Tourism Information Data Processing Method Based on Multi‐Source Data Fusion

Li, YaoGuang; Gan, HeChi

doi:10.1155/2021/7047119

Cited by 10 publications

(9 citation statements)

References 28 publications

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“…To combine the neighbor information of the nodes, we choose the aggregation function [15]. If the neighborhood depth of the target node is k, the aggregation of its neighborhood information can be expressed as h k NðυÞ = max ðfσð W pool h k−1 u + = bÞ,∀u ∈ NðυÞgÞ.…”

Section: Associated User Mining Algorithm Integrating User Attributes...mentioning

confidence: 99%

Implementation of Network Data Mining Algorithm for Associated Users Based on Multi-Information Fusion

Zhang

2022

Journal of Sensors

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In order to accurately and effectively mine relevant users in social networks, we can stop false information and illegal activities in the network, thereby ensuring the safety and integrity of the network environment. A method is proposed for the implementation of a data mining algorithm of a user network based on the fusion of several data. AUMA-MRL (associated user mining algorithm based on multi-information representation learning) proposes an associated user mining algorithm based on node characteristics, neighborhood information, and global network structure information. The steps of the algorithm are as follows: combining each user of the social network into a node using a method where each node is installed separately and combining network user characteristics and user relationship information. A user pair is a network similarity vector that represents the similarity of users in different dimensions. Based on these similarity vectors, a corresponding user separation algorithm is formed. It examines the feasibility and efficiency of the AUMA-MRL algorithm for researching relevant users. The proportion of associated users in the network to be fused is lower than that of nonassociated users, and the prediction has little effect on improving the recall rate of nonassociated users, so the recall rate is slightly lower than the accuracy. This algorithm can quickly get the embedding of new nodes and the similarity vector between new nodes and other nodes in the network, so as to quickly mine the associated users of new nodes in the network and enhance the robustness of the network associated user mining algorithm.

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Section: Associated User Mining Algorithm Integrating User Attributes...mentioning

confidence: 99%

Implementation of Network Data Mining Algorithm for Associated Users Based on Multi-Information Fusion

Zhang

2022

Journal of Sensors

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“…Li and Gan built a multisource data fusion model based on an ensemble learning algorithm. e results show that, according to the results of multisource tourism information fusion, the model prediction accuracy after data processing is about 62% [14]. At present, there are few research studies on multidata fusion in children's movement evaluation system.…”

Section: Introductionmentioning

confidence: 95%

[Retracted] Children’s Motor Intelligence Evaluation System Based on Multi‐data Fusion

Zhao

2022

Contrast Media & Molecular Imaging

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Along with the main feedback information of early childhood education activities, children’s movement evaluation plays an important guiding role in early childhood education activities. Teachers can teach children in accordance with their aptitude according to the results of children’s sports evaluation. Traditional physical education overemphasizes the leading role of teachers, and young children can only passively receive the education of teachers. Teachers always use the same standard to evaluate each child, simply assigning a rating of “strong,” “moderate,” and “poor” to children, ignoring the differences in children’s physical intelligence. The emergence of multidata fusion technology can use the differences and complementarity of various data in evaluation functions to make up for the insufficiency of a single exercise evaluation result, purposefully choose the evaluation method flexibly according to the content of the exercise and the scene of the exercise, and gradually improve the level of children’s exercise evaluation. This paper studied the children’s motor intelligence evaluation system based on multidata fusion. To this end, this paper will focus on children’s autonomous sports games and determine the first-level indicators in the children’s sports evaluation index system as four indicators: classroom performance, physical fitness, motor skills, and extracurricular fitness. It used the principle of multidata fusion, firstly evaluates children’s exercise physiology data through a fuzzy neural network algorithm, and then combines the adaptive weighted data fusion algorithm with D-S evidence theory to evaluate children’s movement intelligence. Experiments showed that the multidata evaluation system can take effective measures to intelligently evaluate children’s comprehensive motor ability. Compared with the traditional evaluation method, the evaluation results are increased by 5%, and the children’s sports evaluation results are more average, which can enhance children’s sports confidence and promote children’s effective exercise.

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“…The development of expert-assisted teaching of artificial intelligence will also greatly promote the development of information technology, trying to make students get rid of the process of “tutoring learning” to accept new knowledge and adopt the method of “learning through activities” [ 9 ]. In the field of artificial intelligence technology, artificial intelligence technology usually uses machine learning to calculate the algorithm of tourism teaching system, and deep learning is the algorithm or method to realize machine learning [ 10 , 11 ]. Therefore, the academic circles regard artificial intelligence, machine learning, and deep learning as approximate inclusion relations.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Tourism Teaching System Based on Artificial Intelligence Technology

Xing

2022

Computational Intelligence and Neuroscience

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The tourism teaching system provides all kinds of teaching resources for students and shares good teachers, which greatly improves the quality of teaching and learning, and it enables students to teach and learn randomly in the system. The mode of educational information dissemination has also changed, resulting in great changes in educational models, ideas, and methods. The tourism teaching system overcomes the shortcomings of traditional education. In the 21st century, the means of education and teaching will be ITS-based, and technology will be used to optimize teaching. With the rapid development of network technology, it has been widely used in the field of education, and the way of educational information dissemination has also changed, which has led to great changes in educational models, concepts, and methods. Therefore, the combination of artificial intelligence technology and tourism teaching system is the focus of current educational research. In this paper, the tourism teaching system is designed based on artificial intelligence technology. Combined with the application status of the tourism teaching system in education, a personalized cognitive student model is designed by combining artificial intelligence technology with student behavior analysis, and an intelligent and personalized educational environment is developed. Design and implement a simple web-based tourism teaching system, use a personalized cognitive student model to model students, and use an expert question bank and self-built question bank to test students’ learning to provide a basis for students’ next learning.

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Tourism Information Data Processing Method Based on Multi‐Source Data Fusion

Cited by 10 publications

References 28 publications

Implementation of Network Data Mining Algorithm for Associated Users Based on Multi-Information Fusion

Implementation of Network Data Mining Algorithm for Associated Users Based on Multi-Information Fusion

[Retracted] Children’s Motor Intelligence Evaluation System Based on Multi‐data Fusion

Design and Implementation of Tourism Teaching System Based on Artificial Intelligence Technology

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