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- ArticleAçık Erişim2026Explainable AI for accurate diagnosis of papillary thyroid carcinoma via fine-needle aspiration cytopathology
Kilicarslan, Ahmet; Tastimur, Canan; Gundogdu, Gokhan; Akin, Erhan
Papillary thyroid carcinoma (PTC) represents most thyroid cancer cases worldwide, making early and accurate diagnosis crucial for successful treatment. This study introduces an explainable AI framework for the automated analysis of fine-needle aspiration (FNA) cytopathology images. By integrating handcrafted features with deep learning-derived abstract features, the proposed model achieves high classification performance (~ 92% accuracy and ~ 96% AUC). The proposed architecture, incorporating multi-scale dilated and deformable convolutions, effectively captures irregularities in cell morphology. Visualizations demonstrate alignment with pathologists’ diagnostic criteria, enhancing the model’s clinical applicability and reliability. The code, dataset, and comprehensive documentation are publicly available via Zenodo (https://zenodo.org/records/18428647), ensuring full reproducibility of the experiments and results presented.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Fabric Faults Robust Classification Based on Logarithmic Residual Shrinkage Network in a Four-Point System
Tastimur, Canan; Akin, Erhan; Agrikli, Mehmet
Accurate and robust detection of fabric defects under noisy conditions is a major challenge in textile quality control systems. To address this issue, we introduce a new model called the Logarithmic Deep Residual Shrinkage Network (Log-DRSN), which integrates a deep attention module. Unlike standard residual shrinkage networks, the proposed Log-DRSN applies logarithmic transformation to improve resistance to noise, particularly in cases with subtle defect features. The model is trained and tested on both clean and artificially noised images to mimic real-world manufacturing conditions. The experimental results reveal that Log-DRSN achieves superior accuracy and robustness compared to the classical DRSN, with performance scores of 0.9917 on noiseless data and 0.9640 on noisy data, whereas the classical DRSN achieves 0.9686 and 0.9548, respectively. Despite its improved performance, the Log-DRSN introduces only a slight increase in computation time. These findings highlight the model’s potential for practical deployment in automated fabric defect inspection.
RDL Kalıcı Kaydıİncele - Conference ObjectAçık Erişim2024Analysis of Barrel Electroplating Line with Process Mining and Petri-Net Model
Ozates, Ayse; Akin, Erhan; Bilgic, Aykut; Akca, Emre; Tastimur, Canan
Nowadays, many companies carry out efficiency studies to increase their profitability. The most important step in productivity studies is to objectively analyze the processes and identify the points that can be improved. Process mining shows the actual functioning within the process. It also identifies points of improvement such as bottlenecks, waiting times and deviations in the process. In this article, it is aimed to analyze the production process of the barrel electrolytic coating line with the process mining method. In order to perform the analysis, firstly, the data related to the production process was obtained from the ERP system in. csvformat. Within the scope of process mining, data obtained from the event log were used to analyze and visualize the processes affecting the coating line and to identify areas where improvements can be made. Process mining techniques were applied to the event log of the production identified after the electrolytic coating process. ProM 6.12, a widely used open source tool for process mining, was used to perform our analysis. After examining the in-line processes of the product produced in the electrolytic coating line, the data from the event logs were processed and analyzed with process discovery algorithms. In the literature, there has been no research to date on using a process mining perspective to detect anomalous flows in electrolytic coating production.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022GMRNet: A Novel Geometric Mean Relation Network for Few-Shot Very Similar Object Classification
Tastimur, Canan; Akin, Erhan
With the widespread use of Deep Learning (DL), the use of DL has increased to provide a solution to the problem of object recognition and classification. In addition to classifying many different types of objects, the Deep Metrics Learning(DML) technique is effective in classifying objects that are visually very similar to each other. In this study, a novel Relation Network (RN) based DML has been designed to classify objects in two different datasets we created. We distinguished groups of objects that had a high degree of similarity to each other. These objects have been categorized using few-shot learning(FSL) since they are quite similar to one another. The impact of changing the number of classes and samples in the database on the network's performance has been studied. It is shown how the network's accuracy varies depending on the N-way (number of classes) and K-shots (number of samples) combinations used in its design. Additionally, the performance of the network has improved by an average of 15% thanks to the contribution of the recently introduced geometric mean module to the RN in our study. The accuracy rate of our recommended RN in screw and spare parts datasets is 96.1% and 92.3%, respectively. The first dataset consists of 1800 screw images with 18 classes, while the second dataset consists of 4100 spare parts images with 20 classes. The effectiveness of our method is expressed by the two datasets that we have extensively experimentally studied.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022Fastener Classification Using One-Shot Learning with Siamese Convolution Networks
Tastimur, Canan; Akin, Erhan
Deep Learning has been widely used in image-based applications such as object classification, object detection, and object recognition in recent years. Classifying highly similar objects is a very difficult problem. It is difficult to classify datasets in this situation where object similarity between classes and differences between classes are high. In this study, Siamese Convolution Neural Network, which is a similarity measurement-based network, has been practiced to classify 6 types of screws, 5 types of nuts, and 7 types of bolts that are very similar to each other. In addition, this neural network formed with the One-Shot Learning technique is trained. Thanks to the OSL technique, there is no need to use large data sets. Also, there is no need to use large amounts of data from each class. Adding a new class to be classified is also made easier by the use of the OSL technique. The performance results of the proposed method are manifested in detail in the article.
RDL Kalıcı Kaydıİncele - Conference ObjectYalnız künye2021A Novel Method Based on Deep Learning and Image Processing Techniques for Wearing Inspection on the Pantograph Surface
Tastimur, Canan; Karaduman, Gulsah; Akin, Erhan
Wear is an important problem in the pantograph-catenary system of electric rail transportation vehicles, which are increasingly used in the world, which receive their energy from the overhead line. This problem causes an increase in unplanned repair costs and disruption of transportation. The pantograph current collector strips move the locomotive with the electrical energy they receive from the catenary. Wear failure on the surface of the current collector strips creates problems in the pantograph-catenary interaction, causing transportation disruption and the safety of transportation is significantly affected. In this study, wear on the current collector strip surface has been detected by the noncontact monitoring method. A Convolution Neural Network (CNN) architecture has been developed to diagnose wear. Before the current collector strip surface images are trained on the CNN architecture, it is aimed to clarify the wear images and to make the diagnosis correctly with some pre-processing techniques on the images. After the pre-processing steps applied to the images, the performance results regarding the training of the dataset and its training with the data with the raw data have been compared, and more successful results have been obtained from the model trained using the pre-processed dataset.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2021Knitting machinery spare classification using deep learning with differential privacy
Tastimur, Canan; Kasap, Songul; Akin, Erhan
Given their widespread use, knitting machines must be maintained regularly. When the spare parts that make up these machines break down or become unusable, they must be replaced with new ones. However, the code/name information of the spare parts is not available to the end user, and can only be accessed with high-cost catalog procurement. Manufacturing companies keep the code/name information of such machine parts confidential. When the literature is examined, there are no studies in which spare parts are classified with machine learning–based algorithms. In line with this, this study focuses on the classification of spare parts using machine learning–based algorithms. The deep learning–based Convolutional Neural Network (CNN) architecture developed in this study can classify highly similar spare parts. In addition, since the code/name information received from the manufacturer and the spare part sample images require confidentiality, the CNN architecture has been developed in combination with the Differential Privacy (DP) method to present the DP-CNN method. As a result of the application of the Differential Privacy method, there has been no great loss of accuracy. This is an important development for our study. In the article, many optimizer algorithms are tested on the proposed method and comparative results are given. A 99.41% accuracy ratio has been obtained with the DP-RMSProp optimization method, which produces the best results. Experimental results of our study are presented in detail.
RDL Kalıcı Kaydıİncele - Conference ObjectAçık Erişim2021A New Bearing Fault Diagnosis Method using Envelope based Feature Extraction
Tastimur, Canan; Karakose, Mehmet; Aydin, Ilhan; Akin, Erhan
The performance of conventional intelligent diagnostic methods depends on the feature extraction of faulty signals. Feature extraction from defective signals requires signal processing techniques, expert knowledge, and human exertion. In this study, a time series based classification technique was developed to detect malfunctions occurring in induction motors. The proposed algorithm only takes the vibration signals as input. The phase space of the vibration signals taken as input is generated according to the time delay and embedding dimension for each motor status. Each motor condition is divided into two classes: faulty and intact. After the envelope transform is enforced into each signal, phase space is obtained. Then the Gaussian mixture model is formed. After with K-means clustering algorithm, the classification has been made up of four classes which are normal, outer race, inner race, and ball. The proposed approach has been tested on normal and defective vibration signals and results with high accuracy have been obtained.
RDL Kalıcı Kaydıİncele - Conference ObjectYalnız künye2019Vibration Signal Processing Based Bearing Defect Diagnosis with Transfer Learning
Tastimur, Canan; Karakose, Mehmet; Akin, Erhan; Varol, A.; Yazici, A.; Yazici, M.A.; Varol, C.; Aygunes, G.S.; Cotur, A.
It is very important to diagnose the fault condition of the bearing machines in order to make the machine run healthier. There are many successful studies in the detection of failures in the bearing machine made by conventional machine learning methods. However, these studies produce successful results in cases where the machines operate under the same condition and feature space is the same. Therefore, a deep learning-based diagnostic has been proposed for changing machine operating conditions. In the scope of our study, Keras and Tensorflow libraries, CNN network from scratch, VGG16 model and VGG19 deep learning models have been used for the classification of vibration images. Freeze the weights for transfer learning and remove the last fully connected layer to update the network in a problem-specific manner. The number of iterations and batch size has been determined by experimental studies. In this study, four faulty conditions have been successfully classified. While the accuracy rate of CNN network from scratch is 25%, the accuracy rate obtained by the VGG16 transfer learning method is 93% and the loss rate is 0.17% and the accuracy rate obtained by the VGG19 transfer learning method is 95% and the loss rate is 0.13%.
RDL Kalıcı Kaydıİncele - Conference ObjectYalnız künye2009Grey Clustering Based Diagnosis of Induction Motor Faults
Saman, Mehmet; Aydin, Ilhan; Akin, Erhan
In this paper, a fault classification method based on grey clustering is proposed for fault detection of induction motors. The amplitudes of rotor frequency related sideband components obtained through fourier transform of one phase stator current are used for broken rotor bar faults. Park's vector components are extracted from three phase motor currents and then new feature is obtained using principal component analysis on park vector components. Obtained features constitute the inputs of grey clustering algorithm. One broken rotor bar, stator faults and stator and multiple faults are diagnosed.
RDL Kalıcı Kaydıİncele - Conference ObjectYalnız künye2009Early fault detection of broken rotor bars using sliding window symbol sequence statistics
Aydin, Ilhan; Karakose, Mehmet; Akin, Erhan
This paper presents a new fault detection approach to detect and locate automatically the broken rotor bar faults of the induction machine. The signatures of the fault are created from the Hilbert transform of one phase motor current. A sliding window symbol sequence statistics-based method is proposed to detect the early faults of broken rotor bars. The different sized periods of phase current are taken as window. The information entropy of symbol tree is used as fault index. One and two broken rotor bar faults are detected on no-load conditions. The results indicate that the method yields a high degree of accuracy in fault identification, as evidenced by given experimental results.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2008A NEW INTELLIGENT FAULT CLASSIFICATION METHOD USING TIME SERIES DATA MINING AND SUPPORT VECTOR MACHINES
Aydin, Ilhan; Karaköse, Mehmet; Akin, Erhan
Induction motors are the most used machines in industrial applications. Although these motors are generally reliably, they can be exposed many faults due to environmental and wear reasons. In this study, broken rotor bar faults occurred in an induction motor are classified using support vector machines and time series data mining methods. Time series to be used for fault diagnosis is obtained by using two components of park's vector approach. The new time series which is obtained for each fault condition is transformed to a phase space. Support vector machines are used to separate healthy and faulty phase spaces. Fuzzy cluster centers are taken as training data to increase training speed of support vector machines. Healthy motor condition and one, two and three broken rotor bar faults are successfully classified at four different operation speeds with developing method.
RDL Kalıcı Kaydıİncele - Conference ObjectAçık Erişim2008Artificial immune inspired fault detection algorithm based on fuzzy clustering and genetic algorithm methods
Aydin, Ilhan; Karakose, Mehmet; Akin, Erhan
Early detection and diagnosis of incipient faults are desired for online condition monitoring and improved operational efficiency of induction motors. In this study, an artificial immune inspired fault detection algorithm based on fuzzy clustering and genetic algorithm is developed to detect broken rotor bar and broken connector faults in induction motors. The proposed algorithm uses only one phase stator current as input without the need for any other signals. The new feature signal called envelop is obtained by using Hilbert transform. This signal is examined in a phase space that is constructed by nonlinear time series analysis method. The artificial immune algorithm called negative selection is used to detect faults. The cluster centers of healthy motor phase space are obtained by fuzzy clustering method and they are taken as self patterns. The detectors of negative selection are generated by genetic algorithm. Self patterns generated by fuzzy clustering speed up the training stage of our algorithm and only small numbers of detectors are sufficient to detect any faults of induction motor. Results have demonstrated that the proposed system is able to detect faults in a three phase induction motor, successfully.
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