Feature Information Recognition of Waste Recycling Resource Set Based on Data Mining-Reference-Cited by-同舟云学术

Feature Information Recognition of Waste Recycling Resource Set Based on Data Mining

Published:2022-01-31 Issue:2 Volume:14 Page:1-14
ISSN:1935-5688
Container-title:International Journal of Information Systems in the Service Sector
language:ng
Short-container-title:

Author:

Li Qin¹,Xiang Peng-Zhi¹,Zhang Chunhong²

Affiliation:

1. School of Chemical Engineering, Open University of Yunnan, China

2. Yunnan University, China

Abstract

Based on data mining, this paper analyzes the development characteristics of waste recycling resources, classifies the characteristics of waste recycling resources, constructs the evaluation level of characteristic information of waste recycling resources set, and quantitatively analyzes the factors influencing the development of waste recycling resources by using data mining method. This paper analyzes the cause and effect classification and importance, analyzing the main factors affecting the development of renewable resources recovery, putting forward to the countermeasures and suggestions on how to develop the recycling of renewable resources and further improving the overall operation and supervision system of waste chain.

Publisher

IGI Global

Subject

Information Systems and Management,Management Science and Operations Research,Strategy and Management,Information Systems,Management Information Systems

Reference15 articles.

1. Understanding the interactability of chikungunya virus proteins via molecular recognition feature analysis;S.Ankur;RSC Advances,2019

2. Vector space based augmented structural kinematic feature descriptor for human activity recognition in videos

3. Guan, Z.H., Ding, X.Q., & Zheng, N. (2019). Two-stage optimal dispatch of active distribution system considering demand-side resources. Smart Power, 46(1), 14-21.

4. Research Progress On Selective Crushing Technology For Typical Solid waste;Q.Huang;Environmental Engineering,2019

5. Hybrid fuzzy integrated convolutional neural network (HFICNN) for similarity feature recognition problem in abnormal netflow detection.(2020). Neurocomputing, 415(11), 332–346.

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