Sirvi Autor "Aktas, Kadir, juhendaja" järgi

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Creating an Explainable AI Tool for First Impression Enhancement in Job Interviews
(Tartu Ülikool, 2024) Gruzdeva, Dariia; Anbarjafari, Gholamreza, juhendaja; Aktas, Kadir, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Tehnoloogiainstituut
In the world of job interviews, first impression plays a big role in candidate selection. However, current Human Resources (HR) technology tends to lack tools that can both provide candidates with meaningful feedback on their first impression and offer transparent, actionable advice for performance improvement. This thesis introduces an explainable artificial intelligence (AI) tool designed to provide advice to candidates for improving their first impressions during job interviews. The proposed tool uses the Big Five personality traits for evaluating and improving job candidates’ interview performances. The thesis focuses on demonstrating the potential of such a tool to provide automated, yet specialized feedback to candidates. The validation of this tool’s effectiveness is showcased through a series of experiments. It was observed that candidates exhibited enhanced performance after engaging with the tool. The findings suggest that this AI tool holds practical value, indicating a promising direction for future integration into HR software platforms. Such integration would not only augment the functionality of these platforms but also advance the goal of improving job interview outcomes through informed data-driven feedback. Further development and refinement are envisioned to fully realize the potential of this tool in professional settings, paving the way for an innovation in HR technology where first impressions are not just evaluated, but systematically improved.
Machine Learning Methods in Anti-Money Laundering
(Tartu Ülikool, 2025) Malkovski, Anton; Aktas, Kadir, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Anti-Money Laundering (AML) is a critical operation in the financial sector, and with the constant growth in transaction volume, traditional AML methods are no longer sufficient for effectively detecting and preventing money laundering activities. Machine learning (ML) has the potential to discover complex patterns within the vast amounts of transactional data and reduce the false positives (FP) in the AML alerts. This thesis analyzes the applicability of machine learning in AML and proposes a full training pipeline that covers model training, hyperparameter optimization, and synthetic data generation. The work focuses on training a machine learning model with focus on reducing FP noise while being able to classify true positive (TP) alerts by augmenting the highly imbalance training dataset with synthetically generated minority class samples using a Variational autoencoder (VAE) and applying Optuna hyperparameter optimization to tune the model. The results of the experiments demonstrate that this method improves the model’s performance while maintaining its ability to generalize to unseen data, finally achieving noise reduction of FP alerts by 40%.