Recently, the tourism trend has been shifting towards the Tourism 2.0 paradigm due to increased travel experiences and the increase in acquiring and sharing information through the Internet. The Tourism 2.0 paradigm requires developing intelligent tourism service tools for positive effects such as time savings and marketing utilization. Existing tourism service tools recommend tourist destinations based on the relationship between tourists and tourist destinations or tourism patterns, so it is difficult to make recommendations in situations where information is insufficient or changes in real time. In this paper, we propose a real-time recommendation system for tourism (R2Tour) that responds to changing situations in real time, such as external factors and distance information, and recommends customized tourist destinations according to the type of tourist. R2Tour trains a machine learning model with situational information such as temperature and precipitation and tourist profiles such as gender and age to recommend the top five nearby tourist destinations. To verify the recommendation performance of R2Tour, six machine learning models, including K-NN and SVM, and information on tourist attractions in Jeju Island were used. As a result of the experiment, R2Tour was verified with accuracy of 77.3%, micro-F1 0.773, and macro-F1 0.415. Since R2Tour trains tourism patterns based on situational information, it is possible to recommend new tourist destinations and respond to changing situations in real time. In the future, R2Tour can be installed in vehicles to recommend nearby tourist destinations or expanded to tasks in the tourism industry, such as a smart target advertising system.

Recent research in AI-based user identification using biometrics is actively underway. User identification based on biometrics is widely used in various industries and energy sectors because it is challenging to counterfeit or tamper with. AI models extract features from a user's bio information and perform identification. However, AI models are vulnerable to adversarial attacks that exploit weight information, resulting in misclassification and reduced reliability, particularly for specific biometrics. Adequate training data is also essential. In this paper, we propose a multi-biometric-based user identification system that is robust against adversarial attacks and effective with limited training data. We evaluate user identification performance based on the multi-modal fusion method. Our proposed method consists of feature extraction from biometrics and fusion for multi-biometric user identification. When evaluating user identification performance using data collected from CASIA-BIT, including face, iris, palmprint, and fingerprint data, our proposed method demonstrates superior accuracy of 24% and an F1-score of 0.3033 compared to single-biometric approaches. Furthermore, when using quad-biometrics, our method improved accuracy of 31% and an F1-score of 0.3717 and an F1-score of 0.3717 compared to dual-biometrics, with an 88.33% faster training speed.