Automatic image interpretation could be achieved by first performing a segmentation of the image, i.e. aggregating similar pixels to form regions, then use a supervised region- based classification. In such a process, the quality of the segmentation step is of great importance. Nevertheless, whereas the classification step takes advantage from some prior knowledge such as learning sample pixels, the segmentation step rarely does. In this paper, we propose to involve machine learning to improve the segmentation process using the watershed transform. More precisely, we apply a fuzzy supervised classification and a genetic algorithm in order to respectively generate the elevation map used in the watershed transform and tune segmentation parameters. The results from our evolutionary supervised watershed algorithm confirm the relevance of machine learning to introduce knowledge in the watershed segmentation process.

The aim of the study is to present a methodology for analyzing pollution emissions in a medium-sized city using modern traffic simulations in the aspect of minimizing exhaust emissions. The scope of the research and the methods of analysis used differ from those applied in big cities projects that can be found in the literature, Therefore the progressive elaboration model has been applied methodically to formulate and carry out the feasibility study. To perform microscopic traffic simulations, the software Simulation of Urban Mobility (SUMO—German Aerospace Center (DLR), Berlin, Germany) was applied. Thanks to the simulations, changes in traffic organization were accurately identified in the context of pollution emissions before they were implemented. The proposed approach allows a smooth flow of vehicles and a reduction of exhaust emissions. The experiments, supported by visual modelling of traffic with respect to pollution emissions, were performed on one of the main arteries of the city of Czestochowa (Poland). The results were used to explain the benefits of planned roadworks and convince the city government of the necessity to modernize the communication network.

Theoretical background: The rapid and exponential technological advancements have far-reaching impacts on management information systems, management practices, and human life. The promising outcomes in Artificial Intelligence and cutting-edge research on semantic networks and natural language processing have motivated the authors to envision the future of management technology. Purpose of the article: Our paper focuses on the new communication facilities and artificial intelligence models used to process management-type queries in natural language. Research methods: The article discusses recently developed technologies, proposed by Google and Microsoft, notably Google Bard and Bing integrated with ChatGPT-4. Both chatbots use Generative AI methods and large language models to understand domain-based queries and generate answers. Main findings: The practical and social implications of new models in management practice are discussed. To illustrate the qualities and weaknesses of the features of new technologies, four examples of management decision-making are discussed. The case studies also show differences between these two technologies. Finally, the paper concludes by summarizing the expectations and limitations of Generative AI applications in management. The paper is one of the first publications describing and demonstrating the idea of interfaces in natural language in business-oriented applications.