scholarly journals Optimization of Teaching Management System Based on Association Rules Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Qing Niu
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Management System ◽  
Computing Time ◽  
Calculation Model ◽  
Teaching Management ◽  
Work Related ◽  
Whole School ◽  
Rule Model ◽  
Low Efficiency

The teaching management department carries all the work related to teaching in the whole school. A scientific, efficient, and complete teaching management system cannot only help the teaching management department improve work efficiency and quality but also greatly reduce many problems caused by manual labour risk. This paper designs and implements a teaching management system based on an improved association rule algorithm. First, aiming at the low efficiency of the Apriori algorithm for mining association rules, an association rule model based on interest is proposed. Second, use the MapReduce calculation model to partition the transaction database, then use the improved Apriori optimization algorithm for mining, and finally merge the mining results to obtain frequent itemsets. Through experiments, the optimized algorithm has greatly improved selection mining and computing time than traditional algorithms.

scholarly journals An Association Rule Model of Course Recommendation in MOOCs: Based on edX Platform

2018 ◽  
Vol 14 (25) ◽  
pp. 284
Author(s):  
Min Tan ◽  
Maoguo Wu
Keyword(s):  
Online Education ◽  
Association Rules ◽  
Association Rule ◽  
Rapid Development ◽  
Business Environment ◽  
Rule Model ◽  
Recommendation Algorithms ◽  
Development Technology ◽  
Fast Development ◽  
Education Platform

The rapid development of the Internet has promoted online education as a new method of education. This paper analyzes the edX platform from various aspects, such as business environment and industry development, technology maturity, and recommendation algorithms and their feasibility. This paper mainly focuses on the curriculum recommendation mechanism under association rules on the edX platform. With the fast development of online education platform, curriculum recommendation technology has matured to a high extent. The recommendation mechanism based on association rules chosen by edX also proves to be feasible at data acquisition level and technical level. However, some defects of this mechanism per se have brought certain risks and limitations to edX’s future sustainable development.

scholarly journals Influence of Selected Model Parameters on the Electromagnetic Levitation Melting Efficiency

2021 ◽  
Vol 11 (9) ◽  
pp. 3827
Author(s):  
Blazej Nycz ◽  
Lukasz Malinski ◽  
Roman Przylucki
Keyword(s):  
Electromagnetic Levitation ◽  
Optimization Methods ◽  
Calculation Model ◽  
Model Parameters ◽  
Starting Point ◽  
Low Efficiency ◽  
The Relationship ◽  
Reactive Metals ◽  
Electromagnetic Levitation Melting ◽  
Metal Melting

The article presents the results of multivariate calculations for the levitation metal melting system. The research had two main goals. The first goal of the multivariate calculations was to find the relationship between the basic electrical and geometric parameters of the selected calculation model and the maximum electromagnetic buoyancy force and the maximum power dissipated in the charge. The second goal was to find quasi-optimal conditions for levitation. The choice of the model with the highest melting efficiency is very important because electromagnetic levitation is essentially a low-efficiency process. Despite the low efficiency of this method, it is worth dealing with it because is one of the few methods that allow melting and obtaining alloys of refractory reactive metals. The research was limited to the analysis of the electromagnetic field modeled three-dimensionally. From among of 245 variants considered in the article, the most promising one was selected characterized by the highest efficiency. This variant will be a starting point for further work with the use of optimization methods.

The Design and Implementation of Teaching Management System Used in Experiment and Training Base

2011 ◽  
Vol 121-126 ◽  
pp. 1120-1124
Author(s):  
Yan Shang ◽  
Hong Sheng Ding ◽  
Chun Yan Wang ◽  
Long Jiang Su ◽  
Yu Can Zhao
Keyword(s):  
Resource Sharing ◽  
Management System ◽  
Control Experiment ◽  
Numerical Control ◽  
Teaching Process ◽  
Teaching Management ◽  
Management Level ◽  
Design And Implementation ◽  
And Training ◽  
Automatic Level

In order to improve the automatic level of teaching management in experiment and training base, a teaching management system is designed and implemented. The teaching process such as checking on work attendance, logging on school reports and equipments’ management can be carried automatically. The efficiency and the management level can be increased, and the resource sharing can be realized. to meet this demand. The use of numerical control experiment and training management system will improve the level of teaching and management of our training base.

Improved Apriori Algorithm in the Power System Fault

2014 ◽  
Vol 556-562 ◽  
pp. 1510-1514
Author(s):  
Li Qiang Lin ◽  
Hong Wen Yan
Keyword(s):  
Power System ◽  
Association Rules ◽  
Fault Analysis ◽  
The Other ◽  
Apriori Algorithm ◽  
Extreme Climate ◽  
Time Space ◽  
Low Efficiency ◽  
Candidate Item ◽  
Improved Algorithm

For the low efficiency in generating candidate item sets of apriori algorithm, this paper presents a method based on property division to improve generating candidate item sets. Comparing the improved apriori algorithm with the other algorithm and the improved algorithm is applied to the power system accident cases in extreme climate. The experiment results show that the improved algorithm significantly improves the time efficiency of generating candidate item sets. And it can find the association rules among time, space, disasters and fault facilities in the power system accident cases in extreme climate. That is very useful in power system fault analysis.

scholarly journals Binary Particle Swarm Optimization-Based Association Rule Mining for Discovering Relationships between Machine Capabilities and Product Features

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Zhicong Kou ◽  
Lifeng Xi
Keyword(s):  
Particle Swarm Optimization ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Particle Swarm ◽  
Performance Comparison ◽  
Binary Particle Swarm Optimization ◽  
Rule Mining ◽  
Swarm Optimization ◽  
Product Features

An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for an efficient and cost-effective product development and production. This paper proposes a new binary particle swarm optimization- (BPSO-) based association rule mining (BPSO-ARM) method for discovering the hidden relationships between machine capabilities and product features. In particular, BPSO-ARM does not need to predefine thresholds of minimum support and confidence, which improves its applicability in real-world industrial cases. Moreover, a novel overlapping measure indication is further proposed to eliminate those lower quality rules to further improve the applicability of BPSO-ARM. The effectiveness of BPSO-ARM is demonstrated on a benchmark case and an industrial case about the automotive part manufacturing. The performance comparison indicates that BPSO-ARM outperforms other regular methods (e.g., Apriori) for ARM. The experimental results indicate that BPSO-ARM is capable of discovering important association rules between machine capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.

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