Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651
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Article Detection and Classification of Femoral Neck Fractures From Plain Pelvic X-Rays Using Deep Learning and Machine Learning Methods(Turkish Assoc Trauma Emergency Surgery, 2025) Sevinc, Huseyin Fatih; Ureten, Kemal; Karadeniz, Talha; Gultekin, Gokhan KorayBackground: Femoral neck fractures are a serious health concern, particularly among the elderly. The aim of this study is to diagnose and classify femoral neck fractures from plain pelvic X-rays using deep learning and machine learning algorithms, and to compare the performance of these methods. Methods: The study was conducted on a total of 598 plain pelvic X-ray images, including 296 patients with femoral neck fractures and 302 individuals without femoral neck fractures. Initially, transfer learning was applied using pre-trained deep learning models: VGG-16, ResNet-50, and MobileNetv2. Results: The pre-trained VGG-16 network demonstrated slightly better performance than ResNet-50 and MobileNetV2 for detecting and classifying femoral neck fractures. Using the VGG-16 model, the following results were obtained: 95.6% accuracy, 95.5% sensitivity, 93.3% specificity, 95.7% precision, 95.5% F1 Score, a Cohen's kappa of 0.91, and the Receiver Operating Characteristic (ROC) curve of 0.99. Subsequently, features extracted from the convolution layers of VGG-16 were classified using common machine learning algorithms. Among these, the k-nearest neighbor (k-NN) algorithm outperformed the others and exceeded the accuracy of the VGG-16 model by 1%. Conclusion: Successful results were obtained using deep learning and machine learning methods for the detection and classification of femoral neck fractures. The model can be further improved through multi-center studies. The proposed model may be especially useful for physicians working in emergency departments and for those not having sufficient experience in evaluating plain pelvic radiographs.Article Prediction of Noise Generated by Rod-Airfoil Configuration: an Investigation Based on Experiments and Machine Learning(Sage Publications Ltd, 2024) Kocak, Eyup; Ayli, EceThis study investigated the effects of various parameters on the SPL (Sound Pressure Level) levels of rod-airfoil configurations. An experimental study was performed to investigate the effects of the rod parameters, such as the configuration of the rod, the distance between the rod and the airfoil, the diameter effect of the rod, and the geometry of the rod, on the performance of the rod-airfoil configuration. An Artificial Neural Network (ANN) model was then developed and applied to accurately predict the SPL of rod-airfoil configurations. The results of the study revealed that the Levenberg-Marquardt (LM) algorithm with 2 hidden neurons produced the best performance in predicting the SPL level, with a training R-squared value of 0.9998 and a testing R-squared value of 0.998715. The findings also indicated that increasing rod diameter increases sound pressure level while reducing gap width increases SPL levels and decreases frequency values. This method offers a more precise and effective technique to forecast the SPL levels of rod-airfoil designs, allowing designers to enhance their creations and lower noise levels. The findings of this study can also be utilized to direct future research in this area and offer important information for a better understanding of the mechanism of rod-airfoil noise creation. To the best of the authors' knowledge, this is the first study to look into rod-airfoil design predictions made using machine learning approaches.Article Citation - WoS: 3Citation - Scopus: 4Predicting Stability Factors for Rotational Failures in Earth Slopes and Embankments Using Artificial Intelligence Techniques(de Gruyter Poland Sp Z O O, 2024) Cemiloglu, Ahmed; Cao, Yingying; Sabonchi, Arkan K. S.; Nanehkaran, Yaser A.This study focuses on slope stability analysis, a critical process for understanding the conditions, durability, mass properties, and failure mechanisms of slopes. The research specifically addresses rotational-type failure, the primary instability mechanism affecting earth slopes. Identifying and understanding key factors such as slope height, slope angle, density, cohesion, friction, water pore pressure, and tensile cracks are essential for effective stabilization strategies. The objective of this study is to develop accurate predictive models for slope stability analysis using advanced intelligent techniques, including data mining mapping and complex decision tree regression (DTR). The models were validated using performance metrics such as mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), and the coefficient of determination (R-2). Additionally, overall accuracy was assessed using a confusion matrix. The predictive model was tested on a dataset of 120 slope cases, achieving an accuracy of approximately 91.07% with DTR. The error rates for the training set were MAE = 0.1242, MSE = 0.1722, and RMSE = 0.1098, demonstrating the model's capability to effectively analyze and predict slope stability in earth slopes and embankments. The study concludes that these intelligent techniques offer a reliable approach for stability analysis, contributing to safer and more efficient slope management.Article Citation - WoS: 3Citation - Scopus: 4Towards an Earthquake-Resistant Architectural Design With the Image Classification Method(Taylor & Francis Ltd, 2024) Akan, Asli Er; Bingol, Kaan; Ormecioglu, Hilal Tugba; Er, Arzu; Ormecioglu, Tevfik Oguz; Er Akan, AslıArchitectural design is an interdisciplinary process which involves multiple stages that are interconnected. In this process, it is common for major decisions to be changed during the final stage, the analysis of the structural system. After making substantial corrections, the architect has to revisit the early stages, the preliminary project. This back-and-forth process can result in significant losses in time and cost. The proposed Irregularity Control Assistant (IC-Assistant) aims to provide architects with feedback on the conformity of structural system decisions to the irregularities defined in the Turkish Building Earthquake Code (TBEC-2018), using image processing methods at the early stages of the design process. The IC-Assistant was preliminarily created to evaluate the torsional irregularity of plan organization using deep learning methods. In this study, the results of the IC-Assistant were verified by structural analysis with the Prota-Structure program. The novelty of this study is the use of the image-classification method in earthquake-resistant architectural design. Up to this point, the method has been mainly used in facial recognition systems. This method minimizes time, human error, and cost losses and includes awareness of load bearing and earthquake resistance as inputs in the early stages of architectural design.Article Citation - WoS: 6Citation - Scopus: 14A Novel Approach for Continuous Authentication of Mobile Users Using Reduce Feature Elimination (Rfe): A Machine Learning Approach(Springer, 2023) Singh, Karan; Khan, Tayyab; Ariffin, Mazeyanti Mohd; Mohan, Senthil Kumar; Baleanu, Dumitru; Ahmadian, Ali; Kumari, SonalMobile phones are a valuable object in our daily life. With the acquisition of the latest technologies, their capabilities and demands increase day by day. However, acquiring the latest technologies makes mobile phones vulnerable to various security threats. Generally, people use passwords, pins, fingerprint locks, etc., to secure their mobile phones. Passwords and pins create so much burden for people always to remember their credentials. These traditional approaches are susceptible to brute force attacks, smudge attacks, and shoulder surfing attacks. Due to the difficulties mentioned above, researchers are leaning more towards continuous authentication. Therefore, this paper introduces an adaptive continuous authentication approach, a behavioral-based mobile authentication mechanism. In (Ehatisham-ul-Haq et al. J Netw Comput Appl 109:24-35, 2018), the authors achieved a good average accuracy of 97.95% with a Support vector machine classifier (SVM). We used LGB and RF and got 95.8% and 98.8% accuracy in user recognition. RF and LGB were trained for all five body positions separately to recognize each User among five users. This model also promises to reduce the system's cost and complexity by introducing the reduce feature elimination (RFE) technique during feature selection. RFE eliminates the less critical feature and reduces the dimension of the feature set. Hence, it demonstrates the benefits of our model for mobile authentication.Article Citation - WoS: 1Citation - Scopus: 3Identifying Criminal Organizations From Their Social Network Structures(Tubitak Scientific & Technological Research Council Turkey, 2019) Genc, Burkay; Sever, Hayri; Cinar, Muhammet SerkanIdentification of criminal structures within very large social networks is an essential security feat. By identifying such structures, it may be possible to track, neutralize, and terminate the corresponding criminal organizations before they act. We evaluate the effectiveness of three different methods for classifying an unknown network as terrorist, cocaine, or noncriminal. We consider three methods for the identification of network types: evaluating common social network analysis metrics, modeling with a decision tree, and network motif frequency analysis. The empirical results show that these three methods can provide significant improvements in distinguishing all three network types. We show that these methods are viable enough to be used as supporting evidence by security forces in their fight against criminal organizations operating on social networks.