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Conference proceedings for MEDICON’23 and CMBEBIH’23

Proceedings of the Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON) and International Conference on Medical and Biological Engineering (CMBEBIH), September 14–16, 2023, Sarajevo, Bosnia and Herzegovina—Volume 1: Imaging, Engineering and Artificial Intelligence in Healthcare. The following papers submitted by the HPC4S3ME young reearchers have been published in the proceedings:

  • Dejan Babic, Luka Filipovic, Sandra Tinaj, Ivana Katnic, Stevan Cakic, “Ten Year Cardiovascular Risk Estimation: A Machine Learning Approach”, Proceedings of MEDICON 2023 Conference, Sarajevo, Sep 2023, pp 605-612
  • Ivan Jovovic, Marko Grebovic, Lejla Gurbeta-Pokvic, Tomo Popovic, Stevan Cakic, “Liver Diseases Classification Using Machine Learning Algorithms”, Proceedings of MEDICON 2023 Conference, Sarajevo, Sep 2023, pp 585-593
  • Tamara Pavlovic, Marko Grebovic, Armin Alibasic, Milica Vukotic, Stevan Sandi, Forecasting Icterus with Machine Learning: An Advanced Classification Model analysis”, Proceedings of MEDICON 2023 Conference, Sarajevo, Sep 2023, pp 673-683
  • Zoja Scekic, Luka Filipovic, Ivana Katnic, Nela Milosevic, Stevan Sandi, “Thyroid Hormones Parameter-Based Classification of Patient Health Status: An analysis of Machine Learning techniques”, Proceedings of MEDICON 2023 Conference, Sarajevo, Sep 2023, pp 673-683

The book can be accessed at the Springer website (link).

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EuroCC and AI4S3 Hackaton

On 23 Dec 2023, our researchers participated in the organisation of EuroCC and AI4S3 Hackaton. The hackaton was organized in the context of EuroCC implementing AI4S3 education project that was funded by the Innovation Fund of Montenegro. There was 24 teams presenting their projects and around 50 attendees. There were students from BSc, MSc and PhD level, as well as some industry representatives.

The Hackaton focused on HPC/AI and Comuter Vision applications in S3 domains
There was 24 teams that presented their projects
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EU AI Act: first regulation on artificial intelligence

The use of artificial intelligence in the EU will be regulated by the AI Act, the world’s first comprehensive AI law. Find out how it will protect you. As part of its digital strategy, the EU wants to regulate artificial intelligence (AI) to ensure better conditions for the development and use of this innovative technology. AI can create many benefits, such as better healthcare; safer and cleaner transport; more efficient manufacturing; and cheaper and more sustainable energy. Read the whole text at European Parlament news portal following this link.

This illustration of artificial intelligence has in fact been generated by AI (source)
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EUROCC2 Workshop “Digital Transformation and HPC/AI” for BSc students

On 8 Dec 2023, a workshop on “Digital Transformation and HPC/AI” unfolded at the University of Donja Gorica. Over the course of this semester, final-year BSc students from the Faculty for Information Systems and Technologies delved into research on HPC and AI technologies within the framework of the Managing Information Technology subject. The EUROCC NCC Montenegro team conducted enlightening lectures and presentations on HPC and AI, providing students with a comprehensive grasp of these technologies and illustrating their pivotal roles in digital transformation.

The workshop was organized for BSc students by EUROCC NCC Monteengro team
Over 20 participants in the workshop

During the workshop, students had the opportunity to showcase the projects they worked on throughout the semester, projects that will be defended during their final exam in Managing Information Technology course. The presented use cases spanned across various fields such as tourism, medicine, agriculture, and digital marketing. This event not only allowed students to share their findings but also fostered a deeper understanding of the practical applications of HPC and AI in real-world scenarios. Our focus was on the possible applications of interest for Montenegro and in the priority domains of Smart Specialisation Strategy for Montenegro (2014-2019).

Students presented their project ideas focused on HPC and AI in Digital transformation
Most of the projects fit into the Montenegrin S3
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HPC4S3.ME project presented at the ANSO InnovateYourFuture Workshop

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 workshop
The presentation included considerations how HPC and AI cen help Montenegrin S3
This event was a parto of Workshop Series organized bz UDG and ANSO
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Cross-project collaboration with AI4S3

At the University of Donja Gorica, the Faculty of Information Systems and Technologies has commenced the implementation of the project “Application of Computer Vision and Deep Learning in Agriculture and Food Production, Medicine, and Energy (AI4S3).” The project is funded within the program to promote the development of an innovation culture and organize education in the fields of smart specialization in Montenegro by the Innovation Fund (https://fondzainovacije.me/).

The implementation of AI4S3 has started

On September 28th, as part of the 9th Festival of Science and Innovation, we had the opportunity to present our plans for the project’s implementation. The key idea of the project is to bring the application of artificial intelligence closer to the S3 sectors, which represent one of the key strategies for digitization in agriculture and supply chain, medicine, tourism, and energy sectors.

Over 65 candidates applied for the training

As previously announced, on September 30th, we organized an entrance test and questionnaire for all applicants. More than 65 candidates took the entrance test and questionnaire, and after analyzing the results, we selected more than 40 of them who will undergo training over the next 2 months.

The training is supported by the Innovation fund of Montenegro

Additionally, on October 7th, we began conducting lectures, starting with Python programming language, which forms the foundation for further training and the development of artificial intelligence models through upcoming modules. This training is a continuation of previous NCC Montenegro and Open Mind Academy efforts. HPC4S3 researchers are providing are taking an active role in the implementation of the training.

Congratulations to everyone, and we look forward to collaborating with all our participants.

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HPC4S3ME In-House Lab Setup

After the physical installaion, we performed system software installation including operating system (Oracle Linux 9) and hardware drivers for GPUs (nVidia T4). This was the next stage of the in-house lab equipment validation. Young researchers have got their user accounts and they can start migrating their projects from their local setups (laptops, Google Colab) and rented HPC resources.

Rack cabinet cotaining computing nodes and UPS
Installation of the operatying system and hardware drivers

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Research paper: “Forecasting Icterus with Machine Learning: An Advanced Classification Model Analysis” (CMBEBIH & MEDICON 2023)

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.

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Research paper: “Thyroid Hormones Parameter-Based Classification of Patient Health Status: An Analysis of Machine Learning Techniques” (CMBEBIH & MEDICON 2023)

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.

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Research paper: “Liver Diseases Classification Using Machine Learning Algorithms” (CMBEBIH & MEDICON 2023)

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.

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