Research Archives - HPC Montenegro

31.03.2025.31.03.2025.

Montenegro Project Among Winners of FFplus HPC Innovation Call (Stats)

The Fortissimo Plus (FFplus) project recently concluded its first Open Call for Business Experiments, aimed at encouraging small and medium-sized enterprises (SMEs) to adopt High-Performance Computing (HPC) technologies. Launched on June 21, 2024, and closed on September 4, 2024, the call attracted 126 proposals from 220 organizations across 30 European countries. After a rigorous evaluation process, 19 sub-projects were selected for funding, involving 43 organizations, including 34 SMEs, demonstrating the initiative’s success in promoting HPC adoption among SMEs.

Summary of FFPlus Open Call 1 results (image: FFPlus)

Among the selected sub-projects is “Transforming Business Culture and Hiring Through High-Performance Computing GenAI-HPC4WB,” a notable initiative from Montenegro by a startup company called Recrewty. This project aims to revolutionize recruitment processes in the Balkan region by integrating generative artificial intelligence (AI), machine learning, and HPC. By analyzing psychometric data, CVs, and interviews using AI models tailored to regional languages—Montenegrin, Serbian, Bosnian, and Croatian—the project seeks to enhance hiring accuracy and efficiency. Utilizing HPC resources enables the processing of large datasets, improving the scalability and precision of these AI-driven assessments.

It is great to see Montenegro on this map (image: FFPlus)

The success of the GenAI-HPC4WB project underscores the growing competence and innovation capacity within Montenegro’s tech community. The HPC Montenegro team played a pivotal role in supporting the proposal’s development, reflecting the country’s commitment to advancing HPC applications and fostering collaboration between academia and industry. This achievement not only highlights Montenegro’s active participation in European HPC initiatives but also sets a precedent for future projects aiming to leverage advanced computing technologies to address regional challenges.

More information on FFPlus website at [link].

29.03.2025.29.03.2025.

Successful application for HPC resources, Faculty of Science and Mathematics of the UoM

A team of researchers from the Center for Computer Science of the Faculty of Science and Mathematics, through the EuroCC2/EuroCC4SEE project, with the support of the NCC team of Montenegro, has gained access to the resources of the Leonardo HPC supercomputer. These resources will be used for the efficient development of a system for automatic segmentation of 3D views of mechanical assemblies obtained using 3D scanners. Identification of assembled parts and their relative positions is an important step for reverse engineering, automation of the disassembly process, quality control, AR and VR, etc. These activities are carried out within the project “AI segmentation and inspection by 3D scanning”, in cooperation with partners from France.

Successful application for HPC resources at Leonardo (Benchmark call)

Access to HPC resources is provided for a period of three months, through a successful application to the Benchmark call. During this period, the goal is to demonstrate the ability to efficiently use advanced computing resources, thus earning the right to apply to the Regular call for HPC resources

28.03.2025.31.03.2025.

Using Generative AI to Transform Poultry Farming with Computer Vision

A new scientific publication by researchers from the University of Donja Gorica and DunavNET explores the innovative use of generative AI in digital agriculture. Titled “Evaluating the FLUX.1 Synthetic Data on YOLOv9 for AI-Powered Poultry Farming”, the study demonstrates how synthetic data, generated using FLUX.1, can effectively enhance deep learning models for chicken detection in smart farms. The paper was published in the Journal of Applied Sciences, a special issue dedicated to the application of computer vision in industry and agriculture [link].

Using generative AI to create sytnhetic data used to train computer vision models for agriculture sector

By combining real and AI-generated images and streamlining annotation with Grounding DINO and SAM2 models, the team achieved impressive detection accuracy—proving that generative AI can bridge the data gap in precision farming. This research is a part of broader efforts including PhD research of mr. Stevan Cakic, as well as collaboration with company that produces smart agriculture platform. The research was done in the context of HPC4S3ME project. This was also supported through EuroCC Montenegro initiatives, showcasing how high-performance computing and AI can drive sustainable innovation in agriculture.

High-level architecture used for experiment execution

ABSTRACT – This research explores the role of synthetic data in enhancing the accuracy of deep learning models for automated poultry farm management. A hybrid dataset was created by combining real images of chickens with 400 FLUX.1 [dev] generated synthetic images, aiming to reduce reliance on extensive manual data collection. The YOLOv9 model was trained on various dataset compositions to assess the impact of synthetic data on detection performance. Additionally, automated annotation techniques utilizing Grounding DINO and SAM2 streamlined dataset labeling, significantly reducing manual effort. Experimental results demonstrate that models trained on a balanced combination of real and synthetic images performed comparably to those trained on larger, augmented datasets, confirming the effectiveness of synthetic data in improving model generalization. The best-performing model trained on 300 real and 100 synthetic images achieved mAP = 0.829, while models trained on 100 real and 300 synthetic images reached mAP = 0.820, highlighting the potential of generative AI to bridge data scarcity gaps in precision poultry farming. This study demonstrates that synthetic data can enhance AI-driven poultry monitoring and reduce the importance of collecting real data.

04.03.2025.15.03.2025.

BioExcel Workshop Balkan Edition

2 Days Hands-On Workshop: Hybrid Learning Experience jointly organized by BioExcel and supported by Sofia University “St. Kliment Ohridski”, Faculty of Chemistry and Pharmacy & Faculty of Physics, DISCOVERER Supercomputer and National Competence Centres in Bulgaria, North Macedonia, Romania, Serbia and Montenegro, this hydrid workshop will offer participants the chance to engage both on-site and online. The workshop will focus on the use of BioExcel core codes such as GROMACS, HADDOCK and PMX with a strong emphasis on hands-on practical sessions and guidance from leading experts in the field.

When & Where: May 21–22, 2025 | Sofia University, Bulgaria & Online
Apply by: April 15, 2025
Don’t miss out and boost your research skills! More info and registration : https://bioexcel-balkan-workshop.com/

28.02.2025.15.03.2025.

HPC/AI for Tuberculosis Detection: Advancing X-Ray Diagnosis with Deep Learning at IT2025

Researchers from the University of Donja Gorica presented a deep learning model for automated tuberculosis detection from chest X-rays at IEEE IT2025 conference. Using a convolutional neural network (CNN), the model classifies images as normal or tuberculosis-positive with an impressive 97.55% accuracy. This breakthrough has the potential to speed up diagnoses, reduce radiologist workload, and improve early detection rates, particularly in low-resource healthcare settings. By leveraging AI for fast and reliable medical imaging analysis, this research highlights the growing role of computer vision in modern healthcare and its ability to enhance efficiency and accuracy in disease detection.

ABSTRACT – This article presents a deep learning model that enables fast and accurate diagnosis of tuberculosis based on chest X-rays. The developed model uses convolutional neural network that enable the automatic classification of chest x-rays into one of two classes: Normal or Tuberculosis with a high degree of accuracy. The model achieved an accuracy of 97.55% on the test data set, indicating its potential to open new perspectives for medical professionals in establishing a tuberculosis diagnosis. This model can significantly speed up the diagnostic process, reducing the workload of medical workers and increasing their productivity in the fight against tuberculosis, one of the most common lung diseases.

28.02.2025.15.03.2025.

Paper on Preserving Cultural Heritage Through Speech Synthesis at IT2025

At the IT2025 IEEE Conference in Žabljak, researchers from the University of Donja Gorica presented a study on voice cloning and text-to-speech (TTS) technology for cultural heritage preservation. Their research compared state-of-the-art AI models, including Realtime Voice Cloning (RVC), Tortoise AI, Bark, and Coqui AI, to evaluate how small, high-quality datasets can produce more accurate and natural-sounding speech than large, unstructured ones. The study highlights the potential of AI in preserving the Montenegrin language and oral traditions, enabling the creation of audiobooks, digital archives, and interactive experiences. This research paves the way for more accessible educational resources and enhanced cultural engagement using AI-driven speech synthesis.

ABSTRACT – This research presents a comparative analysis of modern voice cloning systems, focusing on their ability to generate high-quality speech from limited training data. The paper aims to demonstrate that carefully curated smaller datasets can produce superior results to larger, less structured datasets. The investigation of multiple state-of-the-art models, including Realtime Voice Cloning (RVC), Tortoise AI, Bark, and Coqui AI, establishes optimal data preparation protocols and identifies critical factors in training data quality, with particular emphasis on applications for the Montenegrin language and cultural preservation.

28.02.2025.15.03.2025.

Paper on AI-Driven Breast Cancer Detection with Deep Learning at IT2025

At the IT2025 IEEE Conference in Žabljak, researchers from the University of Donja Gorica presented their latest study on using Artificial Intelligence (AI) for breast cancer diagnostics. The research explores the application of deep learning models, ResNet152 and DenseNet121, to analyze mammographic images. Beyond the clinical results, the study emphasizes the implications of leveraging high-performance computing (HPC) infrastructure to optimize model training and evaluation. By porting the experimental setup to HPC resources, the research opens pathways for faster development cycles, the exploration of more complex architectures, and scalability for real-world implementation.

ABSTRACT – Artificial Intelligence is rapidly advancing the medical field by providing innovative disease diagnosis, treatment, and research approaches. This study explores the application of artificial intelligence in breast cancer diagnostics, focusing on using convolutional neural networks and deep learning to analyze mammographic images. ResNet152 and DenseNet121 models were used to classify malignant changes, achieving AUC scores exceeding 0.9, demonstrating their clinical utility. The research emphasizes how artificial intelligence can enhance screening efficiency, expedite diagnostic processes, and facilitate personalized treatment approaches. Ethical considerations, including patient safety and the transparency of artificial intelligence systems, were also analyzed. The findings underscore the potential of artificial intelligence to transform diagnostic procedures for breast cancer and highlight the importance of further research to integrate these technologies into clinical practice.