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Full-Text Articles in Physical Sciences and Mathematics
Deep Learning Techniques Of Losses In Data Transmitted In Wirelesssensor Networks, Mevlüt Ersoy, Beki̇r Aksoy
Deep Learning Techniques Of Losses In Data Transmitted In Wirelesssensor Networks, Mevlüt Ersoy, Beki̇r Aksoy
Turkish Journal of Electrical Engineering and Computer Sciences
Wireless sensor network (WSN) systems are frequently used today as a result of rapid technological developments. Wireless sensor networks, which form the basis of the Internet of Things (IoT), have a wide range of use in theworld from education to health, and from military applications to home applications. It enables the data obtained fromthe sensors to be transferred between nodes with the help of end-to-end wireless protocols. In parallel with the increasingnumber of nodes in WSN, data tra?ic density also increases. Due to the limitations of the WSN network, lost packetrates also increase with increasing data tra?ic. In this study, …
Ensemble Learning Of Multiview Cnn Models For Survival Time Prediction Of Braintumor Patients Using Multimodal Mri Scans, Abdela Ahmed Mossa, Ulus Çevi̇k
Ensemble Learning Of Multiview Cnn Models For Survival Time Prediction Of Braintumor Patients Using Multimodal Mri Scans, Abdela Ahmed Mossa, Ulus Çevi̇k
Turkish Journal of Electrical Engineering and Computer Sciences
Brain tumors have been one of the most common life-threatening diseases for all mankind. There have beenhuge efforts dedicated to the development of medical imaging techniques and radiomics to diagnose tumor patients quicklyand e?iciently. One of the main aims is to ensure that preoperative overall survival time (OS) prediction is accurate.Recently, deep learning (DL) algorithms, and particularly convolutional neural networks (CNNs) achieved promisingperformances in almost all computer vision fields. CNNs demand large training datasets and high computational costs.However, curating large annotated medical datasets are difficult and resource-intensive. The performances of singlelearners are also unsatisfactory for small datasets. Thus, this study …
Neural Relation Extraction: A Review, Mehmet Aydar, Özge Bozal, Furkan Özbay
Neural Relation Extraction: A Review, Mehmet Aydar, Özge Bozal, Furkan Özbay
Turkish Journal of Electrical Engineering and Computer Sciences
Neural relation extraction discovers semantic relations between entities from unstructured text using deeplearning methods. In this study, we make a clear categorization of the existing relation extraction methods in termsof data expressiveness and data supervision, and present a comprehensive and comparative review. We describe theevaluation methodologies and the datasets used for model assessment. We explicitly state the common challenges inrelation extraction task and point out the potential of the pretrained models to solve them. Accordingly, we investigateadditional research directions and improvement ideas in this field.
Learning Multiview Deep Features From Skeletal Sign Language Videos Forrecognition, Ashraf Ali Shaik, Venkata Durga Prasad Mareedu, Venkata Vijaya Kishore Polurie
Learning Multiview Deep Features From Skeletal Sign Language Videos Forrecognition, Ashraf Ali Shaik, Venkata Durga Prasad Mareedu, Venkata Vijaya Kishore Polurie
Turkish Journal of Electrical Engineering and Computer Sciences
The most challenging objective in machine translation of sign language has been the machine?s inability tolearn interoccluding finger movements during an action process. This work addresses the problem of teaching a deeplearning model to recognize differently oriented skeletal data. The multi-view 2D skeletal sign language video data isobtained using 3D motion-captured system. A total of 9 signer views were used for training the proposed network andthe 6 for testing and validation. In order to obtain multi-view deep features for recognition, we proposed an end-to-endtrainable multistream convolutional neural network (CNN) with late feature fusion. The fused multiview features arethen inputted to …
Turkish Sign Language Recognition Based On Multistream Data Fusion, Cemi̇l Gündüz, Hüseyi̇n Polat
Turkish Sign Language Recognition Based On Multistream Data Fusion, Cemi̇l Gündüz, Hüseyi̇n Polat
Turkish Journal of Electrical Engineering and Computer Sciences
Sign languages are nonverbal, visual languages that hearing- or speech-impaired people use for communication.Aside from hands, other communication channels such as body posture and facial expressions are also valuable insign languages. As a result of the fact that the gestures in sign languages vary across countries, the significance ofcommunication channels in each sign language also differs. In this study, representing the communication channels usedin Turkish sign language, a total of 8 different data streams-4 RGB, 3 pose, 1 optical flow-were analyzed. Inception3D was used for RGB and optical flow; and LSTM-RNN was used for pose data streams. Experiments were conductedby …
Visual Object Detection For Autonomous Transport Vehicles In Smart Factories, Nazlican Gengeç, Onur Eker, Hakan Çevi̇kalp, Ahmet Yazici, Hasan Serhan Yavuz
Visual Object Detection For Autonomous Transport Vehicles In Smart Factories, Nazlican Gengeç, Onur Eker, Hakan Çevi̇kalp, Ahmet Yazici, Hasan Serhan Yavuz
Turkish Journal of Electrical Engineering and Computer Sciences
Autonomous transport vehicles (ATVs) are one of the most substantial components of smart factories of Industry 4.0. They are primarily considered to transfer the goods or perform some certain navigation tasks in the factory with self driving. The recent developments on computer vision studies allow the vehicles to visually perceive the environment and the objects in the environment. There are numerous applications especially for smart traffic networks in outdoor environments but there is lack of application and databases for autonomous transport vehicles in indoor industrial environments. There exist some essential safety and direction signs in smart factories and these signs …
Sleep Staging With Deep Structured Neural Net Using Gabor Layer And Dataaugmentation, Ali Erfani Sholeyan, Fereidoun Nowshiravan Rahatabad, Kamal Setaredan
Sleep Staging With Deep Structured Neural Net Using Gabor Layer And Dataaugmentation, Ali Erfani Sholeyan, Fereidoun Nowshiravan Rahatabad, Kamal Setaredan
Turkish Journal of Electrical Engineering and Computer Sciences
Slow wave sleep (SWS) and rapid eye movement (REM) are two of the most important sleep stages that are considered in many studies. Detection of these two sleep stages will help researchers in many applications to detect sleeprelated diseases and disorders and also in many fields of neuroscience studies such as cognitive impairment and memory consolidation. Since manual sleep staging is time-consuming, subjective, and expensive; designing an efficient automatic sleep scoring system will overcome some of these difficulties. Many studies have proposed automatic sleep staging systems with different methods. In recent years, deep learning methods show their potential in different …
Malignant Skin Melanoma Detection Using Image Augmentation By Oversamplingin Nonlinear Lower-Dimensional Embedding Manifold, Olusola Oluwakemi Abayomi-Alli, Robertas Damasevicius, Sanjay Misra, Rytis Maskeliunas, Adebayo Abayomi-Alli
Malignant Skin Melanoma Detection Using Image Augmentation By Oversamplingin Nonlinear Lower-Dimensional Embedding Manifold, Olusola Oluwakemi Abayomi-Alli, Robertas Damasevicius, Sanjay Misra, Rytis Maskeliunas, Adebayo Abayomi-Alli
Turkish Journal of Electrical Engineering and Computer Sciences
The continuous rise in skin cancer cases, especially in malignant melanoma, has resulted in a high mortality rate of the affected patients due to late detection. Some challenges affecting the success of skin cancer detection include small datasets or data scarcity problem, noisy data, imbalanced data, inconsistency in image sizes and resolutions, unavailability of data, reliability of labeled data (ground truth), and imbalance of skin cancer datasets. This study presents a novel data augmentation technique based on covariant Synthetic Minority Oversampling Technique (SMOTE) to address the data scarcity and class imbalance problem. We propose an improved data augmentation model for …
Evolution Of Histopathological Breast Cancer Images Classification Using Stochasticdilated Residual Ghost Model, Ramgopal Kashyap
Evolution Of Histopathological Breast Cancer Images Classification Using Stochasticdilated Residual Ghost Model, Ramgopal Kashyap
Turkish Journal of Electrical Engineering and Computer Sciences
Breast cancer detection is a complex problem to solve, and it is a topic that is still being studied. Deep learning-based models aid medical science by helping to classify benign and malignant cancers and saving lives. Breast cancer histopathological image classification (BreakHis) and breast cancer histopathological annotation and diagnosis (BreCaHAD) datasets are used in the proposed model. The study led to the resolution of four essential issues: 1) Addresses the color divergence issue caused by strain normalization during image generation 2) Data augmentation uses several factors like as flip, rotation, shift, resize, and gamma value in order to overcome overfitting …
Detection Of Amyotrophic Lateral Sclerosis Disease By Variational Modedecomposition And Convolution Neural Network Methods From Event-Relatedpotential Signals, Fatma Lati̇foğlu, Firat Orhan Bulucu, Rami̇s İleri̇
Detection Of Amyotrophic Lateral Sclerosis Disease By Variational Modedecomposition And Convolution Neural Network Methods From Event-Relatedpotential Signals, Fatma Lati̇foğlu, Firat Orhan Bulucu, Rami̇s İleri̇
Turkish Journal of Electrical Engineering and Computer Sciences
Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is a neurological disease that occurs as a result of damage to the nerves in the brain and restriction of muscle movements. Electroencephalography (EEG) is the most common method used in brain imaging to study neurological disorders. Diagnosis of neurological disorders such as ALS, Parkinson's, attention deficit hyperactivity disorder is important in biomedical studies. In recent years, deep learning (DL) models have been started to be applied in the literature for the diagnosis of these diseases. In this study, event-related potentials (ERPs) were obtained from EEG signals obtained as a …
A Novel Approach For Intrusion Detection Systems: V-Ids, Kenan İnce
A Novel Approach For Intrusion Detection Systems: V-Ids, Kenan İnce
Turkish Journal of Electrical Engineering and Computer Sciences
An intrusion detection system (IDS) is a security mechanism that detects abnormal activities in a network. An ideal IDS must detect intrusion attempts and maybe categorize them for further research and keep false-positive analysis at a very low level. IDSs are used in the analysis of network traffic data at all sizes. Studies on this subject focused on machine learning techniques. Even though the performance rates are high, it is seen that processes such as data understanding, preprocessing, and consistency tests are time-consuming and laborious. For this reason, the use of deep learning (DL) models that automatically perform the mentioned …
Deep Learning For Turkish Makam Music Composition, İsmai̇l Hakki Parlak, Yalçin Çebi̇, Ci̇han Işikhan, Derya Bi̇rant
Deep Learning For Turkish Makam Music Composition, İsmai̇l Hakki Parlak, Yalçin Çebi̇, Ci̇han Işikhan, Derya Bi̇rant
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, we introduce a new deep-learning-based system that can compose structured Turkish makam music (TMM) in the symbolic domain. Presented artificial TMM composer (ATMMC) takes eight initial notes from a human user and completes the rest of the piece. The backbone of the composer system consists of multilayered long short-term memory (LSTM) networks. ATMMC can create pieces in Hicaz and Nihavent makams in Şarkı form, which can be viewed and played with Mus2, a notation software for microtonal music. Statistical analysis shows that pieces composed by ATMMC are approximately 84% similar to training data. ATMMC is an open-source …
Image Forgery Detection Based On Fusion Of Lightweight Deep Learning Models, Amit Doegar, Srinidhi Hiriyannaiah, Siddesh Gaddadevara Matt, Srinivasa Krishnarajanagar Gopaliyengar, Maitreyee Dutta
Image Forgery Detection Based On Fusion Of Lightweight Deep Learning Models, Amit Doegar, Srinidhi Hiriyannaiah, Siddesh Gaddadevara Matt, Srinivasa Krishnarajanagar Gopaliyengar, Maitreyee Dutta
Turkish Journal of Electrical Engineering and Computer Sciences
Image forgery detection is one of the key challenges in various real time applications, social media and online information platforms. The conventional methods of detection based on the traces of image manipulations are limited to the scope of predefined assumptions like hand-crafted features, size and contrast. In this paper, we propose a fusion based decision approach for image forgery detection. The fusion of decision is based on the lightweight deep learning models namely SqueezeNet, MobileNetV2 and ShuffleNet. The fusion decision system is implemented in two phases. First, the pretrained weights of the lightweight deep learning models are used to evaluate …
A Hybrid Approach Based On Transfer And Ensemble Learning For Improvingperformances Of Deep Learning Models On Small Datasets, Tunç Gülteki̇n, Aybars Uğur
A Hybrid Approach Based On Transfer And Ensemble Learning For Improvingperformances Of Deep Learning Models On Small Datasets, Tunç Gülteki̇n, Aybars Uğur
Turkish Journal of Electrical Engineering and Computer Sciences
The need for high-volume data is one of the challenging requirements of the deep learning methods, and it makes it harder to apply deep learning algorithms to domains in which the data sources are limited, in other words, small. These domains may vary from medical diagnosis to satellite imaging. The performances of the deep learning methods on small datasets can be improved by the approaches such as data augmentation, ensembling, and transfer learning. In this study, we propose a new approach that utilizes transfer learning and ensemble methods to increase the accuracy rates of convolutional neural networks for classification tasks …
Brain Tumor Detection From Mri Images With Using Proposed Deep Learningmodel: The Partial Correlation-Based Channel Selection, Atinç Yilmaz
Brain Tumor Detection From Mri Images With Using Proposed Deep Learningmodel: The Partial Correlation-Based Channel Selection, Atinç Yilmaz
Turkish Journal of Electrical Engineering and Computer Sciences
A brain tumor is an abnormal growth of a mass or cell in the brain. Early diagnosis of the tumor significantly increases the chances of successful treatment. Artificial intelligence-based systems can detect the tumor in early stages. In this way, it could be possible to detect a tumor and resolve this problem that may endanger human life early. In the study, the partial correlation-based channel selection formula was presented that allowed the selection of the most prominent feature that differs from the other studies in the literature. Additionally, the multi-channel convolution structure was proposed for the feature network phase of …
Deep-Learning-Based Spraying Area Recognition System Forunmanned-Aerial-Vehicle-Based Sprayers, Shahbaz Khan, Muhammad Tufail, Muhammad Tahir Khan, Zubair Ahmed Khan, Shahzad Anwer
Deep-Learning-Based Spraying Area Recognition System Forunmanned-Aerial-Vehicle-Based Sprayers, Shahbaz Khan, Muhammad Tufail, Muhammad Tahir Khan, Zubair Ahmed Khan, Shahzad Anwer
Turkish Journal of Electrical Engineering and Computer Sciences
Unmanned aerial vehicle (UAV)-based spraying system employing machine learning techniques is a recent advancement in precision agriculture for precise spraying, promoting saving chemicals (pesticide/herbicide), and enhancing their effectiveness. This study aims to develop an efficient deep learning system for UAV-based sprayers, which has the capability to accurately recognize spraying areas. A deep learning system is proposed and developed incorporating a faster region-based convolutional neural network (R-CNN) for the imagery collected. In order to develop a classifier for identifying spraying areas from nonspraying areas, four different agriculture croplands and orchards were considered. All the experiments were performed in agriculture fields through …
Utilizing Resonant Scattering Signal Characteristics Via Deep Learning For Improvedclassification Of Complex Targets, Tuğçe Toprak, Mustafa Alper Selver, Mustafa Seçmen, Emi̇ne Yeşi̇m Zoral
Utilizing Resonant Scattering Signal Characteristics Via Deep Learning For Improvedclassification Of Complex Targets, Tuğçe Toprak, Mustafa Alper Selver, Mustafa Seçmen, Emi̇ne Yeşi̇m Zoral
Turkish Journal of Electrical Engineering and Computer Sciences
Object classification using late-time resonant scattering electromagnetic signals is a significant problem found in different areas of application. Due to their unique properties, spherical objects play an essential role in this field both as a challenging target and a resource of analytical late-time resonant scattering electromagnetic signals. Although many studies focus on their detailed analysis, the challenges associated with target classification by resonant late-time resonant scattering electromagnetic signals from multilayer spheres have not been investigated in detail. Moreover, existing studies made the simplifying assumption that the objects having (one or more) layers constitute equal permeability values at the core and …
Bagging Ensemble For Deep Learning Based Gender Recognition Using Test-Timeaugmentation On Large-Scale Datasets, Taner Danişman
Bagging Ensemble For Deep Learning Based Gender Recognition Using Test-Timeaugmentation On Large-Scale Datasets, Taner Danişman
Turkish Journal of Electrical Engineering and Computer Sciences
We present a bagging ensemble of convolutional networks in combination with the test-time augmentation technique to improve performance on the cross-dataset gender recognition problem. The bagging ensemble combines the predictions from multiple homogeneous models into the ensemble prediction. Augmentation techniques are often used in the learning phase of the CNNs to improve the generalization ability. On the other hand, test-time augmentation is not a common method used in the testing phase of the learned model. We conducted experiments on models trained using different hyperparameters. We augmented the test data and combine the predictive outputs from these network models. Experiments performed …
Deep Learning-Based Covid-19 Detection System Using Pulmonary Ct Scans, Rajit Nair, Adi Alhudhaif, Deepika Koundal, Rumi Iqbal Doewes, Preeti Sharma
Deep Learning-Based Covid-19 Detection System Using Pulmonary Ct Scans, Rajit Nair, Adi Alhudhaif, Deepika Koundal, Rumi Iqbal Doewes, Preeti Sharma
Turkish Journal of Electrical Engineering and Computer Sciences
One of the most significant pandemics has been raised in the form of Coronavirus disease 2019 (COVID19). Many researchers have faced various types of challenges for finding the accurate model, which can automatically detect the COVID-19 using computed pulmonary tomography (CT) scans of the chest. This paper has also focused on the same area, and a fully automatic model has been developed, which can predict the COVID-19 using the chest CT scans. The performance of the proposed method has been evaluated by classifying the CT scans of community-acquired pneumonia (CAP) and other non-pneumonia. The proposed deep learning model is based …
Leukocyte Classification Based On Feature Selection Using Extra Trees Classifier: Atransfer Learning Approach, Diana Baby, Sujitha Juliet Devaraj, Jude Hemanth, Anishin Raj M M
Leukocyte Classification Based On Feature Selection Using Extra Trees Classifier: Atransfer Learning Approach, Diana Baby, Sujitha Juliet Devaraj, Jude Hemanth, Anishin Raj M M
Turkish Journal of Electrical Engineering and Computer Sciences
The criticality of investigating the white blood cell (WBC) count cannot be underestimated, as white blood cells are an important component of the body's defence system. From helping to diagnose hidden infections to insinuating the presence of comorbidities like immunodeficiency, an accurate white blood cell count can contribute significantly to shape a physician?s assessment. The manual process performed by the pathologists for the classification of WBCs is a time consuming and tedious task, which is further disadvantaged by a lack of accuracy. This study concentrates on the automatic detection and classification of WBC without data augmentation into four subtypes such …
Employing Deep Learning Architectures For Image-Based Automatic Cataractdiagnosis, Emrullah Acar, Ömer Türk, Ömer Faruk Ertuğrul, Erdoğan Aldemi̇r
Employing Deep Learning Architectures For Image-Based Automatic Cataractdiagnosis, Emrullah Acar, Ömer Türk, Ömer Faruk Ertuğrul, Erdoğan Aldemi̇r
Turkish Journal of Electrical Engineering and Computer Sciences
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 …
Diagnosis Of Paroxysmal Atrial Fibrillation From Thirty-Minute Heart Ratevariability Data Using Convolutional Neural Networks, Murat Sürücü, Yalçin İşler, Resul Kara
Diagnosis Of Paroxysmal Atrial Fibrillation From Thirty-Minute Heart Ratevariability Data Using Convolutional Neural Networks, Murat Sürücü, Yalçin İşler, Resul Kara
Turkish Journal of Electrical Engineering and Computer Sciences
Paroxysmal atrial fibrillation (PAF) is the initial stage of atrial fibrillation, one of the most common arrhythmia types. PAF worsens with time and affects the patient?s life quality negatively. In this study, we aimed to diagnose PAF early, so patients can start taking precautions before this disease gets worse. We used the atrial fibrillation prediction database, an open data from Physionet and constructed our approach using convolutional neural networks. Heart rate variability (HRV) features are calculated from time-domain measures, frequency-domain measures using power spectral density estimations (fast Fourier transform, Lomb-Scargle, and Welch periodogram), time-frequencydomain measures using wavelet transform, and nonlinear …