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Detecting plagiarism poses a significant challenge for academic institutions, research centers, and content-centric organizations, especially in cases involving subtle paraphrasing and content manipulation where conventional methods often prove inadequate. Our paper proposes FTLM (Fuzzy TOPSIS Language Modeling), a novel method for detecting plagiarism within decision science. FTLM integrates language models with fuzzy sorting techniques to assess plagiarism severity by evaluating the similarity of potential solutions to a reference. The method involves two stages: leveraging language modeling to define criteria and alternatives and implementing enhanced fuzzy TOPSIS. Word usage patterns, grammatical structures, and semantic coherence represent fuzzy membership functions. Moreover, pre-trained language models enhance semantic similarity analysis. This approach highlights the benefits of combining fuzzy logic’s tolerance for imprecision with the semantic evaluation capabilities of advanced language models, thereby offering a comprehensive and contextually aware method for analyzing plagiarism severity. The experimental results on the benchmark dataset demonstrate effective features that enhance performance on the user-defined severity ranking order.

The purpose of this paper is to add some new asymptotic and oscillatory results for third-order neutral delay differential equations with noncanonical operators. Without assuming any extra conditions, by using the canonical transform technique, the studied equation is changed to a canonical type equation, and this reduces the number of classes of nonoscillatory solutions into two instead of four. Then, we obtain Myshkis type sufficient conditions for the nonexistence of Kneser type solutions for the studied equation. Finally, employing these newly obtained criteria, we provide conditions for the oscillation of all solutions of the studied equation. Examples are presented to illustrate the importance and the significance of the main results. [ABSTRACT FROM AUTHOR]