Multilayer Perceptron

Purpose: Electronic Tourism is one of the important components of expanding Tourism by synchronizing this industry with information technology. It has not been long since its emergence. Methodology: this field is a combination of tourism and information technology that is one of the most common types of income-generating businesses which is producing job opportunities in the modern world. The advancement of science alongside communication and information technologies presented many opportunities and threats to this field due to tech such as smartphones and sensors, virtual and augmented reality tools, NFC, RFID, etc. Findings: The disclosure of the tourists' information and the possible abuse of it is one such threat. Therefore privacy and non-disclosure of information should be important factors. Recognition of reputable sites is an important factor in solving this problem. In this study, we have presented a model for recognizing fake and phishing sites which use the CFS+PSO and a combination of Info+Ranger alongside their results to reduce the test dataset features so that it could present a model for categorizing and higher accuracy in recognizing phishing sites by using the Multilayer Perceptron method. The proposed model was successful in recognizing 95.5% of phishing sites. Counclusion: The effect of information technology on the tourism industry and the usage of internet websites for selling and providing tourism services to tourists have created new security challenges. Protecting the privacy and personal information of people and tourists is one of these challenges and the disclosure of such information could lead to abuse by unqualified people and dissatisfaction and distrust of such systems.

The functional logic of classifier models is based on the principle that, to maximize their ability to generalize—an essential factor affecting decision quality in real-world problems—it is crucial to minimize the classification error rate of available historical data. In other words, accuracy is considered the only factor affecting the generalizability of classification methods. However, due to fluctuations in financial variables, stable and reliable forecasts are also necessary for correct and profitable decision-making. Despite the importance of the reliability factor in creating stable and robust results, it has been neglected in the literature on modeling and classification. To address this research gap and enhance decision-making processes in financial applications, a modeling method based on reliability maximization is presented. This paper develops a multilayer perceptron model with the aim of maximizing reliability rather than accuracy. To evaluate the performance of the proposed model, five different financial datasets are selected from the UCI database, and its classification error rate is compared with that of the conventional multilayer perceptron model. The findings show that the reliability factor has a greater impact than the accuracy factor on the generalizability and performance of classification models. The results indicate that the proposed reliability-based multilayer perceptron model demonstrates superior efficiency and performance compared to the conventional multilayer perceptron model and can serve as a viable alternative in financial applications.