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Improvement of machine leaning algorithms performance by data set dimensionality reduction using cellular automata

A significant challenge in Machine Learning is dealing with high-dimensional data. Complexity knowns as the "curse of dimensionality" results in deterioration оf Machine Learning algorithms performance as the dimensionality and dataset size increases. Cellular automata are a dynamical discrete computational system with mathematical functions knows as rules that result in complex global behaviour. We used one-dimensional elementary cellular automata as a tool for dataset size. Model variables were selected for initial status vector generation and its further transformation to format that is suitable for cellular automata rules application known in cellular automata theory as configuration. Then model iterated through all possible cellular automata rules and various epochs variations were applied. Model performance for reduced dataset was compared with benchmark results of original dataset after standard dimensionality reduction technics used. It was concluded that applied cellular automata rules can be used as alternative methods for dataset size reduction without deteriorating model performance.

Author: Alexey Kuchvalskiy

Supervisor: Dmitry Pavlyuk

Degree: Master

Year: 2024

Work Language: English

Study programme: Computer Sciences

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Development of web application for a gym

Bachelor's thesis is devoted to the development of a unique web application for a gym, which will be competitive in the Latvian market. Within the framework of the project, a web application is being developed, including a server part based on Python language, as well as a client part using such languages as HTML, CSS and JavaScript. The key features of the developed application are integration with artificial intelligence ChatGPT and integration with the third-party application Telegram. Artificial intelligence in this work is presented in the form of chatbot technical support and chatbot online trainer, as well as used to create individual training programmes for each client.The main goal of the work is to create a unique product that has no analogues in the Latvian market, which will significantly improve the experience of gym customers and contribute to the achievement of each client's goals. During the development process testing was performed, which confirmed the successful integration of all components of the application. Based on the testing results, recommendations for further development of the web application are offered.

Author: Andrejs Glušenoks

Supervisor: Olga Dribeņeca

Degree: Bachelor

Year: 2024

Work Language: Latvian

Study programme: Computer Science

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Development of a Presentation Generation Web Service Using AI Language Models

This bachelor's thesis presents the development of a presentation generation web service using AI language models. The application integrates APIs such as OpenAI for text generation, Pexels for image retrieval, and Google Slides for presentation creation, providing a comprehensive tool for generating high-quality presentations and videos. The backend is built using Flask, and the frontend uses React, ensuring a seamless user experience. Key features include user authentication, state management, and dynamic content generation.The project involved analyzing existing AI-powered presentation tools, developing the web service with frontend and backend technologies, and integrating various APIs to enhance functionality. Rigorous testing ensured stability, efficiency, and user-friendliness. The resulting application can generate high-quality presentations and convert them into engaging videos with minimal effort.This thesis demonstrates AI's potential in enhancing digital content creation, offering significant improvements in efficiency and user engagement. The developed web service provides a valuable tool for users needing to quickly create professional presentations.

Author: Igors Pticins

Supervisor: Aleksejs Vesjolijs

Degree: Bachelor

Year: 2024

Work Language: English

Study programme: Computer Science

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Unsupervised machine learning approach for hierarchical graph-based representation of natural language text collections.

Managing big data efficiently is important in various fields, much so when data consists of human-written documents. Recent advances in Natural Language Processing (NLP), particularly LLMs, allowed to solve many task in this domain, despite the high demand for labelled data, compute resources and specialized skills.To tackle these limitations, current study proposed a NLP pipeline to identify topic hierarchies in collections of scientific publications. The work focused on evaluation of available unsupervised machine learning methods and quality metrics in NLP, and development of visualization techniques to build a prototype of the pipeline.Proposed solution is based on the hARTM approach optimized for interpretability. It demonstrated the capacity to infer human-interpretable topic hierarchies from collections of scientific texts and construct meaningful hierarchy of topic-based document representations. The visualization approaches rely on MDS to present inter-document similarity and Sankey plots to show document cluster relatedness within topic hierarchy.Utility was demonstrated on two datasets, focusing on interpretability and meaning of the topic hierarchy and associated topic definitions. Potential application areas include personal education and scientific writing.

Author: Jevgenijs Bodrenko

Supervisor: Irina Jackiva

Degree: Master

Year: 2024

Work Language: English

Study programme: Computer Sciences

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Application of machine learning in decision support system

The aim of the work is to improve the accuracy of predicting wait times in an existing queue management system using machine learning. Client-provided data was analyzed, and models were trained using various machine learning algorithms. Performance measures of the models were collected, and the best one was selected. Additionally, software and a database were developed to manage the training process and evaluate the quality of the models. The quality of the software was assessed using industry-standard methodologies and tested.

Author: Jevgēnijs Nikolajevs

Supervisor: Jeļena Kijonoka

Degree: Bachelor

Year: 2024

Work Language: Latvian

Study programme: Computer Science

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