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  • BildiriAçık Erişim2024Scopus SJRQ4
    An Efficient Technique Based on Deep Learning for Automatic Focusing in Microscopic System

    Dogu, Fatma Tuana; Dogan, Hulya; Dogan, Ramazan Ozgur; Ay, Ilyas; Sezen, Sena F.; Seyman, M.

    During microscopic examination process, experts firstly determine the most appropriate focusing point by precisely moving the microscope stage in the Z axis. This manual focusing process may cause variability in results depending on hand-eye coordination and individual experience. Therefore, the development of automatic focusing offers superior quality and consistent imaging. Many researchers have studied automatic focusing systems and proposed many focus functions to extract focus information from multi-focus images. Nevertheless, these focusing systems still consist of several significant limitations such as requiring a supplementary material, longer running time, producing different performance depending on the sample and magnification objective. In this study, an efficient technique based on deep learning is proposed to automatically determine the focused image during microscopic examination processes to minimize these limitations in the literature. This technique takes a sequence of images with the same field of view and different focuses as input and gives the image with maximum focusing as output. The proposed technique is compared with other automatic focusing techniques on the literature. In this study, novel multi-focus image sequences obtained from liver, intestine, heart, kidney, and lung samples is prepared to evaluate the performance of automatic focusing techniques. These sequences are obtained by scanning with 10× and 40× magnification objectives. Running time, accuracy, number of local maximum points, range, and noise levels are used as evaluation criteria in this study. According to evaluation criteria results obtained in this study, it has been proven that the proposed technique provides better performance than other automatic focusing techniques.

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  • BildiriAçık Erişim2024Scopus SJRQ4
    Extension of TODIM Method Based on Distance Measures of Decomposed Fuzzy Sets

    Köseoğlu, Ali; Şahin, Rıdvan; Kahraman, C.; Cevik Onar, S.; Cebi, S.; Oztaysi, B.; Ucal Sari, I.; Tolga, A.C.

    Fuzzy sets allow elements to have partial membership in a set, represented by a membership value between 0 and 1. This feature enables fuzzy sets to model imprecise or vague concepts. Decomposed fuzzy sets, an extension of fuzzy sets, have introduced a novel perspective in the literature by incorporating the Kano model into risk assessment. These sets have also paved the way for new approaches in decision-making methods, highlighting their functional and dysfunctional elements. In this paper, we propose a distance measure for decomposed fuzzy sets and subsequently extend the TODIM method based on this distance measure to DFSs. Then, we provide a numerical example to illustrate its effectiveness and practicality.

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  • BildiriAçık Erişim2024
    AN ANALYSIS OF NEIGHBOURHOOD TYPES FOR POINTNET++ IN SEMANTIC SEGMENTATION OF AIRBORNE LASER SCANNING DATA

    Akbulut, Z.; Ozdemir, S.; Karsli, F.; Dihkan, M.; Isikdag, U.; Bayram, B.

    The objective of the study is to conduct a comprehensive examination of how different neighbourhood types, namely spherical, cylindrical, and k-nearest neighbour (kNN), influence the feature extraction capabilities of the PointNet++ architecture in the semantic segmentation of Airborne Laser Scanning (ALS) point clouds. Two datasets are utilized for semantic segmentation analysis: the Dayton Annotated LiDAR Earth Scan (DALES) and the ISPRS 3D Semantic Labelling Benchmark datasets. In the experiments, the kNN method exhibited approximately 1% higher accuracy in weighted mean F1 and intersection over union (IoU) metrics compared to the spherical and cylindrical neighbourhood types on the DALES dataset. However, in the generalization experiment conducted on the ISPRS dataset, the spherical neighbourhood achieved the best results in these metrics, outperforming the cylindrical neighbourhood by a small margin. Notably, the kNN method was the least accurate, with a decrease in accuracy of approximately 1% in both weighted mean IoU and F1 scores. These findings suggest that the features extracted from spherical and cylindrical neighbourhood types are more generalizable compared to those from the kNN method.

    AtıfScopus1WoS1OpenAlex1
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  • BildiriYayıncıda açık erişim2024
    High Performance Automatic ROI Extraction from Hand Images with Convolutional Neural Networks

    Kosmaz Sunnetci, Betul; Bingol, Ozkan; Gedikli, Eyup; Ekinci, Murat

    Automatic ROI extraction from hand images is a critical step in palmprint recognition. Traditional methods involve steps such as identifying the region of the hand, finding the valleys between the fingers and marking the palm area. Some of these processes are performed manually and some are performed with image processing techniques. However, these methods are time-consuming, vulnerable to human error, and computationally redundant. In this study, a deep learning-based convolutional neural network is developed for high-performance automatic ROI extraction from hand images. The proposed method eliminates the hand segmentation process and provides a solution where the palm region can be automatically selected directly. This reduces processing time, minimizes human error and improves overall accuracy.

    AtıfScopus2OpenAlex1
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  • BildiriYalnız künye2024
    RADIO FREQUENCY PROCESSING OF PEANUT BUTTER: COMPUTATIONAL MODEL DEVELOPMENT AND PROCESS DESIGN

    Coskun, Eda; Ozturk, Samet; Tasci, Caner; Polat, Kubra; Liu, Shuxiang; Li, Rui; Wang, Shaojin; Mert, Behic; Marra, Francesco; Erdogdu, Ferruh

    Within the last decade, Centers for Disease Control and Prevention (CDC) has reported outbreaks of Salmonella, particularly associated with low -moisture foods including peanut butter. In this study, radio frequency (RF) application was carried out for thermal processing of peanut butter samples. For this purpose, a computational model was developed to determine the temperature change of peanut samples using Comsol Multiphysics v6.0 (Comsol AB, Stockholm, Sweden), and experimental studies for model validation were completed in a 10 kW 27.12 MHz RF system at 3300 and 4000 V electrode potentials Following the model validation, industrial scale thermal processing studies were done with up to 30 samples located within the RF cavity. This study demonstrated the feasibility of the RF application for an efficient processing with rather uniform temperature distribution attained within the samples.

    AtıfScopus1
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  • BildiriYalnız künye2023
    Shape 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.

    • SDG 3 · Sağlık ve Kaliteli Yaşam
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  • BildiriAçık Erişim2023
    Solutions of Multiplicative Integral Equations via The Multiplicative Power Series Method

    Bal, Arzu; Yalcin, Numan; Dedeturk, Mutlu

    In this study, definitions of types of multiplicative integral equations are given. And solutions of different types of multiplicative integral equations are investigated using the multiplicative power series method. These are supported by numerical examples.

    AtıfOpenAlex4TR Dizin2
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  • BildiriYalnız künye2023
    Deep 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.

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  • BildiriYalnız künye2023Scopus SJRQ3
    VIRTUAL REALITY TO IMPROVE CANCER PATIENTS' QUALITY OF LIFE: A SYSTEMATIC REVIEW

    Bagcivan, Gulcan; Aydin, Aydanur; Ozkan, Aleyna

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  • BildiriYalnız künye2023Scopus SJRQ1
    Epilepsy in Dystrophinopathies: Series and review of the literature

    Kizek, O.; Topaloglu, P.

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  • BildiriYalnız künye2023
    Anomaly 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.

    • SDG 6 · Temiz Su ve Sanitasyon
    AtıfWoS1OpenAlex5
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  • BildiriYalnız künye2023
    An Extensive Study: Creation of A New Inverted Microscope Image Data Set and Improving Auto-Encoder Models for Higher Accuracy Segmentation of HaCaT Cell Culture Line

    Sevim, Zeynep; Dogan, Hulya; Demir, Zeynep; Sezen, Feride Sena; Dogan, Ramazan Ozgur

    In cell culture investigations, it is assumed that the number of cells is counted automatically without the need for forward planning, additional tools, or consumables. The automatic cell counting process is greatly facilitated and made functional by the segmentation of cell culture lines. Many studies with high-accuracy results have been developed for cell or tissue segmentation in images acquired from microscopic systems, but this success cannot be achieved for segmentation of cell culture line (especially in images acquired from inverted microscope). There are still restrictions in segmentation of cell culture line studies in inverted microscope systems. In this context, a new inverted microscope image data set is created and higher accuracy auto-encoder models are developed for segmentation of HaCat cell culture line. The analyzes of the developed auto-encoder models are evaluated using four different performance evaluation procedures which are Accuracy, Sensitivity, Specificity and Jaccard. In terms of the performance evaluation procedures used in this study, the achievements of the auto-encoder models are clearly satisfactory for the automated segmentation of the HaCat cell culture line.

    AtıfScopus1
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  • BildiriYalnız künye2023Scopus SJRQ4
    Quantum Convolutional Neural Network for Agricultural Mechanization and Plant Disease Detection

    Genemo, Musa; Shakya, S.; Tavares, J.M.R.S.; Fernández-Caballero, A.; Papakostas, G.

    Agricultural research is essential in addressing the challenges of food production and meeting the needs of a growing population. Severe crop disorders can lead to food insecurity, potentially causing disastrous effects on agricultural production, such as yield losses, reduced crop quality, and even complete crop failures. By leveraging computer vision techniques, it is possible to develop systems that detect signs of crop diseases at an early stage, facilitating prompt intervention and management. Environmental changes, such as rainfall and biosolids, can cause plants to become inflamed by microorganisms. While manual observation and assessment of leaf conditions have traditionally been used for crop disease detection, there is increasing interest in leveraging technology to automate and enhance the process. Manual detection of crop diseases based on leaf conditions is subjective, time consuming, and vulnerable to errors. Utilizing computer vision methods to detect plant diseases helps improve plant health. The concept of applying quantum convolutional neural networks (QCNNs) for agricultural mechanization and plant disease detection is an interesting area of research that combines quantum computing and deep learning techniques. However, it is important to note that quantum computing is still in its early stages, and practical implementations of QCNNs for real-world applications, including agriculture, are not yet widespread. The proposed work of paper focuses on the detection of leaf blight disease by classifying leaves into late blight, early blight, and healthy leaves. This work is divided into two stages: segmentation and classification. For segmentation, MobileNetV2 is used, and an emerging quantum machine learning technique named QCNN is used for classification. The main objective is to provide and propose an effective and fast automated technique to detect and classify blight disease, which will help farmers identify blight disease easily. The proposed model achieved a classification accuracy of 0.969. Furthermore, the results were further validated against recent findings in the field. The presented outcome implies that the model’s performance is remarkable.

    • SDG 2 · Açlığa Son
    AtıfScopus12OpenAlex8
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  • BildiriYalnız künye2023
    Current Sources and Actual PV Modules based Photovoltaic (PV) Emulator for Testing MPPT Algorithms and PV Inverters

    Zhou, Zhongfu; Wang, Yidong; Yanarates, Cagfer; MacAulay, John

    PV array emulator has been widely used for testing PV inverters and their control algorithms such as maximum power point tracking (MPPT) algorithms. Various power electronics converter-based PV emulators have been presented in many research papers. This paper presents a PV emulator that is based on indoor solar panels (without solar irradiance) and external current source (dc power supply). The external current source is used to emulate the photo current that is usually generated by solar panel under sunlight. The advantages of the proposed PV emulator are that it can generate similar electrical characteristics of actual PV panel without sunlight, it is suitable for testing PV inverters and MPPT algorithms in indoor conditions where the sunlight is usually very weak. In addition, a PV array emulator that can emulate electrical performance under partial shading conditions was constructed by using two solar panels and two current sources. Test results show that the proposed PV emulator provides a similar performance of real PV array under real sun light. It provides a low-cost solution for testing PV inverters and control algorithms inside a laboratory environment.

    • SDG 7 · Erişilebilir ve Temiz Enerji
    • SDG 9 · Sanayi, Yenilikçilik ve Altyapı
    AtıfScopus2OpenAlex2
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  • BildiriYalnız künye2023
    Pedestrian Inertial Navigation with Multi-Head CNN

    Cetin, Gokhan; Kucuk, Mehmet Ali; Koroglu, Muhammed Taha

    Traditional pedestrian dead-reckoning (DR) methods utilizing inertial measurement units (IMU) have been successfully employed in GPS-denied environments over the past two decades. However, despite continuous advancements in wearable IMU technology, the long-term solution still suffers from the issue of drift. In light of this, Deep Learning (DL) has emerged as a potential candidate for inertial navigation solutions, leveraging its powerful non-linear regression capabilities. Recent research has demonstrated that DL models trained on public datasets can effectively serve as end-to-end solutions. Most studies focus on smartphones, where the IMU is typically situated at the torso, and generally work with transformed data represented in the navigation coordinate frame to estimate pedestrian velocity and heading change at rates lower than the IMU sampling rate. In contrast, this study uses raw foot-mounted IMU data as input to a multi-headed structure consisting of independent convolutional neural networks (CNNs) and targets displacement directly and heading change. The proposed model is trained on a publicly available foot-mounted IMU dataset where pseudo ground-truth (GT) data is generated heuristically to provide supervision for the learning process. The results indicate that the Multi-Head CNN (MHCNN) model is capable of producing pedestrian trajectories at the IMU sampling rate, with positioning errors of less than 2m throughout the duration of the walks. The code for the trained network can be accessed in the associated repository, i.e., github.com/mtkoroglu/PIN-MHCNN.

    AtıfScopus1
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  • BildiriYalnız künye2023
    REGION-TIME-MAGNITUDE ANALYSES OF EARTHQAUKE ACTIVITY IN THE ELBASANI GEOTERMAL ZONE, ALBANIA

    Ormeni, Rrapo; Öztürk, Serkan; Hasimi, Albana; Silo, Erald; Como, Elvin; Trofymchuk, O.; Rivza, B.

    A detailed analysis of the b-value within the Elbasani fault zone, Albania, were studied. From instrumental seismicity in this area have been located 1830 seismic events, with magnitude Ml>0.5 Richter. In the zone of Elbasani, hot mineral water (thermal waters) spot out from natural springs which have been known since the 19th century. The statistical properties of seismicity are analysed by using b-value known as Gutenberg-Richter law. The magnitude of completeness (Mc) value is determined as 2.6, and the bvalue is calculated as 1.03 ± 0.06, and it is determined that the region has a normal stress level. It is seen that the Mc-value varies from 1.6 to 2.9, the b-value changes between 0.9 and 1.3. The regions with the large b-values (> 1.0) are estimated in the eastern parts of the of Elbasani zone. However, the regions with the low b-values (< 1.0) are observed in the north, west and southwest of Elbasani zone. The regions with the small b-values correspond to great-magnitude events, whereas the other large values are related to smallmagnitude earthquakes. These results reveal that b-values for all regions are well represented by a b-value typically close to 1.0. There are clear decreasing trends in temporal changes of b-values before the occurrences of some strong main shocks. The lower b-value indicate that there is still moderate stress accumulation in the southwest area of the Llinxha-Kozan thermal water belt. The b-values smaller than 1.0 indicate potential future earthquake area. Following the strong events, there are remarkable decreases in b-value as a function of time. Changes in b-value with time varies in a large band between 0.6 and 1.4. Probabilities of earthquake activity in different magnitude sizes show comparatively great values changing from 50-100 % for earthquakes of 0.5 ≤ Ml≤ 4.5 and the values relatively lower than 90 % for the earthquakes of 5.0 ≤ Ml. The Elbasani zone has a complex geological structure with a significant number of geothermal water resources and some features of seismicity.

    AtıfOpenAlex1
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  • BildiriYalnız künye2023
    An Efficient 1D Autoencoder-Based Approach for R-Peaks Detection in Electrocardiogram Signals

    Dogan, Ramazan Ozgur; Dogan, Hulya; Kayikcioglu, Temel

    Electrocardiogram (ECG) signal which is composition of multiple segments such as P-wave, QRS complex and T-wave plays a crucial role in evaluating human heart cardiac diagnosis. For an analysis of cardiac diagnosis, it is required that clinicians scan the ECG signal for R-peaks (the highest peak of the QRS complex) detection, which relies on their expertise and takes enormous time. In order to provide more realistic treatment of cardiovascular diseases in the ECG signal with noises and different characteristics, computer-based R-peaks detection approaches have been actively developed in the research article for many years and generally compose of two major operations; preprocessing and decision. Contrary to previous approaches which require high computational costs and complexity, appropriate initial parameters, a novel CNN based approach is presented for R-peaks detection in this paper. Both qualitative and quantitative analysis are performed in the real ECG signal taken from typical MIT-BIH arrhythmia database. In order to validate the success of the recommended approach, total 18 different R-peaks detection approaches reported in the literature are compared by utilizing performance evaluation metrics. Average values of sensitivity, positive predictivity, error rate, accuracy and f-score are computed as 99,96%, 99,95%, 0,09%, 99,90% and 99,95% respectively, which are the best values among well-established studies for R-peaks detection. It is demonstrated with qualitative and quantitative results that better performance is achieved by our suggested approach.

    • SDG 3 · Sağlık ve Kaliteli Yaşam
    AtıfScopus2OpenAlex2
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  • BildiriYalnız künye2023
    Analysis Of The Performance Of The Distributed Maximum Power Point Tracking In Building Integrated Photovoltaic Systems

    Basoglu, Mustafa Engin; Aiordachioaie, D.

    Building-integrated/applied photovoltaic (PV) systems have been widespread recently in terms of electrical energy generation in buildings. These systems are applications with much lower power than land type large-scale systems, addressing the area/building where they are installed rather than centralized production. In this framework, maximum power point (MPP) tracking (MPPT) approaches at module level and sub-module level, balcony railing, Carport or solar tile etc. will be investigated in terms of building integrated PV systems (BIPV). Solar panels act as a building material, insulation material and energy generator in BIPV applications. In building-applied (BAPV) examples, the solar panel is mounted on the building. However, there is no difference between BIPV and BAPV in terms of electrical model. A unique method will be developed for MPPT at module level and sub-module level for such applications. Depending on the power processing strategy in the optimizer power stage, amplifier, synchronous reducer or bidirectional differential power processing topologies will be examined and the most suitable power converter for the module and sub-module level will be determined. Technically, comparisons of the performances of MPPT strategies were made in the MATLAB/Simulink program at the central, module level or sub-module level, and it was shown that the power that can be obtained from the PV module or sub-module increases with the MPPT at the module and sub-module level.

    • SDG 7 · Erişilebilir ve Temiz Enerji
    • SDG 9 · Sanayi, Yenilikçilik ve Altyapı
    AtıfScopus1OpenAlex2
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  • BildiriAçık Erişim2023
    Saw-YOLOv5: Scale-Aware YOLOv5 for Object Detection in Aerial Images

    Doruk, Abdullah Enes; Algul, Mucteba; Akyurek, Feyzullah; Alpaydm, Osman Kursat; Uslu, Fatmatulzehra

    The detection of objects in aerial images is impor-tant for many real world problems related to military defense, transportation, and etc. However, this is a challenging task as a result of the presence of various scales of objects in the same image, the large variety of contexts across aerial images, various brightness levels due to image acquisition at different times of the day and so on. To address these challenges, this paper introduces Saw-YOLOv5 for object detection in aerial images. Saw-YOLOv5 is a deep network based on YOLOv5, which was proposed for object detection in natural images. Saw-YOLOv5 extends YOLOv5 with the addition of several attention modules in its design. The results of our experiments, conducted on the aerial dataset delivered by the Turkey Technology Team for the Artificial Intelligence in Transportation Competition, showed that Saw-YOLOv5 outperforms previous methods, particularly for pedestrian detection, by yielding a mean mAP of 80.23% over all objects.

    AtıfScopus1OpenAlex1
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  • BildiriYalnız künye2023
    A Hybrid CNN-LSTM Framework for Unsupervised Anomaly Detection in Water Distribution Plant

    Sahin, Ali Kivanc; Cavdar, Bora; Dogan, Ramazan Ozgur; Ayas, Selen; Ozgenc, Busra; Ayas, Mustafa Sinasi

    To reduce potential threats to public health and economic losses, water distribution systems, which are critical parts of industrial control systems, require accurate and effective anomaly detection. The successful incorporation of machine learning models into anomaly detection procedures has been made possible by recent developments in computer science. In this research, a hybrid CNN-LSTM framework for anomaly detection in water distribution systems is presented using Python programming language in the JupyterLab environment. The performance of the proposed CNN-LSTM network model is carefully evaluated on the publicly available WADI dataset, which contains several types of attacks for a water distribution testbed, using a statistical window-based anomaly detection approach. The experimental results show acceptable precision, recall and F1 scores of 0.8619, 0.7041, and 0.775, respectively, demonstrating the efficiency of the framework in detecting anomalies in the water distribution system.

    • SDG 6 · Temiz Su ve Sanitasyon
    AtıfScopus13OpenAlex12
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