Publications Archives - HPC Montenegro

25.10.2026.25.10.2026.

MSc Thesis: Computer Vision Models for Liver Tumor Segmentation

On October 21, 2026, MSc candidate Ida Mušović completed her thesis entitled “Development of Computer Vision-Based Predictive Models for Early Diagnosis of Liver Tumors” within the Artificial Intelligence Master’s programme at the Faculty for Information Systems and Technologies, University of Donja Gorica, under the mentorship of Prof. Dr. Tomo Popović.

The research and experimental work were partially supported by NCC Montenegro through HPC infrastructure and expertise within the EuroCC3 project. Through its support for academic research and the development of AI and HPC competencies, NCC Montenegro contributes to advancing the application of advanced computing technologies in healthcare.

Examples of CT images and anatomical segmentation masks generated using the TransUNet model (Source: Ida Mušović, MSc thesis, 2026).

The thesis investigates the application of deep learning and computer vision to automated liver tumor segmentation in CT images, focusing on the comparison of convolutional, hybrid, and transformer-based architectures for computer-aided medical image analysis.

ABSTRACT – This thesis presents the development and comparative evaluation of three deep learning architectures—U-Net, TransUNet, and SegFormer—for automated liver tumor segmentation in CT images. Using the publicly available Liver Tumor Segmentation (LiTS) dataset and a 2.5D image-processing approach, the models were evaluated using Dice similarity and Intersection over Union metrics, with additional analysis of performance across different lesion sizes. SegFormer achieved the highest overall Dice score (82.86%) for tumor segmentation, although differences between the architectures were not statistically significant at the patient level. The study also identified lesion size as an important factor influencing segmentation accuracy, with smaller tumors presenting greater challenges. The findings demonstrate the potential of computer vision to support medical image analysis while emphasizing the need for further validation before clinical application.

24.10.2026.

MSc Thesis Defence: Generating and Evaluating Exam Questions Using LLMs and HPC

On October 21, 2026, MSc candidate Sara Kovačević successfully defended her thesis entitled “Generation and Quality Evaluation of Exam Questions Using Large Language Models and HPC Infrastructure” within the Artificial Intelligence Master’s programme at the Faculty of Information Systems and Technologies, University of Donja Gorica (UDG). The thesis was supervised by Prof. Dr. Tomo Popović. The research was partially supported by NCC Montenegro within the EuroCC3 project, including access to HPC infrastructure and support for experimental work. The study demonstrates the potential of combining generative AI and high-performance computing to support the preparation and evaluation of educational materials, with particular attention to the Montenegrin language.

Ms Sara Kovacevic at her MSc thesis defence

ABSTRACT – This thesis presents the development and evaluation of an automated system for generating exam questions from video lectures using large language models (LLMs). The system integrates automatic transcription, extraction of key concepts, generation of multiple-choice and short-answer questions, and automated quality assessment. Nine LLM configurations were evaluated in English and Montenegrin, using local computing resources, the UDG HPC cluster, and API-based inference services.

The evaluation combined linguistic and structural metrics, Bloom’s taxonomy, and an LLM-as-Judge approach to assess the quality of generated questions. Experimental results highlighted challenges faced by smaller models when generating educational content in Montenegrin, while larger models achieved more consistent results. Experiments on the UDG HPC cluster demonstrated a 17.9× GPU speedup compared with CPU inference for the same model.

The research highlights the potential of AI and HPC technologies to assist educators in preparing assessment materials, while emphasizing the importance of human review and further pedagogical validation before practical deployment.

24.10.2026.24.10.2026.

MSc Thesis Defence: Explainable AI for Type 2 Diabetes Risk Assessment

On October 21, 2026, MSc candidate Sanja Lazić successfully defended her thesis entitled “Interpretable Machine Learning and Deep Learning Models for Type 2 Diabetes Risk Prediction Based on Behavioural and Clinical Indicators” within the Artificial Intelligence Master’s programme at the University of Donja Gorica, Faculty for Information Systems and Technologies, under the mentorship of Prof. Dr. Tomo Popović.

The research and experimental work were partially supported by NCC Montenegro through HPC infrastructure and expertise within the EuroCC3 project, with additional research support from the AI-AGE project. Through its involvement in academic research, mentoring, and competence development, NCC Montenegro contributes to strengthening the application of advanced AI and HPC technologies in healthcare and other scientific domains.

Ms Sanja Lazić during her MSc thesis defence

The thesis investigates the application of machine learning, deep learning, and explainable AI techniques to type 2 diabetes risk assessment, focusing on predictive performance, reliability, and interpretability. The research also demonstrates how AI models can support preventive healthcare through accessible, questionnaire-based risk assessment without requiring laboratory measurements.

ABSTRACT – This thesis presents the development and systematic evaluation of machine learning and deep learning models for type 2 diabetes risk assessment using behavioural, clinical, and demographic indicators. The study analysed 253,680 records and compared five models: Logistic Regression, Random Forest, XGBoost, LightGBM, and a deep neural network. Model performance was evaluated in terms of discrimination, probability calibration, and interpretability. While the deep neural network achieved the highest ROC-AUC (0.7728), LightGBM demonstrated the most balanced overall performance. Explainable AI methods, including TreeSHAP, Integrated Gradients, and DeepLIFT SHAP, were applied to identify factors contributing to model predictions. A prototype web application was developed to demonstrate how estimated risks and their explanations could be presented to users.

The results demonstrate the potential of explainable AI for supporting non-invasive risk assessment and preventive screening, while highlighting the importance of model reliability, transparency, and further validation before clinical application.

16.10.2026.16.10.2026.

HPC Montenegro and Morsko dobro Explore Opportunities for HPC and AI Cooperation

Initial consultations under EuroCC 3 strengthen engagement with major public enterprises and potential large-scale users of advanced computing technologies.

As part of its ongoing activities to engage large organizations and public-sector institutions, HPC Montenegro (NCC Montenegro) held an initial online meeting with representatives of the Public Enterprise for Coastal Zone Management of Montenegro (Morsko dobro) to explore opportunities for cooperation and the potential application of High-Performance Computing (HPC) and Artificial Intelligence (AI).

Online meeting between HPC Montenegro and Morsko dobro

During the meeting, the activities, services, and support opportunities offered by HPC Montenegro within the EuroCC 3 project were presented. The discussion focused on the potential benefits of advanced computing technologies for public enterprises managing complex operations, infrastructure, and significant amounts of data.

Given the responsibilities of Morsko dobro, possible areas for future cooperation include advanced data analysis, coastal and marine environmental monitoring, spatial data processing, and decision-support systems for sustainable coastal management. These opportunities could provide a basis for identifying concrete use cases and further technical consultations.

Possible areas for future cooperation were discussed in order to identify concrete use cases

This initial meeting represents another step in HPC Montenegro’s efforts to engage major public-sector organizations, promote the adoption of HPC and AI technologies, and facilitate access to European supercomputing resources and expertise through the EuroHPC ecosystem.

13.10.2026.24.10.2026.

EuroHPC-Supported Research Advances AI-Based Lecture Transcription for Montenegrin

Results from the Exams 4.0 Proof of Concept project, supported by the Innovation Fund of Montenegro and EuroHPC computing resources, were presented at the international SAMYRAD 2026 conference in Seville, Spain.

The paper “Whisper-Based Lecture Transcription for Montenegrin Higher Education with a Practical Evaluation” was presented at the Smart Algorithms & Models: Your Reach to Advanced Dialogues (SAMYRAD 2026) conference, held on 5–6 October 2026 in Seville, Spain.

Authored by Stevan Čakić, Tomo Popović, Srđan Krčo, and Igor Čulafić, the paper investigates the use of OpenAI Whisper models for automatic transcription of university lectures in Montenegrin, a language not directly supported by Whisper.

The study evaluated three Whisper model variants and three related South Slavic language settings using 30 university lectures covering nearly 38 hours of audio. The experimental work involved generating 4,860 transcripts, with large-scale processing performed on the LUMI EuroHPC supercomputer in Finland.

The results identified Whisper’s turbo variant as the most suitable among the tested models in terms of transcription consistency, evaluated text quality, and processing speed. The research also provided practical recommendations for processing lecture recordings in Montenegrin.

These findings have already been incorporated into a new lecture-transcription module developed within the Exams 4.0 platform, enabling lecturers to transform audio and video recordings into text that can support AI-assisted preparation of educational materials and examination questions.

This R&D effort was conducted as part of the Exams 4.0 Proof of Concept project, implemented by DigitalSmart in collaboration with the University of Donja Gorica and funded by the Innovation Fund of Montenegro.

HPC Montenegro (NCC Montenegro) supported the research team in securing access to EuroHPC computing resources through the EuroHPC JU Development Access programme (project ID: EHPC-DEV-2024D12-018). The computational experiments were performed on LUMI, hosted by CSC in Finland and the LUMI consortium.

This work demonstrates how access to European HPC infrastructure can support applied AI research, foster innovation in higher education, and contribute to the development of digital tools for smaller and underrepresented languages.

11.10.2026.11.10.2026.

AI Research on Premium Domain Name Recommendation Published in IEEE Access

A new scientific paper titled “Premium Domain Name Recommendation via Semantic Retrieval and Reranking of Fixed Aftermarket Inventories” has been published in the international journal IEEE Access. The research was conducted by Milutin Pavićević, Tomo Popović, Srđan Krčo, and Miodrag Vujković from the University of Donja Gorica (UDG) and DunavNET.

The study addresses the challenge of recommending relevant premium domain names from large collections of domains already available for resale. The researchers developed and evaluated an approach combining large language models (LLMs), semantic embeddings, vector search using FAISS, and different reranking strategies. Experiments were conducted on 30,000 domain names sampled from a collection of nearly 700,000 premium domains, with evaluations involving 22 industry experts and 180 students. The results demonstrated the potential of semantic retrieval to improve domain name recommendations while balancing relevance, diversity, and computational cost.

The research also has clear potential for industrial application. Alicorn, a Montenegro-based software company specializing in solutions for the domain name industry, is already developing AI-powered domain name recommendation technologies, including its Name Sentinel platform. The methods investigated in this study could contribute to further improving such solutions, particularly through more effective semantic search and recommendation of premium domains.

The research represents another example of research and innovation supported by European initiatives and connected with the activities of HPC Montenegro (NCC Montenegro), contributing to the development of AI expertise and stronger links between academia and industry.

Read the scientific paper: IEEE Access – DOI: 10.1109/ACCESS.2026.3740779

25.09.2026.11.10.2026.

AI-AGE Research Demonstrates the Potential of Retinal AI for Colorectal Cancer Detection

A new scientific paper developed within the AI-AGE research project, implemented by the University of Donja Gorica (UDG) and the University of Montenegro, has been published in the international journal Current Medical Imaging. The study, titled “Deep Learning Discrimination of Colorectal Cancer using Retinal Fundus Photographs: UK Biobank Case-Control Study,” explores the potential of artificial intelligence (AI) to identify retinal imaging patterns associated with colorectal cancer.

Using 1,600 retinal fundus images from the UK Biobank dataset, the researchers developed a deep learning model based on EfficientNet-B3, achieving an AUROC of 0.945 on an independent participant-level test set. The findings suggest that retinal images may contain valuable information about systemic diseases beyond the eye, opening possibilities for future non-invasive screening and risk assessment approaches. However, further clinical validation is required before such methods can be considered for practical healthcare applications.

The research represents an important achievement for the AI-AGE project and demonstrates the growing potential of advanced AI methods in biomedical research in Montenegro. The project benefits from synergies with HPC Montenegro (NCC Montenegro) and the EuroCC initiatives, which support the development of national competencies in High-Performance Computing (HPC), data analytics, and artificial intelligence. This collaboration contributes to strengthening the research infrastructure and expertise needed for computationally demanding medical AI applications.

The AI-AGE project is implemented by the Faculty for Information Systems and Technologies at UDG and the Faculty of Medicine at the University of Montenegro, with funding from the Ministry of Education, Science and Innovation of Montenegro.

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