Enstitüler

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Browsing Enstitüler by Department "Fen Bilimleri Enstitüsü / Elektronik ve Bilgisayar Mühendisliği Ana Bilim Dalı"

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    Master Thesis
    K-mer Sekans Gösterimine Dayalı MicroRNA-Hastalık İlişkilerinin ve MicroRNA-Tür İlişkilerinin Sınıflandırılması
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Erbaşı, Yalçın Han; Güngör, Burcu; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
    The dysregulated gene expression brings about a variety of diseases, and dysregulation of microRNA (miRNA) has a wide impact on disease development and cellular physiology. Thus, miRNAs play important roles in a variety of fundamental and significant biological processes related to human diseases. There are a lot of research about changes in the function of miRNAs have been published in many human diseases. Computational methods serve as a complementary process to traditional wet-lab experiments, which require many resources and time in terms of detecting potential miRNA-Disease associations. Furthermore, there is a need to present a novel approach that allows assignment of an unknown miRNA to its most likely species. An easy way to filter new data would be to ensure that the new miRNA is classified below the maximum distance to the species known to originate from. In this thesis, a computational model has been proposed for identifying miRNA-disease and miRNA-Species associations by depicting the miRNAs with their k-mer sequence representation and by utilizing machine learning methodologies. The difference of our approach is which we reveal disease and species associated the sequences of miRNA store information. This put a question about the miRNA's chemical compounds and their associations with different types of species and diseases. With this study, the new disease-disease and species-Species associations disclosed can be calculated for many different species and diseases, these approaches can develop to species and disease classification. Lastly, our study may open a door to redefine species and diseases classifications which have been used nowadays, also it may provide the improvement of treatment strategies and early diagnosis
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    Doctoral Thesis
    Nesnelerin İnterneti Tabanlı Araç Tipi Sınıflandırma ve Ağ Anomalisi Tespiti için Makine Öğrenmesi Yaklaşımları
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Kolukısa, Burak; Güngör, Vehbi Çağrı; 0000-0003-0423-4595; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; 01. Abdullah Gül University
    This thesis presents innovative approaches in the realms of Intelligent Transportation Systems (ITS) and Network Intrusion Detection Systems (NIDS) within the Internet of Things (IoT). Leveraging IoT technologies, a low-cost, battery-operated 3-D magnetic sensor has been developed for ITS to enable the classification of vehicle categories. The research presents machine learning and deep learning models that are improved by using oversampling, feature selection and extraction methods, hyperparameter optimization, and converting signals into 2-D images. New methods have been proposed for vehicle type classification to boost classification performance and achieve an accuracy of up to 92.92%. Additionally, the increasing reliance on IoT devices for such applications introduces significant cybersecurity risks. To mitigate these vulnerabilities, a novel logistic regression model trained with a parallel artificial bee colony (LR-ABC) algorithm has been proposed for network anomaly detection. This model incorporates hyperparameter optimization to enhance detection capabilities, showcasing superior performance on popular benchmark NIDS datasets with accuracies of 88.25% and 90.11%. Overall, this research contributes to the advancement of IoT and IoT cybersecurity by offering robust, scalable, and efficient solutions. These innovations not only enhance vehicle type classification and network security in the IoT era but also pave the way for future IoT infrastructure development in an increasingly connected digital landscape.
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    Doctoral Thesis
    Videodan Gece Yangın Tespiti
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Ağırman, Ahmet Kerim; Taşdemir, Kasım; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; 01. Abdullah Gül University
    With the recent advancements in the field of Computer Vision, the central tasks such as object detection, segmentation or object tracking methods attain all-time high accuracies in natural image sets such as ImageNet, COCO, etc. However, due to the innate downsides of digital images acquired in insufficiently illuminated environments, the conventional methods suffer severely. This specific problem remains unsolved. Especially if the environment is pitch dark and the object of interest is emitting light, the dynamic range of the current digital cameras falls short in this situation and the generated digital image contains almost no perceptible visual texture. One prominent example of this is nighttime forest fire videos. In this thesis, detection of nighttime forest fires from video is addressed as an application of the challenging task, scene perception in low light conditions. The first contribution of this dissertation is developing a novel object tracking algorithm for glowing object in the dark environments. The algorithm allows to track fire and nonfire objects throughout the video. The second contribution of the thesis is proposal of new handcrafted features which are designed to capture spatio-temporal behavior of the glowing objects since there is little or no visual textures to be processed. The results showed that the features are descriptive enough to distinguish fire from the other deceptive light sources. The third contribution is employing deep learning models to automatically extract spatial features with CNNs, and temporal features from bidirectional Long Short-Term Memory (BLSTM) networks. The empirical test results show that a CNN + BLSTM pipeline can effectively detect fires at night with a high accuracy. Finally, a new comprehensive nighttime fire video dataset comprising 1358 positive videos and 334535 of fire frames is created.