PMID- 36210986
OWN - NLM
STAT- MEDLINE
DCOM- 20221011
LR  - 20221011
IS  - 1687-5273 (Electronic)
IS  - 1687-5265 (Print)
VI  - 2022
DP  - 2022
TI  - The Analysis of Environmental Cost Control of Manufacturing Enterprises Using 
      Deep Learning Optimization Algorithm and Internet of Things.
PG  - 1721157
LID - 10.1155/2022/1721157 [doi]
LID - 1721157
AB  - Under the background of the Internet of things (IoT), the problems between the 
      actual production and the environment are also prominent. The environmental cost 
      control in the production process of manufacturing enterprises are discussed to 
      reduce the environmental cost and promote the improvement of production 
      efficiency. First, the environmental cost under the background of IoT is 
      analyzed. Also, the environmental cost control methods in the production process 
      of traditional manufacturing enterprises are investigated. Second, based on the 
      principle of traditional genetic algorithm, the fast-nondominated sorting genetic 
      algorithm (NSGA-II) of multiobjective genetic algorithm is introduced to complete 
      the optimization of BP neural network (BPNN) algorithm in deep learning (DL), and 
      the multiobjective GA optimization BPNN model is established. Finally, the 
      multiobjective GA algorithm is used to empirically analyze the environmental cost 
      control capability of a paper-making enterprise. It is compared with enterprises 
      with excellent and poor environmental cost control capabilities in the same 
      industry to find out secondary indexes. The results show that environmental costs 
      have long-term and economic characteristics. The global search ability of BPNN 
      optimized by multiobjective GA is improved, and the local optimal dilemma is 
      avoided. Through empirical analysis, it is found that the comprehensive 
      capability of the environmental cost control of the enterprise is better, scored 
      79 or more, and the indexes of insufficient development and advantages are 
      obtained. As IoT rapidly develops, it is necessary to further improve the ability 
      of enterprises in environmental cost management, which is very important to 
      promote the development of enterprises and enhance their core competitiveness. It 
      is hoped that this investigation can provide certain reference significance for 
      improving the environmental cost management capability of enterprises, increasing 
      production efficiency, and reducing environmental costs.
CI  - Copyright (c) 2022 Jin Qiu and Wenzhuo Chen.
FAU - Qiu, Jin
AU  - Qiu J
AD  - Guangdong University of Science and Technology, Dongguan 523000, China.
FAU - Chen, Wenzhuo
AU  - Chen W
AUID- ORCID: 0000-0001-9639-4419
AD  - Department of Electronics and Information Engineering, North China Institute of 
      Science and Technology, Langfang 065201, China.
LA  - eng
PT  - Journal Article
DEP - 20220930
PL  - United States
TA  - Comput Intell Neurosci
JT  - Computational intelligence and neuroscience
JID - 101279357
SB  - IM
MH  - Algorithms
MH  - Cost Control
MH  - *Deep Learning
MH  - *Internet of Things
MH  - Neural Networks, Computer
PMC - PMC9546652
COIS- All authors declare that they have no conflicts of interest regarding the 
      publication of the paper.
EDAT- 2022/10/11 06:00
MHDA- 2022/10/12 06:00
PMCR- 2022/09/30
CRDT- 2022/10/10 03:47
PHST- 2022/07/20 00:00 [received]
PHST- 2022/08/20 00:00 [revised]
PHST- 2022/08/25 00:00 [accepted]
PHST- 2022/10/10 03:47 [entrez]
PHST- 2022/10/11 06:00 [pubmed]
PHST- 2022/10/12 06:00 [medline]
PHST- 2022/09/30 00:00 [pmc-release]
AID - 10.1155/2022/1721157 [doi]
PST - epublish
SO  - Comput Intell Neurosci. 2022 Sep 30;2022:1721157. doi: 10.1155/2022/1721157. 
      eCollection 2022.