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Automated Representation Learning for Cartesian Genetic Programming Using Neural Networks
Koči, Martin ; Mrázek, Vojtěch (referee) ; Sekanina, Lukáš (advisor)
This master's thesis addresses the integration of neural networks and Cartesian Genetic Programming (CGP). It explores the use of neural networks for automated representation creation for CGP and their application to improve the evolutionary process in CGP. The study covers basic concepts of machine learning, including various types of learning and neural network models. It also touches on evolutionary algorithms with an emphasis on their basic principles, general algorithms, and types of representations. This work also includes principles of representation learning and two fundamental architectures for their creation. It describes the subsequent use of representation learning in genetic programming. The solution design includes data acquisition and preprocessing, representation creation processes, and the utilization of the resulting representations. The thesis also implements two new approaches for creating representations for Cartesian genetic programs. It further explores their use in two new mutation operators, where one is based on direct modification of the vector representation and the other on the selection of genes for mutation based on their similarity. The last of the explored areas is predicting the suitability of candidate solutions using newly emerged representations.
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Application for Automatic Evaluation of the Fidelity of the Generated Facial Image
Šotola, Jiří ; Semerád, Lukáš (referee) ; Goldmann, Tomáš (advisor)
This work focuses on the design and implementation of an application for verifying the fidelity of a synthetically generated images, which, due to the vastness of this topic, is aimed at verifying the similarity of the facial features of the original image and the image generated from it. For this application, a Gen_Verifier model is developed based on Siamese networks, which uses the contrastive loss. This model was trained and tested on the LFW dataset, where it reached an accuracy of 91 %. The StarGAN model is used to test the generated images, which generated facial images with changes in hair color, gender and age. The resulting testing on the generated images showed that the StarGAN model produces faces that are similar in 87.53 % cases.
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Automatic detection of ischemia in ECG
Noremberczyk, Adam ; Potočňák, Tomáš (referee) ; Ronzhina, Marina (advisor)
This thesis discusses the utilization of the artificial neural networks (ANN) for detection of coronary artery disease (CAD) in frequency area. The first part of this thesis is orientated towards the theoretical knowledge. Describes the issue of ECG pathological changes. ECQ are converted to frequency area. Described statistical methods and methods for automatic detection of CAD and MI. Explained the issue of the perceptron and ANN. The second deals with use of Neural Network Toolbox MATLAB®. This part focuses on counting and finding suitable parameters and making connection of band. At the end of the thesis UNS is used to detect ischemic parameters and the results are discussed. Average values for the best settings are 100% accuracy.
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Competitions in Artificial Intelligence
Šafář, Pavel ; Hynčica, Tomáš (referee) ; Honzík, Petr (advisor)
My thesis is focused on the field of artificial intelligence and especially on the competitions in the areas of robotics, computer vision, communication, time series forecasting and game playing programmes. Furthermore I devoted myself to the research of the use of neural network as a tool to solve the Gomoku game problems. The neural network processes the game situations and sets up the output values based on the pre-set models.
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The use of artificial intelligence in cryptography
Lavický, Vojtěch ; Rosenberg, Martin (referee) ; Babnič, Patrik (advisor)
Goal of this thesis is to get familiar with problematics of neural networks and commonly used security protocols in cryptography. Theoretical part of the thesis is about neural networks theory and chooses best suitable type of neural network to use in cryptographic model. In practical part, a new type of security protocol is created, using chosen neural network.
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Comparison of Classification Methods
Dočekal, Martin ; Zendulka, Jaroslav (referee) ; Burgetová, Ivana (advisor)
This thesis deals with a comparison of classification methods. At first, these classification methods based on machine learning are described, then a classifier comparison system is designed and implemented. This thesis also describes some classification tasks and datasets on which the designed system will be tested. The evaluation of classification tasks is done according to standard metrics. In this thesis is presented design and implementation of a classifier that is based on the principle of evolutionary algorithms.
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Demonstrational Program for IZU Course
Míšová, Miroslava ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This bachelor's thesis deals with development of new study aplications for course Fundamentals of Artificial Intelligence. These aplications are based on the older version of JavaApplet, which use features, that are no longer supported. Each applicatoin was made acording to an object-oriented paradigm and than implemented. Special care was taken in order for the UI to be intuitive and easy to use and also for the aplication to be able to be further developed.
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Classification of ECG by artificial neural networks
Loviška, David ; Vítek, Martin (referee) ; Hrubeš, Jan (advisor)
The aim of project with name Classification ECG by artificial neural networks is simplify and speed up working a doctor. That reaches created program that the is capable simply and almost at once classify EKG signal using artificial neuronal nets. Created program will give to the doctor basic information about used electrocardiogram, as are time period and amplitude signal in single surveyed sections. Subsequently will program warn doctor about abnormalities from normal. Part of program is also graphic window with painted signal and on him in color points and partitions marked by program behind special. In next phase program alone classifies gained data and designating without doctor diagnose that doctor can evaluate and in case agreeable it sign and place for true diagnose patient. This program is also fit for data reading from bigger of the number of hours as far as days. It is concerned primarily Holter ECG monitoring.
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