Publications Archives - HPC Montenegro

16.04.2025.16.04.2025.

Conference Paper: AI and HPC Transform Matrix Learning with an Intelligent Tutoring Chatbot

The paper “Transforming Matrix Problem Solving with Intelligent Tutoring Systems” by E. Trubljanin, E. Taruh, S. Cakic, T. Popovic and L. Filipovic was presented at INFOTEH conference an is published by IEEE Xplore. Researchers from the UDG with support from HPC NCC Montenegro have developed an innovative intelligent tutoring system that leverages artificial intelligence (AI) and high-performance computing (HPC) to revolutionize how students learn matrix operations. This chatbot-based solution combines Optical Character Recognition (EasyOCR) with an advanced natural language processing model (Qwen2-Math-7B-Instruct) to interpret both text and image inputs, enabling it to perform matrix operations such as transposition, addition, and multiplication while providing clear, step-by-step explanations. Supported by the university’s HPC infrastructure, the system ensures high-speed processing and real-time feedback, achieving up to 99% accuracy in matrix recognition from high-quality images. Designed with education in mind, this AI-powered tutor enhances interactivity, understanding, and learning outcomes for students tackling complex linear algebra concepts, and sets the stage for future enhancements like handwritten input recognition and support for more advanced operations.

ABSTRACT – This paper presents the integration of optical character recognition (OCR) and advanced natural language processing (NLP) models for automated handling of matrices derived from images and textual inputs, all combined within an implemented chatbot. The motivation for choosing this topic arises from the practical experiences of the authors gained while working with groups of students who encounter the concept of matrices as part of their academic responsibilities. Through the analysis of their results and classroom interactions, it was observed that many students struggle with this area. This paper presents an innovative approach to enhancing matrix problem-solving by leveraging intelligent tutoring systems supported by High-Performance Computing, aiming to improve learning efficiency and student outcomes. By combining the EasyOCR framework and the Qwen2-Math-7B-Instruct model, operations such as transposition, addition, and multiplication of matrices are enabled. The system supports the input of one or two matrices, allowing the selection of operations through textual or image-based queries. The OCR component extracts numerical data from images, while the NLP model interprets user requests and executes operations accurately. The interface allows the addition of a second matrix image only when necessary, enhancing the system’s intuitiveness and efficiency. The results of the recognition accuracy of the OCR model of image input matrices of different dimensions show a high level of accuracy of 95%, while for 2×2 matrices they reach an accuracy of 99%. This work contributes to the development of AI-powered tools for mathematical operations and holds potential applications in education.

28.03.2025.03.04.2025.

BSc Thesis: Attendance System Based on Face Recognition

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

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.

11.03.2025.11.03.2025.

NCC Montenegro Supported Paper to be presented at the INFOTEH conference

At the 24th International Symposium INFOTEH-JAHORINA (March 19-21, 2025), the paper “Transforming Matrix Problem Solving with Intelligent Tutoring Systems” will be presented. It explores the use of OCR and NLP technologies for automated matrix processing through an intelligent tutoring chatbot.

This effort was supported by the NCC Montenegro and resulted in a system that utilizes EasyOCR and the Qwen2-Math-7B-Instruct model for matrix operations with 95% accuracy. Implemented on our HPC cluster, it enables fast and precise processing of user queries, enhancing learning through AI-powered tools. The paper will be presented by Ms. Enisa Trubljanin and Mr Elvis Taruh, students at the Master AI program at UDG.

Click on image for sessions schedule for accepted papers.

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.