Neural networks

The current scenario demands improvements in environmental management practices by firms in order to successfully enter the market. The objective of this paper is to investigate waste management in the manufacturing sector based on the emerging industrial paradigms by reviewing the literature of such paradigms and incorporating a model of artificial neural networks (ANN) to predict the future state of waste management in the Colombian manufacturing sector, the data comes from the industrial environmental survey (EAI) of DANE. Among the findings is that industrial establishments in Colombia do not have an instrument for measuring waste and consequently a high percentage of organic waste is generated. For this reason, an ANN model was proposed as a reference framework to support decisions on industrial environmental policy in the sector. specifically, the methodology used

当前市场环境要求企业优化环境管理实践，方能顺利进入市场。本研究旨在基于新兴工业范式，通过梳理相关工业范式的文献资料，并结合人工神经网络（ANN）模型，对哥伦比亚制造业的废物管理现状展开调研并预测其未来态势，所用数据源自哥伦比亚国家行政统计局（DANE）的工业环境调查（EAI）。研究结果显示，哥伦比亚的工业企业尚未配备废物测量工具，由此产生了高比例的有机废物。基于此，本研究提出人工神经网络（ANN）模型作为参考框架，用以支撑该领域工业环境政策的决策制定。具体而言，所采用的方法论为

搜集汇总

背景与挑战

背景概述

该数据集基于哥伦比亚DANE的工业环境调查（EAI），专注于制造业废物管理研究，利用人工神经网络模型预测未来废物管理状态，旨在为工业环境政策决策提供支持框架。