Despite the fact that technology is improving day by day and that the medical devices (MDs) are being constantly upgraded, their malfunction is not a rare occurrence. The aim of this research is to develop an expert system that can predict whether the device will satisfy functional and safety requirements during a regular inspection. This expert system can be seen as part of Industry 4.0 that is revolutionizing medical device management. In order to develop the system, five machine learning algorithms that are representative of each classifier group, were used: (1) Random Forest, (2) Decision Tree, (3) Support Vector Machine, (4) Naive Bayes, (5) k-Nearest Neighbour. The Decision Tree outperformed other classifiers achieving the classification accuracy of 100% with and without attribute selection applied on the dataset. This study showed that machine learning algorithms can be used in order to predict MDs performance and potential failures in order to make the process of maintenance of medical devices more convenient and sophisticated and it is one step in modernizing medical device management systems by utilizing artificial intelligence.

A car price prediction has been a high-interest research area, as it requires noticeable effort and knowledge of the field expert. Considerable number of distinct attributes are examined for the reliable and accurate prediction. To build a model for predicting the price of used cars in Bosnia and Herzegovina, we applied three machine learning techniques (Artificial Neural Network, Support Vector Machine and Random Forest). However, the mentioned techniques were applied to work as an ensemble. The data used for the prediction was collected from the web portal autopijaca.ba using web scraper that was written in PHP programming language. Respective performances of different algorithms were then compared to find one that best suits the available data set. The final prediction model was integrated into Java application. Furthermore, the model was evaluated using test data and the accuracy of 87.38% was obtained.

Social media is very important factor in analyzing modern society as a whole, their values, norms, and behaviors, as being a part of our everyday life. This study is oriented towards analyzing social media in order to allow users to create their own preferences to follow (analyze) a specific social media source. The web application has been developed to allow a user to follow specific Facebook accounts and categorize the Facebook posts on those accounts based on the user defined taxonomies. Results of this study are various reports generated from the Facebook posts and their statistics that are clustered based on the user defined taxonomies. The benefit of this project is that any user can track in real time when people are talking about some topic, and it enables anyone to have better insight about society as a whole, their values, norms, what they find interesting, and many other things. This tool is also useful for different companies to track the user feedback on social networks for their products.