Yeleny Zulueta-Veliz

A linguistic dynamic decision making problem reveals situations in which the decision data gathered in multiple periods is represented by means of linguistic values. To deal with linguistic variables in linguistic dynamic decision making problems, the 2-tuple linguistic model stands out among computational models because of its accuracy and interpretability. The selection of a suitable time-dependent 2-tuple linguistic aggregation operator is relevant due to its properties can highly modify the computing cost as well as results themselves and their accuracy and interpretability. This paper proposes a new 2-tuple linguistic dynamic hybrid weighted aggregation operator which is suitable to model different attitudes in decision making by simultaneously weighting the given arguments as well as their ordered positions. The novel 2-tuple Linguistic Dynamic Ordered Weighted Averaging–Weighted Average operator weights not only the importance of a particular time period, but also the importance of non-dynamic evaluations in such a time period. Eventually, a practical example is provided to illustrate the developed approach and to demonstrate its practicality and effectiveness.

Group decision-making is an inherently collaborative process that can become increasingly complex when addressing the uncertainty associated with linguistic assessments from experts. A crucial principle for achieving a solution of superior quality lies in the acknowledgment that the same word may bear divergent meanings among different experts. Regrettably, a significant number of existing methodologies for computing with words presuppose a uniformity of meaning for linguistic assessments across all participating individuals. In response to this limitation, we propose an innovative methodology based on the 2-tuple linguistic model in conjunction with the granular computing paradigm. Given that the individual interpretations of words, when articulating preferences, are closely linked to the consistency of each expert, our proposal places particular emphasis on the modification of the symbolic translation of the 2-tuple linguistic value with the overarching objective of maximizing the consistency of their assessments. This adjustment is implemented while preserving the original linguistic preferences communicated by the experts. We address a real-world building refurbishment problem and conduct a comparative analysis to demonstrate the effectiveness of the proposal. Focusing on consistency enhances group decision-making processes and outcomes, ensuring both accuracy and alignment with individual interpretations and preferences.

The main stream of Linguistic Data Summarization involves modeling numerical attributes using linguistic variables, which makes it difficult addressing real-world problems with qualitative or mixed data. Literature highlights challenges such as the limited expressiveness of summaries from classical protoforms, the need to explore relationships between them to find more useful patterns and refining language to improve their interpretability and usefulness. The latter is increasingly significant as the demand for explainability grows with the rise of black-box AI applications. This paper proposes SUMMARIA, an explainable AI approach for generating composite (enriched) linguistic summaries of qualitative data. A framework is formalized integrating Linguistic Data Summarization with the concept of rhetorical relation and defining the structure and quality metrics of a composite linguistic summary. Three abstract forms of composite linguistic summaries representing Evidence, Contrast and Emphasis relations are specified, inspired by Rhetorical Structure Theory. Also, a method based on Association Rule Mining, implements SUMMARIA in problem-solving via five algorithms. An empirical study tested SUMMARIA’s application on two judicial datasets and a substantially different behavior was found for four different scenarios, which reveals its sensitivity to the nature and distribution of the primary data. A human-expert validation was performed showing that the linguistic summaries are understandable and the relation type implicit in them is recognizable by the users.

To contribute to SDG-15 about the conservation of terrestrial ecosystems, the effective management of land resources is required. In this respect, determining the land use and cover (LUC) based on remote sensing constitutes a strength. For the Arimao watershed in the province Cienfuegos of Cuba, the main difficulty in determining the LUC is related to the topographic correction in the mountains of Trinidad. This study aims to validate the effectiveness of seven topographic correction methods using classification accuracy as a criterion. For this purpose, the mountain area was cut out on the Landsat-8 OLI image of December 2020, based on its physical–geographical and geological characteristics. Seven topographic correction algorithms were applied: Cosine correction, Improved cosine, C-correction, Minnaert, Minnaert with the slope, including Riano and others by Law, and the Normalization correction. To evaluate their performance, three criteria were used: visual interpretation, statistical analysis, and assessing classification accuracy taking into account eight cover classes. The obtained results showed a higher effectiveness of the Minnaert correction with slope and roughness coefficient k = 0.3, with an overall accuracy of 94.08%. The user and producer accuracies increased the performance for almost all forest classes. For the mountains of Trinidad, the non-forest classes were not affected by the topographic correction, so it was possible to apply the topographic correction algorithms to the entire area. The results have demonstrated the necessity of applying the criterion of accuracy assessment to select the best topographic correction.