Mr. Igor Radulovic defended his BSc thesis on computer vision and machine learning for creating a prediction model for sign language. The defence took place on 3 October at UDG. This effort was inspired by the AI4S3 course and was supported by mentors from NCC Montenegro and HPC4S3ME team.
ABSTRACT – This thesis explores the use of advanced computer vision and machine learning techniques to develop a system that enables the translation of sign language into speech or written text in real time. The project aims to facilitate the communication of deaf-mute people with people who do not know sign language, in order to overcome language barriers and improve the social status of deaf-mute people in society. Using technologies such as Google Colab, Python, Roboflow, VS Code and Detectron2, a system was developed that recognizes various American Sign Language (ASL) gestures and converts them into understandable information. The system is based on deep neural networks and processes such as model training and instance segmentation, in order to achieve a high level of accuracy and reliability. Through the evaluation of the results, an impressive performance of the model was achieved with an F1 result of 95.6%, while the challenges in the technical limitations remained an important point of future development. This work points to the significant social impact of the application of computer vision in the communication of deaf and mute people, enabling them to integrate and be present in modern society.
Computer vision and machinle learning for sign language
Ms. Jovana Mitric defended her BSc thesis at the Faculty for information sciences and technologies on 3 October, 2024. The topoc was on AI and machine learning for applications in cultural heritage preservation. This research was done in the context of HPC4S3ME project and was supported by the NCC Montenegro team. The future work will explore the use of HPC and expanded datasets to refine and train better models for monuments detection and providing support for Montenegrin tourism development. This work was also successfully presented at the IEEE IT2024 conference.
ABSTRACT – This thesis presents research on artificial intelligence (AI) and machine learning (ML), and their potential application in the preservation of cultural heritage, with a special focus on Montenegro. Computer vision, as a specific field of artificial intelligence, was explored. The paper addresses the implementation of modern technologies, specifically computer vision, in the field of cultural tourism to enhance the visibility and preservation of cultural monuments. By using available tools such as the Roboflow platform for image annotation and Google Colaboratory for model training, a web application was developed using the Flask framework, which recognizes cultural monuments based on images, powered by the YOLO v8 model. Additionally, the thesis discusses the broader context of AI applications in the preservation of cultural heritage and its promotion for tourism purposes, with particular emphasis on the potential for technological enhancement of Montenegro’s tourism offerings. The importance of digital transformation in tourism for Montenegro and its positioning in the global tourism market is highlighted.
Ms Jovan Mitric defended her BSc thesis on AI and machine learning in ultural heritage preservation
Mr Marko Lasice defended his BSc thesis on AI powered real estate pricing. The future work will include larger datasets and explorig the use of HPC and AI to train more precise price estimation models. The work was supported by the NCC Montenegro and HPC4S3ME team members.
ABSTRACT – The development of generative models and exponential progress in artificial intelligence have opened up new application possibilities in many areas of economic life. One of the possibilities is developing an AI model for predicting market prices based on data extracted from the web. This paper introduces the reader to the technique of automated downloading and grouping of data from the web, known as web scraping, and the development of a predictive model that, based on the collected data, would predict real estate prices. The paper presents the practical part of the work, the implementation of a predictive model developed using the decision tree technique. In conclusion, the work contributes to the understanding of how the combination of these techniques improves decision-making processes in the real estate market.
Mr Marko Lasice defended his BSc thesis on AI powered real estate pricing
Mr. Veselin Andric defended his BSc thesis titleld “Prompt endineering for LLMs” at the Faculty for information systems and technologies. The devence took place on 2 Oct 2024 and it was done under mentorship of the EuroCC (NCC Montenegro) and HPC4S3ME teams’ members. This was a part of the effort to promote HPC and AI related technologies in the teaching curricula and research activities at UDG.
ABSTRACT – Prompt engineering is one of the primary areas of Natural language processing (NLP). It is a process that involves designing and improving inputs that are given to a language model such as ChatGPT, with a goal of getting wanted results. This dissertation investigates details of prompt engineering, it’s theoretical foundation, methodologies and practical uses in different tasks of NLP.
Mr. Veselin Andric defended his BSc thesis on Prompt EngineeringThe provided an overview of NLP, LLMs and prompt techniques
At the Avala Hotel in Budva, on September 27, 2024, as part of the international EPhEU event, the association of pharmacy organizations from Europe (September 26 to 28, 2024), a session on Module 6 of continuing education for Montenegrin pharmacists was held, titled “Artificial Intelligence in Pharmacy: Digital Challenges, Myths and Misconceptions Incorporated in Digital Media, the Need for New Competencies for Pharmacists.” This event gathered over 150 experts, both online and in person, from the field of pharmacy, aiming to discuss the role of artificial intelligence in transforming the pharmaceutical sector.
Mr Cakic gave a lecture on AI and applications in Pharmaceutical industry
Mr Stevan Čakić from UDG and NCC Montenegro gave a lecture on the history and development of AI, emphasizing its growing importance in pharmacy. Then a special attention was given to the application of AI in drug development, personalized therapy, and optimization of pharmaceutical services. The discussion addressed the potential of AI and HPC to reshape the way pharmacists perform their daily tasks, as well as the need to improve skills and knowledge to adapt to new technologies.
This hybrid event took place in Avala hotel in Budva and online with around 150 attendees
It was concluded that an interdisciplinary approach is crucial for the proper implementation of AI solutions in pharmacy. Experts from technology, pharmacy, and regulatory fields must work closely together to ensure that AI technologies are used in an ethical and efficient manner. This educational module confirmed that the pharmaceutical industry is on the brink of a major technological transformation, where AI will play a significant role, and the future of pharmacy will depend on the ability of its professionals to adopt new digital competencies.
On Friday, September 27, UDG welcomed Divya Siddarth, one of the 100 most influential young people in the field of artificial intelligence, as chosen by TIME magazine! Divya is a researcher at Microsoft Research, in the Political Economy and Social Technologies (PEST) department, and co-founder of the Collective Intelligence Project. Her work focuses on using technology to create fairer societies, with a particular emphasis on decentralized technologies, artificial intelligence and digital democracy. She was recently recognized as one of the key minds shaping the future of artificial intelligence in the service of the common good! The audience included students, researchers and industry representatives.
Ms Divya Siddarth is one of the 100 most influential young people in the field of artificial intelligenceIt was a pleasure to be a part of this discussion. There was over 200 people in the audienceMs Siddarth is a researcher at Microsoft Research and co-founder of the Collective Intelligence Project
At the Revolutionising Agriculture with HPC and AI: Real-World Applications conference, the project, AIMHiGH, was presented, with DigitalSmart as the coordinator. The project, implemented as part of the HPC Fortissimo initiative and funded through the Horizon 2020 FF4EuroHPC program, was titled AI/ML Enabled by HPC for Edge Camera Devices for the Next Generation Hen Farms. Stevan Cakic presented experience with the AIMHiGH project that introduced cutting-edge HPC and AI/ML technologies that significantly impacted the poultry farming sector. One of the key achievements of the project was enabling real-time monitoring and data-driven insights. Farmers gained access to continuous, real-time surveillance of their poultry through edge cameras equipped with AI models. These models could analyze live footage to identify early signs of illness, stress, or abnormal behavior, allowing immediate intervention. This innovation improved animal welfare, minimized losses, and enhanced overall productivity on the farms.
In addition, the project provided an advanced solution for disease detection and prevention. The AI models developed during the project were capable of detecting subtle signs of illness long before they became visible to the human eye. This early detection allowed for more effective prevention and control, reducing the spread of diseases and ultimately leading to lower usage of antibiotics, which had a positive impact on both costs and animal health. Moreover, the project’s technological solutions helped optimize resource usage, particularly in terms of feed and water management. With real-time behavioral and health data from the birds, farmers could adjust feeding schedules and amounts according to actual needs, reducing waste and cutting down on unnecessary resource consumption. This not only reduced environmental impact but also contributed to more efficient and sustainable production practices.
Presenting HPC and deep learning appraoch
The project’s approach to automating monitoring and decision-making processes also had a transformative effect on production efficiency. By reducing the need for manual supervision, the system allowed farm workers to focus on more critical tasks, leading to higher production outputs while maintaining lower operational costs. A further advantage was the scalability and flexibility of the technology. The use of edge devices allowed these AI models to be easily deployed in various farm setups without requiring expensive infrastructure. This made the technology accessible to both large-scale operations and smaller farms, ensuring that the benefits of AI and HPC could reach a broader segment of the poultry farming industry. Throughout the project, collaboration with key partners played a crucial role. DunavNET contributed its expertise in AI, machine learning, IoT, and software development, while The University of Donja Gorica brought significant HPC expertise through NCC Montenegro and the FoodHub Centre of Excellence. Montenegrin companies Meso-promet Franca and Radinović Company participated actively in the evaluation and piloting phases of the project, ensuring the practical relevance and success of the solutions developed. Overall, the AIMHiGH project was fully aligned with the priorities of Montenegro’s S3 Smart Specialisation Strategy, making significant contributions to technological innovation in the poultry sector, while strengthening the local economy and supporting more sustainable farming practices.
Discussing experiment results and prospects for future work
