Mardin Meslek Yüksekokulu

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Automatic Detection of Brain Tumors With the Aid of Ensemble Deep Learning Architectures and Class Activation Map Indicators by Employing Magnetic Resonance Images
(Elsevier, 2024) Turk, Omer; Ozhan, Davut; Acar, Emrullah; Akinci, Tahir Cetin; Yilmaz, Musa; Türk, Ömer
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.
A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data
(Sage Journals, 2022) Uyulan, Caglar; Erguzel, Turker Tekin; Türk, Ömer; Farhad, Shams; Metin, Bariş; Tarhan, Nevzat; Türk, Ömer
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.
Classification of electroencephalogram records related to cursor movements with a hybrid method based on deep learning
(Wiley Online Library, 2021) Türk, Ömer; 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.
Classification of Epilepsy Types from Electroencephalogram Time Series Using Continuous Wavelet Transform Scalogram-Based Convolutional Neural Network
(ASTM International, 2020) Türk, Ömer; Akpolat, Veysi; Varol, Sefer; Aluçlu, Mehmet Ufuk; Özerdem, Mehmet Siraç; Türk, Ömer
During the supervisory activities of the brain, the electrical activities of nerve cell clusters produce oscillations. These complex biopotential oscillations are called electroencephalogram (EEG) signals. Certain diseases, such as epilepsy, can be detected by measuring these signals. Epilepsy is a disease that manifests itself as seizures. These seizures manifest themselves in different characteristics. These different characteristics divide epilepsy seizure types into two main groups: generalized and partial epilepsy. This study aimed to classify different types of epilepsy from EEG signals. For this purpose, a scalogram-based, deep learning approach has been developed. The utilized classification process had the following main steps: the scalogram images were obtained by using the continuous wavelet transform (CWT) method. So, a one-dimension EEG time series was converted to a two-dimensional time-frequency data set in order to extract more features. Then, the increased dimension data set (CWT scalogram images) was applied to the convolutional neural network (CNN) as input patterns for classifying the images. The EEG signals were taken from Dicle University, Neurology Clinic of Medical School. This data consisted of four classes: healthy brain waves, generalized preseizure, generalized seizure, and partial epilepsy brain waves. With the proposed method, the average accuracy performance of three of the EEG records' classes (healthy, generalized preseizure, and generalized seizure), and that of all four classes of EEG records were 90.16 % (± 0.20) and 84.66 % (± 0.48). According to these results, regarding the specific accuracy ratings of the recordings, the healthy EEG records scored 91.29 %, generalized epileptic seizure records were at 96.50 %, partial seizure EEG records scored 89.63 %, and the preseizure EEG records had a 90.44 % rating. The results of the proposed method were compared to the results of both similar studies and conventional methods. As a result, the performance of the proposed method was found to be acceptable.
The convolutional neural network approach from electroencephalogram signals in emotional detection
(Concurrency Computation, 2021) Türk, Ömer; Özerdem, Mehmet Siraç; Türk, Ömer
Although brain-computer interfaces (BCI) progress rapidly, the desired success has not been achieved yet. One of these BCI is to detect emotional states in humans. An emotional state is a brain activity consisting of hormonal and mental reasons in the face of events. Emotions can be detected by electroencephalogram (EEG) signals due to these activities. Being able to detect the emotional state from EEG signals is important in terms of both time and cost. In this study, a method is proposed for the detection of the emotional state by using EEG signals. In the proposed method, we aim to classify EEG signals without any transform (Fourier transform, wavelet transform, etc.) or feature extraction method as a pre-processing. For this purpose, convolutional neural networks (CNNs) are used as classifiers, together with SEED EEG dataset containing three different emotional (positive, negative, and neutral) states. The records used in the study were taken from 15 participants in three sessions. In the proposed method, raw channel-time EEG recordings are converted into 28 × 28 size pattern segments without pre-processing. The obtained patterns are then classified in the CNN. As a result of the classification, three emotion performance averages of all participants are found to be 88.84%. Based on the participants, the highest classification performance is 93.91%, while the lowest classification performance is 77.70%. Also, the average f-score is found to be 0.88 for positive emotion, 0.87 for negative emotion, and 0.89 for neutral emotion. Likewise, the average kappa value is 0.82 for positive emotion, 0.81 for negative emotion, and 0.83 for neutral emotion. The results of the method proposed in the study are compared with the results of similar studies in the literature. We conclude that the proposed method has an acceptable level of performance.
Developed analytical expression for current harmonic distortion of the PV system’s inverter in relation to the solar irradiance and temperature
(Electrical Engineering, 2021) Adak, Süleyman; Cangi Hasan; Eid Bilal,; Yılmaz Ahmet Sedar
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 THDI 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 THDI 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/m2 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 THDI 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 THDI 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.
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
Energy, exergy and exergoeconomic analysis of solar-assisted vertical ground source heat pump system for heating season
(KOREAN SOC MECHANICAL ENGINEERS, 2018) Unal, Fatih; Temir, Galip; Koten, Hasan
The purpose of this study is to evaluate the experimental performance of a solar assisted vertical ground source heat pump system (VGSHP) for the winter climatic conditions of Mardin, which is in the South-Eastern Anatolia region of Turkey. For this aim, an experimental analysis was performed on solar assisted VGSHP system, which was designed to meet the heating needs of an experimental room, during the heating season (10.01.2013/03.31.2014). The experimentally obtained results were used to calculate energy, exergy and exergoeconomic analyses of the system and its components. The energy efficiency, exergy efficiency and exergoeconomic factors of the entire system were 67.36 %, 27.40 % and 60.51 %, respectively. In this study, the system was proposed for disseminating the use of alternative technologies supported by renewable energy systems and it has been tested for the first time in Mardin to meet its heating needs with convectional systems. The experimental results showed that the proposed solar assisted VGSHP system can be used for residential heating in Mardin and similar regions. As a result, it has been detected that the system is very effective in both reducing energy consumption and decreasing emissions of green-house gases.
FPGA simulation of chaotic tent map-based S-Box design
(Wiley Online Library, 2022) Türk, Ömer; Türk, Ömer
The chaotic system has a characteristically random behavior by nature, and these systems have their own characteristics in a completely deterministic structure. This feature of a chaotic system makes it difficult to predict encryptions designed based on such a system. Thanks to this unpredictable and strong feature, maps produced from chaotic systems are an important alternative in the field of encryption. One of the structures obtained by employing chaotic maps is the substitution box. S-Box, which provides the confusion principle used in block ciphers, is the main block that dynamically replaces unencrypted data with confidential data and makes a significant contribution to ensuring high security in the encryption system. Therefore, S-Boxes hold a critical role in block ciphers. Speed and reliability are important parameters in the creation of this main block. Especially, applications performed on hardware are more reliable and high performance. Therefore, in this study, an S-Box was designed using fieldprogrammable gate arrays (FPGA) simulation from a chaotic tent map to create a fast and reliable S-Box because FPGAs offer solutions that may be important in this field considering their fast and customizable architecture. In the proposed method, the S-Box was created in 0.16 s. In addition, the dynamic properties of the chaotic tent map were analyzed with Lyapunov exponents, and the NIST SP 800-22 test was applied for the information encryption suitability of the proposed chaotic system. Also, to test the reliability of the produced S-Box structures, SAC, non-linearity, bit independence criteria, and input/output XOR distribution table metrics were implemented. The results showed that the proposed chaotic map was dynamic and passed the reliability tests successfully.
Harmonics Mitigation of Stand‑Alone Photovoltaic System Using LC Passive Filter
(Journal of Electrical Engineering & Technology, 2021) Adak, Süleyman
This article investigates modeling and simulation of the of-grid photovoltaic (PV) system, and elimination of harmonic components using an LC passive flter. Pulse width modulation (PWM) inverter is used to convert the direct current to alternating current. It is very important in terms of energy quality that the inverter output current total harmonic distortion (THDI) is below the value given by standards. Harmonic components have negatively efect on of-grid PV power system. THDI should be kept below a certain level in order to prevent damage to the equipment in the of-grid system and to ensure a higher quality energy fow to reduce the total harmonic distortion (THD) of the solar inverter output current; LC passive flter must be connected to the output of the PWM inverter. There are many types of passive flters for solar inverters. One of the most widely used flter types is the LC flter. LC flters are used in of-grid systems. LC flter is smaller in size and lower cost than other flters. But it is more complicated to determine the parameters of the LC flter. Therefore, in order for the system to remain in a steady state, the parameters must be accurately calculated and analyzed. In this study, the output power of the solar inverter, switching frequency, bus voltage etc. values were determined and LC flter parameters were calculated. Since high inductance values are used in LC flters, the voltage drop increases in these flters. To reduce the voltage drop, the DC bus voltage must be increased, which increases the switching losses. LC flter is connected between the inverter and the nonlinear load to flter the harmonic components produced by the DC/DC boost converter, DC/AC inverter and non-linear load. Matlab/Simulink program was used in Simulation and analysis of of-grid solar system. Solar inverter output current THD was measured as 91.55%. After the LC flter is connected to the system, this value has dropped to 2.62%.
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
The quality problems at low irradiance in the grid-connected photovoltaic systems
(Springer Science and Business Media Deutschland GmbH, 2024) Adak, S.; Cangi, H.
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. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
THERMAL ANALYSIS OF DIFFERENT REFUSE DERIVED FUELS SAMPLES
(DoiSerbia, 2021) Ayas, Gizem; Öztop, Hakan F.
As a result of the activities carried out by people to maintain their daily lives in different places such as homes, hospitals, hotels or workplaces, waste consisting of furniture, paint, batteries, food waste, sachets, bottles, fabrics, and fibers with the heterogeneous structure is called municipal solid waste. Secondary fuels with higher heating value, which are generated by recycling of non-recyclable and reusable wastes in municipal solid wastes, are called as refuse derived fuel (RDF). In this study, RDF1 (taken in December, winter season) and RDF2 (taken in June, summer season) samples obtained from different dates were used. The ultimate, proximate, calorific value, X-ray fluorescence, thermogravimetric analysis, and differential scanning calorimetry analysis were performed for these samples. Combustion characterization from RDF samples was investigated in the applied analyzes. The results of the content analysis made were examined separately and compared with the thermogravimetric analysis and differential thermal analysis combustion graph curves. It was revealed that the RDF1 sample had a better combustion compared to the RDF2 sample, as the ash amount and content obtained as a result of the combustion also supported other data. In addition, the results of the analysis show how different the RDF samples taken from the same region in two different months are different from each other.
Thin-Layer Drying Modeling in the Hot Oil-Heated Stenter
(International Journal of Thermophysics, 2020) Ünal, Fatih; Akan, Ahmet Erhan
Although the drying processes have an important place in the textile industry in terms of drying or various textile finishing applications, they are considered as an expensive process in terms of energy and time consumed. Therefore, it is of great importance to simulate with mathematical models the drying behavior of a stenter (ram machine), one of the most preferred convection dryers in the textile industry. For this purpose, in this study, modeling was attempted of the drying behavior of 67 % Cotton + 33 % Polyester containing Thessaloniki knit fabrics, using experimental data obtained from drying processes performed in 9 different drying operations in a 10-chamber hot oil-heated stenter and 12 different empirical and semi-empirical thin-layer models that are frequently used in the literature. R2 values from regression analysis were evaluated as the primary factor in the model fit selection. According to the results obtained, it was understood that the Diffusion Approach model with R2 values ranging from 0.9991 to 0.9999, Two Term Model with R2 values ranging from 0.9995 to 0.9999, and the Modified Henderson and Pabis model with R2 values ranging from 0.9995 to 0.9999 gave the most appropriate results upon simulating drying behavior. In this regard, this study, which contains explanatory information on the drying behavior in a stenter, is thought to be useful to researchers.

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