scholarly journals An Automobile Noise Prediction Model Based on Extension Data Mining Algorithm

2019 ◽  
Vol 33 (5) ◽  
pp. 341-347 ◽  
Author(s):  
Hui Lu ◽  
Tichun Wang
Keyword(s):  
Data Mining ◽  
Prediction Model ◽  
Data Mining Algorithm ◽  
Noise Prediction ◽  
Model Based ◽  
Mining Algorithm

Algorithm applied: attracting MSEs to business associations

2020 ◽  
Vol 35 (1) ◽  
pp. 13-22 ◽  
Author(s):  
Jaqueline de Moraes ◽  
Jones Luís Schaefer ◽  
Jacques Nelson Corleta Schreiber ◽  
Johanna Dreher Thomas ◽  
Elpidio Oscar Benitez Nara
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Management Tool ◽  
Data Mining Algorithm ◽  
Business Associations ◽  
Structured Model ◽  
Content Type ◽  
Model Based ◽  
Mining Algorithm

Purpose This paper aims to propose a structured model based on a data mining algorithm that can calculate, based on business association (BA) attributes, the probability of micro and small enterprises (MSEs) becoming a new member of a BA. Another goal is the probability of a BA attracting new members. Design/methodology/approach As a methodological procedure, the authors used the Naive Bayes data mining algorithm. The collected data were analyzed both quantitatively and qualitatively and then used to define the model, which was tested randomly, while allowing for the possibility of future validation. Findings The findings suggest a structured model based on a data mining algorithm. The model can certainly be used as a management tool for BAs concentrating their efforts on those businesses that are certainly potential new recruits. Further, for an MSE, it serves as a means of evaluating a BA, indicating the possible advantages in becoming a member of a particular association. Research limitations/implications This paper is not intended to be generalized, considering that it only analyzes the BAs of Rio Grande do Sul, Brazil. In this way, when applying this model to other situations, the attributes listed here can be revised and even modified to adapt to the situation in focus. Practical implications The use of the proposed model will make it possible to optimize the time of BA managers. It also gives MSE greater reliability in choosing BA. Social implications Using this model will provide better decision-making and better targeting, thus benefiting both the BAs and the MSEs, which can improve their management and keep jobs. Originality/value This paper contributes to the literature because it is the first to connect BAs, MSEs and Naive Bayes. Also, this study helps in better management for BA managers in their daily activities and provides a better choice of BA for MSE managers. Also, this study contextualizes BAs, MSEs and data mining in an objective way.

scholarly journals Construction of Multimedia Assisted Legal Classroom Teaching Model Based on Data Mining Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yu Lu ◽  
Wang Lizhi
Keyword(s):  
Data Mining ◽  
Phase Space ◽  
Educational Resources ◽  
Classroom Teaching ◽  
Retrieval Algorithm ◽  
Teaching Model ◽  
Data Mining Algorithm ◽  
Teaching System ◽  
Model Based ◽  
Mining Algorithm

In order to quickly and accurately retrieve a required part from massive multimedia educational resources and improve the utilization of educational resources, a multimedia assisted legal classroom teaching model based on data mining algorithm is designed. Firstly, the attributes of multimedia assisted legal classroom teaching resources are judged, and the numerical resources are standardized and discretized. Then, the B+ tree is used to establish the model’s indexes and index library, and the corresponding retrieval algorithm is designed to complete the resource search, establish the data distribution structure model of the multimedia assisted legal classroom teaching system, mine the data, reconstruct the phase space of the fused data information flow, extract the high-order moment features of the specific data in the multimedia assisted legal classroom teaching system in the reconstructed high-dimensional phase space, and realize the accurate mining of the feature data. The experimental results show that the teaching effect of the designed model has more advantages and can promote the improvement of students’ performance.

Sign in / Sign up

Export Citation Format

Share Document