Month: October 2024

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Lecture by prof Kezunovic from Texas A&M on AI/HPC supported risk management in energy sector

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. 

The workshop took place on 25 october at UDG
prof. Kezunovic from Texas A&M gave presentation on a nove approach to Risk managemement in energy sector
Over 60 people attended
How AI/ML supported by HPC can help mitigate risk in energy sector?
Several students from Faculty for information systems and Faculty for applied sciences attended
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Master thesis: HPC/AI for breast cancer detection

Ms. Tamara Pavlovic defended her MSc thesis on the use of HPC/AI for creating prediction models for breast cancer detection on 23 October 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 Montenegro
Ms Pavlovic finalized her thesis during the Breast Cancer Awareness Month (‘Pink October’)
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Master thesis: AI/ML and applications in medicine

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
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Master thesis: Deep learning in energy sector

Ms. Zoja Scekic, a young researcher on HPC4S3ME project, defended her MSc thesis “Deep learning and applications in energy sector” today. This is one of the main project outputs in capacity building aimed at HPC/AI skills for applications in priority domains of Montenegrin S3.

ABSTRACT – This master’s thesis explores the application of advanced deep learning models for predicting day-ahead electricity prices, focusing on the accuracy and efficiency of these models compared to traditional forecasting methods. With the increasing integration of renewable energy sources and the growing complexity of electricity markets, accurate price forecasting has become crucial for market participants, grid operators, and policymakers. The research is structured around four case studies, each employing different deep learning techniques, such as Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and hybrid models like CNN-LSTM. Despite the promising results, the research recognizes limitations related to data quality, model complexity, and computational resource requirements. The study emphasizes the need for further research into optimizing model efficiency, integrating more diverse data sources, and expanding the applicability of these models to different energy markets.

Ms Zoja Scekic defended her MSc thesis on Deep leaning applications in energy sector
This MSc thesis was done in the context of HPC4S3ME with support from EUROCC NCC Montenegro
Friendly support from young researchers from UDG and HPC4S3ME team

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Upcoming Lecture: “Risk Management of Future Large-Scale Electrification”

The global shift towards large-scale electrification brings significant opportunities, yet also introduces complex risks that require our immediate attention. Join us for an insightful lecture by Prof. Mladen Kezunovic, a leader in power engineering and data analytics, as he delves into the challenges and risks posed by the evolution of the electric grid.

Invited lecture from distinguished professor from Texas A&M

Prof. Kezunovic will outline the motivation behind large-scale electrification, addressing the unique vulnerabilities emerging from critical infrastructure interdependencies. This talk will highlight risks such as environmental impacts, aging infrastructure, the rise of distributed energy resources, digitalization challenges, and behavioral factors. Prof. Kezunovic will discuss innovative machine learning and artificial intelligence solutions for predicting and mitigating these risks, offering a glimpse into the future of resilient grid design.

Attendees will gain insight into an essential case study on the State-of-Risk-Prediction for grid outages, shedding light on the shift toward a risk-informed control and protection paradigm. The discussion will touch on a holistic approach encompassing IT management, big data, interoperability, and high-performance computing, emphasizing the necessity of these tools for advancing data analytics and AI-powered solutions in electrification. This lecture is organized with the support of EUROCC NCC Montenegro and HPC4S3ME.

About the Speaker: 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. Don’t miss this chance to learn from one of the foremost experts in the field!

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In-house HPC lab infrastructure update

As planned, our project AI-AGE is advancing high-performance computing (HPC) infrastructure to support AI-driven research on biomarkers of aging in medical applications. This initiative will empower our team with cutting-edge resources, allowing us to enhance our capacity for data analysis and predictive modeling. To meet the demands of sophisticated AI computations, with the support of AI-AGE, we are upgrading our existing HPC setup with a powerful computing node.

New computing infrastructure supported by the AI-AGE project as planned

This new addition includes a rack computing node equipped with a 48 CPU cores with 128GB RAM, NVIDIA L40 48GB GPUs, and 2x480GB internal SSDs. In addition, the project supported NAS storage of 24TB (multiple disks with RAID) dedicated for dataset management. This infrastructure enhancement is designed to integrate smoothly with our existing equipment, augmenting both our computational and storage capabilities while providing significant value for our investment.

System harware installed, configured, and validated

AI-AGE project, supported by the Ministry of education, science and innovation, is implemendet through collaboration between Faculty for information systems and technologies at Uiversity of Donja Gorica, and Faculty of medicine at University of Montenegro. The in-house HPC infrastructure is a result of cross-project collaboration with HPC4S3ME project (IPA programme) and both of these project are done with the support from EUROCC NCC Montenegro. The main goal for the in-house lab is for researchers to gain a hands on experience with physical equipment a their disposal, while for larger computing tasks, we will apply for computing time on some of the EU supercomputers.

Click on image to open AI-AGE project website

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BSc thesis: Hotel chatbot receptionist for smart hospitality

Ms. Sara Kovacevic defended her BSc thesis on the use of AI tools to create a hotel chat bot receptionis for smart hospiality. This research was doen in the context of HPC4S3ME with the support from NCC Montengro an HPC4S3ME. The results were pulished at the IEEE IT2024 conference. The future work will include experimenting with HPC to run different AI tools and models. Her fefence took place on 3 October 2024.

ABSTRACT – The aim of this thesis is to examine the advancements and applications of chatbots in hotels to enhance customer experience and operational efficiency in Montenegro, which aspires to become a prestigious tourist destination. Emphasis is placed on the use of artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC) to develop advanced digital solutions. The automation of guest communication through chatbots reduces the burden on staff and increases customer satisfaction, especially during the tourist season when there are significant fluctuations in the number of visitors. The research analyzes key aspects of implementing chatbot technology, including the challenges and benefits of using the Voiceflow platform for development and testing. It studies data on guest preferences and service personalization, contributing to a better understanding of user needs and tailoring hotel offerings to meet their expectations. The thesis advises further optimization of chatbot functionalities, staff training, and regular collection of guest feedback. These recommendations enable Montenegrin hotels to improve their offerings and stand out in the global market competition. This work represents an important contribution to the advancement of digital solutions in Montenegro and can serve as a starting point for future research.

Ms. Sara Kovacevic defended her BSc thesis on AI powered hotel chatbot receptionist
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BSc thesis: AI models for real estate pricing based on web scraped data

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
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BSc thesis: AI and machine learning for cultural heritage preservation

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
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BSc thesis on computer vision and machine learning for sign language

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
The audience
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