Master Degree / Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/11147/3008

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Author: search.filters.author.Ayav, TolgaPublisher: search.filters.publisher.01. Izmir Institute of Technology

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  • Master Thesis
    Estrus Detection in Cows With Deep Learning Techniques
    (01. Izmir Institute of Technology, 2024) Arıkan, İbrahim; Ayav, Tolga; Ayav, Tolga; Soygazi, Fatih; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    Accurately predicting the estrus period is essential for enhancing the efficiency and lowering the costs of artificial insemination in livestock, a crucial sector for global food production. Precisely identifying the estrus period is critical to avoid economic losses such as decreased milk production, delayed calf births, and loss of eligibility for government subsidies. Since the most obvious movement that needs to be detected during the fertilization period is mounting, it is important to detect this movement. Since manual detection of this movement is difficult and costly, automated methods were needed. Therefore, it is thought that deep learning-based methods can be applied to detect the mounting moment. The proposed method detects the estrus period using deep learning and XAI (Explainable Artificial Intelligence) techniques. Deep learning-based mounting detection is performed using CNN, ResNet, VGG-19 and YOLO-v5 models. The ResNet model in this proposed study detects mounting movement with 99% accuracy. Explainability of deep learning models describes features that aid in decision-making in detecting mounting motion. Grad-CAM and Gradient Inputs models, which are XAI techniques, are used for the black box behind the proposed models. The developed deep learning models reveal that they focus on the udder and back area of the cows during the decision-making phase. In addition, how successfully the Grad-CAM and Gradient Inputs models, which are the XAI models used for the explainability of the deep learning models trained in this study, performed the explanation process was measured by calculating the 'faithfulness', 'maximum sensitivity' and 'complexity' metrics.
  • Master Thesis
    Blackhole Attacks in Iot Networks
    (01. Izmir Institute of Technology, 2020) Sokat, Barış; Erten, Yusuf Murat; Ayav, Tolga; Sokat, Barış; Ayav, Tolga; Erten, Yusuf Murat; 01. Izmir Institute of Technology; 03.04. Department of Computer Engineering; 03. Faculty of Engineering
    IoT technologies are very popular today, and they are used in almost every field. Therefore, the number of IoT devices used is increasing day by day. Like every field in computer networks, security is quite important in IoT networks. However, the resource-constrained nature of IoT devices makes the study of security measures for IoT networks very challenging. In the literature research conducted before this thesis study, it was seen that it can perform a wide variety of RPL-based attacks on IoT networks. One of these attacks is the blackhole attack. Although the black hole attack is functionally simple, the damage it causes in the network can be extremely destructive. As far as it is known, in addition to the limited number of studies in this field of attack, the black hole attack used in the studies in this field has also basic features. The basic feature mentioned here is that the attacker node that will perform a black hole attack drops all the packets that come to it. When the attacker node drops all incoming packets, it causes the topology to change in the network, the number of control messages to increase and the attacker node to be isolated from the network in a short time. However, blackhole attack can be combined with different attacks. Therefore, in this thesis, the node that will perform the black hole attack is designed to allow control messages to pass, while dropping all other packets. Here, it is aimed that the attacker node remains on the network for a longer time. As a result, as long as the attacker node is active, it will be able to drop more packets and the number of control messages in the network will be controlled since the topology does not change. With the black hole attack developed as a result of the simulation tests, the number of control messages released in the network was taken under control and it was observed that the attacker node could remain in the network throughout the simulation period. Thus, the effect of different types of black hole attacks on the network that can be developed has been revealed.
  • Master Thesis
    Predictive Maintenance for Smart Industry
    (01. Izmir Institute of Technology, 2020) Asadzade, Asad; Ayav, Tolga; Ayav, Tolga; 03.04. Department of Computer Engineering; 01. Izmir Institute of Technology; 03. Faculty of Engineering
    After the internet of things developed rapidly, it started to be used in many several industrial areas. Thanks to IoT, data that affect the health of any equipment or other important systems are collected. When these data are processed correctly, important information about the production process is obtained. For example, thanks to this data, systems based on machine learning are created to predict when various components will fail. Thus, maintenance operations are carried out before the component's breakdown, and replacement operations are performed if necessary. This strategy, called predictive maintenance, provides industries with advantages such as maximizing the life of components, reducing extra costs, and time saving. In this study, we applied ARF method, which is based on stream learning, on Turbofan Engine Degradation Simulation Datasets which are provided by NASA to estimate the remaining useful lifetime of jet engines. As a result, we mentioned about the advantages of streaming learning over batch learning and compared our results with other batch learning based studies which are applied on the same datasets.
  • Master Thesis
    Internet of things simulation using cisco packet tracer
    (01. Izmir Institute of Technology, 2020) Thera, David; Erten, Yusuf Murat; Ayav, Tolga; Ayav, Tolga; Erten, Yusuf Murat; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    Nowadays, the term IoT (internet of things) have become extremely important in our life. This technology is used in many fields such as education, health, industries, agriculture and infrastructures. In order to learn and understand how this technology works, many practical learning tools are used. The aim of the thesis is to introduce a iot simulation tool, where student can simulate or build and manage the systems for better understanding of the philosophy behind iot networks. The tool used is Cisco packet tracer which is a software developed by Cisco that is used to create and simulate a virtual network, basically a wireless network, without the need for any network hardware. The tool is free of charge, and suitable to work with almost all the operating systems. Cisco packet tracer allows users to have a practical networking technology knowledge. In this thesis, "Cisco packet tracer" is used to design an internet-based home automation system or smart home.