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Development of an information management application for a logistics company

The thesis "Development of an Information Management Application for a Logistics Company" analyzes information management systems in the logistics sector and proposes the development of a new application. The work includes an analysis of existing logistics information systems, determination of requirements for a new application, design of its architecture, development, testing, and user experience evaluation. The results show that the developed system has demonstrated its ability to improve management processes.

Author: Aleksejs Orlovs

Supervisor: Olga Dribeņeca

Degree: Bachelor

Year: 2024

Work Language: Latvian

Study programme: Computer Science

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PREDICTIVE ANALYTICS FOR ONLINE CASINO REVENUE IN THE AUSTRALIAN MARKET.

This thesis investigates the influence of economic indicators and weather conditions on online slot machine gambling habits and revenues in Australia. By reviewing the interplay of GDP, CPI, and unemployment rates, the study uncovers their impact on gambling behaviors, revealing that a healthier economy boosts gambling expenditures while financial strain reduces participation. Weather's impact was minimal, likely due to the indoor nature of gambling. Predictive models, including Multiple Linear Regression, ARIMAX, and SARIMAX, were developed and evaluated. ARIMAX and SARIMAX models proved more accurate for predicting gross gaming revenue and winning bets, capturing seasonal and external influences effectively. This research provides insights for policymakers and industry stakeholders, emphasizing the need for localized studies to better understand these dynamics and improve strategic planning in the gambling sector. Future work should focus on expanding datasets and incorporating diverse economic and weather patterns to enhance predictive accuracy and industry applicability.

Author: Jānis Želannovs

Supervisor: Nadežda Spiridovska

Degree: Master

Year: 2024

Work Language: English

Study programme: Computer Sciences

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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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Development of Cross-Platform Mobile Application “Health Care for Pets”

The aim of this work was to develop a cross-platform mobile application, “Health Care for Pets,” with the main objective of providing pet owners a comprehensive, user-friendly tool for effective pet care.In the course of the work, an analysis of the subject area was conducted, and existing analogues were analysed. The system and functional requirements, as well as the main users for the application, were identified. Based on the analysis and requirements, the application, server, data access component and database were designed. The system was implemented using Visual Studio, in the C# programming language. The developed application was tested in the final stage.The application created during the bachelor's thesis meets all the requirements.

Author: Roberts Dubovskis

Supervisor: Karina Kostjkina

Degree: Bachelor

Year: 2024

Work Language: English

Study programme: Computer Science

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Improving the accuracy of optical character recognition of stone engravings using image pre-processing methods

This study focuses on the development of preprocessing methods to improve the accuracy of Optical Character Recognition (OCR) for stone engravings. The primary goal is to enhance the precision of widely used OCR tools, particularly for texts engraved on stone surfaces, which present unique challenges that differ from traditional OCR applications. Emphasis is placed on developing image preprocessing methods as a software product. Customized image manipulation scripts were used to improve recognition accuracy and address issues such as contrast, alignment, noise, and resolution. The preprocessing stage was integrated into the workflow designed for image transformation before OCR processing. Subsequently, the recognition improvements were evaluated based on text similarity metrics analysis. Iterative text recognition and repeated recognition of images after applying preprocessing demonstrated significant improvements in OCR accuracy. This work provides a solid foundation for further enhancement of OCR workflows by employing adaptable preprocessing techniques specifically designed for particular problem areas, achieving higher precision in text recognition.

Author: Romans Urbans-Orbans

Supervisor: Aleksandrs Grakovskis

Degree: Bachelor

Year: 2024

Work Language: Latvian

Study programme: Computer Science

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