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Boosting Algorithms for Credit Card Fraud Detection Across Varied Datasets

Manual reviews and rule-based systems, as well as data mining techniques such as clustering and classification algorithms, are crucial for identifying credit card fraud since they help identify fraudulent transactions. Despite obstacles in gathering training data, more data has lately been available, however, a complete comparison of current machine learning approaches has yet to be conducted. Algorithms like XGBoost, AdaBoost, and Gradient Boosting Machine frequently outperform older approaches. This study compares boosting algorithms to traditional approaches using three different credit card transaction datasets: synthetic, balanced with 50% fraudulent transactions, and very unbalanced with only 0.17% fraudulent transactions. The genuine transaction datasets contained 28 anonymized parameters such as time and location. Each method was evaluated using the F1 score, accuracy, precision, and recall. This study makes recommendations on which algorithms to use in real-world scenarios, giving important insights for future research and practical use in credit card fraud detection.

Author: Justs Vīdušs

Supervisor: Nadežda Spiridovska

Degree: Master

Year: 2024

Work Language: English

Study programme: Computer Sciences

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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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Vibration Analysis-Based Fault Diagnosis of Tool Conditions on Electric Motor-Powered Machine Tools Using Convolutional Neural Networks

This thesis explores and evaluates techniques for utilizing vibration analysis and Convolutional Neural Networks (CNNs) to assess the condition of drill bits installed on electric motor-driven drills. By strategically positioning an Inertial Measurement Unit (IMU) sensor to capture acceleration data, a wide range of vibration signals can be gathered in different operational scenarios. The CNN models undergo training and validation utilizing this data to precisely detect various fault conditions and operational states of the drill bits, showcasing the possibility of implementing scalable and reliable fault detection systems in industrial environments. The research attains a Technology Readiness Level (TRL) of 3, as demonstrated by trials that effectively categorize machine conditions using CNNs, hence confirming the critical functions of the proposed technology. The aim of this study is to assess the efficacy of vibration analysis in classifying the operational state of a drilling machine as either good, moderate, or bad.Vibration analysis is a method used to analyze the oscillation patterns of a machine in order to identify problems such as misalignment, imbalance, and wear.

Author: Marawan Mohamed Ahmed Elsayed Youssef

Supervisor: Emmanuel Alejandro Merchan Cruz

Degree: Master

Year: 2024

Work Language: English

Role of Social Media in Promoting Ecotourism

Keywords ECOTOURISM, SOCIAL MEDIA, PROMOTION, INDIAThe research aim is to assess the impact of social media on boosting ecotourism in India. The subject of the research is impact of social media on improving the attractiveness of ecotourism in India. The object of the research is ecotourism in India. Research objectives are as follows: to conduct theoretical analysis regarding the social media and their impact on promoting ecotourism; to conduct industry analysis regarding the ecotourism in India; to develop an empirical research methodology and complete survey and interviews regarding the impact of social media on ecotourism attractiveness; to provide recommendations for the managers involved in ecotourism aimed at improving the attractiveness of ecotourism by means of social media. The results show that there is a significant social media role in transforming the landscape of tourism, particularly in the context of sustainable tourism and ecotourism promotion. The social media inspire people to choose ecotourism if the content is engaging, visually rich and including practical information; they serve as a source of information providing details about destinations, available activities, accommodations and transportation options, and impact decision-making.

Author: Navjot Kaur

Supervisor: Yulia Stukalina

Degree: Master

Year: 2024

Work Language: English

Study programme: Business and Management

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