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基于云的可配置数据流处理架构在农村经济发展中的应用
Haohao Chen1, Fadi Al-Turjman2
1College of Management, Wuhan Technology and Business University, Wuhan, Hubei, China.
PeerJ. Computer science
|December 9, 2024
概括
本研究介绍了RL-CNN-GRU框架,用于增强农业价格预测. 这种新的方法显著提高了准确性,帮助农村信息化和经济发展.
科学领域:
- 农业经济学 农业经济学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 传统的数据处理方法与农业价格预测的复杂性作斗争.
- 准确的价格预测对于农村信息化,农业效率和经济增长至关重要.
研究的目的:
- 开发用于预测农产品价格的先进框架.
- 克服现有的数据处理技术在这个领域的局限性.
主要方法:
- 这项研究提出了强化学习 - 卷积神经网络通道循环单元 (RL-CNN-GRU) 框架.
- 该模型集成了1D-CNN用于特征提取和GRU用于时间模式识别.
- 强化学习被用来优化模型的准确性与多维时间序列数据.
主要成果:
- 与传统模型相比,RL-CNN-GRU框架显示出更高的性能.
- 拟议的框架在价格预测中实现了较低的平均平方误差 (MSE) 和平均绝对误差 (MAE).
- 验证使用公共和专有农业数据集进行.
结论:
- RL-CNN-GRU框架为农业价格预测提供了更准确的工具.
- 这种进步支持农业过程中更好的决策,并有助于农村经济发展.
- 该研究强调了集成机器学习模型在加强农村信息化方面的潜力.
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