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Website Development for Advertisements for Rental and Sale of Real Estate

The purpose of the work is to develop a website for real estate agents and potential buyers, with the ability to publish advertisements for rental and sale of various properties. The objective of the work is to show the knowledge and skills that were acquired through the studies in the TTI (Transport and Telecommunication Institute).In this work were used the following methods:1. Analyzation of the subject area Real Estate;2. Analyzation of existing analogs;3. Design and development of data base, data access, business service, client, and server parts of the web application;4. Quality control.As the result of the work were made the web application for real estate agents and potential buyers was made, where the users can read the description, upload, modify and delete the advertisements. Was created the authentication on the website. Was created the data base of the website to store all of the information about advertisements and users.As the conclusion of the work all of the functional requirements for the website were made, full development cycle of the project was utilized and all of the errors that were raised during the work were fixed.

Author: Aleks Eglītis

Supervisor: Karina Kostjkina

Degree: Bachelor

Year: 2024

Work Language: English

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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COMPARATIVE ANALYSIS OF LLM-BASED APPROACHES FOR SQL GENERATION

The rapid development of Large Language Models has unlocked opportunities for restructuring software development processes in general as well as in such cases as converting natural language into SQL queries. This study seeks to experimentally evaluate the effects of four LLM-based methods on the efficiency and quality of SQL generation. Evaluation is being held based on following metrics: Correctness, Completeness and Consistency. Studied LLM-based SQL generation methods include Specific LLMs tailored for SQL code generation like SQL Coder frameworks for generating SQL code (Vanna.ai, 2023; Llamaindex, 2023) and Multi agent collaborative networks for transforming language into SQL.The research utilizes a mix of literature review case studies and simulations. It offers a comprehensive review of the advancements in LLM-driven SQL generation encompassing concepts, technologies, methodologies, strengths, limitations, and ethical considerations.This research successfully bridges the gap between theoretical foundations and practical application of AI-augmented approaches while promoting the integration of LLM-based SQL generation, into automated software development processes.

Author: Maksim Ilin

Supervisor: Dmitry Pavlyuk

Degree: Master

Year: 2024

Work Language: English

Study programme: Computer Sciences

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