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.
Effects of distributed generations' integration to the distribution networks case study of solar power plant
(İlhami Çolak, 2017) Shobole, Abdulfetah; Baysal, Mustafa; Wadi, Mohammed; Tur, Mehmet Rida
All over the world, the Distributed Generations (DGs) integration to power system has increased in the recent years due to economic, environmental and technical advantages. Turkey which has the huge solar potential has focused on integrating both licensed and unlicensed solar power plants by providing 10 years of purchasing guarantee as an incentive for the electricity producers from solar energy. However, the integration of DGs has several negative effects on the distribution networks (DNs). This work is concerned with investigating the possible challenges that may arise due to integration of PV based DGs on the existing distribution networks. Short circuit current level with respect to variation in MW integration is studied for the case the utility network is weak and strong. When the utility network is strong, the integration effect of inverter based DGs like solar power plants were observed insignificant. However, for the weak utility networks, the integration of inverter based DGs has been observed to have significant influence. Finally, directly integrated DGs (without inverter) are considered to reveal its difference with the non-inverter based DGs. As the case study, the distribution network integration of a solar power project, which is found in the Antalya region of Turkey, is investigated. This is 12 MW solar power plant designed to be connected to the local distribution network in Antalya. It is concluded that the effects of directly integrated DGs are observed more prominent compared to the inverter based DGs. DigSILENT Power Factory simulation tool is used for the study.
Energy Consumption of Defrosting Process in No-Frost Refrigerators
(Yildiz Technical University, 2018) Özkan, D.B.; Ünal, F.
Refrigerators have high energy consumption because they consume energy throughout the day, and they are used in all residences and in most offices. Designing more efficient models and, thus, decreasing the energy consumption of refrigerators have become necessary, owing to the global energy scarcity. The purpose of this study was to decrease energy consumption and increase the efficiency of the defrosting process in no-frost refrigerators. The defrosting process plays an important role in the energy consumption of no-frost refrigerators. The amount of energy needed for defrosting and the time it takes are important factors for manufacturers in terms of energy performance. Recently, a theoretical correlation was developed as a function of the frost thickness, heat flux, and frost density for estimating the defrosting time of an evaporator fin surface. The melting time of the frost on the fin was calculated by a mathematical model and compared to results that were obtained experimentally. The results were differed from the actual melting time as 4.7%. © 2018 Yildiz Technical University.
Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals
(MDPI, 2019) Türk, Ömer; Özerdem, Mehmet Siraç; Türk, Ömer
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%.
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

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