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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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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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CNN-Based pipeline-related artifact and damage recognition in IHC staining as preprocessing step for pathological analysis

This work proposes automated solution for artifact and damage segmentation in biomedical images using machine learning algorithms. The development process includes data preprocessing, label classification using a clustering algorithm and segmentation model. CNN architectures like YOLO and U-NET are utilized for segmentation, and K-Means and DBSC algorithms are evaluated for clustering. The outcomes include a set of data preprocessing precodures, clustering algorithm testing and results analysis, segmentation model and recommendations for further development.

Author: Taisija Kožarina

Supervisor: Jeļena Kijonoka

Degree: Bachelor

Year: 2024

Work Language: English

Study programme: Computer Science

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