Mr Aleksandar vesovic defended his BSc thesis on the use of AI and HPC to develop a solution for attendance records in schools or universities. His mentors were Stevan Cakic and Tomo Popovic. He defended his theses on Friday, 28 March.
The theses and presentation discussed the integration of AI models into a web application and HPC integration
ABSTRACT – This thesis addresses the challenge of tracking student attendance in lectures through facial recognition. The aim of the research is to develop and implement a system that allows for automatic and accurate attendance tracking, thereby eliminating traditional methods that are often prone to errors and manipulation. The study analyzes the latest technologies in artificial intelligence, machine learning, and high- performance computing ( HPC) to achieve optimal accuracy and system efficiency. The implementation was tested on a sample of students and demonstrated high accuracy in facial recognition and attendance recording. This work also considers ethical aspects and p r ivacy concerns, given the sensitivity of the data collected and processed. The results suggest that applying facial recognition technology in an educational setting can significantly improve administrative processes while maintaining student security and privacy. Finally, possible future applications and recommendations for further system discussed.
HPC4S3ME and EUROCC2/EUROCC4SEE team members mentored and supported this research
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 effortsincluding PhD research of mr. Stevan Cakic, as well as collaboration with company that produces smart agriculture platform. 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.
