## 2 Materials and Methods  
### Importance Analysis of Fuzzy AHP-Based Evaluation Criteria  
This study aims to support decision-making on the introduction of control services that can detect life safety abnormalities in a city through thermal cameras. One of the most common approaches is MCDM, which has been utilized to suggest objective priority alternatives based on evaluation criteria according to the defined purpose. MCDM is a proven technique that can present a priority alternative based on the understanding and knowledge level of the evaluation criteria, alternatives, and stratification structures. The typical methodology of MCDM is AHP. AHP hierarchically classifies a number of attributes and identifies the importance of each attribute in selecting an optimal alternative. AHP has the advantage of being able to structure alternatives in complex decision-making situations, and easily integrate quantitative/qualitative and subjective/objective factors based on the experience and knowledge of experts. AHP was developed by [PERSON] and supports decision-making through pairwise comparison between hierarchical elements [61]. Since [PERSON]'s study, numerous researchers have developed diverse derivative techniques. A representative example is Fuzzy AHP, which combines the fuzzy theory suggested by [PERSON] with AHP [62]. Fuzzy Theory is a concept that introduces fuzzy logic and fuzzy sets to overcome the inaccuracy and imprecision of subjective judgment during evaluation. Fuzzy AHP enables a mathematical expression of unclear phenomena (e.g., ambiguous quantitative information, and subjective and uncertain judgment) as well as deriving reasonable decision-making alternatives [3]. Although it is hard to make a direct comparison, some have shown that Fuzzy AHP is mathematically more reliable based on a comparative analysis between the classical method, AHP, and Fuzzy AHP [63]. Numerous studies have also derived priorities for technology/services by evaluating the reliability of the Fuzzy-AHP model [64; 65; 66; 67]. This study performs a relative importance analysis of evaluation criteria by considering the advantages of Fuzzy AHP. The Fuzzy AHP, as utilized in this study, is a supplemented calculation formula by [PERSON] and [PERSON] using a Triangular Fuzzy Number (TFN) based on [PERSON]'s AHP [68]. The triangular fuzzy number M used to calculate the weight was expressed by modifying \(M_{1}=(l_{1},\ m_{1},\ u_{1})\), the membership function of the lower, center, and upper values, to \((a_{1},\ a_{2},\ a_{3})\) to compare the pairs (Figure 1). Here, TFN is expressed in the form of a triangle with three points: (\(l\), \(m\), \(u\); \(l\) = lower limit, \(m\) = middle, \(u\) = upper limit), and their area is the size of TFN [66; 69; 70]. We utilized the fuzzy style outlined in Table 1, which was presented as a Fuzzy AHP extended analysis technique to derive TFN [3; 7; 66; 71; 72]. To derive evaluation criteria per class, the fuzzy scale in Table 1 was utilized to calculate the average value of weights according to pairwise comparison. By utilizing Equations (1)-(4), we derived the TFN \(S_{i}\) = (\(l_{i}\), \(m_{i}\), \(u_{i}\)) value of the \(i\)th attribute, and finally calculated the eigenvector normalization value of the minimum value for defuzzification of the TFN \(S_{i}\) per evaluation criterion [3; 67; 70]. Equation (1) is the sum of the relatively evaluated scores before fuzzification per item. Equation (2) is a triangular fuzzification according to the \(l\), \(m\), and \(u\) values according to the fuzzy scale. Equation (3) is the sum of the relatively evaluated items of each evaluation criterion for each value of \(l\), \(m\), and \(u\). Equation (4) is the final TFN value that takes the reciprocal of the sum of \(l\), \(m\), and \(u\).