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Abnormal Heart Sound Detection Using Ensemble Classifiers
(Institute of Electrical and Electronics Engineers Inc., 2019) Zan, H.; Yildiz, A.; Zan, Hasan; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
Phonocardiogram is used for ambulatory diagnostic to assess health status of heart and detect cardiovascular disease. The goal of this study is to develop automatic classification method of PCG recordings collected from different databases and recorded in a different way. For this purpose, after various time and frequency domain features are extracted from PCG recordings obtained from two databases, recordings are subjected to pre-classification in order determine which database they are obtained from. Before final classification, various time, frequency and time-frequency domain features of classified recordings are extracted. These features are fed into four different classification ensembles trained with training dataset. With final decision rule, proposed algorithm achieved an accuracy of 98.9%, a sensitivity of 93.75% and a specify of 99.5%. © 2018 IEEE.
Citation - WoS: 6
Citation - Scopus: 18
Automatic Detection of Brain Tumors With the Aid of Ensemble Deep Learning Architectures and Class Activation Map Indicators by Employing Magnetic Resonance Images
(Elsevier, 2024) Turk, Omer; Ozhan, Davut; Acar, Emrullah; Akinci, Tahir Cetin; Yilmaz, Musa; Türk, Ömer; Özhan, Davut; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 17.03. Department of Electronics and Automatization / Elektronik ve Otomasyon Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 17. Vocational Higher School / Meslek Yüksekokulu; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
Today, as in every life-threatening disease, early diagnosis of brain tumors plays a life-saving role. The brain tumor is formed by the transformation of brain cells from their normal structures into abnormal cell structures. These formed abnormal cells begin to form in masses in the brain regions. Nowadays, many different techniques are employed to detect these tumor masses, and the most common of these techniques is Magnetic Resonance Imaging (MRI). In this study, it is aimed to automatically detect brain tumors with the help of ensemble deep learning architectures (ResNet50, VGG19, InceptionV3 and MobileNet) and Class Activation Maps (CAMs) indicators by employing MRI images. The proposed system was implemented in three stages. In the first stage, it was determined whether there was a tumor in the MR images Tumor) were detected from MR images (Multi-class Approach). In the last stage, CAMs of each tumor group were created as an alternative tool to facilitate the work of specialists in tumor detection. The results showed that the overall accuracy of the binary approach was calculated as 100% on the ResNet50, InceptionV3 and MobileNet architectures, and 99.71% on the VGG19 architecture. Moreover, the accuracy values of 96.45% with ResNet50, 93.40% with VGG19, 85.03% with InceptionV3 and 89.34% with MobileNet architectures were obtained in the multi-class approach.
Citation - WoS: 16
Citation - Scopus: 17
A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data
(Sage Journals, 2022) Uyulan, Caglar; Erguzel, Turker Tekin; Türk, Ömer; Farhad, Shams; Metin, Bariş; Tarhan, Nevzat; Türk, Ömer; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data is solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal and temporal lobes.
Citation - WoS: 22
Citation - Scopus: 34
Classification and analysis of epileptic EEG recordings using convolutional neural network and class activation mapping
(Biomedical Signal Processing and Control, 2021) Zan, Hasan; Yıldız, Abdulnasir; Said, Sherif; Zan, Hasan; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
Electrical bio-signals have the potential to be used in different applications due to their hidden nature and their ability to facilitate liveness detection. This paper investigates the feasibility of using the Convolutional Neural Network (CNN) to classify and analyze electroencephalogram (EEG) data with their time-frequency representations and class activation mapping (CAM) to detect epilepsy disease. Several types of pre-trained CNNs are employed for a multi-class classification task (AlexNet, GoogLeNet, ResNet-18, and ResNet-50) and their results are compared. Also, a novel convolutional neural network architecture comprised of two horizontally concatenated GoogLeNets is proposed with two inputs scalograms and spectrogram of the eplictic EEG signal. Four segment lengths (4097, 2048, 1024, and 512 sampling points) with three time-frequency representations (short-time Fourier, Wavelet, and Hilbert-Huang transform) are statistically evaluated. The dataset used in this research is collected at the University of Bonn. The dataset is reorganized as normal, interictal, and ictal. The maximum achieved accuracies for 4097, 2048, 1024, and 512 sampling points are 100 %, 100 %, 100 %, and 99.5 % respectively. The CAM method is used to analyze discriminative regions of time-frequency representations of EEG segments and networks' decisions. This method showed CNN models used different time and frequency regions of input images for each class with correct and incorrect predictions.
Classification and analysis of epileptic EEG recordings using convolutional neural network and class activation mapping
(2021) Yildiz, Abdulnasir; Zan, Hasan; Said, Sherif; Zan, Hasan; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
Electrical bio-signals have the potential to be used in different applications due to their hidden nature and their ability to facilitate liveness detection. This paper investigates the feasibility of using the Convolutional Neural Network (CNN) to classify and analyze electroencephalogram (EEG) data with their time-frequency representations and class activation mapping (CAM) to detect epilepsy disease. Several types of pre-trained CNNs are employed for a multi-class classification task (AlexNet, GoogLeNet, ResNet-18, and ResNet-50) and their results are compared. Also, a novel convolutional neural network architecture comprised of two horizontally concatenated GoogLeNets is proposed with two inputs scalograms and spectrogram of the eplictic EEG signal. Four segment lengths (4097, 2048, 1024, and 512 sampling points) with three time-frequency representations (short-time Fourier, Wavelet, and Hilbert-Huang transform) are statistically evaluated. The dataset used in this research is collected at the University of Bonn. The dataset is reorganized as normal, interictal, and ictal. The maximum achieved accuracies for 4097, 2048, 1024, and 512 sampling points are 100 %, 100 %, 100 %, and 99.5 % respectively. The CAM method is used to analyze discriminative regions of time-frequency representations of EEG segments and networks' decisions. This method showed CNN models used different time and frequency regions of input images for each class with correct and incorrect predictions.
Citation - WoS: 3
Citation - Scopus: 3
Classification of electroencephalogram records related to cursor movements with a hybrid method based on deep learning
(Wiley Online Library, 2021) Türk, Ömer; Türk, Ömer; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
In brain computer interface (BCI), many transformation methods are used whenprocessing electroencephalogram (EEG) signals. Thus, the EEG can be represen-ted in different domains. However, designing an EEG-based BCI system withoutany transformation technique is a challenge. For this purpose, in this study, aBCI model is proposed without any transformation. The classification of cursordown and cursor up movements using the EEG signals received from the brain isaimed at in the proposed model. The EEG patterns were classified using twomethods. Firstly, EEG signals were classified by classic convolutional neural net-work (CNN). Secondly, proposed hybrid structure obtained the EEG features,which were classified by k-NN and SVM, using CNN. Classification with CNNarchitecture gave a result of 68.15% while the hybrid method using k-NN andSVM classifiers yielded 97.55% and 97.61% respectively. The hybrid proposedmethod were more successful than the studies in the literature.
Classification of Epilepsy Types from Electroencephalogram Time Series Using Continuous Wavelet Transform Scalogram-Based Convolutional Neural Network
(ASTM International, 2020) Türk, Ömer; Akpolat, Veysi; Varol, Sefer; Aluçlu, Mehmet Ufuk; Özerdem, Mehmet Siraç; Türk, Ömer; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
During the supervisory activities of the brain, the electrical activities of nerve cell clusters produce oscillations. These complex biopotential oscillations are called electroencephalogram (EEG) signals. Certain diseases, such as epilepsy, can be detected by measuring these signals. Epilepsy is a disease that manifests itself as seizures. These seizures manifest themselves in different characteristics. These different characteristics divide epilepsy seizure types into two main groups: generalized and partial epilepsy. This study aimed to classify different types of epilepsy from EEG signals. For this purpose, a scalogram-based, deep learning approach has been developed. The utilized classification process had the following main steps: the scalogram images were obtained by using the continuous wavelet transform (CWT) method. So, a one-dimension EEG time series was converted to a two-dimensional time-frequency data set in order to extract more features. Then, the increased dimension data set (CWT scalogram images) was applied to the convolutional neural network (CNN) as input patterns for classifying the images. The EEG signals were taken from Dicle University, Neurology Clinic of Medical School. This data consisted of four classes: healthy brain waves, generalized preseizure, generalized seizure, and partial epilepsy brain waves. With the proposed method, the average accuracy performance of three of the EEG records' classes (healthy, generalized preseizure, and generalized seizure), and that of all four classes of EEG records were 90.16 % (± 0.20) and 84.66 % (± 0.48). According to these results, regarding the specific accuracy ratings of the recordings, the healthy EEG records scored 91.29 %, generalized epileptic seizure records were at 96.50 %, partial seizure EEG records scored 89.63 %, and the preseizure EEG records had a 90.44 % rating. The results of the proposed method were compared to the results of both similar studies and conventional methods. As a result, the performance of the proposed method was found to be acceptable.
Citation - WoS: 2
Citation - Scopus: 4
The convolutional neural network approach from electroencephalogram signals in emotional detection
(Concurrency Computation, 2021) Türk, Ömer; Özerdem, Mehmet Siraç; Türk, Ömer; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
Although brain-computer interfaces (BCI) progress rapidly, the desired success has not been achieved yet. One of these BCI is to detect emotional states in humans. An emotional state is a brain activity consisting of hormonal and mental reasons in the face of events. Emotions can be detected by electroencephalogram (EEG) signals due to these activities. Being able to detect the emotional state from EEG signals is important in terms of both time and cost. In this study, a method is proposed for the detection of the emotional state by using EEG signals. In the proposed method, we aim to classify EEG signals without any transform (Fourier transform, wavelet transform, etc.) or feature extraction method as a pre-processing. For this purpose, convolutional neural networks (CNNs) are used as classifiers, together with SEED EEG dataset containing three different emotional (positive, negative, and neutral) states. The records used in the study were taken from 15 participants in three sessions. In the proposed method, raw channel-time EEG recordings are converted into 28 × 28 size pattern segments without pre-processing. The obtained patterns are then classified in the CNN. As a result of the classification, three emotion performance averages of all participants are found to be 88.84%. Based on the participants, the highest classification performance is 93.91%, while the lowest classification performance is 77.70%. Also, the average f-score is found to be 0.88 for positive emotion, 0.87 for negative emotion, and 0.89 for neutral emotion. Likewise, the average kappa value is 0.82 for positive emotion, 0.81 for negative emotion, and 0.83 for neutral emotion. The results of the method proposed in the study are compared with the results of similar studies in the literature. We conclude that the proposed method has an acceptable level of performance.
Derin Öğrenme Kullanılarak Fundus Görüntülerinden Katarakt ve Diyabetik Retinopati Tespiti
(BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ, 2023) Aykat, Şükrü; Senan, Sibel; Aykat, Şükrü; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
Diyabetik retinopati ve katarakt ciddi körlüğe ve görme kaybına neden olabilen bazı retina hastalıklarıdır. Gözde meydana gelen bu geri dönüşü olmayan hasarı önlemek için retina hastalıklarının erken teşhisi hayati önem taşımaktadır. Bu çalışmanın problem cümlesi, bu retina hastalıklarının tespiti için derin öğrenme tabanlı sonuçların sunulması olarak verilebilir. Bu amaçla ilk önce ham bir veri seti üzerinde histogram eşitleme yöntemi kullanılarak yeni bir seti oluşturulmuştur. Ardından beş geleneksel derin öğrenme modeline hiperparametre ayarı yapılarak veri setleri üzerinde eğitimler gerçekleştirilmiştir. En son olarak veri setleri üzerinde en yüksek başarıya sahip MobileNet tabanlı bir hibrit model geliştirilmiştir. Önerilen hibrit model, ön işlenmiş veri seti üzerinde %99 doğruluk oranı elde etmiştir. Hibrit modelin sınıflandırma başarısının literatürdeki derin öğrenme modellerinin başarısından daha yüksek olduğu görülmüştür. Bu çalışma diyabetik retinopati ve katarakt hastalarının teşhis sürecine katkı sağlayacaktır.
Citation - WoS: 8
Citation - Scopus: 14
Employing deep learning architectures for image-based automatic cataract diagnosis
(TÜBİTAK, 2021) Acar, Emrullah; Türk, Ömer; Ertuğrul, Ömer Faruk; Aldemir, Erdoğan; Türk, Ömer; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
Various eye diseases affect the quality of human life severely and ultimately may result in complete vision loss. Ocular diseases manifest themselves through mostly visual indicators in the early or mature stages of the disease by showing abnormalities in optics disc, fovea, or other descriptive anatomical structures of the eye. Cataract is among the most harmful diseases that affects millions of people and the leading cause of public vision impairment. It shows major visual symptoms that can be employed for early detection before the hypermature stage. Automatic diagnosis systems intend to assist ophthalmological experts by mitigating the burden of manual clinical decisions and on health care utilization. In this study, a diagnosis system based on color fundus images are addressed for cataract disease. Deep learning-based models were performed for the automatic identification of cataract diseases. Two pretrained robust architectures, namely VGGNet and DenseNet, were employed to detect abnormalities in descriptive parts of the human eye. The proposed system is implemented on a wide and unique dataset that includes diverse color retinal fundus images that are acquired comparatively in low-cost and common modality, which is considered a major contribution of the study. The dataset show symptoms of cataracts in different phases and represents the characteristics of the cataract. By the proposed system, dysfunction associated with cataracts could be identified in the early stage. The achievement of the proposed system is compared to various traditional and up-to-date classification systems. The proposed system achieves 97.94% diagnosis rate for cataract disease grading.
Citation - WoS: 130
Citation - Scopus: 174
Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals
(MDPI, 2019) Türk, Ömer; Özerdem, Mehmet Siraç; Türk, Ömer; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
The studies implemented with Electroencephalogram (EEG) signals are progressing very rapidly and brain computer interfaces (BCI) and disease determinations are carried out at certain success rates thanks to new methods developed in this field. The effective use of these signals, especially in disease detection, is very important in terms of both time and cost. Currently, in general, EEG studies are used in addition to conventional methods as well as deep learning networks that have recently achieved great success. The most important reason for this is that in conventional methods, increasing classification accuracy is based on too many human efforts as EEG is being processed, obtaining the features is the most important step. This stage is based on both the time-consuming and the investigation of many feature methods. Therefore, there is a need for methods that do not require human effort in this area and can learn the features themselves. Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes. Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature. In order to compare the performance of the proposed method, the data set of the University of Bonn was used. The data set consists of five EEG records containing healthy and epilepsy disease which are labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-D and B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% in triple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternary classification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of 93.60%.
Epileptik EEG Sinyallerinin Sınıflandırılması için Bir Boyutlu Medyan Yerel İkili Örüntü Temelli Öznitelik Çıkarımı
(2017) Türk, Ömer; Özerdem, Mehmet Siraç; Türk, Ömer; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
Elektroansefalogram (EEG), epilepsi tespitinde yaygın olarak kullanılan önemli bir veri kaynağıdır. Bu çalışmada da Bonn Üniversitesi Epileptoloji bölümü veritabanından alınan ve A, B, C, D, E olmak üzere 5 işaret grubundan oluşan EEG kayıtları kullanılmıştır. Bir boyutklu medyan yerel ikili örüntü (1B-MYİÖ) yöntemi uygulanarak elde edilen özniteliklerin k-En Yakın Komşu (k-NN) sınıflandırıcısı ile sınıflandırılması amaçlanmıştır. Çalışmada geliştirilen 1BMYİÖ yönteminin öznitelik olarak sınıflandırma başarısı değerlendirilmiştir. Bu sınıflandırma için karışıklık matrisi hesaplanarak model başarım ölçümü yapılmıştır. Çalışmada A-E veri setleri için sınıflandırma performansı %100, A-D veri setleri için %99.00, D-E veri setleri için %98.00, E-CD veri setleri için %99.50 ve A-D-E veri setleri için de %96.00 olarak bulunmuştur. Çalışmada kullanılan 1B-MYİÖ yönteminin, literatürde kullanılan birçok yöntemden daha iyi sonuç verdiği görülmüştür.
Citation - WoS: 8
Citation - Scopus: 10
FPGA simulation of chaotic tent map-based S-Box design
(Wiley Online Library, 2022) Türk, Ömer; Türk, Ömer; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
The chaotic system has a characteristically random behavior by nature, and these systems have their own characteristics in a completely deterministic structure. This feature of a chaotic system makes it difficult to predict encryptions designed based on such a system. Thanks to this unpredictable and strong feature, maps produced from chaotic systems are an important alternative in the field of encryption. One of the structures obtained by employing chaotic maps is the substitution box. S-Box, which provides the confusion principle used in block ciphers, is the main block that dynamically replaces unencrypted data with confidential data and makes a significant contribution to ensuring high security in the encryption system. Therefore, S-Boxes hold a critical role in block ciphers. Speed and reliability are important parameters in the creation of this main block. Especially, applications performed on hardware are more reliable and high performance. Therefore, in this study, an S-Box was designed using fieldprogrammable gate arrays (FPGA) simulation from a chaotic tent map to create a fast and reliable S-Box because FPGAs offer solutions that may be important in this field considering their fast and customizable architecture. In the proposed method, the S-Box was created in 0.16 s. In addition, the dynamic properties of the chaotic tent map were analyzed with Lyapunov exponents, and the NIST SP 800-22 test was applied for the information encryption suitability of the proposed chaotic system. Also, to test the reliability of the produced S-Box structures, SAC, non-linearity, bit independence criteria, and input/output XOR distribution table metrics were implemented. The results showed that the proposed chaotic map was dynamic and passed the reliability tests successfully.
Citation - WoS: 6
Citation - Scopus: 5
How advantageous is it to use computed tomography image-based artificial intelligence modelling in the differential diagnosis of chronic otitis media with and without cholesteatoma?
(European Review for Medical and Pharmacological Sciences, 2023) Türk, Ömer; Temiz, Hakan; Department of Basic Medical Sciences / Temel Tıp Bilimleri Bölümü; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 10. Faculty of Medicine / Tıp Fakültesi; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
Abstract. – OBJECTIVE: Cholesteatoma (CHO) developing secondary to chronic otitis media (COM) can spread rapidly and cause important health problems such as hearing loss. Therefore, the presence of CHO should be diagnosed promptly with high accuracy and then treated surgically. The aim of this study was to investigate the effectiveness of artificial intelligence applications (AIA) in documenting the presence of CHO based on computed tomography (CT) images. PATIENTS AND METHODS: The study was performed on CT images of 100 CHO, 100 non-cholesteatoma (N-CHO) COM, and 100 control patients. Two AIA models including ResNet50 and MobileNetV2 were used for the classification of the images. RESULTS: Overall accuracy rate was 93.33% for the ResNet50 model and 86.67% for the MobilNetV2 model. Moreover, the diagnostic accuracy rates of these two models were 100% and 95% in the CHO group, 90% and 85% in the N-CHO group, and 90% and 80% in the control group, respectively. CONCLUSIONS: These results indicate that the use of AIA in the diagnosis of CHO will improve the diagnostic accuracy rates and will also help physicians in terms of reducing their workload and facilitating the selection of the correct treatment strategy.
Citation - WoS: 7
Citation - Scopus: 7
Identification of cotton and corn plant areas by employing deep transformer encoder approach and different time series satellite images: A case study in Diyarbakir, Turkey
(ScienceDirect, 2023) Türk, Ömer; Şimşek Bağcı, Reyhan; Acar, Emrullah; Türk, Ömer; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
It is very important to determine the crops in the agricultural field in a short time and accurately. Thanks to the satellite images obtained from remote sensing sensors, information can be obtained on many subjects such as the detection and development of agricultural products and annual product forecasting. In this study, it is aimed to automatically detect agricultural crops (corn and cotton) by using Sentinel-1 and Landsat-8 satellite image indexes via a new deep learning approach (Deep Transformer Encoder). This work was carried out in several stages, respectively. In the first stage, a pilot area was determined to obtain Sentinel-1 and Landsat-8 satellite images of agricultural crops used in this study. In the second stage, the coordinates of 100 sample points from this pilot area were taken with the help of GPS and these coordinates were then transferred to Sentinel-1 and Landsat-8 satellite images. In the next step, reflection and backscattering values were obtained from the pixels of the satellite images corresponding to the sample points of these agricultural crops. While creating the data sets of satellite images, the months of June, July, August and September for the years 2016–2021, when the development and harvesting times of agricultural products are close to each other, were preferred. The image data set used in the study consists of a total of 434 images for Sentinel-1 satellite and a total of 693 images for Landsat-8. At the last stage, the datasets obtained from different satellite images were evaluated in three different categories for crop identification with the aid of Deep Transformer Encoder approach. These are: (1-) Crop identification with only Sentinel-1 dataset, (2-) Crop identification only with Landsat-8 dataset, (3-) Crop identification with both Sentinel-1 and Landsat-8 datasets. The results showed that 85%, 95% and 87.5% accuracy values were obtained from the band parameters of Sentinel-1 dataset, Landsat-8 dataset and Sentinel-1&Landsat-8 datasets, respectively
Citation - WoS: 9
Citation - Scopus: 12
Local Pattern Transformation-Based convolutional neural network for sleep stage scoring
(ScienceDirect, 2023) Zan, Hasan; Yildiz, Abdulnasır; Zan, Hasan; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
Sleep stage scoring is essential for the diagnosis and treatment of sleep disorders. However, manual sleep scoring is a tedious, time-consuming, and subjective task. Therefore, this paper proposes a novel framework based on local pattern transformation (LPT) methods and convolutional neural networks for automatic sleep stage scoring. Unlike in previous works in other fields, these methods were not employed for manual feature extraction, which requires expert knowledge and the pipeline behind it might bias results. The transformed signals were directly fed into a CNN model (called EpochNet) that can accept multiple successive epochs. The model learns features from multiple input epochs and considers inter-epoch context during classification. To evaluate and validate the effectiveness of the proposed approach, we conducted several experiments on the Sleep-EDF dataset. Four LPT methods, including One-dimensional Local Binary Pattern (1D-LBP), Local Neighbor Descriptive Pattern (LNDP), Local Gradient Pattern (LGP), and Local Neighbor Gradient Pattern (LNGP), and different polysomnography (PSG) signals were analyzed as sequence length (number of input epochs) increased from one to five. 1D-LBP and LNDP achieved similar performances, outperforming other LPT methods that are less sensitive to local variations. The best performance was achieved when an input sequence containing five epochs of PSG signals transformed by 1D-LBP was employed. The best accuracy, F1 score, and Kohen’s kappa coefficient were 0.848, 0.782, and 0.790, respectively. The results showed that our approach can achieve comparable performance to other state-ofthe-art methods while occupying fewer computing resources because of the compact size of EpochNet.
Local Pattern Transformation-Based convolutional neural network for sleep stage scoring
(2023) Zan, Hasan; Yıldız, Abdulnasır; Zan, Hasan; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
Sleep stage scoring is essential for the diagnosis and treatment of sleep disorders. However, manual sleep scoring is a tedious, time-consuming, and subjective task. Therefore, this paper proposes a novel framework based on local pattern transformation (LPT) methods and convolutional neural networks for automatic sleep stage scoring. Unlike in previous works in other fields, these methods were not employed for manual feature extraction, which requires expert knowledge and the pipeline behind it might bias results. The transformed signals were directly fed into a CNN model (called EpochNet) that can accept multiple successive epochs. The model learns features from multiple input epochs and considers inter-epoch context during classification. To evaluate and validate the effectiveness of the proposed approach, we conducted several experiments on the Sleep-EDF dataset. Four LPT methods, including One-dimensional Local Binary Pattern (1D-LBP), Local Neighbor Descriptive Pattern (LNDP), Local Gradient Pattern (LGP), and Local Neighbor Gradient Pattern (LNGP), and different polysomnography (PSG) signals were analyzed as sequence length (number of input epochs) increased from one to five. 1D-LBP and LNDP achieved similar performances, outperforming other LPT methods that are less sensitive to local variations. The best performance was achieved when an input sequence containing five epochs of PSG signals transformed by 1D-LBP was employed. The best accuracy, F1 score, and Kohen's kappa coefficient were 0.848, 0.782, and 0.790, respectively. The results showed that our approach can achieve comparable performance to other state-of-the-art methods while occupying fewer computing resources because of the compact size of EpochNet.
Mesleki lise öğretmenlerinin, öğrencilerinin ve idarecilerinin hizmetiçi eğitim öncesi ile sonrası etkileşimli tahtaya ilişkin görüşlerinin incelenmesi
(2017) AYKAT, Şükrü; Aykat, Şükrü; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
Bu çalışmanın amacı; FATİH Projesi uygulanan mesleki liselerdeki öğretmenlerin, ETEKUK eğitimi öncesi ve sonrası etkileşimli tahtayı kullanma düzeylerinde, öz yeterliliklerinde ve görüşlerinde farklılık olup olmadığını belirlemektir. Örneklem 2015-2016 eğitim öğretim yılında Mardin ili Midyat ilçesinde FATİH Projesi çerçevesinde etkileşimli tahta kurulumu yapılmış Telkari Mesleki ve Teknik Anadolu Lisesi’nde görev yapan ETEKUK eğitimine katılan öğretmenler ve okul idarecileri ile okulda eğitim gören öğrencilerden oluşmaktadır. Bu çalışmada karma yöntem modeli kullanılmıştır. Veriler ölçek, anket ve yarı yapılandırılmış görüşme formları ile toplanmıştır. Nicel veriler ortalama ve t-Testi, nitel veriler ise içerik analizi ile çözümlenmiştir. Araştırma sonunda öğretmenlerin ETEKUK eğitimi sonrasında etkileşimli tahta kullanma öz yeterliliklerinde ve etkileşimli tahta kullanma düzeylerinde anlamlı bir değişimin olmadığı görülmüştür. Ayrıca öğretmenlerin ETEKUK eğitiminden önce etkileşimli tahta için materyal geliştiremedikleri ETEKUK eğitiminden sonra ise kısmen materyal hazırlayabildikleri görülmüştür. Bu da ETEKUK eğitim içeriğinin etkileşimli tahta için materyal geliştirmede yeterli olmadığını göstermektedir. Öğrenciler ise; etkileşimli tahta kullanılan dersleri tercih ettiklerini, öğretmenlerin etkileşimli tahtayı kullanmaya devam etmelerini istediklerini belirtmişlerdir. Etkileşimli tahta arızaları dersi olumsuz yönde etkilediğini de belirtmişlerdir. Öğretmenler, öğrenciler ve okul idarecileri derste etkileşimli tahta kullanılmasıyla; öğrenci başarısının arttığını, derse ilgi ve katılımı olumlu yönde etkilediğini belirtmişlerdir.
Multi-task learning for arousal and sleep stage detection using fully convolutional networks
(2023) Zan, Hasan; Yıldız, Abdulnasır; Zan, Hasan; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
Objective. Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, as frequent and excessive occurrences of arousals disrupt sleep stage patterns and lead to poor sleep quality, negatively impacting physical and mental health. Polysomnography is a traditional method for arousal and sleep stage detection that is time-consuming and prone to high variability among experts. Approach. In this paper, we propose a novel multi-task learning approach for arousal and sleep stage detection using fully convolutional neural networks. Our model, FullSleepNet, accepts a full-night single-channel EEG signal as input and produces segmentation masks for arousal and sleep stage labels. FullSleepNet comprises four modules: a convolutional module to extract local features, a recurrent module to capture long-range dependencies, an attention mechanism to focus on relevant parts of the input, and a segmentation module to output final predictions. Main results. By unifying the two interrelated tasks as segmentation problems and employing a multi-task learning approach, FullSleepNet achieves state-of-the-art performance for arousal detection with an area under the precision-recall curve of 0.70 on Sleep Heart Health Study and Multi-Ethnic Study of Atherosclerosis datasets. For sleep stage classification, FullSleepNet obtains comparable performance on both datasets, achieving an accuracy of 0.88 and an F1-score of 0.80 on the former and an accuracy of 0.83 and an F1-score of 0.76 on the latter. Significance. Our results demonstrate that FullSleepNet offers improved practicality, efficiency, and accuracy for the detection of arousal and classification of sleep stages using raw EEG signals as input.
Citation - WoS: 8
Citation - Scopus: 7
Multi-task learning for arousal and sleep stage detection using fully convolutional networks
(IOP Publishing, 2023) Zan, Hasan; Yıldız, Abdulnasir; Zan, Hasan; 08.01. Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; 08. Faculty of Engineering and Architecture / Mühendislik Mimarlık Fakültesi; 01. Mardin Artuklu University / Mardin Artuklu Üniversitesi
Objective: Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, as frequent and excessive occurrences of arousals disrupt sleep stage patterns and lead to poor sleep quality, negatively impacting physical and mental health. Polysomnography is a traditional method for arousal and sleep stage detection that is time-consuming and prone to high variability among experts. Approach: In this paper, we propose a novel multi-task learning approach for arousal and sleep stage detection using fully convolutional neural networks. Our model, FullSleepNet, accepts a full-night single-channel EEG signal as input and produces segmentation masks for arousal and sleep stage labels. FullSleepNet comprises four modules: a convolutional module to extract local features, a recurrent module to capture long-range dependencies, an attention mechanism to focus on relevant parts of the input, and a segmentation module to output final predictions. Main results: By unifying the two interrelated tasks as segmentation problems and employing a multi-task learning approach, FullSleepNet achieves state-of-the-art performance for arousal detection with an area under the precision-recall curve of 0.70 on Sleep Heart Health Study and Multi-Ethnic Study of Atherosclerosis datasets. For sleep stage classification, FullSleepNet obtains comparable performance on both datasets, achieving an accuracy of 0.88 and an F1-score of 0.80 on the former and an accuracy of 0.83 and an F1-score of 0.76 on the latter. Significance: Our results demonstrate that FullSleepNet offers improved practicality, efficiency, and accuracy for the detection of arousal and classification of sleep stages using raw EEG signals as input.

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