nccmne, Author at HPC Montenegro

01.08.2026.01.08.2026.

HPC Montenegro at the Eastern European Machine Learning Summer School (EEML 2026) in Cetinje

NCC HPC Montenegro took part as a partner of the Eastern European Machine Learning Summer School (EEML 2026), held from 27 July to 1 August 2026 in Cetinje. The school brought together around 300 participants from across the European Union — roughly half of them from Eastern Europe — along with researchers and companies from Montenegro, making it one of the largest AI and machine learning gatherings in the region.

On Tuesday and Wednesday (28–29 July), HPC Montenegro hosted a booth where our team spent two full days talking with participants about high-performance computing (HPC) for AI and machine learning. These conversations connected us with representatives from industry and academia — both from Montenegro and from across Europe — and gave us the chance to understand their computing needs and show how HPC resources can support their work.

On Thursday, 30 July 2026, HPC Montenegro delivered a welcome talk to all participants. We presented the activities of NCC HPC Montenegro, traced the history of the EuroCC initiative through the EuroCC1, EuroCC2 and EuroCC4SEE projects, and reported on how researchers and organisations in Montenegro have been using EuroHPC access. We invited participants from Montenegro to collaborate with us and to request our services, and we helped participants from the wider region and other parts of the EU find and connect with the National Competence Centres (NCCs) in their own countries.

Our involvement in EEML 2026 reflects the twofold mission of NCC HPC Montenegro: providing training support to help build HPC and AI skills across the region, and engaging with industry to bring the benefits of high-performance computing to companies and the wider economy.

We are especially proud that several members of the HPC Montenegro team provided hands-on support and training to participants throughout EEML, helping them with practical work and with mastering machine learning tools and concepts. Alongside this, members of our team and students also took part as volunteers in the organisation of the school and in EEML activities.

You can find out more about the school at www.eeml.eu. To learn more about NCC HPC Montenegro, or to discuss collaboration and access to our services, get in touch with our team.

31.07.2026.01.08.2026.

NCC HPC Montenegro featured at the meeting of the President of Montenegro with AI experts

Representatives of NCC HPC Montenegro attended a meeting of the President of Montenegro and the President’s Cabinet dedicated to the development of artificial intelligence, held on 28 July 2026 in Cetinje. Around ten AI experts from Montenegro took part. Among those present were the President of the Montenegrin Academy of Sciences and Arts (CANU), university representatives, members of the diaspora, representatives of the Montenegrin AI Association (MAIA), and representatives from industry. Participants exchanged views with the President and his associates on the directions for developing this field in the country.

The meeting also touched on the Master’s programme in Artificial Intelligence — the first programme of its kind in Montenegro, delivered with the support of NCC HPC Montenegro and the EuroCC projects. HPC Montenegro presented the goals and projects of the EuroCC initiative and the services offered by the NCC, with particular attention to supporting industry in accessing HPC resources. In the discussion on the need for data centres and HPC infrastructure in Montenegro, NCC HPC Montenegro was recognised as part of the platform for further steps in this area.

For NCC HPC Montenegro, this was an important opportunity to be presented at the highest level of public administration and to bring its capacities and results closer to decision-makers. We look forward to taking part in the further steps and activities aimed at strengthening the artificial intelligence ecosystem in Montenegro.

More about the meeting: the President of Montenegro’s Instagram and the post on X.

27.07.2026.27.07.2026.

BSc Thesis: BabyFlow.AI – A Mobile Assistant for Early Parenthood Support

Marija Gardašević successfully defended her BSc thesis, “Application of Artificial Intelligence in the Development of a Mobile Assistant for Early Parenthood Support,” at the Faculty for Information Systems and Technologies, University of Donja Gorica. Supervised by Prof. Tomo Popović, the thesis resulted in BabyFlow.AI, a functional Flutter-based mobile prototype for recording and reviewing a baby’s daily routines, including sleep, feeding, mood, meals and basic health-related information. The prototype may be considered an early proof of concept for a future digital parenting-support product, subject to further user testing, integration of more advanced analytics and validation in cooperation with relevant professionals.

ABSTRACT – The thesis presents the development of BabyFlow.AI, a functional mobile application prototype designed to help parents organize and monitor a baby’s daily routine. The application combines modules for sleep, feeding, pumping, mood, meals, calming-noise sessions, vaccination records, growth tracking and relevant health-information resources, while storing user data locally on the device. Basic analytical insights are generated from user entries, and a separate demonstrative Python machine-learning model illustrates a simple data-processing and classification workflow. BabyFlow.AI is not intended for medical, clinical or diagnostic use, but represents a practical proof of concept that could support the future development of privacy-conscious digital services for parents and childcare providers.

27.07.2026.27.07.2026.

BSc Thesis: AI and HPC for Cybersecurity of Connected Vehicles

Anđela Vuković successfully defended her BSc thesis, “Application of AI and HPC Technologies in the Cybersecurity of Connected Vehicles,” at the Faculty for Information Systems and Technologies, University of Donja Gorica. Supervised by Prof. Tomo Popović, with the support of Arnad Lekić, the thesis combines an analysis of automotive cybersecurity with a practical AI model for detecting anomalies in CAN communication. The developed demonstrator can be regarded as an early proof of concept for potential industrial applications, subject to further testing with real vehicle data and deployment-oriented validation.

ABSTRACT – This thesis examines the application of Artificial Intelligence (AI) and High-Performance Computing (HPC) in the cybersecurity of connected vehicles. In addition to analysing contemporary cybersecurity risks, communication technologies and protection mechanisms, the thesis presents a practical implementation of an autoencoder model for detecting anomalous CAN messages. The experimental model demonstrated the potential of AI-based anomaly detection, while HPC is considered an important enabler for processing larger datasets, training more advanced models and simulating cyberattack scenarios. Although developed using simulated data, the implementation provides an initial proof of concept that could be extended and validated for use by automotive manufacturers, cybersecurity providers, testing laboratories and fleet operators.

20.07.2026.27.07.2026.

HPC Montenegro Partners with EEML 2026 in Cetinje

HPC Montenegro is proud to partner with the Eastern European Machine Learning Summer School (EEML 2026), taking place from 27 July to 1 August 2026 in Cetinje, Montenegro. The event will bring together leading researchers, lecturers and participants from the international machine learning and artificial intelligence community.

Members of the HPC Montenegro team will participate in the organization and implementation of the summer school, together with colleagues from the University of Donja Gorica and the University of Montenegro who are also active members of the Montenegrin AI Association (MAIA).

HPC NCC Moneengro is a partner organization supporting EEML

HPC Montenegro will also host an information stand during the event, providing participants from academia, industry and the public sector with an opportunity to learn more about available high-performance computing resources, EuroHPC opportunities, training activities and support for the development of AI and HPC applications.

30.06.2026.10.07.2026.

MSc Thesis Defence: Quantization of Edge AI Models in IoT Systems

On June 29, 2026, an MSc thesis entitled “Quantization of Edge AI Models in IoT Systems” by Mr. Zarko Perunicic was successfully defended within the Artificial Intelligence Master’s programme at the University of Donja Gorica. Through its participation in the programme, mentoring activities, and support for practical research in AI, HPC, and IoT, NCC Montenegro contributes to developing advanced competencies in the efficient deployment of artificial intelligence models on resource-constrained devices. The thesis addresses an important Edge AI challenge by evaluating model quantization strategies for computer vision applications in IoT environments.

ABSTRACT – Edge AI systems in Internet of Things (IoT) environments require artificial intelligence models that are sufficiently small, fast, and reliable to operate on resource-constrained devices. This thesis examines how quantization, as a model optimization method, affects the performance of a computer vision model in the task of grape leaf disease classification. MobileNetV2 was used as the reference model, and its optimized variants were then prepared in the TensorFlow Lite environment using FP16 and INT8 quantization modes, including dynamic INT8 quantization, full INT8 quantization based on a representative dataset, and an INT8 variant obtained through quantization-aware training (QAT) on an additional, more challenging dataset. The experiments were conducted on cleaned and restructured subsets, following quality control of publicly available datasets and the removal of redundant and visually equivalent samples. Under controlled conditions, latency, execution stability, peak RAM usage, model size, and accuracy were analyzed.

On the more controlled dataset, full post-training INT8 quantization achieved the most favorable balance among efficiency, stability, and model size while preserving accuracy, whereas dynamic INT8 quantization, despite reducing model size, can measurably slow down model execution. On the more challenging field dataset, this pattern changed partially: although full INT8 quantization remained the fastest variant, the INT8 model obtained through QAT provided the most favorable overall balance between accuracy, model size, and latency. The results show that the effect of quantization depends not only on numerical precision, but also on data characteristics, the calibration procedure, and the compatibility of the model with the execution environment. It is therefore concluded that the choice of quantization strategy should be empirically validated for a specific application scenario rather than assumed in advance.

29.06.2026.10.07.2026.

MSc Thesis Defence: Synergy of Computer Vision and Natural Language Processing in Tuberculosis Diagnostics and Education

On June 29, 2026, MSc candidate Nikola Kavarić successfully defended his thesis entitled “Synergy of Computer Vision and Natural Language Processing in Tuberculosis Diagnostics and Education” within the Artificial Intelligence Master’s programme at the University of Donja Gorica. Through its support for the programme, mentoring activities, and development of competencies in artificial intelligence and high-performance computing, NCC Montenegro contributes to preparing young researchers to develop interdisciplinary AI solutions for healthcare. The thesis investigates the combination of computer vision and Retrieval-Augmented Generation approaches for detecting signs of tuberculosis and providing educational explanations of medical findings.

ABSTRACT – The aim of this thesis is the development and evaluation of a system that combines computer vision and Retrieval-Augmented Generation (RAG) models for the automatic detection of signs of tuberculosis in chest X-ray images and the educational explanation of findings. The initial hypothesis was that it is possible to develop a functional prototype capable of recognizing pathological changes in X-ray images and generating informative, literature-grounded responses for users. Within this research, a CNN model for binary classification and YOLO models for the localization of pathological changes were developed and evaluated. The CNN model achieved an accuracy of 97% on the test set, representing a solid and measurable contribution. The YOLO models adequately demonstrated the concept of localization, with certain limitations related to dataset size and class imbalance. In addition to the visual module, a RAG prototype was implemented, utilizing a local medical document base to generate responses to user queries. The integration was implemented at the prototype level, without clinical validation. Based on the obtained results, the hypothesis was partially confirmed — to a significant extent for the CNN classification component within the test dataset used, while the YOLO and RAG components, due to dataset limitations and the absence of expert-verified reference answers, should be treated as proof-of-concept components. The thesis demonstrates that a modular combination of these technologies can serve as a useful foundation for the development of educational tools in the field of medical diagnostics.