The intention of this paper is to propose some similarity measures between Fermatean fuzzy sets (FFSs). Firstly, we propose some score based similarity measures for finding similarity measures of FFSs and also propose score based cosine similarity measures between FFSs. Furthermore, we introduce three newly scored functions for effective uses of Fermatean fuzzy sets and discuss some relevant properties of cosine similarity measure. Fermatean fuzzy sets introduced by Senapati and Yager can manipulate uncertain information more easily in the process of multi-criteria decision making (MCDM) and group decision making. Here, we investigate score based similarity measures of Fermatean fuzzy sets and scout the uses of FFSs in pattern recognition. Based on different types of similarity measures a pattern recognition problem viz. personnel appointment is presented to describe the use of FFSs and its similarity measure as well as scores. The counterfeit results show that the proposed method is more malleable than the existing method(s). Finally, concluding remarks and the scope of future research of the proposed approach are given.

Explosive material detection considers the identification and classification of explosive materials using techniques from traditional sniffer dogs to cutting-edge sensing technology like thermal imaging, X-ray scanners, and chemical sensors. Explosive detection is applied in various locations, including airports, government buildings, and public areas, to prevent terrorist attacks and criminal actions that attempt to employ explosive devices. The effectiveness of these procedures is dependent on the detection materials, equipment, and environment, so new techniques are continuously explored to increase precision, sensitivity, and detection speed. Because explosive substances present a critical risk to infrastructure, security, and public safety, extensive analysis of existing detection methods is needed. This paper highlights key areas for further research and development in explosive materials detection by addressing identified limitations and challenges. Specifically, advancements in technology, interdisciplinary collaboration, and the integration of AI techniques offer significant opportunities for improving detection accuracy, reducing false positives, and ensuring safer environments for individuals and society.

Control chart pattern (CCP) recognition can act as a problem identification tool in any manufacturing organization. Feature-based rules in the form of decision trees have become quite popular in recent years for CCP recognition. This is because the practitioners can clearly understand how a particular pattern has been identified by the use of relevant shape features. Moreover, since the extracted features represent the main characteristics of the original data in a condensed form, it can also facilitate efficient pattern recognition. The reported feature-based decision trees can recognize eight types of CCPs using extracted values of seven shape features. In this paper, a different set of seven most useful features is presented that can recognize nine main CCPs, including mixture pattern. Based on these features, decision trees are developed using CART (classification and regression tree) and QUEST (quick unbiased efficient statistical tree) algorithms. The relative performance of the CART and QUEST-based decision trees are extensively studied using simulated pattern data. The results show that the CART-based decision trees result in better recognition performance but lesser consistency, whereas, the QUEST-based decision trees give better consistency but lesser recognition performance.