Artificial Intelligence Generated Content Neural Network Model for Enterprise Management Decision-Making System

Research was conducted on Artificial Intelligence Generated Content neural network model solutions for the technical issues of enterprise decision-making systems. The enterprise decision-making system utilizes Artificial Intelligence Generated Content, neural network technology, and algorithms to establish modules for neural network data preprocessing, anomaly detection, predictive analysis, decision support, adaptive machine learning, and visual decision-making models, integrating the generative and controllable aspects of artificial intelligence into the decision-making system. In simulation experiments, this system improved output accuracy by 21.6% compared to general information systems and provided 89.7 times more effective reference data, indicating that the proposed technical solution is feasible.

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Publication Details

Published
2024-12-06
DOI
https://doi.org/10.1145/3727993.3727999
Primary Topic
Energy Load and Power Forecasting
Type
article
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article

Artificial Intelligence Generated Content Neural Network Model for Enterprise Management Decision-Making System

Xueling Wei, Tao Lin
Energy Load and Power Forecasting
article

Artificial Intelligence Generated Content Neural Network Model for Enterprise Management Decision-Making System

Xueling Wei, Tao Lin
article en

Abstract

Research was conducted on Artificial Intelligence Generated Content neural network model solutions for the technical issues of enterprise decision-making systems. The enterprise decision-making system utilizes Artificial Intelligence Generated Content, neural network technology, and algorithms to establish modules for neural network data preprocessing, anomaly detection, predictive analysis, decision support, adaptive machine learning, and visual decision-making models, integrating the generative and controllable aspects of artificial intelligence into the decision-making system. In simulation experiments, this system improved output accuracy by 21.6% compared to general information systems and provided 89.7 times more effective reference data, indicating that the proposed technical solution is feasible.

Baise University (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 72%
Energy Load and Power Forecasting
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