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Full-Text Articles in Physical Sciences and Mathematics
Defect Classification Of Railway Fasteners Using Image Preprocessing And Alightweight Convolutional Neural Network, İlhan Aydin, Mehmet Sevi̇, Mehmet Umut Salur, Erhan Akin
Defect Classification Of Railway Fasteners Using Image Preprocessing And Alightweight Convolutional Neural Network, İlhan Aydin, Mehmet Sevi̇, Mehmet Umut Salur, Erhan Akin
Turkish Journal of Electrical Engineering and Computer Sciences
Railway fasteners are used to securely fix rails to sleeper blocks. Partial wear or complete loss of these components can lead to serious accidents and cause train derailments. To ensure the safety of railway transportation, computer vision and pattern recognition-based methods are increasingly used to inspect railway infrastructure. In particular, it has become an important task to detect defects in railway tracks. This is challenging since rail track images are acquired using a measuring train in varying environmental conditions, at different times of day and in poor lighting conditions, and the resulting images often have low contrast. In this study, …
Temporal Bagging: A New Method For Time-Based Ensemble Learning, Göksu Tüysüzoğlu, Derya Bi̇rant, Volkan Kiranoğlu
Temporal Bagging: A New Method For Time-Based Ensemble Learning, Göksu Tüysüzoğlu, Derya Bi̇rant, Volkan Kiranoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
One of the main problems associated with the bagging technique in ensemble learning is its random sample selection in which all samples are treated with the same chance of being selected. However, in time-varying dynamic systems, the samples in the training set have not equal importance, where the recent samples contain more useful and accurate information than the former ones. To overcome this problem, this paper proposes a new time-based ensemble learning method, called temporal bagging (T-Bagging). The significant advantage of our method is that it assigns larger weights to more recent samples with respect to older ones, so it …
Swft: Subbands Wavelet For Local Features Transform Descriptor For Cornealdiseases Diagnosis, Samer Al-Salihi, Sezgi̇n Aydin, Nebras Hussein
Swft: Subbands Wavelet For Local Features Transform Descriptor For Cornealdiseases Diagnosis, Samer Al-Salihi, Sezgi̇n Aydin, Nebras Hussein
Turkish Journal of Electrical Engineering and Computer Sciences
Human cornea is the front see-through shield of the eye. It refracts light onto the retina to induce vision.Therefore, any defect in the cornea may lead to vision disturbance. This deficiency is estimated by sets of topographicalimages measured, and assessed by an ophthalmologist. Consequently, an important priority is the early and accuratediagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms. Images producedby a Pentacam device can be subjected to rotation or some distortion during acquisition; therefore, accurate diagnosisrequires the use of local features in the image. Accordingly, a new algorithm called subbands wavelet for …
Prediction Of Railway Switch Point Failures By Artificial Intelligence Methods, Burak Arslan, Hasan Ti̇ryaki̇
Prediction Of Railway Switch Point Failures By Artificial Intelligence Methods, Burak Arslan, Hasan Ti̇ryaki̇
Turkish Journal of Electrical Engineering and Computer Sciences
In recent years, railway transport has been preferred intensively in local and intercity freight and passenger transport. For this reason, it is of utmost importance that railway lines are operated in an uninterrupted and safe manner. In order to carry out continuous operation, all systems must continue to operate with maximum availability. In this study, data were collected from switch motors, which are the important equipment of railways, and the related equipment and these data were evaluated with sector experience and the results related to the failure status of the switch points were revealed. The obtained results were processed with …
Polyhedral Conic Kernel-Like Functions For Svms, Gürkan Öztürk, Emre Çi̇men
Polyhedral Conic Kernel-Like Functions For Svms, Gürkan Öztürk, Emre Çi̇men
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, we propose a new approach that can be used as a kernel-like function for support vector machines (SVMs) in order to get nonlinear classification surfaces. We combined polyhedral conic functions (PCFs) with the SVM method. To get nonlinear classification surfaces, kernel functions are used with SVMs. However, the parameter selection of the kernel function affects the classification accuracy. Generally, in order to get successful classifiers which can predict unknown data accurately, best parameters are explored with the grid search method which is computationally expensive. We solved this problem with the proposed method. There is no need to …
Classification Of The Likelihood Of Colon Cancer With Machine Learning Techniques Using Ftir Signals Obtained From Plasma, Suat Toraman, Mustafa Gi̇rgi̇n, Bi̇lal Üstündağ, İbrahi̇m Türkoğlu
Classification Of The Likelihood Of Colon Cancer With Machine Learning Techniques Using Ftir Signals Obtained From Plasma, Suat Toraman, Mustafa Gi̇rgi̇n, Bi̇lal Üstündağ, İbrahi̇m Türkoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Colon cancer is one of the major causes of human mortality worldwide and the same can be said for Turkey. Various methods are used for the determination of cancer. One of these methods is Fourier transform infrared (FTIR) spectroscopy, which has the ability to reveal biochemical changes. The most common features used to distinguish patients with cancer and healthy subjects are peak densities, peak height ratios, and peak area ratios. The greatest challenge of studies conducted to distinguish cancer patients from healthy subjects using FTIR signals is that the signals of cancer patients and healthy subjects are similar. In the …
Gait Pattern Discrimination Of Als Patients Using Classification Methods, Süleyman Bi̇lgi̇n, Zahi̇de Eli̇f Akin
Gait Pattern Discrimination Of Als Patients Using Classification Methods, Süleyman Bi̇lgi̇n, Zahi̇de Eli̇f Akin
Turkish Journal of Electrical Engineering and Computer Sciences
Amyotrophic lateral sclerosis (ALS) is a mortal and idiopathic neurodegenerative disturbance of the human motor system. The disturbances of locomotion due to neurodegenerative diseases (NDDs) consisting of ALS, Parkinson disease (PD), and Huntington disease (HD) cause some abnormal fluctuations in gait signals. The investigation into gait patterns of NDDs provides significant information in order to develop new biomedical diagnosis devices. The main objective of this study is to evaluate the best discrimination method of ALS among control subjects (Co.), PD patients, and HD patients. The D2, D4, D5, and D6 detailed components, which were determined as critical features extracted from …
Feature Extraction Using Sequential Cumulative Bin And Overlap Mean Intensity Foriris Classification, Ahmad Nazri Ali, Shahrel Azmin Suandi, Mohd Zaid Abdullah
Feature Extraction Using Sequential Cumulative Bin And Overlap Mean Intensity Foriris Classification, Ahmad Nazri Ali, Shahrel Azmin Suandi, Mohd Zaid Abdullah
Turkish Journal of Electrical Engineering and Computer Sciences
This paper examines an approach generalizing a variant of the local binary pattern (LBP) method for iris feature extraction. The proposed method employs two different LBP variants called the sequential cumulative bin and overlap mean intensity for projecting the one-dimensional local iris textures into a binary bit pattern. The assigned bit, either 1 or 0 as a bit code, replaces the original intensity value using a specific condition for the respective reference element. The ratio value from the total transition of 1 to 0 along the row axis represents the feature of each iris image. The extraction only utilizes a …
Intellimote: A Hybrid Classifier For Classifying Learners' Emotion In A Distributed E-Learning Environment, Lopa Mandal, Rohan Das, Samar Bhattacharya, Pramatha Nath Basu
Intellimote: A Hybrid Classifier For Classifying Learners' Emotion In A Distributed E-Learning Environment, Lopa Mandal, Rohan Das, Samar Bhattacharya, Pramatha Nath Basu
Turkish Journal of Electrical Engineering and Computer Sciences
A huge collection of textual, graphical, audio, and video contents are readily available on the Internet to be used for the purpose of learning. Sentimental feedbacks of learners posted at the end of many of these contents may be considered as genuine reactions of the learners who have gone through the contents. Such learners' sentiments are important inputs for judging the acceptability of a learning material. Analyzing such feedbacks using sentiment analysis techniques can identify the best reusable learning contents that may be used for developing new courseware. This can significantly reduce the time and effort of authoring, which is …
Common Spatial Pattern-Based Feature Extraction From The Best Time Segment Of Bci Data, Önder Aydemi̇r
Common Spatial Pattern-Based Feature Extraction From The Best Time Segment Of Bci Data, Önder Aydemi̇r
Turkish Journal of Electrical Engineering and Computer Sciences
Feature extraction is one of the most crucial stages in the field of brain computer interface (BCI). Because of its ability to directly influence the performance of BCI systems, recent studies have generally investigated how to modify existing methods or develop novel techniques. One of the most successful and well-known methods in BCI applications is the common spatial pattern (CSP). In existing CSP-based methods, the spatial filters were extracted either by using the whole data trial or by dividing the trials into a number of overlapping/nonoverlapping time segments. In this paper, we developed a CSP-based moving window technique to obtain …
A New Fuzzy Membership Assignment And Model Selection Approach Based On Dynamic Class Centers For Fuzzy Svm Family Using The Firefly Algorithm, Omid Naghash Almasi, Modjtaba Rouhani
A New Fuzzy Membership Assignment And Model Selection Approach Based On Dynamic Class Centers For Fuzzy Svm Family Using The Firefly Algorithm, Omid Naghash Almasi, Modjtaba Rouhani
Turkish Journal of Electrical Engineering and Computer Sciences
The support vector machine (SVM) is a powerful tool for classification problems. Unfortunately, the training phase of the SVM is highly sensitive to noises in the training set. Noises are inevitable in real-world applications. To overcome this problem, the SVM was extended to a fuzzy SVM by assigning an appropriate fuzzy membership to each data point. However, suitable choice of fuzzy memberships and an accurate model selection raise fundamental issues. In this paper, we propose a new method based on optimization methods to simultaneously generate appropriate fuzzy membership and solve the model selection problem for the SVM family in linear/nonlinear …
Heart Sound Signal Classification Using Fast Independent Component Analysis, Yücel Koçyi̇ği̇t
Heart Sound Signal Classification Using Fast Independent Component Analysis, Yücel Koçyi̇ği̇t
Turkish Journal of Electrical Engineering and Computer Sciences
No abstract provided.
A Wavelet-Based Feature Set For Recognizing Pulse Repetition Interval Modulation Patterns, Kenan Gençol, Nuray At, Ali̇ Kara
A Wavelet-Based Feature Set For Recognizing Pulse Repetition Interval Modulation Patterns, Kenan Gençol, Nuray At, Ali̇ Kara
Turkish Journal of Electrical Engineering and Computer Sciences
No abstract provided.
Bone Age Determination In Young Children (Newborn To 6 Years Old) Using Support Vector Machines, Gür Emre Güraksin, Harun Uğuz, Ömer Kaan Baykan
Bone Age Determination In Young Children (Newborn To 6 Years Old) Using Support Vector Machines, Gür Emre Güraksin, Harun Uğuz, Ömer Kaan Baykan
Turkish Journal of Electrical Engineering and Computer Sciences
Bone age is assessed through a radiological analysis of the left-hand wrist and is then compared to chronological age. A conflict between these two values indicates an abnormality in the development process of the skeleton. This study, conducted on children aged between 0 and 6 years, proposes a computer-based diagnostic system to eliminate the disadvantages of the methods used in bone age determination. For this purpose, primarily an image processing procedure was applied to the X-ray images of the left-hand wrist of children from different ethnic groups aged between 0 and 6 years. A total of 9 features, corresponding to …
A Fault Detection, Diagnosis, And Reconfiguration Method Via Support Vector Machines}, Rana Ortaç Kabaoğlu
A Fault Detection, Diagnosis, And Reconfiguration Method Via Support Vector Machines}, Rana Ortaç Kabaoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a fault detection, diagnosis, and reconfiguration method based on support vector machines. This method is appropriate for certain or predetermined faults and involves a fault detection and diagnosis unit and an online controller selection type reconfiguration mechanism. In this method, when a fault is detected and diagnosed by the fault detection and diagnosis unit, a suitable controller, which has been determined via an optimization algorithm in an off-line fashion, is activated to maintain proper closed-loop performance of the system in an on-line manner. In the detection, diagnosis, and reconfiguration stages of the method, support vector classification and …
A Penalty Function Method For Designing Efficient Robust Classifiers With Input Space Optimal Separating Surfaces, Ayşegül Uçar, Yakup Demi̇r, Cüneyt Güzeli̇ş
A Penalty Function Method For Designing Efficient Robust Classifiers With Input Space Optimal Separating Surfaces, Ayşegül Uçar, Yakup Demi̇r, Cüneyt Güzeli̇ş
Turkish Journal of Electrical Engineering and Computer Sciences
This paper considers robust classification as a constrained optimization problem. Where the constraints are nonlinear, inequalities defining separating surfaces, whose half spaces include or exclude the data depending on their classes and the cost, are used for attaining robustness and providing the minimum volume regions specified by the half spaces of the surfaces. The constraints are added to the cost using penalty functions to get an unconstrained problem for which the gradient descent method can be used. The separating surfaces, which are aimed to be found in this way, are optimal in the input data space in contrast to the …
A Combined Protective Scheme For Fault Classification And Identification Of Faulty Section In Series Compensated Transmission Lines, Resul Çöteli̇
A Combined Protective Scheme For Fault Classification And Identification Of Faulty Section In Series Compensated Transmission Lines, Resul Çöteli̇
Turkish Journal of Electrical Engineering and Computer Sciences
The fault detection process is very difficult in transmission lines with a fixed series capacitor because of the nonlinear behavior of protection device and series-parallel resonance. This paper proposes a new method based on S-transform (ST) and support vector machines (SVMs) for fault classification and identification of a faulty section in a transmission line with a fixed series capacitor placed at the middle of the line. In the proposed method, the fault detection process is carried out by using distinctive features of 3-line signals (line voltages and currents) and zero sequence current. The relevant features of these signals are obtained …
Hybrid Spr Algorithm To Select Predictive Genes For Effectual Cancer Classification, Aruna Sundaram, Nandakishore Lellapalli Venkata, Rajagopalan Sarukai Parthasarathy
Hybrid Spr Algorithm To Select Predictive Genes For Effectual Cancer Classification, Aruna Sundaram, Nandakishore Lellapalli Venkata, Rajagopalan Sarukai Parthasarathy
Turkish Journal of Electrical Engineering and Computer Sciences
Designing an automated system for classifying DNA microarray data is an extremely challenging problem because of its high dimension and low amount of sample data. In this paper, a hybrid statistical pattern recognition algorithm is proposed to reduce the dimensionality and select the predictive genes for the classification of cancer. Colon cancer gene expression profiles having 62 samples of 2000 genes were used for the experiment. A gene subset of 6 highly informative genes was selected by the algorithm, which provided a classification accuracy of 93.5%.
A Video-Based Eye Pupil Detection System For Diagnosing Bipolar Disorder, Gökay Akinci, Edi̇z Polat, Orhan Murat Koçak
A Video-Based Eye Pupil Detection System For Diagnosing Bipolar Disorder, Gökay Akinci, Edi̇z Polat, Orhan Murat Koçak
Turkish Journal of Electrical Engineering and Computer Sciences
Eye pupil detection systems have become increasingly popular in image processing and computer vision applications in medical systems. In this study, a video-based eye pupil detection system is developed for diagnosing bipolar disorder. Bipolar disorder is a condition in which people experience changes in cognitive processes and abilities, including reduced attentional and executive capabilities and impaired memory. In order to detect these abnormal behaviors, a number of neuropsychological tests are also designed to measure attentional and executive abilities. The system acquires the position and radius information of eye pupils in video sequences using an active contour snake model with an …
Detection Of Microcalcification Clusters In Digitized X-Ray Mammograms Using Unsharp Masking And Image Statistics, Peli̇n Kuş, İrfan Karagöz
Detection Of Microcalcification Clusters In Digitized X-Ray Mammograms Using Unsharp Masking And Image Statistics, Peli̇n Kuş, İrfan Karagöz
Turkish Journal of Electrical Engineering and Computer Sciences
A fully automated method for detecting microcalcification (MC) clusters in regions of interest (ROIs) extracted from digitized X-ray mammograms is proposed. In the first stage, an unsharp masking is used to perform the contrast enhancement of the MCs. In the second stage, the ROIs are decomposed into a 2-level contourlet representation and the reconstruction is obtained by eliminating the low-frequency subband in the second level. In the third stage, statistical textural features are extracted from the ROIs and they are classified using support vector machines. To test the performance of the method, 57 ROIs selected from the Mammographic Image Analysis …