The COVID-19 pandemic has caused the acceleration of digitization and the consideration of digital financial inclusion as a means to minimize negative economic consequences and increase the resilience of households and SMEs. The purpose of this article was to assess the impact of the COVID-19 pandemic on digital financial inclusion by constructing and calculating an integral index of digital financial inclusion (DFI) based on Global Findex Database indicators. The approach to calculating the DFI index and two sub-indices that characterized passive participation in financial relations and active use of digital technologies was based on a linear mathematical model of the integrated indicator and on the use of the Fishburn formula to calculate the weight coefficients. The obtained results proved the acceleration of digital financial inclusion in 2021 and revealed significant differences in DFI between countries and groups of countries according to income level as well as problems of financial exclusion of the most vulnerable groups of population, especially in developing countries. The obtained results regarding the level of DFI are discussed from the point of view of COVID-19 impacts: both directly by influencing consumer behavior and decisions regarding digital financial services and from a broader perspective by influencing business entities, financial service providers, and regulation.

No abstract

No abstract

This paper presents an application for the monitoring of leaks in flood embankments by reconstructing images in electrical tomography using logistic regression machine learning methods with elastic net regularisation, PCA and wave preprocessing. The main advantage of this solution is to obtain a more accurate spatial conductivity distribution inside the studied object. The described method assumes a learning system consisting of multiple equations working in parallel, where each equation creates a single point in the output image. This enables the efficient reconstruction of spatial images. The research focused on preparing, developing, and comparing algorithms and models for data analysis and reconstruction using a proprietary electrical tomography solution. A reliable measurement solution with sensors and machine learning methods makes it possible to analyse damage and leaks, leading to effective information and the eventual prevention of risks. The applied methods enable the improved resolution of the reconstructed images and the possibility to obtain them in real-time, which is their distinguishing feature compared to other methods. The use of electrical tomography in combination with specific methods for image reconstruction allows for an accurate spatial assessment of leaks and damage to dikes.

No abstract