Browsing by Author "Avots, Egils, juhendaja"

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Emotion Recognition using EEG signal data from EMO2018 Dataset
(Tartu Ülikool, 2024) Rebriks, Aleksandrs; Avots, Egils, juhendaja; Juuse, Liina, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Tehnoloogiainstituut
Emotion Recognition (ER) is developing area within the artificial intelligence field that is focused on comprehending and further interpreting of human emotions through various modalities. Despite that, these approaches are often not ubiquitous as they are affected by external factors. With recent physiology research connecting development of emotions to the central nervous system, usage of brain signals became a highly practical option for emotion recognition. One of the most promising methods of emotion recognition using brain signals for emotion recognition involves using Electroencephalography (EEG). Despite being more complex than classical machine learning or deep learning approaches, EEG-based emotion recognition is potentially more accurate and robust, with applications in mental health monitoring, researches in applied physiology or human-computer interactions. This thesis studies existing approaches of EEG-based emotion recognition methods for private EMO2018 dataset. We adopted methods of Fast Fourier Transform with additional processing for key features extraction and tested different Deep Learning models. Our results show performances of utilized Deep learning models with best accuracy of 88.6% from Hybrid Neural Network approach.
Image context analysis for use in social media
(Tartu Ülikool, 2024) Hajiyev, Yagub; Avots, Egils, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Tehnoloogiainstituut
In our digital world some of the shared images have been misunderstood and such events can negatively affect person’s life and mental health. Therefore, before a person shares an image, it would be beneficial to know if the image is appropriate for the intended use. To address this problem, this thesis aims at understanding the context of the images in social media. The context of the image is described with two methods: Image tagging and Image captioning. Afterwards, a large language model is used to understand if the image is appropriate for personal, social or business use. And in this way, the person will be more aware of images shared in social media.
Image similarity usage in order to find similar items in E-commerce dataset
(Tartu Ülikool, 2024) Dossymbekov, Adilet; Avots, Egils, juhendaja; Makarov, Aleksandr, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Tehnoloogiainstituut
This thesis looks into the e-commerce situation among small businesses and examines the possibility of the application of neural networks to business-to-consumer interactions. The goal of the study is to test the ability of NN to work with limited data and hardware. Pre-trained ResNet-50 model was used as a base, and fine-tuned with the 4 class dataset, consisting of 2906 images of clothes categorised into four classes. The experiments were conducted on a budget-friendly hardware setup. Both the dataset and hardware reflect the limited resources of the small and micro businesses The final model was evaluated, reaching an accuracy of 70-80%, These results suggest the possibility of NN usage in limited data and computational resource conditions and provide the base for further developments in implementing NN among small businesses.