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Exploitation of Machine Learning for Identification of Feeder Rod Movement
Vele, Patrik ; Vašíček, Zdeněk (referee) ; Šimek, Václav (advisor)
The aim of this diploma thesis is to create a device that uses machine learning methods to recognize the movements of a feeder fishing rod based on data from an inertial measurement unit. The introductory part is devoted to the feeder fishing technique, the selection of important movements and the possibilities of attaching the detection device to the rod. This is followed by the creation of a theoretical basis in the field of machine learning, familiarization with the inertial measurement unit and the issue of classification. The acquired knowledge is used to select appropriate techniques for solving the task of recognizing the movements of the rod. In the practical part, a detection device based on the ESP32 platform is designed and created. This is initially used as a motion sensor, which, in combination with the processing of the measured values, serves as a generator of a training data set. The work continues with the implementation of the convolutional neural network, the learning process on the created dataset and the integration of the most successful model into the detection device. The conclusion is devoted to testing in practice, evaluation and possibilities of future development. The result is a small, battery-powered device that, when attached to any feeder rod, provides highly successful detection of all key movements during the hunt. In addition, thanks to wireless communication via ESP-NOW, it is possible to send the results to various devices.
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Design of Methods for Encrypted Traffic Visualization
Hlučková, Pavla ; Martinásek, Zdeněk (referee) ; Malina, Lukáš (advisor)
This thesis deals with design of methods for encrypted traffic visualization. It generally describes selected encrypted traffic protocols, whose data samples were collected later on to form a dataset. Furthermore, it focuses on the topic of IP flow monitoring and decribes the means of carrying out such monitoring. An important part of this thesis is the dataset created from the samples of mentioned protocols and the visualizations of different statistics and metadata gatherable from (extended) IP flows of these protocols. The designed methods of visualization are implemented using the Python programming language and the Jupyter Notebook technology.
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Advanced analysis of moving objects in the image
Medynskyi, Ivan ; Sikora, Pavel (referee) ; Kiac, Martin (advisor)
This work focuses on image processing using the OpenCV library and detecting moving objects in video using convolutional neural networks. The resulting application can detect moving objects in video or in real time and includes a user interface that allows the user to easily control the application. Part of this application is the YOLO convolutional model, which is designed to detect moving objects.
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Machine Learning from Intrusion Detection Systems
Dostál, Michal ; Očenášek, Pavel (referee) ; Hranický, Radek (advisor)
The current state of intrusion detection tools is insufficient because they often operate based on static rules and fail to leverage the potential of artificial intelligence. The aim of this work is to enhance the open-source tool Snort with the capability to detect malicious network traffic using machine learning. To achieve a robust classifier, useful features of network traffic were choosed, extracted from the output data of the Snort application. Subsequently, these traffic features were enriched and labeled with corresponding events. Experiments demonstrate excellent results not only in classification accuracy on test data but also in processing speed. The proposed approach and the conducted experiments indicate that this new method could exhibit promising performance even when dealing with real-world data.
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Malicious Domain Detection from External Data Sources
Horák, Adam ; Ryšavý, Ondřej (referee) ; Hranický, Radek (advisor)
This thesis presents a study on the development of a malicious domain detection system based on external data sources. The research examines suitable domain lists for the task, available domain data sources, and the information they provide. The thesis presents a comprehensive analysis of feature selection methods and evaluates their effectiveness in building an accurate classifier. The resulting model is both effective and fast, making it suitable for practical use. The thesis concludes that the proposed approach offers a promising solution for detecting malicious domains in real-world scenarios.
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QR code detection using deep learning
Černohous, Matěj ; Kříž, Petr (referee) ; Přinosil, Jiří (advisor)
This bachelor thesis deals with the design of an algorithm for detecting and decoding QR codes in images using deep learning techniques. The work involved the construction of 2 datasets, a YOLOv7 neural network model for detecting QR codes in images, a YOLOv4-tiny neural network model for detecting position markers of QR codes, and a Python program utilizing these models to read QR codes in images. For evaluation, the algorithm was compared with other options for QR code reading.
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Exploitation of Machine Learning for Identification of Feeder Rod Movement
Vele, Patrik ; Vašíček, Zdeněk (referee) ; Šimek, Václav (advisor)
The aim of this diploma thesis is to create a device that uses machine learning methods to recognize the movements of a feeder fishing rod based on data from an inertial measurement unit. The introductory part is devoted to the feeder fishing technique, the selection of important movements and the possibilities of attaching the detection device to the rod. This is followed by the creation of a theoretical basis in the field of machine learning, familiarization with the inertial measurement unit and the issue of classification. The acquired knowledge is used to select appropriate techniques for solving the task of recognizing the movements of the rod. In the practical part, a detection device based on the ESP32 platform is designed and created. This is initially used as a motion sensor, which, in combination with the processing of the measured values, serves as a generator of a training data set. The work continues with the implementation of the convolutional neural network, the learning process on the created dataset and the integration of the most successful model into the detection device. The conclusion is devoted to testing in practice, evaluation and possibilities of future development. The result is a small, battery-powered device that, when attached to any feeder rod, provides highly successful detection of all key movements during the hunt. In addition, thanks to wireless communication via ESP-NOW, it is possible to send the results to various devices.
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Simple Recommender System
Gorčák, Damián ; Rychlý, Marek (referee) ; Bartík, Vladimír (advisor)
Recommender systems are very important in searching for items all over the internet. There are many algorithms for creating recommendations. The main goal of this thesis was to find suitable datasets and make application, which would process them. After that, chosen algorithms for recommender systems are compared with selected datasets
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Advanced image analysis using deep neural networks
Hynek, Vojtěch ; Přinosil, Jiří (referee) ; Kiac, Martin (advisor)
This bachelor thesis deals with the problem of object detection in images using a convolutional neural network. The result of this work is a custom dataset, a neural network model YOLOv4 and a script used to process the resulting model data. The dataset contains 8080 images on which 14 objects are annotated. The neural network model was reduced in depth, which significantly increased the speed of the detection itself. The script processing the resulting data calculates the 3D and GPS coordinates of the detected object in space. The paper concludes by summarizing the results of the model and at the same time suggesting how the quality of the dataset could be improved.
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Advanced analysis of moving objects in the image
Medynskyi, Ivan ; Sikora, Pavel (referee) ; Kiac, Martin (advisor)
This work focuses on image processing using the OpenCV library and detecting moving objects in video using convolutional neural networks. The resulting application can detect moving objects in video or in real time and includes a user interface that allows the user to easily control the application. Part of this application is the YOLO convolutional model, which is designed to detect moving objects.
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