Digital transformation plays a pivotal role in streamlining administrative procedures, stimulating economic growth, enhancing governance efficiency, and improving quality of life. This research aims to assess the efficacy of digital transformation initiatives, using a case study involving 20 cities in Vietnam. Eight criteria were employed to evaluate digital transformation progress: digital awareness index, digital institutional framework index, digital infrastructure index, digital human resources index, cybersecurity index, digital government performance index, digital business performance index, and digital social activity index. SIWEC, a subjective weighting method, was utilized to determine the significance of each criterion in this study. The performance ranking of the cities' digital transformation efforts was then conducted using the Probability method. The results showed, firstly, that the criteria identified as most important when using the subjective weighting method, SIWEC, were consistent with those when using the objective weighting method, PSI. Secondly, the integration of SIWEC and Probability methods yielded a digital transformation ranking of the cities that exhibited a high degree of correlation with rankings obtained using other established methodologies.
The paper presents the ranking of Artificial Intelligence researchers in Serbia for 2025. The ranking is presented primarily according to the researchers' h-index. Researchers with matching h-index are ranked by the number of citations. The minimum h-index of the 15 ranked researchers is 17. The h-index can be determined from the following online databases: Web of Science, Scopus, Google Scholar and the Publish or Perish program. The h-index, also known as the Hirsch index, is based on references. The ranking will be edited using the Google Scholar web database. Google Scholar is Google's scientific search engine, launched in 2004. We also present the ORCID iD number and the g-index of the researchers.
When working with correlation analysis and the visualization of its results and processes, the systematic identification of correlation classes is often overlooked. Such classes can be described as sets of attributes of the studied dataset that share similar correlation patterns-like the strength and direction of the relationship measured between a pair of attributes-and can be used as a method of identification of significant and insignificant correlations while introducing an attribute hierarchy for automated predictive analysis. This work proposes correlation n-star graphs, a graph-based visualization model designed to support automatic identification of these classes in multidimensional datasets. The work focuses on the design of the visualization model, a Python implementation of the proposed concept, and an experimental evaluation on three benchmark datasets assessing both qualitative and quantitative aspects of the visualization and correlation classification.
The paper presents the ranking of 2025. The ranking is presented primarily according to the researchers' h-index. Researchers with matching h-index are ranked by the number of citations. The minimum h-index of the 15 ranked researchers is 17. The h-index can be determined from the following online databases: Web of or Perish program. The h-index, also known as the Hirsch index, is based on references. The ranking will be edited using the Google Scholar web database. Google Scholar is Google's scientific search engine, launched in 2004. We also present the ORCID iD number and the g-index of the researchers.
This paper investigates the prediction of finger motion intention using surface electromyography (sEMG) signals and supervised machine learning models. sEMG sensors provide a noninvasive means of capturing muscle activity, enabling applications in prosthetics, rehabilitation, and human-computer interface. The study explores signal preprocessing techniques, time-frequency domain transformations, and machine learning algorithms including SVM, Random Forest, and XG-Boost. The results highlight the challenges posed by noise and variability in sEMG signals, and suggest strategies for improving prediction accuracy and real-world applicability.
