Category: Presentations

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.

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).

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.

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.

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.

Mr Dejan Babic presented the research paper “Ten Year Cardiovascular Risk Estimation: A Machine Learning Approach” on a joint event CMBEBIH & MEDICON 2023 which held place in Sarajevo, Bosnia and Herzegovina from September 14th to September 16th. The paper presented a comprehensive analysis of the use of Machine Learning and Artificial Intelligence techniques in the sense of predicting the risk, and therefore evaluate their potential in identifying patients with possibilities of developing cardiovascular disease.

ABSTRACT – Cardiovascular disease is one of the most common causes of death in the world. Usual practice in medicine is estimation of 10 years risk of patients developing cardiovascular disease. This practice enables taking preventive measures such as early medical treatments and lifestyle changes. The key step is to accurately assess the risk where the patients can be treated accordingly. With advancement of Artificial Intelligence and ML in medicine, this study aims to present a comprehensive analysis of the use of these techniques in the sense of predicting the risk, and therefore evaluate their potential in identifying patients with possibilities of developing cardiovascular disease. First, an analysis of the working dataset is provided. Then, various traditional classification models are implemented and trained on the dataset, and their performance is compared. The classifying models used in this research include SVC, KNN etc. The results of this study are presented in systematic comparison, where the performances of all the models are represented with discussion about challenges and possible limitations of the data regarding this particular problem. Normalization and oversampling techniques were used and reduced the overfitting problem.