Our young researchers Ms. Zoja Scekic and Ms Tamara Pavlovic are wrapping up their master theses submission and the defence will be scheduled for October. These efforts are mentored and driven though the HPC4S3ME project and represent the main outputs of the project. One of the theses focuses on the HPC/AI applications in energy sector, while the other is focused on applications in medicine, both sectorial priorities of Montenegrin S3 .
The first master’s thesis examines the use of advanced deep learning models for day-ahead electricity price prediction, comparing their accuracy and efficiency with traditional methods. With the increasing integration of renewable energy and the complexity of electricity markets, accurate forecasting is essential. The research includes four case studies using different techniques: Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and hybrid CNN-LSTM models. Despite promising results, limitations regarding data quality, model complexity, and computational demands are acknowledged. The study highlights the need for further optimization and broader applications across energy markets.
The second master’s thesis explores the use of AI in breast cancer diagnosis, utilizing computer vision algorithms to analyze mammographic images. By applying convolutional neural networks (CNNs) like ResNet152 and DenseNet121, the study demonstrates how AI can improve early detection, streamline screening processes, and support more personalized treatment approaches. With AUC scores surpassing 0.9, the models show strong potential for clinical use. The thesis also addresses ethical considerations, including patient safety and AI transparency, while emphasizing the need for further research in AI-driven medical diagnostics.
