In a world rich with linguistic-valued data, traditional methods often lead to significant information loss when converting such data into other formats. This paper presents a novel approach for constructing an interval linguistic-valued intuitionistic fuzzy concept lattice, which adeptly manages qualitative linguistic information by leveraging the strengths of interval-valued intuitionistic fuzzy sets to represent both fuzziness and uncertainty. First, the interval linguistic-valued intuitionistic fuzzy concept lattice is constructed by integrating interval intuitionistic fuzzy sets, capturing the bidirectional fuzzy linguistic information between objects, which encompasses both positive and negative aspects. Second, by analyzing the expectations of concept extent relative to intent, and considering both the membership and non-membership perspectives of linguistic expressions, we focus on the extraction of linguistic association rules. Finally, comparative analyses and examples demonstrate the effectiveness of the proposed approach, showcasing its potential to advance the management of linguistic data in various domains.

This paper introduces the MMTADAN, an innovative algorithm designed to enhance cross-domain image classification. By integrating multi-scale feature extraction with Taylor series-based detail enhancement and adversarial domain adaptation, the MMTADAN effectively aligns features between the source and target domains. The proposed approach addresses the critical challenge of generalizing classification models across diverse datasets, demonstrating significant improvements in performance. The findings suggest that retaining essential image details through multi-scale extraction and Taylor series enhancement can lead to better classification outcomes, making the MMTADAN a valuable contribution to the field of image classification.