Comparison of Feature Selection Techniques in Knowledge Discovery Process

The process of knowledge discovery in data consists of five steps. Data preparation, which includes data cleaning and feature selection, takes away from 60% to 95% total time of the whole process. Thus, it is crucial phase of the process. The purpose of this research is to investigate feature selection techniques performance by conducting empirical research. Our comparison of three feature selection techniques reveals significant difference in feature selection techniques performance

Estimation and Comparison of Underground Economy in Croatia and European Union Countries: Fuzzy Logic Approach

Underground economy (UE) is one of the undesired facts in every country. The size of the underground economy is an important parameter in determining the effectiveness of fiscal and monetary policy, the rate of economic growth, and income distribution. From a scientific point of view analysis of the UE is faced with severe data problems because underground activities are not recorded and anyone engaged in it has an incentive to hide them. Therefore, economists have developed a variety of methods to estimate the size of the underground economy. The aim of this paper is to estimate and compare the size of the Croatian underground economy with the underground economy of European Union (EU) countries in the period of 2004 till 2012. The purpose of this paper is to address this issue in three ways. First, we review existing estimates of the size of the underground economy. Second, we apply a novel calculation method for estimation: fuzzy logic. Third, we calculated and compared underground economy index for 25 European Union countries and compared it, with special focus on Croatian underground economy index. Results indicated that Croatia has the thirteenth largest underground economy among measured members of the European Union. This study is the first of its kind with recent data to measure the size of underground economy in European Union countries by employing fuzzy logic approach

A Multidimensional Model of the New Work Environment in the Digital Age to Increase a Company’s Performance and Competitiveness

The purpose of the paper is to develop a multidimensional model of the new work environment in the digital age to increase a company’s performance and competitiveness in VUCA (volatility, uncertainty, complexity, and ambiguity) business environment. The multidimensional model covers the implementation of an agile work environment through the prism of using artificial intelligence technology to increase company’s performance and competitiveness. Researched determined multidimensional aspects for successful implementation of work environment in the digital age are, therefore 1) drivers for shifting towards agility, 2) implementation of agile leadership, 3) implementation of an agile work environment, 4) implementation of AI technology in work environment, 5) company’s performance, 6) competitiveness. The main survey involved randomly selected 473 medium-sized and large companies in Slovenia. Structural equation modelling was used for statistical data analysis. The results show that drivers for shifting towards agility have a positive effect on implementation of agile leadership. Also, results show that implementation of agile leadership and implementation of AI technology in work environment have a positive effect on implementation of an agile work environment. Moreover, results show that implementation of an agile work environment has positive effect on company’s performance and competitiveness. The paper highlights the important multidimensional aspects of the successful implementation of an agile work environment to increase the company’s performance and competitiveness. Also, our results will contribute to the proper implementation of the work environment in the digital age and give owners or top managers a broad insight into the various aspects that must be considered in their business governance in today’s rapidly changing business environment

Development of a big data bank for PV monitoring data, analysis and simulation in COST Action 'PEARL PV'

COST Action entitled PEARL PV aims at analyzing data of monitored PV systems installed all over Europe to quantitatively evaluate the long-term performance and reliability of these PV systems. For this purpose, a data bank is being implemented that can contain vast amounts of data, which will enable systematic performance analyses in combination with simulations. This paper presents the development process of this data bank