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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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Enhancement Strategies for Retrieval-Augmented Generation Systems

This thesis systematically explores the enhancement of Retrieval-Augmented Generation (RAG) systems within Large Language Models, emphasizing optimization of retrieval parameters and generation accuracy. We investigate optimal configurations in RAG systems, including chunk size and overlap percentages, top-k selection, query transformations, different retrieval methods, different LLMs, namely GPT-3.5-Turbo and GPT-4, discovering that a chunk size of 500 tokens generally offers the best performance. Vector search using cosine similarity emerges as the most effective retrieval method, significantly enhancing both context precision and recall across various tasks and knowledge bases. Experimentation within the CRUD-RAG framework demonstrates its applicability in diverse tasks from content creation to knowledge refinement. Our findings indicate that enhancements in retrieval settings can markedly improve the performance of RAG systems, making them more efficient and adaptable for complex information synthesis and retrieval tasks. These results affirm the potential of systematic enhancements to improve AI-driven language models in practical applications, contributing significant insights and practical approaches to the evolving landscape of RAG system research.

Author: Sigita Lapiņa

Supervisor: Dmitry Pavlyuk

Degree: Master

Year: 2024

Work Language: English

Study programme: Computer Sciences

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