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 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 HPC and 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. Link for the PKFE: https://pkfe.me/edukativni-programi/
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!
Ms Divya Siddarth is one of the 100 most influential young people in the field of artificial intelligence
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!
It was a pleasure to be a part of this discussion. There was over 200 people in the audienceDivya 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 AIMHiGH project, coordinated by DigitalSmart, was presented as part of the HPC Fortissimo initiative under the Horizon 2020 FF4EuroHPC program. The project, titled “AI/ML Enabled by HPC for Edge Camera Devices for the Next Generation Hen Farms,” brought cutting-edge HPC and AI/ML technologies to the poultry farming sector. Key achievements included real-time monitoring and data-driven insights through AI-powered edge cameras that continuously surveilled poultry, detecting early signs of illness and abnormal behavior. This innovation allowed farmers to intervene promptly, improving animal welfare, reducing losses, and enhancing productivity.
S. Cakic presented the project AIMHiGH that utilised HPC and AI for computer vision in smart agriculture
Additionally, the AI models developed during the project provided advanced solutions for early disease detection, resource optimization, and automation, leading to more sustainable and efficient farming practices. The system reduced manual supervision needs, cut operational costs, and optimized feed and water usage based on real-time data. The technology’s scalability and flexibility made it accessible to both large and small farms. Collaboration with partners like DunavNET, the University of Donja Gorica, and Montenegrin companies Meso-promet Franca and Radinović ensured practical relevance, aligning the project with Montenegro’s Smart Specialisation Strategy while supporting innovation and local economic growth in the poultry sector.
Presenting HPC and deep learning approach to the problemDiscussing the experiment results and prospects of the future wok
The paper “Hotel Chatbot Receptionist for Smart Hospitality” by Ms Sara kovacevic, Tomo Popovic, Ivan Jovovic, and Stevan Cakic was presented at the 28th IEEE International Conference on Information Technology. This is a great example of the engagement of young researchers to utilise HPC/AI tehcnology in the priority domains of S3 Montenegro, in this case tourism and hospitality.
ABSTRACT – The dynamic changes in the global business landscape are being driven by cutting-edge technologies such as artificial intelligence and machine learning, blockchain, and high-performance computing. Recognizing the pivotal role of digital transformation, particularly in the tourism sector, Montenegro has started embracing innovative solutions. The continuous evolution of technology has significantly influenced the tourism industry presenting an opportunity for digital transformation in the sector. The introduction of chatbots in Montenegrin hotels and resorts emerges as a potential game-changer. This implementation aims not only to reduce waiting times at reception but also to elevate the overall user experience. By adopting hotel chatbots in different hotels, each establishment can have a dedicated knowledge base tailored to its specific policies and regulations. This approach ensures a seamless integration of technology that not only enhances operational efficiency, but also enriches the offerings within the tourism and hospitality sector in Montenegro.
The paper is a result of invlovement of young researchers in HPC/AI research in S3 domain
Ms Sara Kovacevic, a BSc student, presenting the paper at IT2024 conference
HPC4S3ME project was presented at the 28th IEEE IT2024 Conference that took place 21-24 Feb 2024 in Zabljak, Montenegro. The presentation took place in a poster section dedicated to project presentations. We had a chance to talk to conference attendees, our colleauges from Montenegro and abroad. Also, the next day we had a large group of students participating in the EuroCC Workshop on HPC and Industry Applications, where we had a chance to discuss the project posters and take their attention to HPC4S3ME and other projects presented at the conference. Furhtermore, during the conference we had a scientific paper that was based on the research conducetd bz young researchers.
HPC4S3ME presented in the special section dedicated to project resultsThe attendance was over 100 people during the conferenceAll the project posters were present throughout the whole conference
HPC4S3.ME project was presented and featured at the ANSO InnovateYourFuture Workshop organized by UDG and supported by Alliance of International Science Organizations ANSO. This was a part of cross-project collaboration with ANSO InnovateYourFuture project that focuses on Competency traininjg in IoT and AI. The presentation was given by prof. Tomo Popovic, Stevan Cakic, and Dejan Babic, on 18 Nov 2023. There was around 60 attendees in the audience, mainly youg researchers interested in AI applications.
Presenters discussed the objectives of the project, described HPC lab implemented through the project, and explained priority domains of Montenegrin S3 and how IoT/AI and HPC can be utilised to develop and innovate. These application domains include agriculture and food value chain, health and tourism, and energy sectors.
HPC4S3.ME was featured at the ANSO Workshop on 18 Nov 2023.Around 60 young researchers were attending the workshopThe presentation included considerations how HPC and AI cen help Montenegrin S3This event was a parto of Workshop Series organized bz UDG and ANSO
Ms Tamara Pavlovic presented research paper “Forecasting Icterus with Machine Learning: An Advanced Classification Model Analysis” on a joint event CMBEBIH & MEDICON 2023 which held place in Sarajevo, Bosnia and Herzegovina from September 14th to September 16th. The study utilized multiple Machine Learning classification models to predict icterus type on a custom dataset and demonstrated the models’ performances in estimating icterus type. The authors are Ms Tamara Pavlovic (MSc candidate), Mr Marko Grebovic (PhD candidate), dr Armin Alibasic, prof. Milica Vukotic, and Mr Stevan Sandi (PhD candidate).
Ms Tamara Pavlovic presented the research paper “Forecasting Icterus with Machine Learning: An Advanced Classification Model Analysis”
ABSTRACT – Icterus is a medical condition characterized by the yellowing of the skin and sclera caused by the accumulation of bilirubin in the body. There are three main types of icterus: extrahepatic, intrahepatic, and prehepatic, and accurately diagnosing the type is crucial for treatment. Machine learning classification techniques can aid in the accurate and timely diagnosis of icterus types. The present study utilized multiple Machine Learning classification models to predict icterus type on a custom dataset and demonstrated the models’ performances in estimating icterus type. The MLP Classifier with five hidden layers achieved the best results. However, the models still struggled to differentiate between instances of extrahepatic and prehepatic types, indicating the need for improvements to enhance the models’ performance.
Ms Zoja Scekic presented the research paper “Thyroid Hormones Parameter-Based Classification of Patient Health Status: An Analysis of Machine Learning Techniques” on a joint event CMBEBIH & MEDICON 2023 which held place in Sarajevo, Bosnia and Herzegovina from September 14th to September 16th. The paper proposes utilization of machine learning (ML) algorithms for disease diagnosis based on patients’ thyroid hormones levels. The authors are Ms Zoja Scekic, dr Luka Filipovic, prof. Ivana Katnic, prof. Nela Milosevic, and Mr Stevan Sandi.
Results of several ML models
ABSTRACT – Thyroid autoimmune diseases are widely spread and present in the world population. The problem with these diseases is that giving the diagnosis is often challenging, as the symptoms are similar with some other health problems (for example, depression). This paper proposes utilization of machine learning (ML) algorithms for disease diagnosis based on patients’ thyroid hormones levels. The experiment was done on a dataset that consisting of 2000 data samples with following parameters: TSH, FT4, TT3, SHBG, and T total hormones. Dataset was prepared before feeding into machine learning models. ML algorithms used in the experiment were Logistic Regression, Random Forest Classifier, Naive Bayes, and Support Vector Machines (SVM). For evaluation, multiple metrics were used: confusion matrix, precision, recall, and accuracy. The whole process could be more efficient and accurate with use of good quality artificial intelligence systems as help in making a better diagnosis.
Mr Ivan Jovovic presented his research paper “Liver Diseases Classification Using Machine Learning Algorithms” on a joint event CMBEBIH & MEDICON 2023 which held place in Sarajevo, Bosnia and Herzegovina from September 14th to September 16th. The research aims to show how Artificial Intelligence (AI) can help predict liver diseases based on the patient’s parameters.
Mr Ivan Jovovic presented the research paper “Liver Diseases Classification Using Machine Learning Algorithms”
ABSTRACT – This study aims to show how Artificial Intelligence (AI) can help predict liver diseases based on the patient’s parameters. The focus is to provide a reliable and accurate tool to support healthcare professionals in decision making process. Alongside dataset preparation and feature engineering, three well known machine learning algorithms (ML) were used to achieve this goal: Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB), alongside with simple Artificial Neural Network (ANN). The best-performing model was SVM, with an average precision of 88%, outcome that is comparable to the accuracy obtained in the studies analyzed in this paper, but with a slightly different approach, especially in dataset preparation.
