Informatica

Accurately differentiating between fungal and bacterial infections is critical for treatment but remains challenging due to slow manual diagnostics and class imbalance in medical datasets. This paper introduces OD-Abs, a one-class outlier detection classifier based on an autoencoder architecture to distinguish infection types in microscopic images. By training solely on the reference fungal class, the model learns to identify bacterial instances as outliers. Our approach yields statistically significant improvements compared to other one-class approaches, achieving an F1-score of 0.967, an AUC of 0.980, and a specificity of 0.992. Additionally, we explore the explainability of our method by analysing reconstruction error maps from both a technical and biological perspective and conclude that the model’s decision-making aligns with biologically relevant morphological features. This proof of concept study establishes the OD-Abs autoencoder model as a highly robust tool for handling the task of classifying images of bacteria and fungi from abscesses.

The field of Quantum Genetic Algorithm (QGA) has seen extensive efforts to develop reasonable algorithmic designs mainly due to its potential as well as the intrinsic difficulty to reproduce the necessary processes with the limited amount of quantum resources available, although it still remains one of rather theoretical concepts of approach to achieving quantum optimization. In this paper, we propose a dynamic encoding scheme that combines quantum adaptive search with iterative approximation of the search region. The method reuses the same quantum index register while updating the classical coordinate mapping associated with its basis states, thereby increasing local coordinate resolution without globally refining the entire continuous domain. Through noiseless statevector simulations on benchmark functions, we compare the proposed method with selected QGA variants in terms of final optimization accuracy and simulated qubit usage. The results show improved optimization performance under similar maximum qubit constraints, establishing a simulation-level resource advantage.

This paper firstly distinguishes between risk and Risk Factor (RF), as fundamental concepts in risk management. The need for the current study lies in the fact that although there are various risk analysis methods, but there is no well-defined frameworks for RF analysis. Manifestly, most of the researches on RF have concentrated on identifying them, expressing relationships among them, and descriptive discussion on their root causes, rather than numerically analysing them. Additionally, most of the risk-related methods (e.g. the classical Failure Mode and Effects Analysis (FMEA)) are very sensitive to nuanced changes on inputs. Thus, the paper is to design a robust RF analysis method. The paper proposes a new method called Risk Influential Factor Analysis (RIFA) for evaluation of RFs. Additionally, for getting reliable results, weighting RFs is done by a novel idea named Weight Estimation by Steady Trend (WEST). The WEST is established on the basis of a well-known branch of Multiple Attribute Decision-Making (MADM), called surrogate weighting. Therewith, in analysis section, the paper shows that (I) employing the suggested hybrid RIFA-WEST methodology leads to robust results, (II) compared to the classical FMEA, the RIFA has not many drawbacks to it, and (III) the WEST is comparable with many of the existing surrogate weighting methods. The proposed methodology is applied in a case study from one of the upstream oil engineering disciplines, i.e. Chemical Enhanced Oil Recovery (CEOR).

The research introduces a credible decision-support tool for dealing with uncertainty for supplier selection in the aerated concrete industry. The developed framework includes criteria selection, determination of expert and criterion weights, and alternative ranking within a hyperbolic fuzzy environment. Criteria selection is done via the Laplacian score. Expert weights are methodically determined via entropy measure. Criteria are weighted using the LOPCOW and RANCOM methods, and alternatives are ranked using the hyperbolic extension of the DEPART method. The model is employed to solve a circular supplier selection problem in the aerated concrete industry. Comprehensive sensitivity and comparison checks are conducted.

Determining criteria importance is a crucial task in multi-criteria decision-making problems, and the Best-Worst Method (BWM) has emerged as an effective weighting technique due to its reduced number of pairwise comparisons. Although BWM has been extended to handle group decision-making and linguistic information, existing approaches do not adequately address disagreements among evaluators, which may lead to dissatisfaction with the resulting criteria weights. To address this limitation, this paper proposes a consensus-based BWM for linguistic multi-criteria group decision-making. The proposed approach obtains consensual collective weights by minimally modifying the evaluators’ initial linguistic preferences. The resulting solution can support moderators and evaluators during consensus-reaching processes by facilitating the identification of disagreements and the generation of appropriate recommendations. The proposal is developed within the 2-tuple linguistic framework and provides both numerical and linguistic representations of the resulting weights. In addition, a novel stochastic consistency index is introduced to assess the reliability of evaluators’ preferences. The feasibility and effectiveness of the proposal are illustrated through a real-world case study and analysed by means of comparative and sensitivity analyses.

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.

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.