The Analysis of Environmental Cost Control of Manufacturing Enterprises Using Deep Learning Optimization Algorithm and Internet of Things

Abstract

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.

Unleashing the Potentials of Artificial Intelligence for Micro, Small, and Medium Enterprises: A Systematic Literature Review

“…This sub-category is the second most prevalent, with three identified studies [20], [24], [28]. Each study was conducted in the same year.…”

“…Neural network techniques and methods predominantly characterize the use of deep learning. Firstly, a Backpropagation Neural Network (BPNN), a type of Feedforward Neural Network (FFNN), is utilized, and researchers optimize its performance using genetic algorithms [20]. Another combination is the integration of Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) for sentiment analysis, representing each word as a vector [23].…”

“…Other swarm optimization techniques include Chaotic Salp Swarm Optimization-based Feature Selection (CSSO-FS) for feature selection, and Quantum Behaved Particle Swarm Optimization (QPSO) to enhance model performance [18]. Another technique is Non-dominated Sorting Genetic Algorithm II (NSGA-II), which inherits principles from genetic algorithms [20]. The last technique found is Factor Analysis, used to obtain common factors in an effort to identify the effects of reducing overfitting in the model [21].…”

“…customer churn using ensemble methods, Random Forest (RF), and Light Gradient-Boosting Machine (LightGBM)[17] Sales Q1(Faritha Banu et al, 2022) Model for predicting and managing customer churn and non-churn in the business sector using advanced selection, optimalization, and classification techniques deep learning-based method to enhance the classification accuracy of sentiment analysis on product comments[19] Sales Q1(Qiu & Chen, 2022) Model for analysing environmental costs using Back Propagation Neural Network (BPNN) optimized by the NSGA-II algorithm[20] Financial Q2(Chen & Long, 2023) Model for predicting financial risks based on a neural network with a combination of Factor Analysis (FA), Particle Swarm Optimization (PSO), and Long Short-Term Memory (LSTM) techniques[21] customer churn using Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR)[22] analysis and goal extraction from customer feedback using a combination of Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN)[23] Sales Q2Li et al, 2022) Implementation of Probabilistic Neural Network (PNN) for environmental cost control classification and Particle Swarm Optimization (PSO) algorithm as the decision-making system[24] Financial Q3(J et al, 2023) Customer churn prediction using a Support Vector Machine algorithm and Hybrid Recommendation Strategy to prevent churn[25] financial risks using an optimized structure combining Temporal Convolutional Network-Long (TCN) with Long Short-Term Memory (LSTM)[26] Financial Q4Wisesa et al, 2020) Model for predicting sales in the B2B context using various machine learning algorithms, with Gradient Boost performing the best[27] Sales ~(Sánchez-Torres et al, 2022) …”