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.
Education in modern business conditions plays a strategic role in the development of knowledge-based and innovation-driven societies. Investment in education represents one of the key determinants of democratization and the economic empowerment of individuals. The goal of contemporary education is to strengthen professional knowledge and to develop key competencies, including digital skills, which enable individuals to be prepared for new professional challenges and actively participate in current socio-economic trends. This paper aims to analyze, through conducted empirical research, the impact of formal education and acquired digital competencies on employability in the Republic of Serbia. Special emphasis is placed on the relationship between the level of education, digital literacy, and employment opportunities, to provide recommendations for improving educational policies and professional training programs.
This study presents a novel account of artificial intelligence-driven digital transformation (AIDT) in the proposed relationship between artificial intelligence readiness (AIR) and business firms were analysed with the PLS-SEM approach for testing the proposed AIDT model. Results confirmed that the link between AIR and BPE is positive and significant. Furthermore, it is confirmed that AIDT significantly mediates the positive relationship between AIR and BPE. Further, discussion on tangible and intangible aspects of AIR offers deep AI-related theoretical insights into the AIDT related concept of organizational resource-based view. Policymakers and AI practitioners can use the AIDT model as a driver for enhancing BPE.
This paper examines users' perceptions of online banking applications in the Republic of Serbia, focusing on the scales of efficiency, availability, and ease of contact. The research aims to examine user experiences across different devices, the effect of the length of use, and the influence of frequency of use on perceptions of online banking applications. The questionnaire was completed by 181 online banking application users, in the period from August 2024 to March 2025. The results of statistical tests, including the Mann-Whitney and Kruskal-Wallis, revealed no statistically significant differences between mobile and computer/laptop users in terms of efficiency, availability, and ease of contact; the length of time using online banking did not affect users' perceptions of these factors; and the frequency of use also had no significant impact on users' experiences. The findings suggest that users across different usage patterns generally have similar experiences and perceptions regarding online banking applications in the Republic of Serbia. The study's implications are relevant for both researchers and practitioners aiming to enhance the design and user experience of online banking applications.
Credit analysis has faced significant limitations in the context of digital transformation, where traditional models based on retrospective financial indicators can no longer adequately meet modern market demands alone. Conversely, contemporary digital models, despite their technological sophistication, still often lack transparency and raise ethical and regulatory concerns. This paper theoretically develops a hybrid creditworthiness assessment model that combines the reliability of traditional indicators with the adaptability of digital tools. The model incorporates explainable artificial intelligence (XAI), real-time and unstructured data analysis, and visual risk dashboards. The methodological approach is descriptive and theoretical, involving comparative analysis and normative evaluation. Findings suggest the hybrid creditworthiness assessment model can meet the requirements of accuracy, speed, transparency, and adaptability. The research provides theoretical value in redefining credit analysis frameworks and practical relevance in developing digital credit assessment solutions, particularly for sectors operating outside the traditional financial structure.
This paper aims to analyze the use of online banking services in Serbia, with a special focus on the frequency of use, the most common services, the level of client satisfaction and sociodemographic differences in satisfaction. In the period from August to December 2024, a total of 196 clients of online banking services in Serbia participated in the research. The research results showed that online banking has become a daily habit for a large number of clients, with the most frequently used services being bill payment, fund transfer and checking account balances. The results indicate that the key factors that influenced clients to use online banking services were flexibility, speed and ease of transaction execution, as well as ease of account monitoring and transaction management. Clients are largely satisfied with online banking services, and applied statistical tests did not show that there are statistically significant differences in client satisfaction relating to gender, age, education or length of use of online banking services.
Digital technologies have been a catalyst for creative destruction in various industries. They have disrupted existing routines and structures while creating new opportunities for innovation. Through creative destruction, digital technologies fundamentally challenge existing routines, capabilities, and structures by which organizations presently operate, adapt, and innovate. The origins of modern technological change provide the context necessary to understand present-day technological transformation, to investigate the impact of the new digital technologies, and to examine the phenomenon of digital disruption of established industries and occupations. How these contemporary technologies will transform industries and institutions, or serve to create new industries and institutions, will unfold in time. The implications of the relationships between these pervasive new forms of digital transformation and the accompanying new business models, business strategies, innovation, and capabilities are being worked through at global, national, corporate, and local levels. Whatever the technological future holds, it will be defined by continual adaptation, perpetual innovation, and the search for new potential. The author of this study concluded that digital entrepreneurship thrives on the principles of creative destruction, using technology to innovate and disrupt existing markets. This cycle of innovation and obsolescence drives economic growth and fosters a dynamic and competitive business environment, which indicates a close connection between Creative Destruction, Technology and Digital Entrepreneurship.
Digital transformation has become an essential factor shaping contemporary business processes, particularly within the small and medium-sized enterprises (SMEs) sector, which often faces resource constraints and rapid market fluctuations. The purpose of this study is to explore the impact of digital orientation on SME profitability, considering digital orientation through five critical dimensions: strategic planning, technological investment, budget allocation, organisational agility, and employee training. Although existing frameworks acknowledge the relevance of digital transformation, they frequently lack a comprehensive approach to integrating these dimensions in the context of business performance. The research adopted a quantitative approach, employing a five-point Likert scale based on responses from 740 SME participants. Findings indicate a statistically significant positive correlation between digital orientation and perceived profitability, confirming that a strategic focus on digital resources and employee competency development significantly contributes to sustainable growth within the SME sector. Future research should prioritise support for struggling SMEs through government aid, tailored training, and stronger public-private partnerships. Overcoming these barriers will enable full use of digital technologies and enhance long-term competitiveness. In addition, focusing solely on profitability overlooks other vital indicators such as growth, profit, and market share. Further efforts should aim to bridge the digital divide and promote broader digital transformation across SMEs.
As cyber threats continue to evolve, based security tools, play a crucial role in Network attackers and collecting intelligence on their tactics. This survey provides a systematic and comprehensive review of honeypot solutions within modern NIDS, offering an in-depth categorization of different types of honeypots, examining their integration with NIDS, and evaluating their effectiveness in detecting sophisticated cyberattacks. In addition to presenting an overview of existing honeypot technologies, this survey critically analyzes recent advances and identifies key challenges, including deployment complexities, evasion techniques, and resource constraints. By synthesizing findings from a wide range of research studies, this work highlights the current state of honeypot technology and its role in contemporary trends such as AI-driven honeypots, the integration of large language models (LLMs), deception-based cyber defense, and cloud-based implementations are explored. This survey also synthesizes findings from recent review studies, providing a structured overview of the latest advances in honeypot-based security solutions.
Technology companies continuously innovate and develop new devices and software solutions that facilitate user interaction with smart technologies. The pace of development varies, with some devices advancing more rapidly than others. This article focuses on developing applications for the Tobii Eye Tracking device, which allows users to control a computer solely with their eye movements without needing a mouse or keyboard. We present two applications we have developed using the Unity game engine. These applications can assist individuals with disabilities in communicating via a computer or serve as a demonstration of how eye-tracking technology can be used for gaming. The first application introduces users to this technology. The player moves across the screen and attempts to collect points by focusing on green balls while avoiding red ones. The second application focuses on writing and drawing using only eye movements. This application is particularly useful for individuals with disabilities, providing them with an accessible way to interact with a computer.
This paper describes applying the Purdue reference model enhanced for security zones and conduits according to IEC 62443 standards to the ICS network architecture of the representative mobile offshore drilling unit. It describes previous relevant cybersecurity incidents on the oil rigs, outlines the applicable standards, and provides a detailed description of each level of the Purdue reference model and the related security zones and conduits concept. This research included an application to a reference architecture for a representative mobile offshore drilling unit. The aim is to demonstrate that proper implementation of this architecture significantly increases the technological unit's overall cybersecurity level.
The increasing adoption of smart health systems has revolutionized the healthcare industry, offering improved diagnosis, personalized However, the security and privacy of sensitive patient data remain one of the major concerns. Federated machine learning has emerged as a promising approach to address these challenges by enabling collaborative learning across multiple healthcare institutions without sharing raw data. This paper investigates and detects the inference attack accuracy of federated machine learning algorithms in the context of smart healthcare. The research examines popular algorithms such as multiple datasets specific to smart healthcare. The findings highlight the effectiveness of these algorithms in protecting sensitive healthcare data, with FedDP consistently achieving the highest accuracy. The study contributes to the field by providing evidence of effective methods for safeguarding patient privacy in smart healthcare. Furthermore, the paper explores the strengths, limitations, and trade-offs of different algorithms, enabling researchers and practitioners to make intelligent decisions in selecting appropriate algorithms for privacy-preserving analytics. The study also discusses the ethical considerations, the data collection process, and the experimental methodology employed. The results of this research enhance the understanding of federated machine learning in smart healthcare and contribute to the development of robust mechanisms for data privacy and security. The findings foster trust among patients, healthcare providers, and stakeholders, paving the way for the secure and responsible use of advanced technologies in healthcare.
As cyber threats continue to evolve, organizations increasingly rely on advanced security mechanisms to detect and mitigate malicious activities. Honeypots, as deceptionbased security tools, play a crucial role in Network Intrusion Detection Systems (NIDS) by defrauding attackers and collecting intelligence on their tactics. This survey provides a systematic and comprehensive review of honeypot solutions within modern NIDS, offering an in-depth categorization of different types of honeypots, examining their integration with NIDS, and evaluating their effectiveness in detecting sophisticated cyberattacks. In addition to presenting an overview of existing honeypot technologies, this survey critically analyzes recent advances and identifies key challenges, including deployment complexities, evasion techniques, and resource constraints. By synthesizing findings from a wide range of research studies, this work highlights the current state of honeypot technology and its role in contemporary cybersecurity strategies. Furthermore, emerging trends such as AI-driven honeypots, the integration of large language models (LLMs), deception-based cyber defense, and cloud-based implementations are explored. This survey also synthesizes findings from recent review studies, providing a structured overview of the latest advances in honeypot-based security solutions.
The chromatic index of a graph denotes the number of colors needed for such a coloring of this graph, that no two adjacent edges are colored with the use of the same color. The value of this graph property is commonly used in several real-world problems, such as the determination of the number of time slots for traffic lights placed in the intersection system, or the allocation of registers to variables in the compilation of code. Since the problem of chromatic index identification is NP-complete, the conventional computing methods are of high time complexity. This motivated the recent use of machine and deep learning models for the approximate determination of the value of the graph property. In this study, the Kolmogorov-Arnold Network is designed, implemented, and experimentally evaluated in the context of the selected task. This model has been utilized for its high decision-making quality and strong interpretability, which are both explored in the presented work through conventional classification metrics such as accuracy and precision and through means of visualization and symbolic approach to the interpretation of the decisionmaking process.
The process of diagnostic analysis of data often incorporates human experts for problems outside of computer science and data analysis itself. These experts then interpret and explain the events, trends, and relationships identified in the studied data based on their previous domain knowledge. The main insufficiency of this approach to diagnostic data analysis is the lack of experts or their frequent unavailability, which motivated the previous use of pre-trained large language models as a surrogate for human experts in solving simple domain-specific problems. In this work, the web-focused application for diagnostic analysis of data based on the combination of large language models and correlation analysis is designed, implemented, and examined on two case studies of commonly utilized benchmarking datasets. The proposed model emphasizes the use of interactive visualization techniques in the context of correlation maps, in which the significant relationships between the value of attributes of a dataset are identified, while the large language model offers a brief explanation of these relationships from the point of view of the data domain.
Current trends and future developments in the field of data storage, processing, analysis, output and visualization include various possible directions and future developments of these systems. This paper discusses the current state of data ecosystems and possible future developments based on an analysis of publications and studies from the last time. Commonly known trends point to approaches that enable more efficient data management, faster data analysis and, of course, the integration of different types of data coming from different sources. Additionally, the study evaluates the ETL process performance, identifying key metrics, optimizations, and bottlenecks in data processing workflows. The benefits and development of generative models are also changing the way we work with data, with implications for improved visualizations and general process automation. Thus, in the area of analysis, it can be assumed there will be an even greater emphasis on interactive and intuitive reports that make it as easy as possible to understand complex data structures. In terms of the use of system resources, the use of Cloud services is the basis for comprehensive use of scalability and flexibility at lower infrastructure costs. Another area that the paper discusses is the current growing demand for real-time data analysis due to the increasing need for quick decisions based on data evaluation.
