Informatica

A new population-based metaheuristic called Zebrafish Optimization Algorithm (ZFO) is proposed to find the global optimum solution using foraging behaviour of zebrafish larvae (Danio rerio). In ZFO, an exploration and exploitation trade-off is realized by simulation of exploratory dispersion, collaborative shoaling and directional searching. The movement strategy incorporates a stagnation-avoiding reinitialization mechanism, ensuring that diversity is maintained and premature convergence is avoided. To demonstrate the efficiency of ZFO, 23 benchmark functions including unimodal, multimodal and fixed dimension multimodal functions are tested and their optimal solutions are searched. Results obtained by ZFO are compared with existing metaheuristic algorithms, namely Cuttlefish Optimization, Jellyfish Search, Pufferfish Optimizer and Krill Herd algorithm. The Friedman rank test and Wilcoxon signed-rank test show that ZFO yields better-quality solutions than the compared algorithms in terms of convergence speed and robustness. The novel characteristic of ZFO is that it combines biologically inspired zebrafish dynamics and adaptive diversification strategy to enhance the capability of global search.

Propaganda techniques are a key tool for creating misleading content, often disseminated in native languages to increase their impact. Therefore, it is increasingly important to develop detection models not only for high-resource languages but also for low-resource languages, which still face significant limitations in propaganda detection. This study presents the first approach to automated propaganda technique detection in Lithuanian using the HALT-PROP corpus. We adapt the standard framework to account for frequent overlap between techniques. Experiments with the Lithuanian transformer LT-MLKM-modernBERT show that BILOU tagging improves span identification, while sentence classification based on span-level information enhances technique detection for most techniques. The results also indicate that training separate binary classifiers is more effective than multi-label classification in this setting. Overall, the proposed approach outperforms GPT-5.3 on most techniques and provides a strong baseline for propaganda technique detection in Lithuanian.

Limited proficiency in sign language creates communication barriers, motivating the development of robust Automatic Sign Language Recognition (SLR) systems. We address isolated SLR in a low-resource setting using few-shot metric-based meta-learning. Sign videos are encoded with spatiotemporal convolutional backbones and classified using a prototypical network, enabling generalization to unseen classes from small support sets. We compare the SlowFast architecture with state-of-the-art video models on the LSA64 benchmark under strict class-disjoint protocols. SlowFast achieves 94.33% accuracy, outperforming competing backbones and demonstrating an effective and data-efficient approach for low-resource isolated SLR.

Lean Six Sigma (LSS) is defined as an innovative business strategy for achieving operational excellence through continuous improvement in the manufacturing sector. By embracing LSS principles, manufacturers can create an adaptable and capable system to preserve a competitive positioning, while reducing waste and defects in the business processes. The integration of sustainability with LSS has contributed to the upward attention among scholars and practitioners worldwide by advancing knowledge of how manufacturers can improve their sustainable performance through LSS practices. For any manufacturing firm, the challenge lies in exploring enablers that support successful adoption of sustainable LSS. Consequently, this study aims to develop an intuitionistic fuzzy decision-making framework for identifying and assessing the enablers influencing an integrated sustainable LSS in electric manufacturing companies. The proposed framework integrates the Weight by Envelope and Slope (WENSLO) and Modified Preference Selection Index (MPSI) models taking into account the developed score and distance formulae under the setting of intuitionistic fuzzy sets. Using an integrated intuitionistic fuzzy WENSLO-MPSI model, this study further evaluated thirteen sustainable LSS enablers of five electric manufacturing companies, followed by sensitivity and comparative analyses. The findings indicated that “Linking SLSS to business strategies”, “Green design principles” and “Effective scheduling” are the most significant enablers to implement sustainable LSS in an electrical manufacturing company.

Agile methodology follows the Agile Manifesto, encompassing principles, frameworks, and tools for implementation. Selecting an appropriate agile method is a complex multi-criteria decision problem. To address uncertainty objectively, this study employs rough number theory, while Copula-Dombi aggregation operators preserve information and capture interrelationships. A group decision-making framework is developed, with criteria weights derived using cross-entropy and dispersion measures. A case study is conducted to demonstrate the applicability of the proposed framework. The results indicate Dynamic System Development Model as the most suitable method, while project vision and customer involvement emerged as the most influential criteria, demonstrating robustness and practical relevance.

This study proposes an integrated AI-driven quantum spherical fuzzy decision framework for multi-criteria evaluation under uncertainty. The model combines AI-based decision-maker weighting, quantum spherical fuzzy Bayesian networks for criteria weighting, and WASPAS for ranking. Decision makers are clustered using k-means to reduce bias, while interdependencies and uncertainty are captured probabilistically. The framework is applied to assess renewable energy investment competencies in G7 economies. Results highlight the importance of customer-centric expectations and real-time financial performance. The model offers a flexible, robust, and scalable approach, improving reliability, transparency, and decision quality in complex environments.

Multi-Criteria Decision-Making (MCDM) is a vital tool for handling complex decision problems under uncertainty. Fuzzy set theory and its extensions, such as Single-Valued Neutrosophic Sets (SVNS), enhance decision-making by addressing ambiguity, indeterminacy, and partial information. Among MCDM techniques, TOPSIS has gained prominence for ranking alternatives, and its integration with some MCDM approaches has been widely applied. However, no prior study has combined the Analytic Hierarchy Process (AHP) with Neutrosophic-TOPSIS. This study proposes a hybrid AHP-SVNS-TOPSIS framework, where AHP determines the weights of evaluation criteria, and Neutrosophic-TOPSIS ranks alternatives under uncertain conditions. The model is applied to assess hydropower plant (HPP) performance, considering impacts from urbanization, climate change, and machine failures. The generator’s efficiency is the most important parameter, based on the results of the suggested model. Existing research validates the outcomes of the suggested model.

The notion of quasi-closed element plays a central role in several branches of mathematics and computer sciences, for instance, in the Duquenne-Guigues basis of attribute implications. This paper deals with the extension of quasi-closed elements to the fuzzy setting by extending the well-known characterisation of quasi-closed elements in the crisp case, which is given in terms of closure systems. Specifically, we provide two distinct definitions, one considering crisp closure systems and another for fuzzy ones. Finally, we obtain a characterisation for each one of these notions.

The purpose of this study is to evaluate the significant dimensions of customer-centric innovation in renewable energy projects. We construct a novel fuzzy decision-making model to address this objective. In the first stage, significant indicators are identified using balanced scorecard-based determinants and weighted through the multi-step wise weight assessment ratio analysis (M-SWARA) method integrated with quantum spherical fuzzy sets. In the second stage, the energy efficiency of renewable energy alternatives is assessed for customer-centric innovation performance via technique for order preference by similarity to ideal solution (TOPSIS). We offer priority strategies for green energy investors to enhance customer-centric innovation with more reasonable costs. Methodologically, the proposed model provides important advantages by effectively handling uncertainty through quantum spherical fuzzy structures, incorporating the golden ratio in degree calculations, and capturing interdependencies among criteria through the improved M-SWARA approach. The findings reveal that customization is the most critical indicator for improving customer-centric innovation performance, followed by efficiency, while optimization and innovation have relatively lower importance. The ranking results indicate that solar energy projects demonstrate the highest performance in managing customer-centric innovation, followed by wind and geothermal energy alternatives.

Taking into account the irrational elements and regret aversion of decision makers (DMs) during the decision-making process, regret theory (RT) and the TODIM methods have been integrated into a decision-making framework to develop an enhanced multi-attribute decision-making (MADM) method (PDHL-RT-TODIM) within probabilistic double hierarchy linguistic (PDHL) environment. Specifically, extending the perceived utility function in RT to determine the regret and joy values of the overall advantage flow of alternatives calculated by TODIM method in PDHL environment. Then, a correlation coefficient (CC) and standard deviation (SD) integral (CCSD) method was created using the probabilistic double hierarchy linguistic set (PDHLTS) distance metric and PDHL weight arithmetic operator to establish the objective weights of attributes. Additionally, the effectiveness of this proposed method was illustrated through numerical examples for information system investment project selection, and its stability, efficiency, and benefits were further confirmed through sensitivity analysis and comparisons with existing methods.