and superior corrosion resistance compared to conventional copper surfaces. Antibacterial tests compared the coated surfaces with untreated AISI430 steel. Results showed that Cu(Ag)-coated surfaces exhibited improved antibacterial properties. This study shows how Cu(Ag) electrochemical coatings can be used to make surfaces that are antibacterial, long-lasting, and resistant to corrosion.
- MakaleAçık Erişim2024Scopus SJRQ2Hybrid Deep Learning Model for Earthquake Time Prediction
Utku, Anıl; Akcayol, Muhammet Ali
Earthquakes are one of the most dangerous natural disasters that have constantly threatened humanity in the last decade. Therefore, it is extremely important to take preventive measures against earthquakes. Time estimation in these dangerous events is becoming more specific, especially in order to minimize the damage caused by earthquakes. In this study, a hybrid deep learning model is proposed to predict the time of the next earthquake to potentially occur. The developed CNN+GRU model was compared with RF, ARIMA, CNN and GRU. These models were tested using an earthquake dataset. Experimental results show that the CNN+GRU model performs better than others according to MSE, RMSE, MAE and MAPE metrics. This study highlights the importance of predicting earthquakes, providing a way to help take more effective precautions against earthquakes and potentially minimize loss of life and material damage. This study should be considered an important step in the methods used to predict future earthquakes and supports efforts to reduce earthquake risks.
AtıfScopus6WoS5TR Dizin4İncele - MakaleAçık Erişim2023Scopus SJRQ3Rotor Field-Oriented Control of Doubly Fed Induction Generator in Wind Energy Conversion System
Omac, Zeki; Erdem, Irfan
In this study, robust and high-performance vector control of a rotor side converter (RSC) was performed for stability and efficient operation doubly fed induction generator (DFIG) based on the variable speed wind turbine (VSWT). The mathematical model of the DFIG is simulated in the computer. Amplitude and frequency of the voltage in the DFIG were controlled for different values of load and variable speeds. In the experimental study, a DFIG-based wind turbine system was set up in the laboratory. The field position of the stator was detected from stator voltages by a phase-locked loop (PLL) circuit. The rotor position was measured with an incremental encoder connected to the rotor shaft of the DFIG. The angular position of the slip was calculated by the difference between the rotor and the stator field positions. The frequency and amplitude of rotor currents were determined with the angular position of slip. To generate output voltages of converter feeding rotor windings, the space vector pulse width modulation (SVPWM) technique was used. In the experimental study, the RSC was controlled with the DS1103 board. The prepared experiment set was tested at different operating speeds.
AtıfScopus4WoS3OpenAlex4İncele - MakaleAçık Erişim2023Scopus SJRQ3Effect of Spectrogram Parameters and Noise Types on The Performance of Spectro-temporal Peaks Based Audio Search Method
Koseoglu, Murat; Uyanik, Hakan
Audio search algorithms are used to detect the queried file in large databases, especially in multimedia applications. These algorithms are expected to perform the detection in a reliable and robust way within the shortest time. In this study, based on spectral peaks method, an audio fingerprint algorithm with a few minor modifications was developed to detect the matching audio file in target database. This method has two stages as the audio fingerprint extraction and matching. In the first stage, fingerprint features are extracted from spectral peaks on the spectrograms of audio files by hash functions. This state-of-art technique reduces the processing load and time considerably compared to traditional methods. In the second stage, fingerprint data of the queried file are compared with the data created in the first stage in the database. The algorithm was demonstrated, and the effect of spectrogram parameters (window size, overlap, number of FFT) was investigated by considering reliability and robustness under different noise sources. Also, it was aimed to contribute to new audio retrieval studies based on spectral peaks method. It was observed that the variation in the spectrogram parameters significantly affected the number of matchings, reliability and robustness. Under high noise conditions, the optimal spectrogram parameters were determined as 512 (window size), 50% (overlap), 512 (number of FFT). It was seen in general that the algorithm successfully detected the queried file in the database even in high noise conditions for these parameters. No significant effect of music genre was observed.
AtıfScopus9WoS6OpenAlex11TR Dizin1İncele - MakaleAçık Erişim2022Scopus SJRQ2Using network traffic analysis deep learning based Android malware detection; [Aǧ trafiǧi analizi ile derin öǧrenme tabanli Android kötücül yazilim tespiti]
Utku, Anil
Mobile devices, which are becoming more and more widespread today, have turned into hand-held computers thanks to the multimedia communication and applications. Today, the multimedia applications have been supported by traditional mobile phones. With their increasing functionality, mobile devices are used for many purposes such as enriched internet experience, financial transactions, access to social media platforms, sharing, music and video. Performing transactions where sensitive personal data transfers such as banking and shopping are carried out on mobile devices make mobile devices the target of attackers. In this study, a deep learning based malware detection system has been developed based on the interactions of mobile applications on the network. The developed LSTM-based deep learning model has been analysed comparatively with NB, RF, SVM, MLP, CNN, RNN and GRU using accuracy, precision, recall and F-1 metrics. The experimental results showed that the developed LSTM-based deep learning model is more successful in malware detection than others with 95% accuracy.
AtıfScopus4WoS3OpenAlex4TR Dizin1İncele - MakaleAçık Erişim2020Scopus SJRQ3A new family of archimedean copula via trigonometric generator function
Susam, Selim Orhun
In this paper, new type of Archimedean copula based on cotangent generator function, and dependence characteristics are investigated. Also, the estimation procedure of the dependence parameter is given. A Monte Carlo study is performed to measure the performance of estimators. To demonstrate the goodness of fit performance of the new family, a real data analysis is performed.
AtıfScopus13WoS10OpenAlex11TR Dizin1İncele - DergiGazi University Journal of Science
- DergiJournal of the Faculty of Engineering and Architecture of Gazi University