Cloud-based configurable data stream processing architecture in rural economic development
Haohao Chen, Fadi Al-Turjman · 2024
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Description
Farm prices swing on weather, soil, and history together, and older forecasting tools handle that tangle poorly. Haohao Chen and Fadi Al-Turjman build a cloud-based system to predict agricultural prices more sharply. Their framework stacks three learning methods. A convolutional network extracts features from the data. A gated recurrent unit follows patterns over time. Reinforcement learning then tunes the whole model.
The inputs span historical prices, weather, soil conditions, and other drivers. On public and private datasets, the system beat traditional models. Its errors, measured two standard ways, came out lower. The authors frame the work as rural informatization: better forecasts support better planting and selling decisions. That, in turn, supports rural economic growth.
Catalog record
| CREATOR | Haohao Chen, Fadi Al-Turjman |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 49 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |