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Neutrosophic topological spaces (NTS) offer a novel framework for uncertainty modeling by incorporating degrees of truth, indeterminacy, and falsity. This paper investigates the potential applications of NTS in computer science. We provide background on neutrosophic sets and their extension to topological spaces. We then explore how NTS could be used for uncertainty modeling in data analysis (e.g., handling noisy data in sensor networks), pattern recognition (e.g., improving image classification with imprecise features), and information retrieval (e.g., enhancing search results by considering relevance uncertainty). We discuss the challenges associated with applying NTS and highlight promising areas for future research, such as developing efficient algorithms for NTS operations. Overall, this paper aims to stimulate further exploration of how neutrosophic topological spaces can contribute to advancements in various computer science domains.
Salama et al. (Sun,) studied this question.