With the deep cross-border integration of tourism and big data, the personalized demand of tourist groups is increasingly strong. Precision marketing has become a new marketing mode that the tourism industry needs to pay close attention to and explore. Based on the advantages of big data platform and location-based service, starting from the precise marketing demand of tourism, we design data flow mining technology framework for user’s mobile behavior trajectory based on location services in mobile e-commerce environment to get user track data that incorporates location information, consumption information, and social information. Data mining clustering technology is used to analyze the characteristics of users’ mobile behavior trajectories, and the precise recommendation system of tourism is constructed to provide support for tourism decision making. It can target the tourist group for precise marketing and make tourists travel smarter.
This paper proposes an SFNN (a sales factor model using a neural network), which uses a backpropagation multilayer perceptron neural network and weight matrix operation, to study the mechanism of the influencing factors of online product sales in the e-commerce platform. To achieve this objective, this study analyzes the factors and relative strength of online product sales based on four aspects: online reviews, review system curation, online promotional marketing, and seller guarantees. The empirical analysis of the SFNN model based on the data of Taobao.com shows whether the 14 factors, in relation to the four aspects, have any impact on product sales. In addition, the findings indicate that the number of sentiment words greatly affects product sales. Other factors affecting online product sales significantly include the review volume, the number of uploaded pictures, the negative review rate, the discount rate, 7+ day returns and money-back guarantees, and the freight insurance. This study examines the interactions among the various factors affecting product sales on the e-commerce platform and provides management inspiration for ecommerce enterprises to manipulate online reviews, undertake effective promotion and fulfill after-sales promises.
The accurate segmentation of cervical cell images is one of the key steps of the cervical cancer computer-aided diagnosis system. For the problem of overlapping cell and boundary blurring in cervical cell clusters, the researchers propose a segmentation algorithm based on the nuclear radial boundary enhancement for overlapping cell of cervical cytology images. This method not only suppresses the noise of cervical cytology images but also preserves the contrast of overlapping cell boundary. The researchers generate the weight graph by the candidate contour points and contour line segment attributes and utilize the dynamic programming algorithm to find the shortest path in the weight graph. The shortest path corresponds to the coarse segmentation contour in the cell image. The level set model is used to finely segment the obtained coarse cell segmentation boundary, so as to obtain the final cervical cell boundary. Through the quantitative and qualitative evaluation results, such as dice similarity coefficient, true positive rate, and false positive rate, it can be seen that the overlapping cell segmentation algorithm in this paper has achieved better segmentation results. Compared with other current overlap cell segmentation algorithms, the segmentation results obtained in this paper have greater advantages.
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