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The epidemic situation is spreading repeatedly among people, accelerating the promotion of intelligent hotel. The mobile infrared temperature measurement robot can quickly and accurately complete the body temperature examination of groups and individuals in the scene of dense crowd flow, The AI products in the hotel can achieve "contactless" service and effectively reduce the risk of human contact. This study is based on SPSS data analysis of artificial intelligence background, hotel robot imports intention research, using quantitative research method for data analysis. The results show that when the customer's perception of the usability of the intelligent temperature measurement doorman robot is improved, the perceived usefulness will also be relatively improved, the more customers feel that the intelligent temperature measurement robot is useful, the better their attitude towards using it, and the better their willingness to use it. The results are helpful to understand the customers' real attitude and willingness to accept the trend of artificial intelligence in the hotel industry, and find out the optimal way to provide intelligent hotel operators with the method of using artificial intelligence.

Dilated capillaries are an important characteristic of basal cell carcinoma (BCC). Detecting capillaries in images can improve a computer-aided skin cancer diagnosis system. In this study, we investigate the feasibility to extract capillaries from clinical images of skin lesions recorded by a regular digital camera. First, we used a compact set of 1 curvilinear and 2 color parameters to train a support vector machine (SVM) classifier to identify capillary pixels. Second, the identified pixels were grouped by a region-growing algorithm to form capillary candidates. Last, the likelihood to be a true capillary was estimated based on the distance to the red color in the “CIE Lab” color space. The method was tested on a dataset of 21 BCC images with visible capillaries and 28 benign pigmented lesions without visible capillaries. The accuracy, sensitivity, and specificity of the proposed method were 89.8% (44/49), 90.5% (19/21), and 89.3% (25/28) respectively. We found capillaries recorded by a regular digital camera can be detected successfully.

This paper proposes an intelligent learning management system with load-balancing function based on Grid structure that allows for the replication of learning materials and balance of network traffic. The system consists of the Learning Management System (LMS) for processing basic data, learning data and learning records of learners, and the Learning Content Management Systems (LCMS) based on the Grid structure for managing and storing the related teaching materials. The Web Service cross-platform distribution configuration of this research provides common communications between systems and enhances the capability of integrating learning resources. The Grid architecture of the system ensures a load-balancing functionality for LCMS of different domains. The connection program embedded in the PC of the learner via LMS connects to the embedded Ganglia agent for access to required teaching materials from each LCMS. The LCMS with the minimum load is then selected from suitable LCMS as the source of the teaching materials. The proposed intelligent learning environment based on Grid structure is designed to improve the overload and the expansibility problems existing in the traditional decomposed LMS system.