Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Dogan, Ramazan Ozgur(10)
- Kayikçioglu, Temel(8)
- Bingöl, Ozkan(5)
- Ekinci, Murat(5)
- Yildirim, Omer(5)
- Albayrak, Cenk(4)
- Türk, Kadir(4)
- Akpulat, Murat(3)
- Köse, Cemal(3)
- Çakmak, Recep(3)
- Altas, Ismail Hakki(2)
- Dogan, Hulya(2)
- Gedikli, Eyup(2)
- Kayikcioglu, Temel(2)
- Küçük, Mehmet Ali(2)
- Simsek, Cemaleddin(2)
- Ulutas, Guzin(2)
- Özcan, Zubeyir(2)
- Altas, Ismail H.(1)
- Altas, Ismail Hakk(1)
- Ayas, Mustafa Sinasi(1)
- Ayas, Selen(1)
- Baser, Oguzhan(1)
- Basturk, S(1)
- Bozkurt, Ferhat(1)
- Cakmak, Recep(1)
- Demir, Yasin(1)
- Ilarslan, M(1)
- Imamoglu, Mustafa Bilgehan(1)
- Ince, F(1)
- Incir, Ramazan(1)
- Kaplan, Erdar(1)
- Karakaya, Sukru(1)
- Kaya, Haydar(1)
- Kaya, Ismail(1)
- Kayikçioglu, Ilknur(1)
- Kaynak, O(1)
- Kobya, Mehmet Emin(1)
- Kul, Sibel(1)
- Küçükugurlu, Busranur(1)
Yayınlar
- BildiriAçık Erişim2024Experimental Demonstration of an Optic-DAC Based Transmitter Structure
Baser, Oguzhan; Küçük, Mehmet Ali; Albayrak, Cenk; Türk, Kadir
In this paper, an optical-DAC (ODAC) based transmitter design is proposed for indoor visible light communication (VLC) applications. The proposed design is practically realized and the communication performance of the experimental VLC system is investigated. In the ODAC transmitter, multilevel communication signal can be generated optically by independently switching light emitting diode (LED) groups with different powers. In this way, multilevel modulation techniques in VLC systems can be realized through a simple transmitter structure that can be digitally driven. In the proposed transmitter design, unlike the existing ODAC-based VLC transmitters in the literature, metal oxide semiconductor field effect transistors (MOSFET) are used as switching elements in order to drive high-power LED for ambient illumination and communication. In the experimental studies, an offline VLC system using the designed ODAC-based transmitter with a power of approximately 10 W is tested at different switching speeds and distances. The results show that by implementing 8 PAM modulation in the proposed transmitter design, a data transmission rate of 6.264 Mbps is successfully achieved.
- BildiriAçık Erişim2024Reconfigurable Optical Filter Based Receiver Structure For Visible Light Communication
Küçük, Mehmet Ali; Albayrak, Cenk; Türk, Kadir
In this paper, a novel receiver structure is proposed to mitigate the inter-channel interference (ICI) effects that limit the data rate in multi-color visible light communication (MC-VLC) systems. Reconfigurable optical filters (ROFs) are used at the receiver side of MC-VLC systems to enable the separation of color channels. ROFs are optical elements that have the ability to electrically change their transmission spectrum. Utilizing this feature of ROFs, a receiver structure is developed that, unlike traditional receiver structures, can adaptively adjust the transmit spectrum based on channel state information. This adaptive adjustment is aimed at maximizing communication performance by effectively separating color channels in MC-VLC systems. The obtained results demonstrate that the proposed receiver structure achieves higher communication performance compared to traditional receivers.
- BildiriAçık Erişim2024Cross-view Gait Recognition Based on Fine-Tuned Deep Networks
Yaprak, Busranur; Gedikli, Eyup; Bingöl, Ozkan; Dogan, Ramazan Ozgur
Gait recognition is a biometrics-based computer vision process used to identify people based on their walking styles. Compared to other types of biometrics, gait offers a more advantageous recognition process as it does not require high-resolution and close-range images and obtains without contact. But besides this, gait biometrics is highly affected by cross-view variation, and under this variation recognition performance decreases significantly. In this study, performance evaluations of fine-tuned VGG-16 and ResNet-50 deep CNN networks on the cross-view gait recognition problem are performed. For this purpose, Gait energy images (GEI) and Silhouettes obtained from CASIA-B, the most comprehensive data set in gait recognition, are given as input to the networks. The experimental results showed that the VGG-16 network achieved higher recognition rates in cross-view gait recognition.
- BildiriAçık Erişim2024A Study on the Segmentation and Classification of Diabetic Retinopathy Images Using the K-Means Clustering Method
Incir, Ramazan; Bozkurt, Ferhat
Diabetic Retinopathy (DR) is a retinal disease caused by diabetes, representing one of the most prevalent causes of vision loss affecting millions of people worldwide. Swift detection and treatment of this condition are crucial for preventing the disease. Various deep learning and machine learning algorithms have been employed for disease detection and classification, often overlooking the data preprocessing stage. In the data preprocessing phase of this study, segmentation of important lesions, such as hard exudates, was conducted using the K-Means clustering method. The identified lesions were highlighted on the original images. The resulting dataset was then classified using pre-trained architectures, namely EfficientNetV2-M, ResNet50, MobileNet, and DenseNet121. After training on the APTOS dataset, the EfficientNetV2-M model achieved an accuracy of 95.16%. The classification results indicated the contribution of the lesion highlighting process during data preprocessing to the overall classification accuracy.
- BildiriYalnız künye2023Shape of Masses in Contrast Mammography Images and First Order Statistical Features Classification Based on
Kobya, Mehmet Emin; Ture, Hayati; Dogan, Ramazan Ozgur; Kayikcioglu, Temel; Teymur, Aykut; Kul, Sibel
Mammography is a valuable tool in the diagnosis and staging of primary breast cancer. Contrast-enhanced mammography (CEM) combines an iodinated contrast agent with traditional mammography to increase diagnostic accuracy, especially in women with more dense parenchymal background patterns. In this study, a Decision Support System (DSS) is proposed that classifies masses using radiomic features obtained from masks of benign and malignant masses in images obtained with the CEM technique. An open-access database containing 2006 CEM images was used in this study. Radiomic features obtained from these images were used to identify differences between benign and malignant masses. The DSS uses these features to classify masses using a decision tree algorithm. This study highlights the importance of the use of iodinated contrast agents to improve diagnostic accuracy in women with more dense parenchymal background patterns. Additionally, it emphasizes the potential benefits of a DSS, in combination with radiomic features, in the diagnosis and staging of breast cancer. In conclusion, this study demonstrates that the use of radiomic features in mammography images obtained with the CEM technique is an effective way to classify benign and malignant masses.The use of this technology could be an important tool in the early diagnosis and accurate classification of breast cancer.
- BildiriYalnız künye2023Deep Learning Based Focus Measurement Operator for 3D Imaging in Microscopic Systems
Dogan, Hulya; Dogan, Ramazan Ozgur
Since 3D imaging in microscopic systems makes cell and tissue structures more prominent and visible, it provides higher performance in image processing applications such as automatic segmentation, recognition, classification, alignment and stitching. For this reason, 3D imaging in microscopic systems has been one of the most comprehensive and long-studied research areas in the literature. It seems that the most frequently preferred strategy for 3D imaging is shape from focus. Shape from focus is a strategy that uses a sequence of 2D images with the same perspective and different focused regions to create the 3D shape of the sample. Focus measurement operators are applied to extract the focus properties of the input images in shape from focus strategies. These operators have several limitations, such as more precise characterization of curves and edges in images. Researchers often use a pre- or post-processing technique to minimize these limitations, rather than proposing a new focus measurement operator. In this study, a deep learning based focus measurement operator, which does not require a pre- or post-processing technique, is developed to eliminate these limitations. The qualitative and quantitative results obtained in the study show that the proposed focus measurement operator is more effective in 3D imaging than other operators in the literature.
- BildiriYalnız künye2023Anomaly Detection in Predicted Water Treatment Data using Hybrid CNN-LSTM Network Model
Özgenç, Busra; Ayas, Selen; Dogan, Ramazan Ozgur; Çavdar, Bora; Sahin, Ali Klvanc; Ayas, Mustafa Sinasi
Water treatment systems are among the industrial control systems where it is essential to detect anomalies accurately and efficiently due to the potential threat to public health. With advances in computer science, machine learning models have been successfully used in the anomaly detection process in recent years. In this paper, a hybrid CNN-LSTM network model is proposed to detect anomalies in water systems. Using a statistical window-based anomaly detection approach, the performance of the proposed model in detecting different types of attacks is analyzed on the open SWaT dataset. Experimental results show that the precision, recall and F1-score values of the proposed model are 0.994, 0.973 and 0.983, respectively, and can be successfully used to detect anomalies in water treatment systems.
- BildiriYalnız künye2022Segmentation of Pectoral Muscle Region in MLO Mammography Images by Backboned U-Net
Dogan, Ramazan Ozgur; Türe, Hayati; Kayikçioglu, Temel
The pectoral muscle region on MLO mammography images appears prominently similar to suspicious areas. For this reason, Computer-Aided Detection (CAD) systems remove this region to reduce false-positive rates in the mass detection process. In some cases, the pectoral muscle region is exposed to distortions due to the superposition effects caused by the mammography technique. As a result, segmentation error rates of the pectoral muscle region, whose characteristic features are deteriorated, appear. In this study, a method to identify impaired pectoral muscle regions with MobileNetV2 backboned U-Net Deep Learning method is proposed. The proposed method was tested on 84 and 201 mammography images taken from both MIAS and InBreast databases and segmented with 1.81% and 1.92% false-negative (FN) and 0.25% and 0.37% false positive (FP) rates, respectively. Particularly for distorted pectoral muscle regions, the proposed method has been shown to outperform some pioneering studies in this area.
- BildiriYalnız künye2021BroomyCell: An Introductory Educational Software for Neuroscience
Ozcan, Zubeyir; Yildirim, Omer; Kayikcioglu, Temel
Nerve cells are the basic building blocks of the nervous system. Therefore, the first step in neuroscience education is the introduction of nerve cells. However, both the complexity of nerve cells and the difficulty of their experimental examination make it difficult to test nerve cells practically. Therefore, simulation of nerve cells is found to be significantly effective. The difficulty of examining this type of cells experimentally shows its effect on neuroscience education. This brings to the fore the necessity of virtual laboratory environments designed for use in education. In this study, BroomyCell, which is designed by us as an introductory educational software in neuroscience education, was introduced. With this desktop application, it is aimed that potential neuroscientists learn their fields experimentally and without the need for a physical laboratory.
- BildiriAçık Erişim2021Pneumonia Detection from Pediatric Lung X-Ray Images with Convolutional Neural Network Method
Demir, Yasin; Bingöl, Ozkan
Pneumonia can be cause diseases such as cough, fever and respiratory problems and to death in the progressive situations. Mostly lung x-ray images are used for diagnosis of pneumonia. In this study, an unique convolutional neural network model that can help experts in detection of pneumonia using lung x-ray images below 5 years of age. In the model, open access to 5840 images has been used, and various data increasing methods were used to increase the performance of the training and the success of the method. With the proposed convolutional neural network model, an accuracy rate of 94.55% has been achieved.
- BildiriAçık Erişim2021Automatic Sleep Scoring System Based On Autoregressive Model
Melek, Mesut
Electroencephalography (EEG) signals recorded during sleep are divided into 30-second segments and analyzed by experts and classified into sleep stages, and in this way, diagnosis and treatment of diseases are made. Computer-assisted sleep scoring systems can effectively reduce the burden of experts by classifying sleep stages. In this study, after preprocessing of the EEG signals, four features were obtained for each EEG band (20 features in total) with the autoregressive model and then classified with three different classifiers common in machine learning. The proposed method was tested on the data set called ISRUC sleep (Subgroup 3, ISRUC3) and the results were compared with the results of other studies. The results show that the proposed method is more successful than other existing methods, despite the use of fewer features.
- BildiriYalnız künye2019Meta-Heuristic Algorithms Based Multi-Level Thresholding
Küçükugurlu, Busranur; Gedikli, Eyup
Thresholding is a very important stage in computer vision applications. An ideal single thresholding algorithm is not available for all environments. Multi-level thresholding in environments with multiple objects is constantly being developed for interpretation of images. Kapur entropy and Otsu approaches are among the most successful algorithms in the literature. In this study, it is tried to increase the performance of Otsu and Kapur algorithms by using meta-heuristic optimization approaches. The results of the Firefly Algorithm (FF) and Real Coded Genetic Algorithm (RGA) were evaluated with PSNR, SSIM and CPU processing time criteria.
- BildiriYalnız künye2018Determining Complexity within the Crowd Movements
Akpulat, Murat; Ekinci, Murat
The segmentation of the moving and non-moving parts in an image within the crowd analysis studies is a crucial result in terms of understanding the behaviour. However, in many studies carried out, moving areas are assessed as making a set of movement that are similar in terms of location and of acceleration. In this study, what kind of interaction there exists among the people in a high-density crowd movement was studied. Initially, the motion data is represented as trajectories by using the optical flow and particle advection methods. The image is divided into equal hexagons and each area's topological entropy value is determined through Finite Time Braid Entropy (FTBE). This value depends on the complexity of the spiral structure that the trajectories generate throughout the movement and this value shows how much effective it is in the area. Eventually, through the complexity map that was generated, the behaviour of the crowd could be better understood and assessed. Also, in this way, possible abnormal conditions in the area can be predicted by monitoring each region and by setting off from the changes in the values of Braid entropy in time. The method was tested using the high-density crowd movements in the UCF database and results were presented.
- BildiriYalnız künye2018Network Master: A Versatile and User-Friendly Educational Software for Simulation of Neural Networks
Yildirim, Omer; Özcan, Zubeyir; Turhan, Hilal; Kayikçioglu, Temel
In this study, an interface in which neural networks consisting of biological cells could be modeled and simulated using Neuron hoc programming. With this interface, the resulting action potentials and the behavior of the nerve network can be investigated in detail by making the desired stimuli to mathematical models of neural networks. The most important feature of the work is that the designed interface is completely user friendly. Another advantage of this software is that users can easily perform simulations of the neural network models without writing code.
- BildiriYalnız künye2018Cell Master: A Versatile and User-Friendly Educational Software for Simulation of Neuronal Dynamics
Özcan, Zubeyir; Kayikçioglu, Ilknur; Yildirim, Omer; Köse, Cemal; Kayikçioglu, Temel
In this study, A user-friendly, versatile and easily accessible educational software design was done by using NEURON hoc programming in order to investigate the dynamics, biophysical properties of nerve cells and their responses to various stimulation. With the help of interfaces which prepared for users to be assisted visually, it is aimed to be able to easily perform almost any application desired. This software allows users to perform their simulations without the need to write any code. Considering the features given in detail in the study, it has been suggested to use the software in neuroscience education.
- BildiriYalnız künye2018Hourly Solar Irradiation Prediction by Artificial Neural Network Based on Similarity Analysis of Time Series
Ulutas, Ahsen; Çakmak, Recep; Altas, Ismail Hakki; Ozyildirim, BM; Yildirim, T
Solar energy is a renewable energy source which has intermittent and variable characteristic. Solar irradiation must be predicted in advance in an electrical grid which has solar energy based electrical power generation systems in order to operate the electrical grid stable and efficient. In this study, a multi-layered, feed forward artificial neural network (ANN) has been designed to predict the hourly solar irradiation of next day. The designed ANN has been trained by data which has been obtained via similarity analysis. Total solar irradiation on horizontal plane, relative humidity and temperature data of Trabzon province for 2015-2017 have been used as the training data set. Hourly solar irradiation prediction has been performed by utilizing the designed ANN and test data set. The prediction results have been evaluated as to root mean square (RMS), mean absolute error (MAE) and mean absolute percentage error (MAPE) performance criteria. The obtained performance criteria results show that the proposed ANN could make prediction with acceptable error.
- BildiriYalnız künye2018R-peaks Detection with Convolutional Neural Network in Electrocardiogram Signal
Dogan, Ramazan Ozgur; Kayikçioglu, Temel
The performance of electrocardiogram applications is closely related to the succesful detection of R-peaks. Therefore, successful detection of R-peaks is of high importance. Electrocardiogram signals vary from person to person, so detection of R-peaks is getting difficult. In this study, Evolutionary Neural Network which is a deep learning model and imitates human vision, is used to detect R-peaks. The MIT-BIH Arrhythmia Database with 48 different records was used as the database and experimental results with very high accuracy were obtained.
- BildiriYalnız künye2018Detecting Cardiac Arrhythmia by Using Time Differences of Double Wavelength PPG Peaks
Yildirim, Omer; Kayikçioglu, Temel
Nowadays, photopletismogram (PPG) signals are used in respiration and blood pressure measurement, monitoring of the sleep pattern and also detecting of mental disorders, mainly blood oxygen saturation and heartrate measurement. The reason of the use of PPG signals instead of signals such as electroencephalogram (EEG) or electrocardiogram (ECG) is easy to obtain and real-time monitoring of these data, as well as being easily used by people in daily life and easily integrated into wearable technologies. In this study, detecting of cardiac arrhythmia was made successfully by using double wavelength PPG signals instead of ECG signals
- BildiriYalnız künye2017Real Time Sending and Monitoring Electronic Health Records
Dogan, Ramazan Ozgur; Kayikçioglu, Temel; Yagci, Yavuz
Innovations in the field of health increase with the developing technology nowadays. These technological advances in the health field have enabled us to meet with telemedicine applications. Telemedicine is the use of computer technologies and the Internet infrastructure so that we can access health services without going to the hospital. In telemedicine applications, patients can access past electronic health records of patients can be accessed as well as access real-time instant electronic health records is provided. Real-time telemedicine applications usually aim to provide remote access to the biological signs of the patient and to diagnose them through these signals. In this study, it is mentioned how a real time telemedicine application can be performed, which features should be provided, and which performance criteria should be taken into consideration while developing. In addition, performance values are examined for a sample implementation.
- BildiriYalnız künye2017Observing the Entropy of Crowd Movements
Akpulat, Murat; Ekinci, Murat
Providing security and managing it as well as trying to understand how it behaves have become an important research topic in our day. The aim of this study is to calculate the entropy value of the crowd movement regionally and to track how it. changes in time. Because, excessive changes in the entropy value in time might point to abnormal behaviour in the region. The motion information obtained from the optical flow method from the image in parallel to the target was converted to motion orbits by particle advection. Motion orbits are mapped into by the Braid Theory and entropy value is calculated. Braid Theory is a mathematical method that is used to express entropy as a qualitative value by determining the interaction among the movement orbits. The results are tested according to the video sequence in the PETS2009 and UCF database and they show that this method is functional for observing how the entropy changes in time within the crowd movement.