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Conference Object Citation - Scopus: 1Similarity-Inclusive Link Prediction With Quaternions(Scitepress, 2021) Kurt, Zuhal; Gerek, Omer Nezih; Bilge, Alper; Ozkan, KemalThis paper proposes a Quaternion-based link prediction method, a novel representation learning method for recommendation purposes. The proposed algorithm depends on and computation with Quaternion algebra, benefiting from the expressiveness and rich representation learning capability of the Hamilton products. The proposed method depends on a link prediction approach and reveals the significant potential for performance improvement in top-N recommendation tasks. The experimental results indicate the superior performance of the approach using two quality measurements - hits rate, and coverage - on the Movielens and Hetrec datasets. Additionally, extensive experiments are conducted on three subsets of the Amazon dataset to understand the flexibility of this algorithm to incorporate different information sources and demonstrate the effectiveness of Quaternion algebra in graph-based recommendation algorithms. The proposed algorithms obtain comparatively higher performance, they are improved with similarity factors. The results show that the proposed quaternion-based algorithm can effectively deal with the deficiencies in graph-based recommender system, making it a preferable alternative among the other available methods.Article Güvenlik Sistemleri için Silah ve Bıçak Tanıma(2021) Işık, Şahin; Özkan, Şerif Ercan; Kurt, ZuhalBu çalışma, halka açık yerlerde güvenlik sorunlarının üstesinden gelmek için etkili ve yenilikçi bir çözüm sunmaktadır. Alternatif bir video gözetim sistemi olarak, önerilen yöntem videolardan silah ve bıçak nesnelerini gerçek zamanlı olarak algılar ve yerelleştirir. Evrişimsel Sinir Ağı tabanlı nesne algılama ile bağlantılı olarak, en yüksek performansa sahip silah ve bıçak nesnelerini tespit etmek için Hızlı-Bölgesel Tabanlı Evrişimsel Sinir Ağı yapısı uygulanmıştır. Test görüntüleri üzerinde simülasyon gerçekleştirdikten sonra, geliştirilen sistemin F1-skor performansı yaklaşık %70 tanıma oranı olarak elde edilmiştir. Eğitilen Faster R-CNN modeli, uçak, otobüs durağı, stadyum ve güvenliğin önemli bir faktör olduğu kamu taşıtları da dâhil olmak üzere farklı halka açık yerler için kullanılabilir. Ayrıca, geliştirilen yöntem, tehlikeli nesnelerin raporlanması ve bu tür nesnelerin neden olduğu risklerin en aza indirilmesi açısından yerel gözetim sistemine gömülebilir.Article Recurrent Neural Networks for Spam E-Mail Classification on an Agglutinative Language(2020) Işık, Şahin; Kurt, Zuhal; Anagun, Yildiray; Özkan, KemalIn this study, we have provided an alternative solution to spam and legitimate email classification problem. The different deep learning architectures are applied on two feature selection methods, including the Mutual Information (MI) and Weighted Mutual Information (WMI). Firstly, feature selection methods including WMI and MI are applied to reduce number of selected terms. Secondly, the feature vectors are constructed with concept of the bag-of-words (BoW) model. Finally, the performance of system is analyzed with using Artificial Neural Network (ANN), Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BILSTM) models. After experimental simulations, we have observed that there is a competition between detection results of using WMI and MI when commented with accuracy rates for the agglutinative language, namely Turkish. The experimental scores show that the LSTM and BILSTM give 100% accuracy scores when combined with MI or WMI, for spam and legitimate emails. However, for particular cross validation, the performance WMI is higher than MI features in terms e-mail grouping. It turns out that WMI and MI with deep learning architectures seem more robust to spam email detection when considering the high detection scores.Article Citation - WoS: 9Citation - Scopus: 11Using Deep Learning Approaches for Coloring Silicone Maxillofacial Prostheses: a Comparison of Two Approaches(Wolters Kluwer Medknow Publications, 2023) Kurt, Meral; Kurt, Zuhal; Isik, SahinAim: This study aimed to compare the performance of two deep learning algorithms, attention-based gated recurrent unit (GRU), and the artificial neural networks (ANNs) algorithm for coloring silicone maxillofacial prostheses. Settings and Design: This was an in vitro study. Materials and Methods: A total of 21 silicone samples in different colors were produced with four pigments (white, yellow, red, and blue). The color of the samples was measured with a spectrophotometer, then the LFNx01, aFNx01, and bFNx01 values were recorded. The relationship between the LFNx01, aFNx01, and bFNx01 values of each sample and the amount of each pigment in the compound of the same sample was used as the training dataset, entered into each algorithm, and the prediction models were obtained. While generating the prediction model for each sample, the data of the corresponding sample assigned as the target color were excluded. LFNx01, aFNx01, and bFNx01 values of each target sample were entered into the obtained models separately, and recipes indicating the ratios for mixing the four pigments were predicted. The mean absolute error (MAE) and root mean square error (RMSE) values between the original recipe used in the production of each silicone and the recipe created by both prediction models for the same silicone were calculated. Statistical Analysis Used: Data were analyzed with the Student t-test (alpha=0.05). Results: The mean RMSE values and MAE values for the ANN algorithm (0.029 & PLUSMN; 0.0152 and 0.045 & PLUSMN; 0.0235, respectively) were found significantly higher than the attention-based GRU model (0.001 & PLUSMN; 0.0005 and 0.002 & PLUSMN; 0.0008, respectively) (P < 0.001). Conclusions: Attention-based GRU model provided better performance than the ANN algorithm with respect to the MAE and RMSE values.Article Citation - WoS: 13Citation - Scopus: 24Evaluation of Efficientnet Models for Covid-19 Detection Using Lung Parenchyma(Springer London Ltd, 2023) Kurt, Zuhal; Isik, Sahin; Kaya, Zeynep; Anagun, Yildiray; Koca, Nizameddin; Cicek, SuemeyyeWhen the COVID-19 pandemic broke out in the beginning of 2020, it became crucial to enhance early diagnosis with efficient means to reduce dangers and future spread of the viruses as soon as possible. Finding effective treatments and lowering mortality rates is now more important than ever. Scanning with a computer tomography (CT) scanner is a helpful method for detecting COVID-19 in this regard. The present paper, as such, is an attempt to contribute to this process by generating an open-source, CT-based image dataset. This dataset contains the CT scans of lung parenchyma regions of 180 COVID-19-positive and 86 COVID-19-negative patients taken at the Bursa Yuksek Ihtisas Training and Research Hospital. The experimental studies show that the modified EfficientNet-ap-nish method uses this dataset effectively for diagnostic purposes. Firstly, a smart segmentation mechanism based on the k-means algorithm is applied to this dataset as a preprocessing stage. Then, performance pretrained models are analyzed using different CNN architectures and with our Nish activation function. The statistical rates are obtained by the various EfficientNet models and the highest detection score is obtained with the EfficientNet-B4-ap-nish version, which provides a 97.93% accuracy rate and a 97.33% F1-score. The implications of the proposed method are immense both for present-day applications and future developments.
