Acta Informatica Pragensia, 2026 (vol. 15), issue 3

Editorial

Challenges and Advances in Applied Informatics: Selected Extended Papers from Three International Conferences

Waad Alhoshan, Hakim Bendjenna, Subrata Chakraborty, Kim-Mey Chew, Stephanie Chua, Dalel Kanzari, Siaw-Hong Liew, Phei Chin Lim

Acta Informatica Pragensia 2026, 15(3), 621-622 | DOI: 10.18267/j.aip.33015  

The editorial summarises the special issue that is based on contributions from three international conferences focused on mathematics, informatics, data-driven analytics, intelligent systems, and information technology. This special issue consists of seven articles.

Article

Similarity Ranking-Based Instance Selection for Enhancing k-NN Classification Performances

Abdul Muqtasid bin Rushdi, Mohammad bin Hossin, Suhaila binti Saee, Norita binti Md Norwawi

Acta Informatica Pragensia 2026, 15(3), 623-637 | DOI: 10.18267/j.aip.310507  

Background: The k-nearest neighbours (k-NN) is a well-established classifier in machine learning. Yet, its performance drops and computational costs rise with extensive or redundant datasets. Furthermore, current instance selection (IS) approaches often face scalability problems and are sensitive to parameter settings.Objective: This study seeks to design a straightforward and efficient IS algorithm that reduces both dataset size and computational demands while preserving or enhancing the accuracy of k-NN classification.Methods: We propose Euclidean ranking-based instance selection (ERbIS), a novel IS approach that prioritises samples based on their...

PQAC–BIoMT: Post-Quantum Authentication and Access Control Framework for Blockchain-Enabled IoMT Systems

Rachida Hireche, Houssem Mansouri, Yasmine Harbi, Al-Sakib Khan Pathan, Saad Harous

Acta Informatica Pragensia 2026, 15(3), 638-660 | DOI: 10.18267/j.aip.321155  

Background: In recent years, the Internet of Medical Things (IoMT) has transformed the healthcare sector through real-time patient monitoring and continuous data collection. However, transmitting sensitive medical information over public networks exposes IoMT systems to significant security threats, while emerging quantum computing technologies challenge the reliability of traditional cryptographic systems.Objective: The objective of this study is to propose PQAC-BIoMT, a secure and robust model for remote user authentication and access control in IoMT environments, capable of withstanding both conventional and quantum attacks.Methods: This article...

Evaluation and Selection of Startup Ideas Using MCDM: A Comparative Study of TOPSIS, AHP, VIKOR, PROMETHEE II and ELECTRE II

Sara Rekkal, Kahina Rekkal, Mohamed Amine Yakoubi

Acta Informatica Pragensia 2026, 15(3), 661-679 | DOI: 10.18267/j.aip.32295  

Background: The selection of startup ideas to fund and develop is a complex decision-making problem regarding a multitude of conflicting criteria such as profitability, growth potential, risk, initial costs and social impact. Traditional single-criteria approaches are therefore insufficient to grasp this multidimensional complexity. This is the reason why multi-criteria decision-making (MCDM) methods are well adapted to this context.Objective: The aim of this study is to compare several widely recognized MCDM methods to find the most promising startup ideas. Besides, we compare the consistency, robustness and convergence of the rankings obtained under...

Adversarially Robust Intrusion Detection for OneM2M-Based IoT Systems: A Real-Time Edge Security Framework

Hamza Jamiri, Abdellah Zyane

Acta Informatica Pragensia 2026, 15(3), 680-697 | DOI: 10.18267/j.aip.32910  

Background: Machine learning-based intrusion detection systems are being used increasingly to keep internet of things (IoT) environments safe, but the fact that they can be tweaked by adversaries limits their dependability, especially in resource-constrained, real-time deployments.Objective: This study proposes and evaluates an adversarially robust intrusion detection framework, integrated into the OneM2M service layer, for real-time edge IoT security, where the goal is to keep communications safe, even when attackers act in crafty ways.Methods: Within the OneM2M analytics engine, the framework combines statistical anomaly filtering, feature...

Review

Gamification Meets Large Language Models: A Systematic Review of Applications and Challenges

Nurfauza Jali, Lleyton Geboh Leslie, Cheah Wai Shiang, Sadok Ben Yahia, Syahrul Nizam Junaini

Acta Informatica Pragensia 2026, 15(3), 698-718 | DOI: 10.18267/j.aip.311174  

Background: The convergence of large language models (LLMs) and gamification has emerged as a promising direction for enhancing user engagement, personalisation and interaction quality across digital systems. While prior studies have largely examined LLMs or gamification in isolation, limited work has systematically explored their combined role in redefining engagement paradigms beyond static reward-based mechanisms.Objective: This review systematically examines how LLMs are integrated into gamified systems, identifies their application contexts and functional roles and synthesises reported benefits, challenges and methodological trends across existing...

Coordination in Multi-Agent Systems: Overview of Metaheuristic Approaches

Ferial Laassami, Mohammed Elhabib Souidi, Abdeldjalil Ledmi

Acta Informatica Pragensia 2026, 15(3), 719-739 | DOI: 10.18267/j.aip.3266  

Background: In the field of distributed artificial intelligence, multi-agent systems (MAS) collaborate to resolve complex problems. To operate efficiently, these systems require effective coordination mechanisms. Metaheuristic algorithms are now standard for decentralized optimization and agent task distribution.Objective: This study evaluates the strengths and limitations of common metaheuristics used for MAS path planning and task coordination. Additionally, it introduces comparative simulation studies to evaluate the performance of baseline and dynamic metaheuristic methods specifically in MAS path planning.Methods: This paper presents...

Miscellanea

Operationalising Large Language Models for Financial Sentiment Analysis: The PipeFinePT Modular Pipeline

Gonçalo Carnaz, João M. Carvalho, Rui Pedro Marques

Acta Informatica Pragensia 2026, 15(3), 740-754 | DOI: 10.18267/j.aip.328171  

Background: Financial decision-making increasingly relies on automated analysis of large volumes of textual information. However, financial sentiment analysis resources remain scarce for low-resource linguistic contexts such as European Portuguese, limiting reproducible evaluation and adoption of advanced AI techniques within financial information systems.Objective: This study aims to design and evaluate a modular pipeline capable of operationalizing large language models (LLMs) for sentiment classification of Portuguese financial news while assessing performance, robustness, and deployment trade-offs across different model categories.Methods: A domain-specific...