Big Data and Cognitive Computing

Metaheuristic optimization algorithms have demonstrated their effectiveness in solving complex optimization tasks, such as those related to Intrusion Detection Systems (IDSs). It was widely used to enhance the detection rate of various types of cyber attacks by reducing the feature space or tuning the model’s hyperparameters. The Artificial Rabbit Optimizer (ARO) mimics rabbits’ intelligent foraging and hiding behavior. The ARO algorithm has seen widespread adoption in the optimization field. The widespread use of the ARO algorithm occurs due to its simple design and ease of implementation. However, ARO can get trapped in local optima due to its limited diversity in population dynamics. Although the transition between phases is managed via an energy shrink factor, fine-tuning this balance remains challenging and unexplored. These limitations could limit the ARO algorithm’s effectiveness in high-dimensional space, as with IDS systems. This paper introduces a novel enhancement of the original ARO by integrating Fuzzy Rule Interpolation (FRI) to compute the energy factor during the optimization process dynamically. In this work, we integrate the FRI along with the ARO algorithm to improve solution accuracy, maintain population diversity, and accelerate convergence, particularly in high-dimensional and complex problems such as IDS. The integration of the FRI and ARO aimed to control the exploration-exploitation balance in the IDS application area. To validate our proposed hybrid approach, we tested it on a diverse set of intrusion datasets, covering eight different benchmark intrusion detection datasets. The suggested hybrid approach has been demonstrated to be effective in handling various intrusion classification tasks. For binary intrusion classification tasks, it achieved accuracy rates ranging from 96% to 99.9%. In the case of multiclass intrusion classification tasks, the accuracy was slightly more consistent, falling between 91.6% and 98.9%. The suggested approach effectively reduced the number of feature spaces, achieving reduction rates from 56% up to 96%. Furthermore, the proposed approach outperformed other state-of-the-art methods in terms of detection rate.

Large language models (LLMs) are now routine writing tools across various domains, intensifying questions about when text should be treated as human-authored, artificial intelligence (AI)-generated, or collaboratively produced. This rapid review aims to identify cue families reported in empirical studies as distinguishing AI from human-authored text and to assess how stable these cues are across genres/tasks, text lengths, and revision conditions. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we searched four online databases for peer-reviewed empirical articles (1 January 2022–1 January 2026). After deduplication and screening, 40 studies were included. Evidence converged on five cue families: surface, discourse/pragmatic, epistemic/content, predictability/probabilistic, and provenance. Surface cues dominated the literature and were the most consistently operationalized. Discourse/pragmatic cues followed, particularly in discipline-bound academic genres where stance and metadiscourse differentiated AI from human writing. Predictability/probabilistic cues were central in detector-focused studies, while epistemic/content cues emerged primarily in tasks where grounding and authenticity were salient. Provenance cues were concentrated in watermarking research. Across studies, cue stability was consistently conditional rather than universal. Specifically, surface and discourse cues often remained discriminative within constrained genres, but shifted with register and discipline; probabilistic cues were powerful yet fragile under paraphrasing, post-editing, and evasion; and provenance signals required robustness to editing, mixing, and span localization. Overall, the literature indicates that AI–human distinction emerges from layered and context-dependent cue profiles rather than from any single reliable marker. High-stakes decisions, therefore, require condition-aware interpretation, triangulation across multiple cue families, and human oversight rather than automated classification in isolation.

Active athletes represent a specific target for learning and development. Their schedules, including training sessions and competitions, leave little time for education. However, athletes still need skills beyond sports to ensure they are prepared for future employment. Our study approaches this issue by identifying appropriate settings for athletes’ learning and development. (1) Based on the background of current athletes’ education, it addresses the gap of not enough attention being paid to transferable practices from corporate attitudes to learning and development. (2) The study’s methodology primarily uses the case study concept because this conveys the video content we created for the athletes’ learning and development. This is combined with the method of content analysis of selected examples from corporate learning and development and the design thinking workshop, with the engagement of important stakeholder groups: athletes (2 participants), lecturers (2 participants), and representatives of sports organizations (1 participant). The other 9 workshop participants were master’s students in a managerial study programme because of their age similarities with the current athletes and the applicability of the courses they were studying to athletes’ education. (3) The designed process was created as a digital twin using haptic artefacts and the S2M technology (version 1.0) within the OMiLAB platform (version 1.6). Our results show that video content tailored to the athletes’ constraints is a viable solution that improves their career prospects. (4) The study’s practical implications are supported by the expert validation of the model provided by the inside of the large sports organizations’ management.