Perceived value is the customer’s subjective understanding of the value they obtain and is their subjective evaluation of the product or service they enjoy. This value is deducted from the cost of the product or service. In order to understand and predict the specific cognition of consumers on the value of products or services and distinguish it from the objective value of products or services in the general sense, this paper uses the in-depth learning method based on LSTM to establish a model to predict the perceived benefits of consumers. It is a challenging task to analyze the emotion of consumers or recognize the perceived value of consumers from various texts of online trading platforms. This paper proposes a new short-text representation method based on bidirectional LSTM. This method is very effective for forecasting research. In addition, we also use the attention mechanism to learn the specific emotional vocabulary. Short-text representation can be used for emotion classification and emotion intensity prediction. This paper evaluates the proposed classification model and regression data set. Compared with the baseline of the corresponding data set, the contrast of the results was 93%. The research shows that using deep neural network to predict the perceived utility of consumer comments can reduce the intervention of artificial features and labor costs and help predict the perceived utility of products to consumers.
Abstract-The innovation of business model driven by science and technology has become one of the key drivers to promote the leap-forward growth of economy. Through literature review, this paper analyzes the theory of scientific and technological innovation to drive the business model innovation logic, summarizes the Internet age, the age of the Internet, and before the typical business model intelligent era, in order to grasp the future direction of the business model innovation for the enterprise, make the management decision to provide corresponding theoretical reference.
Nowadays, airplane is the fastest transportation tool, and more and more passengers choose it when they are traveling. so, Airport security analysis can help to upgrade Airport security system. For analyzing Airport security system, the Model of M / M / c queuing system in Queuing Theory is built to explore the flow of passengers in security screening system. The data obtained from reality in reference are linear fitted and the Gray Model is employed to analyze the data, and the average service rate of different Zones is obtained. In addition, added The principle of human traffic to make the model more complete.
The mathematical model based on Queuing TheoryAirport Security Screening Process Analysis. Referring to literatures and our boarding experience at airport, we make the interpretations of Figure 1 as below: The ID Check channel is in one-to-one relationship with the subsequent detection channel. That is, the ID Check channel can't be detected by another subsequent detection channel And then, Passengers are ready to do Millimeter Wave Scan and X-ray Scan, where they should remove the package of electronic products and shoes, belts, jackets in a advance. (Pre-Check travelers would not remove the shoes, belts, jackets) During the Millimeter Wave Scan and X-ray, passengers should go for a tap check in Zone D if there is a threat. If there is not a threat, passengers could pass the TSA Security Screening, and then waiting for their plane.
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