Institute of Communications Engineering

Description: This research focuses on developing and evaluating transformer-based architectures for automatic depression detection using speech data. The study investigates both linguistic and acoustic characteristics of speech and explores their effectiveness in identifying depression across different cultural and linguistic backgrounds. By leveraging advanced deep learning models and cross-cultural datasets, the work aims to improve the accuracy, generalizability, and robustness of speech-based mental health assessment systems.

 

Description: This thesis investigates the use of Genetic Programming to create explainable machine learning models for depression prediction. The approach focuses on producing symbolic, human-readable models and applying interpretation techniques that clearly communicate the decision-making process to both mental health professionals and the general public. The aim is to combine strong predictive performance with transparency, fostering trust and understanding among all stakeholders.

  1. Byeon, H. Advances in Machine Learning and Explainable Artificial Intelligence for Depression Prediction. International Journal of Advanced Computer Science and Applications 2023, 14, 520–526. doi.org/10.14569/IJACSA.2023.0140656.
  2. Mimikou, C.; Kokkotis, C.; Tsiptsios, D.; Tsamakis, K.; Savvidou, S.; Modig, L.; Christidi, F.; Kaltsatou, A.; Doskas, T.; Mueller, C.; Serdari, A.; Anagnostopoulos, K.; Tripsianis, G. Explainable Machine Learning in the Prediction of Depression. Diagnostics2025, 15 (11), 1412. doi.org/10.3390/diagnostics15111412.
  3. Hu, T. Genetic Programming for Interpretable and Explainable Machine Learning. In Genetic Programming Theory and Practice XIX; Trujillo, L., Winkler, S. M., Silva, S., Banzhaf, W., Eds.; Springer Nature: Singapore, 2023; pp 81–90. doi.org/10.1007/978-981-19-8460-0_4.
  4. Mei, Y.; Chen, Q.; Lensen, A.; Xue, B.; Zhang, M. Explainable Artificial Intelligence by Genetic Programming: A Survey. IEEE Transactions on Evolutionary Computation 2023, 27 (3), 621–641. https://doi.org/10.1109/TEVC.2022.3225509.
  5. Maddigan, P.; Lensen, A.; Xue, B. Explaining Genetic Programming Trees Using Large Language Models. arXiv March 6, 2024. doi.org/10.48550/arXiv.2403.03397.