Acta Informatica Pragensia, 2026 (vol. 15), issue 1
Article
ResNetMF: Improving Recommendation Accuracy and Speed with Matrix Factorization Enhanced by Residual Networks
Mustafa Payandenick, YinChai Wang, Mohd Kamal Othman, Muhammad Payandenick
Acta Informatica Pragensia 2026, 15(1), 1-21 | DOI: 10.18267/j.aip.280651 
Background: Recommendation systems are essential for personalized user experiences but struggle to balance accuracy and efficiency.Objective: This paper presents ResNetMF, an innovative hybrid framework designed to address these limitations by combining the strengths of matrix factorization (MF) and deep residual networks (ResNet). Matrix factorization excels at capturing explicit linear relationships between users and items, while ResNet is employed to model non-linear residuals.Methods: By focusing on refining the baseline MF output through incremental improvements, ResNetMF minimizes redundant computations and significantly enhances recommendation...
Analysis of Benford’s Law Conformity with Web of Science Citations of Documents
David Jiri Slosar
Acta Informatica Pragensia 2026, 15(1), 22-35 | DOI: 10.18267/j.aip.281768
Background: Benford’s law is a statistical phenomenon that predicts the probability of a particular digit at a particular position in a number. This law has been successfully applied in a number of areas, such as accounting. In the area of scientometrics, research has been devoted mostly to journal data.Objective: This paper investigates the conformity of Benford’s law with the citation counts of records retrieved from the Web of Science database. We evaluate the conformity levels with Benford’s law in the complete dataset. We determine the effect of document type (article, proceedings paper and review), year of publication (2014–2018)...
Data Science Framework for Adaptive Expert Systems: Psychological Profiling and Knowledge Fusion in Higher Education
Tomislav Mesic, Miloslav Hub
Acta Informatica Pragensia 2026, 15(1), 36-53 | DOI: 10.18267/j.aip.282550
Background: Traditional expert systems in education rely on static knowledge bases and rule-based logic, limiting their ability to adapt to the diverse and evolving needs of students. Recent advancements in artificial intelligence and psychological profiling offer new pathways for building personalized support systems.Objective: This study presents a data science framework for developing adaptive expert systems that personalize support delivery using dynamic psychological profiling, with a focus on the higher education domain.Methods: The proposed system integrates five core components: MIND (a multimodal information orchestrator), UEX (expert system...
Personalized Learning Analytics Through Static Code Analysis in Computer Science Education
Marek Horváth, Emília Pietriková, Filip Gurbáµ
Acta Informatica Pragensia 2026, 15(1), 54-71 | DOI: 10.18267/j.aip.283602
Background: Learning programming is often difficult for beginners, primarily because of the challenge of providing timely and personalized feedback in large educational environments. While automated assessment systems have improved efficiency in grading and feedback, they typically focus on correctness and often lack personalized guidance concerning code quality, readability, and maintainability.Objective: This study aims to investigate whether integrating static code analysis into automated assessment systems to provide personalized feedback can effectively enhance students code quality, learning process, and engagement in programming courses.Methods:...
Automated Machine Learning in Action: Performance Evaluation for Predictive Analytics Tasks
Nicolas Leyh
Acta Informatica Pragensia 2026, 15(1), 72-89 | DOI: 10.18267/j.aip.288612
Background: As organizations increasingly seek data-driven insights, the demand for machine learning (ML) expertise outpaces the current workforce supply. Automated Machine Learning (AutoML) frameworks help close this gap by streamlining the ML pipeline, making advanced modeling accessible to non-specialists.Objective: This study evaluates the performance of four open-source AutoML frameworks—Auto-Keras, Auto-Sklearn, H2O, and TPOT—in predictive analytics, focusing on both binary and multiclass classification. The goal is to identify performance strengths and limitations under varying dataset conditions and propose improvements for framework...
Blockchain-Based Framework for Enhancing Interoperability and Security in EHR Exchange Using Lightweight ECC Proxy Re-Encryption
Devaramane Yogaraj Ashwini, Reval Prabhu Puneeth
Acta Informatica Pragensia 2026, 15(1), 90-108 | DOI: 10.18267/j.aip.290127
Background: The sharing of electronic health records among hospitals is crucial for ensuring consistent patient treatment. However, the process remains challenging due to the existence of varied systems, privacy concerns and interoperability issues. It is often difficult to maintain an equilibrium of security, efficiency and compliance across all platforms.Objective: The objective of this article is to develop a framework that enables secure, efficient and interoperable Electronic health records (EHR) sharing across healthcare systems.Methods: The proposed work introduces a Lightweight elliptic curve cryptography proxy re-encryption (LWECC-PRE) framework...
Evaluating Intrinsic Motivation in Robot-Supported Quiz-Based Learning: A Comparative Study of Verbal-Only and Multimodal Feedback with Sound Input
Rezaul Tutul, Ilona Buchem, André Jakob, Niels Pinkwart
Acta Informatica Pragensia 2026, 15(1), 109-125 | DOI: 10.18267/j.aip.292118
Background: Maintaining high motivation in robot-led educational activities is challenging when interactions rely solely on verbal communication. Incorporating multimodal feedback combining gestures, sounds and music may provide a richer and more engaging learning experience.Objective: This study aims to examine whether integrating multimodal feedback with a real-time, fair first-responder detection system in a robot-led quiz game enhances students’ intrinsic motivation and engagement compared to a verbal-only, sequential turn-taking interaction.Methods: A two-group experiment was conducted with 48 university students randomly assigned to two...
Evaluating AI Text Detection Tools for Distinguishing Human-Written from AI-Generated Abstracts in Persian-Language Journals of Library and Information Science
Amrollah Shamsi, Ting Wang, Maryam Amraei, Narayanaswamy Vasantha Raju
Acta Informatica Pragensia 2026, 15(1), 126-134 | DOI: 10.18267/j.aip.293284
Background: Researchers are using artificial intelligence (AI) tools in academic writing. However, their use may compromise the integrity and originality of the work. Hence, AI text detection tools have come to increase transparency. Objective: This study aims to evaluate the accuracy of AI text detection tools in recognizing human-written and AI-written abstracts in library and information science (LIS).Methods: Seven Persian academic journals in LIS were selected. ZeroGPT and GPTZero as AI text detectors were used. AI-generated abstracts were produced by AI chatbots (ChatGPT 4.0, DeepSeek and Qwen).Results: Despite performing strongly in detecting...
Optimizing Osteoporosis Detection with Cascaded Convolutional Neural Network and Real-Coded Genetic Algorithm
Hemalatha Balan, Madhavi Latha Pandala, Venkatasubramanian Srinivasan, Venkatachalam Kandasamy
Acta Informatica Pragensia 2026, 15(1), 135-156 | DOI: 10.18267/j.aip.294133
Background: Osteoporosis is a condition characterized by bones that are porous and brittle, increasing the risk of fractures. It is often asymptomatic until substantial harm develops, making it crucial to treat at the onset of the disorder.Objective: This study aims to develop a practical, in-depth and adaptable framework utilizing constructed clinical and demographic datasets for the early detection of osteoporosis.Methods: We design a cascade convolutional neural network with adaptive weight fusion and fine-tune it with a real-coded genetic algorithm. An anonymized clinical and demographic record publicly available bone mineral density dataset was...
Modular Local Classification via Cluster-Guided Feature Selection in Tabular Data
Leila Boussaad
Acta Informatica Pragensia 2026, 15(1), 157-172 | DOI: 10.18267/j.aip.295100
Background: Many real-world tabular datasets are heterogeneous, with distinct regions of the feature space exhibiting different feature–label relationships. Conventional global classifiers often miss these local patterns, reducing both predictive accuracy and interpretability. Objective: This study aims to design a modular classification framework that combines local specialization with global consistency to enhance predictive performance and interpretability in heterogeneous tabular data.Methods: The author proposes Cluster-guided local feature selection with top-2 voting and fallback (CGLFS+), which integrates unsupervised clustering, cluster-specific...
Review
Data Quality in Estimates from Probability-Based Online Panels: Systematic Review and Meta-Analysis
Andrea Ivanovska, Michael Bosnjak, Vasja Vehovar
Acta Informatica Pragensia 2026, 15(1), 173-197 | DOI: 10.18267/j.aip.279760
Background: General population surveys now increasingly use nonprobability samples from access panels instead of probability-based methods, which often leads to lower-quality estimates. In response, many official and academic surveys have adopted probability-based online panels (PBOPs), which use probability sampling and retain participants for follow-up surveys. While these panels reduce costs compared to one-time surveys, they still face low response rates and other challenges that may affect data quality.Objective: This study aimed to assess the accuracy of PBOPs by synthesising evidence on relative bias (RB), and to examine how RB varies by country,...
Digital Twins in the Context of Ensuring Sustainable Industrial Development
Yuliia Biliavska, Valentyn Biliavskyi
Acta Informatica Pragensia 2026, 15(1), 198-220 | DOI: 10.18267/j.aip.291127
Background: Currently, there is a megatrend towards digitalisation and servitisation using digital technologies and digital twins to support the digital transformation of the economy. In the literature, new digital technologies are seen as creating added value, strengthening customer relationships and accelerating the process of servitisation from manufacturing. The implementation of such a complex of technologies and business solutions can lead to the adaptation of the product and service life cycle, as well as the entire business model, to full servitisation.Objective: This study reveals the role of digital twins in the context of entrepreneurship...
Drone Delivery Global Research Landscape: A Bibliometric Analysis
Abdulwahab Funsho Atanda, Daniel Yong Wen Tan, Huong Yong Ting, Wasiu Olakunle Oyenuga, Abdulrauf Uthman Tosho
Acta Informatica Pragensia 2026, 15(1), 221-252 | DOI: 10.18267/j.aip.296100
Background: Rapid technological advancements have revolutionized research into unmanned aerial vehicles (UAVs), commonly known as drones, particularly in delivery applications. However, despite numerous related publications, there remains a lack of systematic reviews that synthesize challenges, trends and recent advances in drone delivery. To address this gap, the present study conducts a bibliometric analysis to examine evolutionary trends and emerging applications of UAVs between 2015 and 2024.Objective: This study aims to identify established and emerging trends in drone delivery research by analysing articles, journals, authors, institutions, countries...
Antecedents of Test Automation Adoption in DevOps Continuous Testing: A Systematic Literature Review Through the TOE Framework
Anuji Isara Vithana, Kelum Asanga Akurugoda Gamage, Dilani Wickramaarachchi, Ruwan Wickramarachchi, Shan Jayasinghe
Acta Informatica Pragensia 2026, 15(1), 253-273 | DOI: 10.18267/j.aip.29790
Background: The rapid evolution of software engineering has positioned DevOps practices and Continuous Testing (CT) as critical approaches for achieving speed, quality, and reliability in software delivery. Test automation is central to CT, yet its adoption remains inconsistent due to a complex interplay of technological, organizational, and environmental conditions.Objective: This study employs a systematic literature review guided by the Technology–Organization–Environment (TOE) framework to identify, categorize, and synthesize the antecedents that influence the test automation adoption in DevOps continuous testing.Methods: Using the...
Knowledge-Based and Intelligent Engineering Trends in Smart Cities: A Bibliometric Analysis of Machine Learning Applications
Rituraj Jain, Ashish Sharma, Nausheen Khilji, Ramesh Babu Putchanuthala, Venkateswararao Pulipati, Mysore KeshavaRao Harikeerthan, Himanshu Gupta
Acta Informatica Pragensia 2026, 15(1), 274-306 | DOI: 10.18267/j.aip.29898
Background: Artificial intelligence (AI) and machine learning (ML) have become a revolutionary force in the development of smart cities and are changing the way cities are built, run and managed. With the rapid acceleration of the degree of urbanization and technological convergence, the state of research in this interdisciplinary area is a question that is very important for both researchers and policy makers.Objective: This study aims to present an all-round analysis of the smart city applications of ML, in terms of both bibliometric and thematic analysis. The focus is on identifying trends in publication, major contributors, emerging topics of research...