On Saturday, December 21st, the University of Donja Gorica will host an HPC/AI Workshop and Student Conference, where participants from the AIFusion and HPC4S3ME projects will present their results.
HPC/AI Workshop and student conference are organized in context of HPC4S3ME and AI Fusion projects
The event will include:
Presentation of key results and achievements of both projects,
NCC Montenegro and EuroCC2/EuroCC4SEE presentation,
Presentation of student projects,
Panel discussion,
Coctail and networking.
Location: AP Amphitheatre, University of Donja Gorica Time: 10:00am – 16:00pm
The full agenda:
After the program, the socializing will continue with a cocktail. Join us to celebrate the results and exchange ideas in the field of HPC and AI
The EuroCC2 project team from UoM (Enis Kočan and Božo Krstajić), as part of service activities and interaction with the industry, held a meeting with the management and employees from the top technical leadership of the FiveG group and the NeurobotX company, which specializes in application of AI and VR in the gaming industry and the development of simulation nautical models for training purposes (Ivan Šoć, CEO of the FiveG group; Aleksa Čović, NeurobotX and Marko Šćepanović NeurobotX), on November 11, 2024, at their premises.
We met with FiveG on Nov 11
After the presentation of the services that the NCC of Montenegro can provide to companies within the EuroCC2 project, the current projects of the FiveG company were jointly analyzed, from the aspect of their potential implementation on HPC resources. Following a comprehensive analysis, at least 2 projects were identified that would benefit significantly from being executed on HPC resources. Consequently, we agreed on future collaboration activities aimed at preparing these identified projects for implementation in an HPC environment and their participation in the upcoming conference, which will feature a special event for companies interested in utilizing HPC resources through HPC NCC Montenegro.
We arranged the participation in IT2025 activities and continuation of the collaboration
During the Fall 2024, the NCC team at UDG developed a new training offering aimed at the the use of generative AI, mos specifically at real-life LLM applications and digital transformation. The training was developed based on the communications with students and industry representatives. The initial enrollment in November was over 40 students, where most of them are expected to finish the training by 20 December.
The study of Prompt Engineering represents a cornerstone technique for effective interaction with advanced language models such as GPT-4, LLama and beyond. This course equips students with the knowledge and skills necessary to harness the transformative potential of AI technologies, emphasizing innovative, responsible, and industry-specific applications. In an era of digital transformation, where real-time decision-making and intelligent automation shape industries, the demand for high-performance computing (HPC) is critical. By exploring advanced natural language processing (NLP) models, students will not only develop effective querying techniques but also understand the computational requirements and infrastructure needed to implement these solutions at scale.
Unlocking the Power of AI: The Role of High-Performance Computing in Real-Life LLM Applications and Digital Transformation
As large language models become more sophisticated, their computational demands grow exponentially. Applications such as real-time customer interactions, predictive analytics, and decision support in fields like healthcare and education require HPC infrastructure to ensure performance and scalability. This course bridges the gap between theoretical understanding and practical implementation, highlighting how HPC enables the deployment of robust AI solutions, thus driving innovation in the digital age. Whether you’re preparing to lead AI projects in academia or industry, this course provides the essential knowledge to leverage AI technologies responsibly and effectively, positioning you at the forefront of digital transformation.
Course Content Overview (12 Modules)
Introduction to Prompt Engineering – Fundamentals, significance, and applications of prompt structuring.
Understanding AI Models – Overview of how language models process inputs and generate outputs.
Contextual Importance – Strategies to define and provide context for optimal AI performance.
Crafting Effective Prompts – Techniques for prompt structuring, tone control, and style customization.
As planned, the invited lecture “Risk Management of Future Large-Scale Electrification” by prof. Mladen Kezunovic took place on 25 October 2024 in Enterpreneurial nest at UDG. Threre was over 60 attendees including students, academics from Montenegrin universities and representatives from the industry. This workshop was organized in the context of HPC4S3ME project and supported by EUROCC NCC Montenegro team.
What are the risks? Methodology for risk management and mitigation? What data do we have and how do we manage all that data? How can AI/ML supported by HPC help?
Dr. Mladen Kezunovic is a University Distinguished Professor at Texas A&M with over 35 years of expertise in power engineering. Renowned globally, Dr. Kezunovic has authored over 600 papers and consulted for 50+ companies worldwide. His extensive research and industry contributions, notably in fault modeling, data analytics, and smart grids, have earned him IEEE Life Fellow status and recognition from the US National Academy of Engineering.
prof. Kezunovic from Texas A&M gave presentation on a nove approach to Risk managemement in energy sectorThe workshop took place on 25 october at UDGOver 60 people attendedHow AI/ML supported by HPC can help mitigate risk in energy sector?
Ms. Tamara Pavlovic defended her MSc thesis on the use of HPC/AI for creating prediction models for breast cancer detection on 23.10.2024. With the support from NCC Montenegro, Ms Pavlovic did her research in the context of the HPC4S3ME project and the focus was on AI and computer vision applications in medicine. From the motivational point of view, we congratulate Tamara for finalizing and defending her thesis during the Breast Cancer Awareness Month (‘Pink October’) as people around the world adopt the pink colour and display a pink ribbon to raise awareness about breast health.
ABSTRACT – Artificial Intelligence (AI) is revolutionizing numerous sectors, including medicine, by offering innovative methods for diagnosing, treating, and researching diseases. This master’s thesis focuses on the application of AI in the diagnosis of breast cancer, using computer vision algorithms to analyze mammographic images. Through a combination of convolutional neural networks (CNNs) and deep learning, models have been developed that identify malignant changes, potentially contributing to earlier and more precise disease detection. The thesis examines in detail how AI can improve the efficiency of screening processes, reduce the time required for diagnosis, and enable a more personalized approach to treatment. In addition to technological progress, ethical issues such as patient safety and the transparency of AI systems are also considered. The results of this study confirm that the application of AI in breast cancer diagnostics can significantly enhance medical procedures. The models tested, ResNet152 and DenseNet121, demonstrated quite good performance in classifying breast cancer. Their AUC scores, which exceed the threshold of 0.9, indicate their potential for use in clinical practice. These findings not only contribute to the improvement of diagnostic processes but also open up opportunities for further research and development of AI technologies in medicine.
This research was done in th context of HPC4S3ME and with the support from EUROCC NCC MontenegroMs Pavlovic finalized her thesis during the Breast Cancer Awareness Month (‘Pink October’)
Mr Mato Martinovic defended his MSc thesis on 23 octiber 2024. His research focused on detecting plant deseases for applications in vineyards. He was experimenting with HPC/AI and computer vision. He is one of the latest graduates from the AI master program created under EUROCC project and his mentoring was done with the support of EUROCC NCC Montenegro.
ABSTRACT – This research analyzes the use of computer vision in the field of viticulture. The thesis describes problems in viticulture, computer vision and its use in this field. The paper analyzed the performance of ResNet50, VGG16 and MobileNet models in the classification of diseases and grapevine species. The models achieved accuracy of 98.67%, 97.28%, and 98.72% on the original test data set, while on the extended one, they achieved 87.47%, 72.07%, and 86.64%, respectively, when classifying diseases. In species classification, the models achieved accuracies of 70%, 78% and 88% on the original test data set, and 66%, 51% and 72% on the extended one, respectively. The VGG-16 model had the largest difference in accuracy over extended data, while ResNet had the smallest decrease in accuracy in both cases, which implies that ResNet generalizes the data better. The paper presents the process of creating a platform that allows users to post an image and receive a prediction value through a mobile application.
HPC/AI and computer vision for applications in smart viticulture
Mr. Luka Jeremic defended his MSc thesis on 23 October 2024. The title of the thesis was AI and applications in medicine. His research was mentored by HPC4S3ME team members and it was done in the context of AI master program at the Faculty for information systems and technologie at UDG. This program and Master students are supporter by EUROCC NCC Montenegro.
ABSTRACT – This research explores the application of artificial intelligence in medicine, with a focus on the classification of brain, liver, and blood cell diseases. The main objective is to evaluate the effectiveness of algorithms in recognizing and classifying diseases of these organs. Through the development of a prototype information system, the study analyzes how artificial intelligence can improve diagnostics and contribute to the advancement of personalized medicine. The methodology includes a literature review, the development of computer vision models, and the assessment of model accuracy using real medical data. The results show that models based on deep neural networks can enhance the accuracy and speed of diagnostics, allowing for more precise disease classification. The paper also highlights the barriers and challenges in implementing these technologies, including the need for ethical considerations and training of medical staff. The conclusions suggest that this approach has the potential to significantly improve medicine, but further research and refinement are necessary.
Mr Jeremic defended his master thesis on AI/ML and applications in medicine
