İnce, Türker
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Ince, T
Turker Ince
Ince, Turker
İnce, Turker
İnce, T
Turker Ince
Ince, Turker
İnce, Turker
İnce, T
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turker.ince@ieu.edu.tr
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05.06. Electrical and Electronics Engineering
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Current Staff
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1NO POVERTY
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2ZERO HUNGER
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3GOOD HEALTH AND WELL-BEING
7
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4QUALITY EDUCATION
3
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5GENDER EQUALITY
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6CLEAN WATER AND SANITATION
0
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7AFFORDABLE AND CLEAN ENERGY
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
11
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Documents
95
Citations
9377
h-index
31
Documents
62
Citations
7243
Scholarly Output
92
Articles
42
Views / Downloads
119/487
Supervised MSc Theses
4
Supervised PhD Theses
1
WoS Citation Count
6799
Scopus Citation Count
8950
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0
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5
WoS Citations per Publication
73.90
Scopus Citations per Publication
97.28
Open Access Source
33
Supervised Theses
5
| Journal | Count |
|---|---|
| Ieee Transactıons on Bıomedıcal Engıneerıng | 5 |
| Expert Systems Wıth Applıcatıons | 4 |
| Proceedings - International Conference on Pattern Recognition | 2 |
| Ieee Transactıons on Systems Man And Cybernetıcs Part B-Cybernetıcs | 2 |
| 2023 Photonics and Electromagnetics Research Symposium, PIERS 2023 - Proceedings | 2 |
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Article Citation - WoS: 24Citation - Scopus: 33Early Bearing Fault Diagnosis of Rotating Machinery by 1d Self-Organized Operational Neural Networks(IEEE-Inst Electrical Electronics Engineers Inc, 2021) İnce, Türker; Malik, Junaid; Devecioglu, Ozer Can; Kiranyaz, Serkan; Avcı, Onur; Eren, Levent; Gabbouj, MoncefPreventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs. Recently many studies investigated machine learning monitoring methods especially based on Deep Learning networks focusing mostly on detecting bearing faults; however, none of them addressed bearing fault severity classification for early fault diagnosis with high enough accuracy. 1D Convolutional Neural Networks (CNNs) have indeed achieved good performance for detecting RM bearing faults from raw vibration and current signals but did not classify fault severity. Furthermore, recent studies have demonstrated the limitation in terms of learning capability of conventional CNNs attributed to the basic underlying linear neuron model. Recently, Operational Neural Networks (ONNs) were proposed to enhance the learning capability of CNN by introducing non-linear neuron models and further heterogeneity in the network configuration. In this study, we propose 1D Self-organized ONNs (Self-ONNs) with generative neurons for bearing fault severity classification and providing continuous condition monitoring. Experimental results over the benchmark NSF/IMS bearing vibration dataset using both x- and y-axis vibration signals for inner race and rolling element faults demonstrate that the proposed 1D Self-ONNs achieve significant performance gap against the state-of-the-art (1D CNNs) with similar computational complexity.Article Citation - WoS: 64Citation - Scopus: 76Self-Organized Operational Neural Networks With Generative Neurons(Pergamon-Elsevier Science Ltd, 2021) Kiranyaz, Serkan; Malik, Junaid; Abdallah, Habib Ben; İnce, Türker; Iosifidis, Alexandros; Gabbouj, MoncefOperational Neural Networks (ONNs) have recently been proposed to address the well-known limitations and drawbacks of conventional Convolutional Neural Networks (CNNs) such as network homogeneity with the sole linear neuron model. ONNs are heterogeneous networks with a generalized neuron model. However the operator search method in ONNs is not only computationally demanding, but the network heterogeneity is also limited since the same set of operators will then be used for all neurons in each layer. Moreover, the performance of ONNs directly depends on the operator set library used, which introduces a certain risk of performance degradation especially when the optimal operator set required for a particular task is missing from the library. In order to address these issues and achieve an ultimate heterogeneity level to boost the network diversity along with computational efficiency, in this study we propose Self-organized ONNs (Self-ONNs) with generative neurons that can adapt (optimize) the nodal operator of each connection during the training process. Moreover, this ability voids the need of having a fixed operator set library and the prior operator search within the library in order to find the best possible set of operators. We further formulate the training method to back-propagate the error through the operational layers of Self-ONNs. Experimental results over four challenging problems demonstrate the superior learning capability and computational efficiency of Self-ONNs over conventional ONNs and CNNs. (C) 2021 The Author(s). Published by Elsevier Ltd.Article Citation - WoS: 5Citation - Scopus: 5The Effect of Automated Taxa Identification Errors on Biological Indices(Pergamon-Elsevier Science Ltd, 2017) Arje, Johanna; Karkkainen, Salme; Meissner, Kristian; Iosifidis, Alexandros; İnce, Türker; Gabbouj, Moncef; Kiranyaz, SerkanIn benthic macroinvertebrate biomonitoring systems, the target is to determine the status of ecosystems based on several biological indices. To increase cost-efficiency, computer-based taxa identification for image data has recently been developed. Taxa identification errors can, however, have strong effects on the indices and thus on the determination of the ecological status. In order to shift the biomonitoring process towards automated expert systems, we need a clear understanding on the bias caused by automation. In this paper, we examine eleven classification methods in the case of macroinvertebrate image data and show how their classification errors propagate into different biological indices. We evaluate 14 richness, diversity, dominance and similarity indices commonly used in biomonitoring. Besides the error rate of the classification method, we discuss the potential effect of different types of identification errors. Finally, we provide recommendations on indices that are least affected by the automatic identification errors and could be used in automated biomonitoring. (C) 2016 Elsevier Ltd. All rights reserved.Patent Method and Apparatus for Performing Motor-Fault Detection Via Convolutional Neural Networks(2020) Kiranyaz, Serkan; İnce, Türker; Eren, LeventConference Object Citation - WoS: 2Citation - Scopus: 8Multi-Dimensional Evolutionary Feature Synthesis for Content-Based Image Retrieval(2011) Kiranyaz S.; Pulkkinen J.; İnce, Türker; Gabbouj, MoncefLow-level features (also called descriptors) play a central role in content-based image retrieval (CBIR) systems. Features are various types of information extracted from the content and represent some of its characteristics or signatures. However, especially the (low-level) features, which can be extracted automatically usually lack the discrimination power needed for accurate description of the image content and may lead to a poor retrieval performance. In order to efficiently address this problem, in this paper we propose a multidimensional evolutionary feature synthesis technique, which seeks for the optimal linear and non-linear operators so as to synthesize highly discriminative set of features in an optimal dimension. The optimality therein is sought by the multi-dimensional particle swarm optimization method along with the fractional global-best formation technique. Clustering and CBIR experiments where the proposed feature synthesizer is evolved using only the minority of the image database, demonstrate a significant performance improvement and exhibit a major discrimination between the features of different classes. © 2011 IEEE.Conference Object Citation - WoS: 2Citation - Scopus: 4A Comparison of Feature Selection Algorithms for Cancer Classification Through Gene Expression Data: Leukemia Case(IEEE, 2017) Taşçı, Aslı; İnce, Türker; Guzelis, CuneytIn this study, three different feature selection algorithms are compared using Support Vector Machines as classifier for cancer classification through gene expression data. The ability of feature selection algorithms to select an optimal gene subset for a cancer type is evaluated by the classification ability of selected genes. A publicly available micro array dataset is employed for gene expression values. Selected gene subsets were able to classify subtypes of the considered cancer type with high accuracies and showed that these feature selection methods were applicable for bio-marker gene selection.Article Citation - WoS: 53Citation - Scopus: 65Enhanced Bearing Fault Detection Using Multichannel, Multilevel 1d Cnn Classifier(Springer, 2022) Ozcan, Ibrahim Halil; Devecioglu, Ozer Can; İnce, Türker; Eren, Levent; Askar, MuratElectric motors are widely used in many industrial applications on account of stability, solidity and ease of use. Mechanical bearing faults have the highest statistical occurrence percentage among all of the motor fault types. Accurate and advance detection of the bearing faults is critical to avoid unpredicted breakdowns of electric motors. Through early detection of bearing faults, it would be possible to solve the problem at a lower cost by repairing and/or replacing relevant parts. Most of the fault detection works in the literature attempted to detect binary {healthy, faulty} motor fault case based on a single input. In this study, we propose an enhanced performance bearing fault diagnosis system based on multichannel, multilevel 1D-CNN classifier processing vibration data collected from multiple accelerometers mounted on bearings in a test bed. Effectiveness and feasibility of the proposed method are validated by applying it to the benchmark IMS bearing vibration dataset for inner race and rolling element faults and comparing the results with the conventional single-axis data-based fault detection.Conference Object Citation - WoS: 1Citation - Scopus: 4Polarimetric Sar Image Classification Using Radial Basis Function Neural Network(Electromagnetics Acad, 2010) İnce, TürkerThis paper presents a robust radial basis function (RBF) network based classifier for polarimetric synthetic aperture radar (SAR) images. The proposed feature extraction process utilizes the covariance matrix, the gray level co-occurrence matrix (GLCM) based texture features, and the backscattering power (Span) combined with the H/alpha/A decomposition, which are projected onto a lower dimensional feature space using principal component analysis. For the classifier training two popular techniques are explored: conventional backpropagation (BP) and particle swarm optimization (PSO). By using both polarimetric covariance matrix and decomposition based pixel values and textural information (contrast, correlation, energy, and homogeneity) in the feature set, classification accuracy is improved. An experimental study is performed using the fully polarimetric San Francisco Bay and Flevoland data sets acquired by the NASA/Jet Propulsion Laboratory Airborne SAR, (AIRSAR.) at L-band to evaluate the performance of the proposed classifier. Classification results (in terms of confusion matrix, overall accuracy and classification map) compared with competing state of the art algorithms demonstrate the effectiveness of the proposed RBF network classifier.Book Part Citation - Scopus: 1Convolutional Neural Networks and Applications on Civil Infrastructure(CRC Press, 2022) Avci O.; Abdeljaber O.; Kiranyaz S.; İnce, Türker; Inman D.J.; Kiranyaz, Serkan; Avci, Onur; Abdeljaber, Osama; Inman, Daniel J.[No abstract available]Book Part Citation - Scopus: 4Biosignal Time-Series Analysis(Elsevier, 2022) Kiranyaz S.; İnce, Türker; Chowdhury M.E.H.; Degerli A.; Gabbouj, Moncef; Kiranyaz, Serkan; Chowdhury, Muhammad E. H.; Degerli, AysenIn this chapter, recent state-of-the-art techniques in biosignal time-series analysis will be presented. We shall start with the problem of patient-specific ECG beat classification where the objective is to discriminate the arrhythmic beats from the normal (healthy) beats of an individual patient. So, we will answer the ultimate question of how to design person-specific, real-time, and accurate monitoring of ECG signals. We shall then move on to the recent solution of a related problem, an early warning system that can alert an individual the instant his/her heart deviates from its normal rhythm. This is a far challenging problem since the detection of the arrhythmia beats should be performed without knowing them. © 2022 Elsevier Inc. All rights reserved.