Mardin Meslek Yüksekokulu

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Citation - WoS: 7
Citation - Scopus: 13
Palmprint recognition system based on deep region of interest features with the aid of hybrid approach
(SpringerLink, 2023) Türk, Ömer; Çalışkan, Abidin; Acar, Emrullah; Ergen, Burhan
Palmprint recognition system is a biometric technology, which is promising to have a high precision. This system has started to attract the attention of researchers, especially with the emergence of deep learning techniques in recent years. In this study, a deep learning and machine learning-based hybrid approach has been recommended to recognize palmprint images automatically via region of interest (ROI) features. The proposed work consists of several stages, respectively. In the first stage, the raw images have been collected from the PolyU database and preprocessing operations have been implemented in order to determine ROI areas. In the second stage, deep ROI features have been extracted from the preprocessed images with the aid of deep learning technique. In the last stage, the obtained deep features have been classified by employing a hybrid deep convolutional neural network and support vector machine models. Finally, it has been observed that the overall accuracy of the proposed system has achieved very successful results as 99.72% via hybrid approach. Moreover, very low execution time has been observed for whole process of the proposed system with 0.10 s.
Citation - WoS: 32
Citation - Scopus: 32
Development software program for extraction of photovoltaic cell equivalent circuit model parameters based on the Newton–Raphson method
(SpringerLink, 2022) Adak, Süleyman; Cangi, Hasan; Yılmaz, Ahmet Serdar; Arifoğlu, Uğur
Finding the equivalent circuit parameters for photovoltaic (PV) cells is crucial as they are used in the modeling and analysis of PV arrays. PV cells are made of silicon. These materials have a nonlinear characteristic. This distorts the sinusoidal waveform of the current and voltage. As a result, harmonic components are formed in the system. The PV cell is the smallest building block of the PV system and produces voltages between 0.5 V and 0.7 V. It serves as a source of current. The amount of radiation hitting the cell determines how much current it produces. In an ideal case, a diode and a parallel current source make up the equivalent circuit of the PV cell. In practice, the addition of a series and parallel resistor is made to the ideal equivalent circuit. There are many equivalent circuits in the literature on modeling the equivalent circuit of a PV cell. The PV cell single–diode model is the most used model due to its ease of analysis. In this study, the iterative method by Newton–Raphson was used to find the equivalent circuit parameters of a PV cell. This method is one of the most widely used methods for determining the roots of nonlinear equations in numerical analysis. In this study, five unknown parameters (Iph, Io, Rs, Rsh and m) of the PV cell equivalent circuit were quickly discovered with the software program prepared based on the Newton–Raphson method in MATLAB
Citation - WoS: 3
Citation - Scopus: 3
Investigating the limestone quarries as geoheritage sites: Case of Mardin ancient quarry
(De Gruyter, 2023) Karataş, Lale; Alptekin, Aydın; Yakar, Murat
Abstract: Abandoned quarries are valuable as a tourism element, as they exhibit the building material of the buildings built in the geographical area they are located in as historical objects. However, in order to determine how the quarries can be used for tourism purposes, it is necessary to determine the constraints on the choice of solution in spatial arrangements. The aim of this study is to investigate how the ancient limestone quarry of Mardin, which is a natural and cultural geological heritage, can be used for tourism and to develop suggestions. Within the scope of the study, in order to examine the possibilities of how an idle quarry located in Mardin province in Turkey can be used for tourism, the constraints in the selection of the post-use solution will be determined. In order to determine whether the Mardin quarry is accessible and safe to visit, various field studies were carried out in the study area, laboratory experiments and analyses. The caves were scanned with a 3D laser scanner, and its plans and sections were obtained. The findings were evaluated and suggestions were developed for the use of the ancient limestone quarry for tourism.
Citation - WoS: 8
Citation - Scopus: 21
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
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: 6
Citation - Scopus: 6
Design and Implementation of a Maximum Power Point Tracking System for a Piezoelectric Wind Energy Harvester Generating High Harmonicity
(Sustainability, 2021) Kurt, Erol; Özhan, Davut; Bizon, Nicu; Lopez-Guede, Jose Manuel
In this work, a maximum power point tracking (MPPT) system for its application to a new piezoelectric wind energy harvester (PWEH) has been designed and implemented. The motivation for such MPPT unit comes from the power scales of the piezoelectric layers being in the order of μW. In addition, the output generates highly disturbed voltage waveforms with high total harmonic distortion (THD), thereby high THD values cause a certain power loss at the output of the PWEH system and an intense motivation is given to design and implement the system. The proposed MPPT system is widely used for many different harvesting studies, however, in this paper it has been used at the first time for such a distorted waveform to our best knowledge. The MPPT consists of a rectifier unit storing the rectified energy into a capacitor with a certain voltage called VOC (i.e., the open circuit voltage of the harvester), then a dc-dc converter is used with the help of the MPPT unit using the half of VOC as the critical value for the performance of the control. It has been demonstrated that the power loss is nearly half of the power for the MPPT-free system, the efficiency has been increased with a rate of 98% and power consumption is measured as low as 5.29 μW
Citation - WoS: 18
Citation - Scopus: 20
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
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: 27
Citation - Scopus: 27
Perspective of safflower (Carthamus tinctorius) as a potential biodiesel feedstock in Turkey: characterization, engine performance and emissions analyses of butanol–biodiesel–diesel blends
(Taylor and Francis Ltd., 2017) Al-Samaraae R.R.; Atabani A.E.; Uguz G.; Kumar G.; Arpa O.; Ayanoglu A.; Mohammed M.N.; Farouk H.
Safflower (Carthamus tinctorius) is widely farmed in Turkey. This study investigates the physicochemical properties of safflower biodiesel and its blends with Euro diesel and butanol. A polynomial curve-fitting method was used to predict kinematic viscosity and density of the ternary blends. Furthermore, characteristics such as DSC, FT-IR, UV-Vis and TGA were adopted to evaluate the influence of butanol addition on biodiesel–diesel blends. Engine performance parameters such as BP, torque and BSFC and emissions such as CO, HC, NOx and EGT were studied. Safflower methyl ester satisfied both EN 14214 and ASTM D 6751 standards regarding viscosity, flash point and density. However, iodine value was quite high. Oxidation stability fails to satisfy either standard. This is due to the high level of unsaturated fatty acids (91.05%). A reduction in BP, torque, HC and CO coupled with an increase in BSFC, NOx emissions and EGT was observed for all blends compared to Euro diesel. Overall, all blends appear to be good alternatives to biodiesel–diesel blends. This work supports that biodiesel can be blended with diesel and butanol as ternary blends (up to 20%) for use as a fuel in compression ignition (CI) engines. Therefore, combustion characteristics of blends shall be further investigated. © 2017 Informa UK Limited, trading as Taylor & Francis Group
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
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: 11
Citation - Scopus: 13
Multi-task learning for arousal and sleep stage detection using fully convolutional networks
(IOP Publishing, 2023) Zan, Hasan; Yıldız, Abdulnasir
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: 26
Citation - Scopus: 28
Developed Analytical Expression for Current Harmonic Distortion of the Pv System's Inverter in Relation To the Solar Irradiance and Temperature
(Springer, 2021) Cangi, Hasan; Eid, Bilal; Yilmaz, Ahmet Serdar; Adak, Suleyman
This paper deals with modeling and simulation of the total harmonic distortion of the current (THDI) dispatched from the inverter and connected to nonlinear load. The change of THD(I)was examined in relation to the ambient temperature (T) and solar irradiance (G). The developed model is being used to extract parameters for a given THD(I)as a function of temperature and solar radiation. This study outlines the working principle of photovoltaic (PV) panel as well as PV array. Off-grid PV system is modeled by using Matlab/Simulink program, and detailed analytical study has been carried out in this work. The design, modeling and simulation of this study are performed from 50 up to 988 W/m(2)for solar irradiance. Harmonic components have negative effects on the steady-voltage stability of the PV system. Therefore, analytical expression is needed for steady-state stability analysis in order to reduce negative effects. Hence, two analytical expressions of THD(I)were obtained by two new different methods which are statistical package for the social sciences program and genetic expression programming. Eventually, two different methods have been verified by the Matlab/Simulink program in order to find out THD(I)and demonstrated the effectiveness of the proposed strategy. As a result of this study, it is observed that input current THDI of nonlinear load is too high at low irradiance. It is suggested that active harmonic filters should be used at low irradiance in order to produce better quality energy and avoid damages in the PV system.
Citation - WoS: 11
Citation - Scopus: 14
Development software program for finding photovoltaic cell open-circuit voltage and fill factor based on the photovoltaic cell one-diode equivalent circuit model
(Springer, 2024) Adak, Süleyman; Cangi, Hasan
The photovoltaic (PV) cell is the smallest building block of the PV solar system and produces voltages between 0.5 and 0.7 V. It acts as a current source in the equivalent circuit. The amount of radiation hitting the cell determines how much current it produces. The equivalent circuit of an ideal PV cell consists of a diode and a parallel current source. In order to express losses in applications, series and parallel resistance are added to the ideal equivalent circuit of the PV cell. There are many equivalent circuits in the literature for modeling the equivalent circuit of a PV cell. The single-diode equivalent circuit is the most widely used model because of its simplicity and ease of analysis. There are several methods available to estimate and analyze the parameters of PV cell models, such as Newton Raphson method, Lambert-W function, etc. In this study, the Newton Raphson method was used to find the equivalent circuit parameters of a PV cell. Fill factor is used to determine the quality of electricity generated by the photovoltaic cell. Open-circuit voltage is the maximum voltage value that the PV cell can transmit. The analysis of PV cell fill factor and open-circuit voltage was carried out using the developed software program. Then, the open-circuit voltage and fill factor were found using the software program prepared in MATLAB and given in Appendix.
Qualitative properties of certain non-linear differential systems ofsecond order
(Elsevier, 2017) TUNÇ, CEMİL; DİNÇ, Yavuz
In this paper, we study the boundedness and square integrability of solutions in certain non-linear systems of differential equations of second order. We establish two new theorems, which include suitable sufficient conditions guaranteeing the boundedness and square integrability of solutions to the considered systems. The presented proofs simplify previous works since the Gronwall inequality is avoided which is the usual case. The technique of proof involves the integral test, and two examples are included to illustrate the results.
Citation - WoS: 19
Citation - Scopus: 20
The Quality Problems at Low Irradiance in the Grid-Connected Photovoltaic Systems
(Springer, 2024) Adak, Suleyman; Cangi, Hasan
Solar photovoltaic (PV) energy is one of the most prominent topics that have attracted the attention of researchers in recent years. The use of solar energy is increasing rapidly in the world. Although using PV energy has various advantages, it has some disadvantages. Among these disadvantages, power factor (PF) and total harmonic distortion (THD) issues are discussed in this article. When solar PV systems are integrated into the grid, various power quality problems arise. In addition, due to low power quality and high harmonics, power system components overheat and start operating in undesirable regions; causes great damage. The magnitude of PF and THD is dependent on solar irradiation values. In order to determine how the power quality in the grid-connected solar system is affected by changes in solar irradiation (G), results for various irradiation situations are presented and analyzed. In addition, at low irradiance values, the amplitude of harmonic components and reactive power increases, whereas the power factor of the PV system decreases. Low power factor and high amplitude of harmonics cause the efficiency of the solar system to decrease. In this study, PF and THDI values were measured on a particular cloudy day for analysis. An analysis of the solar PV system was conducted using Matlab/simulation program to model the grid-connected PV system. Thus, the analytical expression of the PF and THDI, which are dependent on irradiation, was found with a new method by using the Statistical Package for the Social Sciences (SPSS) program and the curve fitting method. Obtaining the analytical expressions for both solar irradiation (G) and power factor (PF) used the SPSS program and also solar irradiation (G) and total harmonic distortion (THDI) used the MATLAB curve fitting method which contributed to the science comparing to the existing literature. It can be prevented the low power quality by using such these expressions at low solar irradiation cases.
Citation - WoS: 141
Citation - Scopus: 186
Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals
(MDPI, 2019) Türk, Ömer; Özerdem, Mehmet Siraç
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%.
Citation - WoS: 3
Citation - Scopus: 4
Classification of electroencephalogram records related to cursor movements with a hybrid method based on deep learning
(Wiley Online Library, 2021) Türk, Ömer
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.
Multi-task learning for arousal and sleep stage detection using fully convolutional networks
(2023) Zan, Hasan; Yıldız, Abdulnasır
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: 10
Citation - Scopus: 11
Qualitative properties of certain non-linear differential systems of second order
(TAYLOR & FRANCIS LTD, 2017) Tunc, Cemil; Dinc, Yavuz
In this paper, we study the boundedness and square integrability of solutions in certain non-linear systems of differential equations of second order. We establish two new theorems, which include suitable sufficient conditions guaranteeing the boundedness and square integrability of solutions to the considered systems. The presented proofs simplify previous works since the Gronwall inequality is avoided which is the usual case. The technique of proof involves the integral test, and two examples are included to illustrate the results. (C) 2016 The Authors. Production and hosting by Elsevier B.V. on behalf of Taibah University.
MONITORING OF WINE PROCESS AND PREDICTION OF ITS PARAMETERS WITH MID-INFRARED SPECTROSCOPY
(2015) Canal, Canan; Ozen, Banu
It was aimed to predict the chemical (ethanol, glycerol, organic acids, titratable acidity, °Brix, sugars, total phenolic and anthocyanin content) and microbiological parameters of red, rose and white wines during their processing from must to bottling using mid-infrared (IR) spectroscopy in combination with one of the multivariate statistical analysis techniques, partial least square (PLS) regression. Various spectral filtering techniques were employed before PLS regression analysis of mid-IR data. The best results were obtained from the second-order derivation for the chemical parameters except for alcohols. PLS models developed for the prediction of some of the chemical parameters have R2 values greater than 0.9, with low root mean square error values; however, prediction of microbial population from mid-IR spectroscopy did not provide accurate results. IR spectroscopic and chemical–chromatographic data were also used to investigate the differences between processing steps, and principal component analysis allowed clear separation of the beginning of the process from the rest.

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