Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Yildirim, Ozal(19)
- Utku, Anıl(11)
- Talo, Muhammed(10)
- Utku, Anil(10)
- Baloglu, Ulas Baran(9)
- Acharya, U. Rajendra(7)
- Ergen, Burhan(7)
- Doğan, Gürkan(5)
- Karakose, Mehmet(5)
- Yilmazer, Merve(5)
- Can, Umit(4)
- Demir, Yakup(4)
- Imak, Andaç(4)
- Acharya, U Rajendra(3)
- Aydin, Galip(3)
- Ertargin, Merve(3)
- Gunay, Mihriban(3)
- Tan, Ru-San(3)
- Yıldırım, Özal(3)
- Akcayol, M. Ali(2)
- Ay, Betul(2)
- Barut, Abdulkadir(2)
- Iliev, Teodor(2)
- Karabatak, Murat(2)
- Kaya, Sema Kayapinar(2)
- Korkmaz, Adem(2)
- Kosunalp, Selahattin(2)
- Kutlu, Banu(2)
- Orhan, Ahmet(2)
- Pilatin, Abdulmuttalip(2)
- Rajendra Acharya, U.(2)
- Sengur, Abdulkadir(2)
- Tan, Ru San(2)
- Türkoğlu, Muammer(2)
- Çelebi, Adalet(2)
- Çelik, Yusuf(2)
- Şengür, Abdulkadir(2)
- Agdas, Mehmet T.(1)
- Agdas, Mehmet Tevfik(1)
- Akcayol, Muhammet Ali(1)
Yayınlar
- MakaleYalnız künye2019Scopus SJRQ1Deep long short-term memory networks-based automatic recognition of six different digital modulation types under varying noise conditions
Daldal, Nihat; Yıldırım, Özal; Polat, Kemal
In this paper, a new method based on deep learning has been proposed in order to recognize noise-digital modulation signals at varying noise levels automatically. The 8-bit data from six different modulations have been obtained by adding noise levels from 5 to 25 dB. The used digital modulation types are Amplitude Shift Keying, Frequency Shift Keying, Phase Shift Keying, Quadrature Amplitude Shift Keying, Quadrature Frequency Shift Keying, and Quadrature Phase Shift Keying. To recognize the noise-digital modulation signals automatically, a new deep long short-term memory networks (LSTMs) model has been proposed and then applied to these signals successfully. A significant advantage of the proposed system is that deep learning method has been trained and tested with raw digital modulation signals without applying any feature extraction from the signals. In this study, the noise modulation signals of 5–25 dB have been classified and compared with each other. The innovative aspect of the study is to classify the modulation with the LSTM method without dealing with the extraction of signal characteristics. Without noise, added digital modulation signals had been classified as the success rate of 97.22%, while with all noise-added signals have been classified as the success rate of 94.72% with deep LSTM model. The experimental results show that the proposed deep LSTM model has been achieved remarkable results in recognition of noised six different modulation signals with a fully end-to-end structure.
AtıfScopus35WoS26OpenAlex35
İncele - MakaleYalnız künye2019Scopus SJRQ1A new approach for arrhythmia classification using deep coded features and LSTM networks
Yildirim, Ozal; Baloglu, Ulas Baran; Tan, Ru-San; Ciaccio, Edward J.; Acharya, U. Rajendra
Background and objective: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG)signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues. Methods: A convolutional auto-encoder (CAE)based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM)classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network. Results: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD)rate, and an accuracy of over 99.0% was observed. Conclusions: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.
AtıfScopus377OpenAlex388
İncele - BildiriAçık Erişim2019Bigailab-4race-50K: Race Classification with a New Benchmark Dataset
Talo, Muhammed; Ay, Betul; Makinist, Semiha; Aydin, Galip
Face analysis is the process of extracting useful information such as gender, age and race from a face image. In this study, we look at ways of closing the gap between the capabilities of automatic facial recognition and race classification methods. For this purpose, we take the following two steps. (1) We offer a public race dataset consisting of labeled images to overcome the challenges of real-world race estimation tasks. (2) We present a benchmark study to find race of a human face using a pre-trained Convolutional Neural Network (CNN) model on the large-scale face dataset. We are able to achieve a remarkable accuracy of 97.6% in race classification task.
AtıfScopus3OpenAlex5İncele - MakaleAçık Erişim2019Scopus SJRQ2Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals
Yildirim, Ozal; Talo, Muhammed; Ay, B.; Baloglu, Ulas Baran; Aydin, G.; Acharya, U. Rajendra
In this study, a deep-transfer learning approach is proposed for the automated diagnosis of diabetes mellitus (DM), using heart rate (HR) signals obtained from electrocardiogram (ECG) data. Recent progress in deep learning has contributed significantly to improvement in the quality of healthcare. In order for deep learning models to perform well, large datasets are required for training. However, a difficulty in the biomedical field is the lack of clinical data with expert annotation. A recent, commonly implemented technique to train deep learning models using small datasets is to transfer the weighting, developed from a large dataset, to the current model. This deep learning transfer strategy is generally employed for two-dimensional signals. Herein, the weighting of models pre-trained using two-dimensional large image data was applied to one-dimensional HR signals. The one-dimensional HR signals were then converted into frequency spectrum images, which were utilized for application to well-known pre-trained models, specifically: AlexNet, VggNet, ResNet, and DenseNet. The DenseNet pre-trained model yielded the highest classification average accuracy of 97.62%, and sensitivity of 100%, to detect DM subjects via HR signal recordings. In the future, we intend to further test this developed model by utilizing additional data along with cloud-based storage to diagnose DM via heart signal analysis.
AtıfScopus145OpenAlex152İncele - MakaleAçık Erişim2019Scopus SJRQ2A deep learning model for automated sleep stages classification using PSG signals
Yildirim, Ozal; Baloglu, Ulas Baran; Acharya, U Rajendra
Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environment. However, the automated monitoring of sleep stages can help detect neurological disorders accurately as well. In this study, a flexible deep learning model is proposed using raw PSG signals. A one-dimensional convolutional neural network (1D-CNN) is developed using electroencephalogram (EEG) and electrooculogram (EOG) signals for the classification of sleep stages. The performance of the system is evaluated using two public databases (sleep-edf and sleep-edfx). The developed model yielded the highest accuracies of 98.06%, 94.64%, 92.36%, 91.22%, and 91.00% for two to six sleep classes, respectively, using the sleep-edf database. Further, the proposed model obtained the highest accuracies of 97.62%, 94.34%, 92.33%, 90.98%, and 89.54%, respectively for the same two to six sleep classes using the sleep-edfx dataset. The developed deep learning model is ready for clinical usage, and can be tested with big PSG data.
AtıfScopus255OpenAlex265İncele - MakaleAçık Erişim2019Scopus SJRQ2Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals
Ay, Betul; Yildirim, Ozal; Talo, Muhammed; Baloglu, Ulas Baran; Aydin, Galip; Puthankattil, Subha D.; Acharya, U. Rajendra
Depression affects large number of people across the world today and it is considered as the global problem. It is a mood disorder which can be detected using electroencephalogram (EEG) signals. The manual detection of depression by analyzing the EEG signals requires lot of experience, tedious and time consuming. Hence, a fully automated depression diagnosis system developed using EEG signals will help the clinicians. Therefore, we propose a deep hybrid model developed using convolutional neural network (CNN) and long-short term memory (LSTM) architectures to detect depression using EEG signals. In the deep model, temporal properties of the signals are learned with CNN layers and the sequence learning process is provided through the LSTM layers. In this work, we have used EEG signals obtained from left and right hemispheres of the brain. Our work has provided 99.12% and 97.66% classification accuracies for the right and left hemisphere EEG signals respectively. Hence, we can conclude that the developed CNN-LSTM model is accurate and fast in detecting the depression using EEG signals. It can be employed in psychiatry wards of the hospitals to detect the depression using EEG signals accurately and thus aid the psychiatrists.
AtıfScopus279OpenAlex291İncele - BildiriYalnız künye2018Geometric Methods in Deep Learning
Ünal, Inan; Yildirim, Ozal; Ozyildirim, BM; Yildirim, T
Deep learning algorithms have recently become the most widely used machine learning approaches. Remarkable approaches have emerged in the field of machine learning studies with the use of geometric methods for deep learning. The manifold theory, which is simply regarded as the general state of an Euclidean space, has become applicable in the field of deep learning and this field has been named Geometric Deep Learning. In this study, applications of manifold theory and differential geometry to deep learning is presented.
- MakaleYalnız künye2018Scopus SJRQ2Arrhythmia detection using deep convolutional neural network with long duration ECG signals
Yıldırım, Özal; Pławiak, Paweł; Tan, Ru-San; Acharya, U. Rajendra
This article presents a new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis. Cardiovascular disease prevention is one of the most important tasks of any health care system as about 50 million people are at risk of heart disease in the world. Although automatic analysis of ECG signal is very popular, current methods are not satisfactory. The goal of our research was to design a new method based on deep learning to efficiently and quickly classify cardiac arrhythmias. Described research are based on 1000 ECG signal fragments from the MIT - BIH Arrhythmia database for one lead (MLII) from 45 persons. Approach based on the analysis of 10-s ECG signal fragments (not a single QRS complex) is applied (on average, 13 times less classifications/analysis). A complete end-to-end structure was designed instead of the hand-crafted feature extraction and selection used in traditional methods. Our main contribution is to design a new 1D-Convolutional Neural Network model (1D-CNN). The proposed method is 1) efficient, 2) fast (real-time classification) 3) non-complex and 4) simple to use (combined feature extraction and selection, and classification in one stage). Deep 1D-CNN achieved a recognition overall accuracy of 17 cardiac arrhythmia disorders (classes) at a level of 91.33% and classification time per single sample of 0.015 s. Compared to the current research, our results are one of the best results to date, and our solution can be implemented in mobile devices and cloud computing.
AtıfScopus801OpenAlex854İncele - BildiriAçık Erişim2018Scopus SJRQ2Evaluating deep learning models for sentiment classification
Ay Karakuş, Betül; Talo, Muhammed; Hallaç, İbrahim Rıza; Aydin, Galip
Deep learning has emerged as an effective solution to various text mining problems such as document classification and clustering, document summarization, web mining, and sentiment analysis. In this paper, we describe our work on investigating several deep learning models for a binary sentiment classification problem. We used movie reviews in Turkish from the website www.beyazperde.com to train and test the deep learning models. We also report a detailed comparison of the models in terms of accuracy and time performances. Two major deep learning architectures used in this study are Convolutional Neural Networks and Long Short-Term Memory. We built several variants of these models by changing the number of layers, tuning the hyper-parameters, and combining models. Additionally, word embeddings were created by applying the word2vec algorithm with a skip-gram model on a large dataset (∼ 13 M words) composed of movie reviews. We investigate the effect of using the pre-word embeddings with these models. Experimental results have shown that the use of word embeddings with deep neural networks effectively yields performance improvements in terms of run time and accuracy.
AtıfScopus55WoS40OpenAlex64İncele - MakaleAçık Erişim2018Scopus SJRQ3Recognition of road type and quality for advanced driver assistance systems with deep learning
Tumen, Vedat; Yildirim, Ozal; Ergen, Burhan
To develop effective advanced driving assistance systems, it is important to accurately recognize current driving environments and make critical decisions about driving processes. Preventing accidents through the interaction between the driving assistance systems and the environment and ensuring optimum driving dynamics are the main topics in this field. Vehicles need to recognize the road type and quality at a high accuracy to ensure the most suitable driving for the road type. It is also important to use both uncomplicated and cost-effective systems when performing this detection. In this study, a deep learning-based approach that can be used in vehicle driver assistance systems is proposed to automatically recognize road type and quality. Using this approach, it is possible to determine the road type and the quality of the road using only driving images as the input data. A new convolutional neural network model is designed for classification of the driving images. Driving images obtained from Google Street View are used to evaluate the recognition system for an actual driving environment. The proposed approach shows that the road types were determined with accuracy of 91.41 %, and the pothole road–smooth road distinction was successful at 91.07 %. It can be said that the proposed method is an effective structure that can be used for advanced driving support systems, V2I communications systems, and similar intelligent transportation systems.
AtıfScopus22WoS16OpenAlex25İncele - MakaleYalnız künye2018Scopus SJRQ3An efficient compression of ECG signals using deep convolutional autoencoders
Yildirim, Ozal; Tan, Ru San; Acharya, U. Rajendra
Background and objective: Advances in information technology have facilitated the retrieval and processing of biomedical data. Especially with wearable technologies and mobile platforms, we are able to follow our healthcare data, such as electrocardiograms (ECG), in real time. However, the hardware resources of these technologies are limited. For this reason, the optimal storage and safe transmission of the personal health data is critical. This study proposes a new deep convolutional autoencoder (CAE) model for compressing ECG signals. Methods: In this paper, a deep network structure of 27 layers consisting of encoder and decoder parts is designed. In the encoder section of this model, the signals are reduced to low-dimensional vectors; and in the decoder section, the signals are reconstructed. The deep learning approach provides the representations of the low and high levels of signals in the hidden layers of the model. Hence, the original signal can be reconstructed with minimal loss. Very different from traditional linear transformation methods, a deep compression approach implies that it can learn to use different ECG records automatically. Results: The performance was evaluated on an experimental data set comprising 4800 ECG fragments from 48 unique clinical patients. The compression rate (CR) of the proposed model was 32.25, and the average PRD value was 2.73%. These favourable observation suggest that our deep model can allow secure data transfer in a low-dimensional form to remote medical centers. We present an effective compression approach that can potentially be used in wearable devices, e-health applications, telemetry and Holter systems.
AtıfScopus201OpenAlex206İncele - MakaleYalnız künye2018Scopus SJRQ2A novel wavelet sequences based on deep bidirectional LSTM network model for ECG signal classification
Yildirim, Özal
Long-short term memory networks (LSTMs), which have recently emerged in sequential data analysis, are the most widely used type of recurrent neural networks (RNNs) architecture. Progress on the topic of deep learning includes successful adaptations of deep versions of these architectures. In this study, a new model for deep bidirectional LSTM network-based wavelet sequences called DBLSTM-WS was proposed for classifying electrocardiogram (ECG) signals. For this purpose, a new wavelet-based layer is implemented to generate ECG signal sequences. The ECG signals were decomposed into frequency sub-bands at different scales in this layer. These sub-bands are used as sequences for the input of LSTM networks. New network models that include unidirectional (ULSTM) and bidirectional (BLSTM) structures are designed for performance comparisons. Experimental studies have been performed for five different types of heartbeats obtained from the MIT-BIH arrhythmia database. These five types are Normal Sinus Rhythm (NSR), Ventricular Premature Contraction (VPC), Paced Beat (PB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The results show that the DBLSTM-WS model gives a high recognition performance of 99.39%. It has been observed that the wavelet-based layer proposed in the study significantly improves the recognition performance of conventional networks. This proposed network structure is an important approach that can be applied to similar signal processing problems.
AtıfScopus807WoS607OpenAlex837İncele - BildiriAçık Erişim2017Face recognition based on convolutional neural network
Coskun, Musab; Ucar, Aysegul; Yildirim, Ozal; Demir, Yakup
Face recognition is of great importance to real world applications such as video surveillance, human machine interaction and security systems. As compared to traditional machine learning approaches, deep learning based methods have shown better performances in terms of accuracy and speed of processing in image recognition. This paper proposes a modified Convolutional Neural Network (CNN) architecture by adding two normalization operations to two of the layers. The normalization operation which is batch normalization provided acceleration of the network. CNN architecture was employed to extract distinctive face features and Softmax classifier was used to classify faces in the fully connected layer of CNN. In the experiment part, Georgia Tech Database showed that the proposed approach has improved the face recognition performance with better recognition results.
AtıfScopus250OpenAlex295İncele - BildiriAçık Erişim2017Facial emotion recognition on a dataset using Convolutional Neural Network
Tümen, Vedat; Söylemez, Ömer Faruk; Ergen, Burhan
Nowadays, deep learning is a technique that takes place in many computer vision related applications and studies. While it is put in the practice mostly on content based image retrieval, there is still room for improvement by employing it in diverse computer vision applications. In this study, we aimed to build a Convolutional Neural Network (CNN) based Facial Expression Recognition System (FER), in order to automatically classify expressions presented in Facial Expression Recognition (FER2013) database. Our presented CNN achieved % 57.1 success rate on FER2013 database.
AtıfScopus68OpenAlex83İncele