Browsing by Author "Kiranyaz S."

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Citation - Scopus: 4
Biosignal Time-Series Analysis
(Elsevier, 2022) Kiranyaz S.; İnce, Türker; Chowdhury M.E.H.; Degerli A.; Gabbouj, Moncef
In 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.
Citation - Scopus: 1
Classification of Holter Registers by Dynamic Clustering Using Multi-Dimensional Particle Swarm Optimization
(2010) Kiranyaz S.; İnce, Türker; Pulkkinen J.; Gabbouj M.
In this paper, we address dynamic clustering in high dimensional data or feature spaces as an optimization problem where multi-dimensional particle swarm optimization (MD PSO) is used to find out the true number of clusters, while fractional global best formation (FGBF) is applied to avoid local optima. Based on these techniques we then present a novel and personalized long-term ECG classification system, which addresses the problem of labeling the beats within a long-term ECG signal, known as Holter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so called master key-beats) each of which is representing a cluster of homogeneous (similar) beats. We tested the system on a benchmark database where the beats of each Holter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and the proposed systematic approach produced results that were consistent with the manual labels with 99.5% average accuracy, which basically shows the efficiency of the system. © 2010 IEEE.
Citation - Scopus: 4
Classification of Polarimetric Sar Images Using Evolutionary Rbf Networks
(2010) İnce, Türker; Kiranyaz S.; Gabbouj, Moncef
This paper proposes an evolutionary RBF network classifier for polarimetric synthetic aperture radar ( SAR) images. The proposed feature extraction process utilizes the full covariance matrix, the gray level co-occurrence matrix (GLCM) based texture features, and the backscattering power (Span) combined with the H/?/A decomposition, which are projected onto a lower dimensional feature space using principal component analysis. An experimental study is performed using the fully polarimetric San Francisco Bay data set 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 to the Wishart and a recent NN-based classifiers demonstrate the effectiveness of the proposed algorithm. © 2010 IEEE.
Citation - Scopus: 5
Collective Network of Evolutionary Binary Classifiers for Content-Based Image Retrieval
(2011) Kiranyaz S.; Uhlmann S.; Pulkkinen J.; Gabbouj, Moncef; İnce, Türker
The content-based image retrieval (CBIR) has been an active research field for which several feature extraction, classification and retrieval techniques have been proposed up to date. However, when the database size grows larger, it is a common fact that the overall retrieval performance significantly deteriorates. In this paper, we propose collective network of (evolutionary) binary classifiers (CNBC) framework to achieve a high retrieval performance even though the training (ground truth) data may not be entirely present from the beginning and thus the system can only be trained incrementally. The CNBC framework basically adopts a "Divide and Conquer" type approach by allocating several networks of binary classifiers (NBCs) to discriminate each class and performs evolutionary search to find the optimal binary classifier (BC) in each NBC. In such an evolution session, the CNBC body can further dynamically adapt itself with each new incoming class/feature set without a full-scale re-training or re-configuration. Both visual and numerical performance evaluations of the proposed framework over benchmark image databases demonstrate its scalability; and a significant performance improvement is achieved over traditional retrieval techniques. © 2011 IEEE.
Citation - Scopus: 1
Convolutional Neural Networks and Applications on Civil Infrastructure
(CRC Press, 2022) Avci O.; Abdeljaber O.; Kiranyaz S.; İnce, Türker; Inman D.J.
[No abstract available]
Citation - Scopus: 291
Convolutional Neural Networks for Patient-Specific Ecg Classification
(Institute of Electrical and Electronics Engineers Inc., 2015) Kiranyaz S.; İnce, Türker; Hamila R.; Gabbouj, Moncef
We propose a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can fuse feature extraction and classification into a unified learner. In this way, a dedicated CNN will be trained for each patient by using relatively small common and patient-specific training data and thus it can also be used to classify long ECG records such as Holter registers in a fast and accurate manner. Alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. The experimental results demonstrate that the proposed system achieves a superior classification performance for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB). © 2015 IEEE.
Citation - Scopus: 1
Dynamic Data Clustering Using Stochastic Approximation Driven Multi-Dimensional Particle Swarm Optimization
(Springer Verlag, 2010) Kiranyaz S.; İnce, Türker; Gabbouj, Moncef
With an ever-growing attention Particle Swarm Optimization (PSO) has found many application areas for many challenging optimization problems. It is, however, a known fact that PSO has a severe drawback in the update of its global best (gbest) particle, which has a crucial role of guiding the rest of the swarm. In this paper, we propose two efficient solutions to remedy this problem using a stochastic approximation (SA) technique. For this purpose we use simultaneous perturbation stochastic approximation (SPSA), which is applied only to the gbest (not to the entire swarm) for a low-cost solution. Since the problem of poor gbest update persists in the recently proposed extension of PSO, called multi-dimensional PSO (MD-PSO), two distinct SA approaches are then integrated into MD-PSO and tested over a set of unsupervised data clustering applications. Experimental results show that the proposed approaches significantly improved the quality of the MD-PSO clustering as measured by a validity index function. Furthermore, the proposed approaches are generic as they can be used with other PSO variants and applicable to a wide range of problems. © 2010 Springer-Verlag Berlin Heidelberg.
Citation - Scopus: 20
Exploring Sound vs Vibration for Robust Fault Detection on Rotating Machinery
(Institute of Electrical and Electronics Engineers Inc., 2024) Kiranyaz S.; Devecioglu O.C.; Alhams A.; Sassi S.; Ince T.; Avci O.; Gabbouj M.
Robust and real-time detection of faults has become an ultimate objective for predictive maintenance on rotating machinery. Vibration-based Deep Learning (DL) methodologies have become the de facto standard for bearing fault detection as they can produce state-of-the-art detection performances under certain conditions. Despite such particular focus on the vibration signal, the utilization of sound, on the other hand, has been widely neglected. As a result, no large-scale benchmark motor fault dataset exists with both sound and vibration data. The novel and significant contributions of this study can be summarized as follows. This study presents and publically shares the Qatar University Dual-Machine Bearing Fault Benchmark dataset (QU-DMBF), which encapsulates sound and vibration data from two different motors operating under 1080 working conditions. Then, we focus on the major limitations and drawbacks of vibration-based fault detection due to numerous installation and operational conditions. Finally, we propose the first DL approach for sound-based fault detection and perform comparative evaluations between the sound and vibration signals over the QU-DMBF dataset. A wide range of experimental results shows that the sound-based fault detection method is significantly more robust than its vibration-based counterpart, as it is entirely independent of the sensor location, cost-effective (requiring no sensor and sensor maintenance), and can achieve the same level of the best detection performance by its vibration-based counterpart. This study publicly shares the QU-DMBF dataset, the optimized source codes in PyTorch, and comparative evaluations with the research community. Authors
Citation - WoS: 16
Citation - Scopus: 21
Generalized Model of Biological Neural Networks: Progressive Operational Perceptrons
(Institute of Electrical and Electronics Engineers Inc., 2017) Kiranyaz S.; İnce, Türker; Iosifidis A.; Gabbouj, Moncef
Traditional Artificial Neural Networks (ANNs) such as Multi-Layer Perceptrons (MLPs) and Radial Basis Functions (RBFs) were designed to simulate biological neural networks; however, they are based only loosely on biology and only provide a crude model. This in turn yields well-known limitations and drawbacks on the performance and robustness. In this paper we shall address them by introducing a novel feed-forward ANN model, Generalized Operational Perceptrons (GOPs) that consist of neurons with distinct (non-)linear operators to achieve a generalized model of the biological neurons and ultimately a superior diversity. We modified the conventional back-propagation (BP) to train GOPs and furthermore, proposed Progressive Operational Perceptrons (POPs) to achieve self-organized and depth-adaptive GOPs according to the learning problem. The most crucial property of the POPs is their ability to simultaneously search for the optimal operator set and train each layer individually. The final POP is, therefore, formed layer by layer and this ability enables POPs with minimal network depth to attack the most challenging learning problems that cannot be learned by conventional ANNs even with a deeper and significantly complex configuration. © 2017 IEEE.
Citation - Scopus: 7
Improved Domain Adaptation Approach for Bearing Fault Diagnosis
(IEEE Computer Society, 2022) İnce, Türker; Kilickaya S.; Eren L.; Devecioglu O.C.; Kiranyaz S.; Gabbouj, Moncef
Application of domain adaptation techniques to predictive maintenance of modern electric rotating machinery (RM) has significant potential with the goal of transferring or adaptation of a fault diagnosis model developed for one machine to be generalized on new machines and/or new working conditions. The generalized nonlinear extension of conventional convolutional neural networks (CNNs), the self-organized operational neural networks (Self-ONNs) are known to enhance the learning capability of CNN by introducing non-linear neuron models and further heterogeneity in the network configuration. In this study, first the state-of-the-art 1D CNNs and Self-ONNs are tested for cross-domain performance. Then, we propose to utilize Self-ONNs as feature extractor in the well-known domain-adversarial neural networks (DANN) to enhance its domain adaptation performance. Experimental results over the benchmark Case Western Reserve University (CWRU) real vibration data set for bearing fault diagnosis across different load domains demonstrate the effectiveness and feasibility of the proposed domain adaptation approach with similar computational complexity. © 2022 IEEE.
Incremental Evolution of Collective Network of Binary Classifier for Content-Based Image Classification and Retrieval
(2011) Kiranyaz S.; Uhlmann S.; Pulkkinen J.; İnce, Türker; Gabbouj M.
In this paper, we propose an incremental evolution scheme within collective network of (evolutionary) binary classifiers (CNBC) framework to address the problem of incremental learning and to achieve a high retrieval performance for content-based image retrieval (CBIR). The proposed CNBC framework can still function even though the training (ground truth) data may not be entirely present from the beginning and thus the system can only be evolved incrementally. The CNBC framework basically adopts a "Divide and Conquer" type approach by allocating several networks of binary classifiers (NBCs) to discriminate each class and performs evolutionary search to find the optimal binary classifier (BC) in each NBC. This design further allows such scalability that the CNBC can dynamically adapt its internal topology to new features and classes with minimal effort. Both visual and numerical performance evaluations of the proposed framework over benchmark image databases demonstrate its efficiency and accuracy for scalable CBIR and classification. © 2011 IEEE.
Citation - WoS: 2
Citation - Scopus: 8
Multi-Dimensional Evolutionary Feature Synthesis for Content-Based Image Retrieval
(2011) Kiranyaz S.; Pulkkinen J.; İnce, Türker; Gabbouj, Moncef
Low-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.
Citation - WoS: 6
Citation - Scopus: 8
Multifrequency Polsar Image Classification Using Dual-Band 1d Convolutional Neural Networks
(Institute of Electrical and Electronics Engineers Inc., 2020) Ahishali M.; Kiranyaz S.; İnce, Türker; Gabbouj, Moncef
In this work, we propose a novel classification approach based on dual-band one-dimensional Convolutional Neural Networks (1D-CNNs) for classification of multifrequency polarimetric SAR (PolSAR) data. The proposed approach can jointly learn from C- and L-band data and improve the single band classification accuracy. To the best of our knowledge, this is the first study that introduces 1D-CNNs to land use/land cover classification domain using PolSAR data. The proposed approach aims to achieve maximum classification accuracy by one-time training over multiple frequency bands with limited labelled data. Moreover, the proposed dual-band 1D-CNN approach yields a superior computational efficiency compared to the deep 2D-CNN based approaches. The performed experiments using AIRSAR PolSAR image over San Diego region at C- and L-bands have shown that the proposed approach is able to simultaneously learn from the C- and L-band SAR data and achieves an elegant classification performance with minimal complexity. © 2020 IEEE.
Citation - Scopus: 23
Patient-Specific Epileptic Seizure Detection in Long-Term Eeg Recording in Paediatric Patients With Intractable Seizures
(2013) Zabihi M.; Kiranyaz S.; İnce, Türker; Gabbouj, Moncef
The contemporary diagnosis of epileptic seizures is dominated by non-invasive EEG signal analysis and classification. In this paper, we propose a patient-specific seizure detection technique, which selects the optimal feature subsets and trains a dedicated classifier for each patient in order to maximize the classification performance. Our method exploits time domain, frequency domain, time-frequency domain and non-linear feature sets. Then, by using Conditional Mutual Information Maximization (CMIM) as the feature selection method the optimal feature subset is chosen over which the Support Vector Machine is trained as the classifier. In this study, both train and test sets contain 50% of seizure and non-seizure segments of the EEG signal. From the CHB-MIT Scalp benchmark EEG dataset, we used the EEG data from four subjects with overall 21 hours of recording. Support Vector Machine (SVM) with linear kernel is used as the classifier. The experimental results show a delicate classification performance over the test set: I.e., an average of 90.62% sensitivity and 99.32% specificity are acquired when all channels and recordings are used to form a composite feature vector. In addition, an average of 93.78% sensitivity and a specificity of 99.05% are obtained using CMIM.
Citation - Scopus: 7
Polarimetric Sar Images Classification Using Collective Network of Binary Classifiers
(2011) Uhlmann S.; Kiranyaz S.; Gabbouj, Moncef; İnce, Türker
In this paper, we propose the application of collective network of (evolutionary) binary classifiers (CNBC) to address the problems of feature/class scalability and classifier evolution, to achieve a high classification performance over full polarimetric SAR images even though the training (ground truth) data may not be entirely accurate. The CNBC basically adopts a "Divide and Conquer" type approach by allocating an individual network of binary classifiers (NBCs) to discriminate each SAR image class and performing evolutionary search to find the optimal binary classifier (BC) in each NBC. Such design further allows dynamic class and SAR image feature scalability in such a way that the CNBC can gradually adapt itself to new features and classes with minimal effort. Experiments demonstrate the classification accuracy and efficiency of the proposed system over the fully polarimetric AIRSAR San Francisco Bay data set. © 2011 IEEE.
Citation - WoS: 2
Citation - Scopus: 2
Sar Imagery Classification in Extended Feature Space by Collective Network of Binary Classifiers
(2011) Uhlmann S.; Kiranyaz S.; İnce, Türker; Gabbouj, Moncef
Polarimetric SAR image classification has been an active research field where several features and classifiers have been proposed in the past. Using numerous features can be a desirable option so as to achieve a better discrimination over certain classes, yet key questions such as how to avoid "Curse of Dimensionality" and how to combine them in the most effective way still remains unanswered. In this paper, we investigate SAR image classification using a large set of features, where the focus is particularly drawn on the extension of image processing features e.g. texture, edge and color. We propose a dedicated application of the Collective Network of (evolutionary) Binary Classifiers (CNBC) framework to address these problems with the aim of achieving high feature scalability. We furthermore tested several SAR and image processing feature constellations over three well-known SAR image classifiers and make comparative evaluations with CNBC. Experimental results over the full polarimetric AIRSAR San Francisco Bay and Flevoland images show that additional image processing features are able to improve SAR image classification accuracy and moreover, the CNBC proves useful and can scale well especially whenever high number of features and classes are encountered. © 2011 EURASIP.
Citation - Scopus: 1
Stochastic approximation driven particle swarm optimization
(2009) Kiranyaz S.; İnce, Türker; Gabbouj, Moncef
Particle Swarm Optimization (PSO) is attracting an ever-growing attention and more than ever it has found many application areas for many challenging optimization problems. In this paper, we draw the focus on a major drawback of the PSO algorithm: the poor gbest update. This can be a severe problem, which causes pre-mature convergence to local optima since gbest as the common term in the update equation of all particles, is the primary guide of the swarm. Therefore, we basically seek a solutionfor the social problem in PSO, i.e. "Who will guide the guide?" which resembles the rhetoric question posed by Plato in his famous work on government: "Who will guard the guards?" (Quis custodiet ipsos custodes?). Stochastic approximation (SA) is purposefully adapted into two approaches to guide (or drive) the gbest particle (with simultaneous perturbation) towards the right direction with the gradient estimate of the underlying surface (or function) whilst avoiding local traps due to its stochastic nature. Wepurposefully used simultaneous perturbation SA (SPSA) for its low cost and since SPSA is applied only to the gbest (not the entire swarm), both approaches have thus a negligible overhead cost over the entire PSO process. Yet we have shown over a wide range ofnon-linear functions that both approaches significantly improve the performance of PSO especially ifthe parameters of SPSA suits to the problem in hand. ©2009 IEEE.
Citation - Scopus: 5
Unsupervised Design of Artificial Neural Networks Via Multi-Dimensional Particle Swarm Optimization
(Institute of Electrical and Electronics Engineers Inc., 2008) Kiranyaz S.; İnce, Türker; Yildirim A.; Gabbou M.
In this paper, we present a novel and efficient approach for automatic design of Artificial Neural Networks (ANNs) by evolving to the optimal network configuration(s) within an architecture space. The evolution technique, the so-called multi-dimensional Particle Swarm Optimization (MD PSO) re-forms the native structure of PSO particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. So in a multidimensional search space where the optimum dimension is unknown, swarm particles can seek for both positional and dimensional optima. This eventually removes the necessity of setting a fixed dimension a priori, which is a common drawback for the family of swarm optimizers. With the proper encoding of the network configurations and parameters into particles, MD PSO can then seek for positional optimum in the error space and dimensional optimum in the architecture space. The optimum dimension converged at the end of a MD PSO process corresponds to a unique ANN configuration where the network parameters (connections, weights and biases) can then be resolved from the positional optimum reached on that dimension. The efficiency and performance of the proposed technique is demonstrated over one of the hardest synthetic problems. The experimental results show that MD PSO evolves to optimum or near-optimum networks in general. © 2008 IEEE.