Author: nccmne

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Master thesis: HPC/AI in precision agriculture

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
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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
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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.

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.

Invited lecture from distinguished professor from Texas A&M

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. 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.

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

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: Quadruped robot with integrated self-balancing and AI capabilities.

Mr Igor Culafic, a student at the Faculty of applied sciences, defended his BSc thesis titled “Quadruped robot with integrated self-balancing and AI capabilities”. Igor received support from UDG to build the robot and implement experimenting with AI and ML for this robot platform.

ABSTRACT – This paper presents the development of a quadruped robot equipped with artificial intelligence (AI) capabilities for mapping the environment and adapting to various terrains and surfaces for movement. The project is inspired by the Spot Robot Dog project by the Boston Dynamics team, utilizing one of the versions of the open-source project known as Spot Micro, specifically using the branch project named Nova SM3. The complexity of this endeavour lies in the integration of electronics, robotics, and artificial intelligence, requiring expertise in AI model training, soldering, 3D printing, programming, and robotics. This multidisciplinary initiative represents a synthesis of knowledge acquired during studies at the Faculty of Electrical Engineering and Computing of the University of Donja Gorica, serving as a comprehensive demonstration of applied engineering skills and an innovative approach to robotics.

A BSc thesis at Faculty for applied sciences
The use of 3D printing, electronics, robotics, and AI model training
Model training and evaluation in the simulator
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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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