Bilgisayar Teknolojileri Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12514/175

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Balina Optimizasyon Algoritması Kullanılarak Türkiye’nin Uzun Vadeli Enerji Tüketimi Tahmini
(IEEE Xplore, 2021) Babaoğlu, Merve; Haznedar, Bülent
Enerji, ülkelerin sürdürülebilir kalkınmaları için en önemli konu başlıklarından biridir. Kullanılan enerjinin tükenebilir olması, birçok enerji kaynağını ithal ediyor olması ve çevresel faktörlerden dolayı Türkiye için gelecekte enerji ihtiyacının ne kadar olabileceğinin tahmin edilebilmesi büyük önem taşımaktadır. Bu çalışmada Türkiye’nin 2040 yılına kadarki enerji tüketim tahminini yapabilmek adına, sezgisel algoritmalardan balina optimizasyon algoritması (BOA) tercih edilmiştir. Balina optimizasyon algoritmasının performansını belirleyebilmek için elde edilen veriler, genetik algoritma (GA) sonuçları ile karşılaştırılmıştır. Tüm modeller doğrusal olarak düzenlenip sonuç alınmıştır. Enerji talebini etkileyen gayri safi yurtiçi hasıla (GSYH), nüfus, ithalat ve ihracat gibi bağımsız değişkenlerin 1990-2019 yılları arasındaki verileri kullanılmıştır. Sonuçların doğruluğunu hesaplayabilmek için geçmiş 30 yılın modellenmesi sağlanmıştır. En uygun model elde edildikten sonra gelecek 20 yıl için 4 farklı senaryoya göre tahminler yapılmıştır.
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.
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.
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.
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.
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; Türk, Ömer
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
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; Türk, Ömer
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.

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