Summary: Information technology companies currently use data mining techniques in different areas with the goal of increasing the quality of decision‐making and to improve their business performance. The study described in this paper uses a data mining approach to produce an effort estimation of a software development process. It is based on data collected in a Croatian information technology company. The study examined 27 software projects with a total effort exceeding 42 000 work hours. The presented model employs a modified Cross‐Industry Standard Process for Data Mining, where prior to model creation, additional clustering of projects is performed. The results generated by the proposed approach generally had a smaller effort estimation error than the results of human experts. The applied approach has proved that sound results can be gained through the use of data mining within the studied area. As a result, it would be wise to use such estimates as additional input in the decision‐making process.

This paper presents an artificial intelligence-based model for the classification of maritime vessel images obtained by cameras operating in the visible part of the electromagnetic spectrum. It incorporates both the deep learning techniques for initial image representation and traditional image processing and machine learning methods for subsequent image classification. The presented model is therefore a hybrid approach that uses the Inception v3 deep learning model for the purpose of image vectorization and a combination of SVM, kNN, logistic regression, Naïve Bayes, neural network, and decision tree algorithms for final image classification. The model is trained and tested on a custom dataset consisting of a total of 2915 images of maritime vessels. These images were split into three subsections: training (2444 images), validation (271 images), and testing (200 images). The images themselves encompassed 11 distinctive classes: cargo, container, cruise, fishing, military, passenger, pleasure, sailing, special, tanker, and non-class (objects that can be encountered at sea but do not represent maritime vessels). The presented model accurately classified 86.5% of the images used for training purposes and therefore demonstrated how a relatively straightforward model can still achieve high accuracy and potentially be useful in real-world operational environments aimed at sea surveillance and automatic situational awareness at sea.

Turnover of the personnel represents a serious issue for management of software projects. The buildup of competences and phasing in of the people into the project requires both time and effort. This paper presents a case study of a large in-house agile software development project. The research goal was to determine the effects that turnover has on the expert effort estimation. In order to do this, paper examines relations across empirical data on a studied project. Study findings are the following: a) it is necessary to distinguish types of turnover, b) the general and planned turnover do not necessarily have a negative effect on estimation accuracy, and c) the unplanned turnover can have a significant negative impact on the reliability of the estimates and therefore should be treated with special attention. Results suggest that these facts should be taken into account both by the management and human resources.

The primary means of informing the population in modern society is through news portals. This paper analyses the characteristics and effects that such way of communication creates. The influence was studied in particular on an example of current global phenomenon “vaccination” (hr. cijepljenje). The research method follows the CRISP-DM process adapted to the digitalized form of textual data. The analysed corpus, in the form of natural spoken language, was scraped from Croatian news portals. The subsequent processing extracts information from unstructured textual sources and provides valuable insights, like how much a particular topic is represented in the article. Modeling is based on the application of multiple text mining algorithms, like Words Cloud, Topic Modelling, Concordance and Sentiment Analysis. The implemented model produces indicators for objective information interpretation. The findings suggest that the portals associated the notion of vaccination with the COVID-19 pandemic. Furthermore, this term was often used in a political context. The words used and predominantly negative character of texts dealing with vaccination has led to the transmission of negative emotions to readers. A significant aspect of the study is the fact that it was conducted on the corpus of texts written in Croatian - a relatively small and morphologically complex language.