This paper presents an analysis of digital skills and network infrastructure in the European Union (EU) countries based on data from the Digital Economy and Society Index (DESI) 2024. We analyze the current state of digital skills and network infrastructure in EU countries, which in the DESI framework is called digital infrastructure, identifying key trends and differences between EU member states. The analysis shows significant differences in the relative share of citizens with a certain level of digital skills across countries, both among ordinary users of digital services and among information and communication technology professionals. The analysis of digital infrastructure includes fixed broadband coverage, mobile broadband, and edge networks, the latter of which are expected to become an important component of future digital infrastructure. The results highlight the importance of harmonizing the development of digital skills and digital infrastructure to support the EU’s digital transformation. Significant attention is paid to 5G technology. The feasibility of including a new additional indicator in DESI for next-generation 5G technology in the frequency range of 24.25–52.6 GHz is shown. The value of this indicator can be used to assess the readiness of the EU economy for technological leaps that place extremely high demands on reliability and data transmission delays. The results of the current state and the analysis of digital skills and infrastructure contribute to understanding the potential for the future development of the EU digital economy.

This article describes the using of various clustering methods for in-depth analysis and further researches of the spread of the virus COVID-19 in Italy during February-April 2020.

The article describes the method of cost-sensitive classification for imbalanced dataset based on neural-like structure of successive geometric transformations model using piecewise-linear approach to classification. The proposed method characterized by high learning speed and accuracy of classification.

The precise categorization of brief texts holds significant importance in various applications within the ever-changing realm of artificial intelligence (AI) and natural language processing (NLP). Short texts are everywhere in the digital world, from social media updates to customer reviews and feedback. Nevertheless, short texts’ limited length and context pose unique challenges for accurate classification. This research article delves into the influence of data sorting methods on the quality of manual labeling in hierarchical classification, with a particular focus on short texts. The study is set against the backdrop of the increasing reliance on manual labeling in AI and NLP, highlighting its significance in the accuracy of hierarchical text classification. Methodologically, the study integrates AI, notably zero-shot learning, with human annotation processes to examine the efficacy of various data-sorting strategies. The results demonstrate how different sorting approaches impact the accuracy and consistency of manual labeling, a critical aspect of creating high-quality datasets for NLP applications. The study’s findings reveal a significant time efficiency improvement in terms of labeling, where ordered manual labeling required 760 min per 1000 samples, compared to 800 min for traditional manual labeling, illustrating the practical benefits of optimized data sorting strategies. Comparatively, ordered manual labeling achieved the highest mean accuracy rates across all hierarchical levels, with figures reaching up to 99% for segments, 95% for families, 92% for classes, and 90% for bricks, underscoring the efficiency of structured data sorting. It offers valuable insights and practical guidelines for improving labeling quality in hierarchical classification tasks, thereby advancing the precision of text analysis in AI-driven research. This abstract encapsulates the article’s background, methods, results, and conclusions, providing a comprehensive yet succinct study overview.

Hierarchical classification, which organizes items into structured categories and subcategories, has emerged as a powerful solution for handling large and complex datasets. However, traditional flat classification approaches often overlook the hierarchical dependencies between classes, leading to suboptimal predictions and limited interpretability. This paper addresses these challenges by proposing a novel integration of tree-based models with hierarchical-aware split criteria through adjusted entropy calculations. The proposed method calculates entropy at multiple hierarchical levels, ensuring that the model respects the taxonomic structure during training. This approach aligns statistical optimization with class semantic relationships, enabling more accurate and coherent predictions. Experiments conducted on real-world datasets structured according to the GS1 Global Product Classification (GPC) system demonstrate the effectiveness of our method. The proposed model was applied using tree-based ensemble methods combined with the newly developed hierarchy-aware metric Penalized Information Gain (PIG). PIG was implemented with level-wise entropy adjustments, assigning greater weight to higher hierarchical levels to maintain the taxonomic structure. The model was trained and evaluated on two real-world datasets based on the GS1 Global Product Classification (GPC) system. The final dataset included approximately 30,000 product descriptions spanning four hierarchical levels. An 80-20 train–test split was used, with model hyperparameters optimized through 5-fold cross-validation and Bayesian search. The experimental results showed a 12.7% improvement in classification accuracy at the lowest hierarchy level compared to traditional flat classification methods, with significant gains in datasets featuring highly imbalanced class distributions and deep hierarchies. The proposed approach also increased the F1 score by 12.6%. Despite these promising results, challenges remain in scaling the model for very large datasets and handling classes with limited training samples. Future research will focus on integrating neural networks with hierarchy-aware metrics, enhancing data augmentation to address class imbalance, and developing real-time classification systems for practical use in industries such as retail, logistics, and healthcare.