Mardin Meslek Yüksekokulu

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Citation - WoS: 10
Citation - Scopus: 9
Identification of cotton and corn plant areas by employing deep transformer encoder approach and different time series satellite images: A case study in Diyarbakir, Turkey
(ScienceDirect, 2023) Türk, Ömer; Şimşek Bağcı, Reyhan; Acar, Emrullah
It is very important to determine the crops in the agricultural field in a short time and accurately. Thanks to the satellite images obtained from remote sensing sensors, information can be obtained on many subjects such as the detection and development of agricultural products and annual product forecasting. In this study, it is aimed to automatically detect agricultural crops (corn and cotton) by using Sentinel-1 and Landsat-8 satellite image indexes via a new deep learning approach (Deep Transformer Encoder). This work was carried out in several stages, respectively. In the first stage, a pilot area was determined to obtain Sentinel-1 and Landsat-8 satellite images of agricultural crops used in this study. In the second stage, the coordinates of 100 sample points from this pilot area were taken with the help of GPS and these coordinates were then transferred to Sentinel-1 and Landsat-8 satellite images. In the next step, reflection and backscattering values were obtained from the pixels of the satellite images corresponding to the sample points of these agricultural crops. While creating the data sets of satellite images, the months of June, July, August and September for the years 2016–2021, when the development and harvesting times of agricultural products are close to each other, were preferred. The image data set used in the study consists of a total of 434 images for Sentinel-1 satellite and a total of 693 images for Landsat-8. At the last stage, the datasets obtained from different satellite images were evaluated in three different categories for crop identification with the aid of Deep Transformer Encoder approach. These are: (1-) Crop identification with only Sentinel-1 dataset, (2-) Crop identification only with Landsat-8 dataset, (3-) Crop identification with both Sentinel-1 and Landsat-8 datasets. The results showed that 85%, 95% and 87.5% accuracy values were obtained from the band parameters of Sentinel-1 dataset, Landsat-8 dataset and Sentinel-1&Landsat-8 datasets, respectively
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: 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.
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: 62
Citation - Scopus: 100
A comprehensive review on detection of cyber-attacks: Data sets, methods, challenges, and future research directions
(ScienceDirect, 2022) Ahmetoglu, Huseyin; Das, Resul
Rapid developments in network technologies and the amount and scope of data transferred on networks are increasing day by day. Depending on this situation, the density and complexity of cyber threats and attacks are also expanding. The ever-increasing network density makes it difficult for cyber-security professionals to monitor every movement on the network. More frequent and complex cyber-attacks make the detection and identification of anomalies in network events more complex. Machine learning offers various tools and techniques for automating the detection of cyber attacks and for rapid prediction and analysis of attack types. This study discusses the approaches to machine learning methods used to detect attacks. We examined the detection, classification, clustering, and analysis of anomalies in network traffic. We gave the cyber-security focus, machine learning methods, and data sets used in each study we examined. We investigated which feature selection or dimension reduction method was applied to the data sets used in the studies. We presented in detail the types of classification carried out in these studies, which methods were compared with other methods, the performance metrics used, and the results obtained in tables. We examined the data sets of network attacks presented as open access. We suggested a basic taxonomy for cyber attacks. Finally, we discussed the difficulties encountered in machine learning applications used in network attacks and their solutions.
Citation - WoS: 3
Citation - Scopus: 3
Material analysis for restoration application: a case study of the world’s first university Mor Yakup Church in Nusaybin, Mardin
(SpringerLink, 2023) Karataş, Lale; Alptekin, Aydın; Yakar, Murat
The Mor Yakup Church, located in the Nusaybin District of Mardin, is known as the world’s frst educational university in history and represents one of the oldest Christian medieval monuments. In this study, it is aimed to determine the factors of the strength problems of the structure by investigating the characterization of building materials and what kind of factors afect the material behavior with various observational and experimental methods. It was determined that the main deterioration types in the materials of the building were erosion, fractures, loss of parts and the dissolve of the joint mortars between the masonry work on the facades. Since the materials used in the construction of the building are unable in terms of physico-mechanics, it has been determined that the severe continental climate conditions prevailing in the region easily cause such physical deterioration on the construction materials. In addition, the presence of clays in the conventional mortar used in the building has been defned as an internal problem that causes the material to get tired with the osmotic pressure it creates by absorbing water. A very high rate of salinization was detected in the building materials of the building and it was observed that this salting was caused by the acid efect caused by air pollution and the portland cement used in the previous repairs in the building. Finally, this study presents restoration recommendations to repair the material deterioration in the building and to prevent its occurrence in the future.

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