Novel algorithm improves sample projection accuracy in linguistic landscape research, enhancing analyses of language patterns and diversity.
This study presents a novel approach to addressing methodological challenges in linguistic landscape (LL) research through the development of an advanced algorithm for language pattern sample projection. Leveraging fuzzy number-based generation with Mersenne number principles, our proposed algorithm offers a sophisticated solution for projecting the number of language signs in diverse LL contexts. Through a comparative analysis with existing methodologies, we highlight the advantages of our approach, such as exponential growth potential with avoidance of language pattern repetitions. Experimental results demonstrate the efficacy and reliability of our proposed algorithm in accurately capturing linguistic diversity within LL environments, contributing to improved methodological objectivity in LL research. We recommend the widespread adoption of our algorithms to enhance linguistic analyses in public spaces and advance our understanding of language dynamics and sociocultural interactions.
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Kawonga et al. (2025) studied this question.
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