Cloud-based configurable data stream processing architecture in rural economic development
Purpose This study aims to address the limitations of traditional data processing methods in predicting agricultural product prices, which is essential for advancing rural informatization to enhance agricultural efficiency and support rural economic growth. Methodology The RL-CNN-GRU framework combi...
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PeerJ Inc.
2024-11-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2547.pdf |
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| author | Haohao Chen Fadi Al-Turjman |
| author_facet | Haohao Chen Fadi Al-Turjman |
| author_sort | Haohao Chen |
| collection | DOAJ |
| description | Purpose This study aims to address the limitations of traditional data processing methods in predicting agricultural product prices, which is essential for advancing rural informatization to enhance agricultural efficiency and support rural economic growth. Methodology The RL-CNN-GRU framework combines reinforcement learning (RL), convolutional neural network (CNN), and gated recurrent unit (GRU) to improve agricultural price predictions using multidimensional time series data, including historical prices, weather, soil conditions, and other influencing factors. Initially, the model employs a 1D-CNN for feature extraction, followed by GRUs to capture temporal patterns in the data. Reinforcement learning further optimizes the model, enhancing the analysis and accuracy of multidimensional data inputs for more reliable price predictions. Results Testing on public and proprietary datasets shows that the RL-CNN-GRU framework significantly outperforms traditional models in predicting prices, with lower mean squared error (MSE) and mean absolute error (MAE) metrics. Conclusion The RL-CNN-GRU framework contributes to rural informatization by offering a more accurate prediction tool, thereby supporting improved decision-making in agricultural processes and fostering rural economic development. |
| format | Article |
| id | doaj-art-1d0eb34a77cc42adaf2acbd7dbc4102a |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-1d0eb34a77cc42adaf2acbd7dbc4102a2025-08-20T02:33:00ZengPeerJ Inc.PeerJ Computer Science2376-59922024-11-0110e254710.7717/peerj-cs.2547Cloud-based configurable data stream processing architecture in rural economic developmentHaohao Chen0Fadi Al-Turjman1College of Management, Wuhan Technology and Business University, Wuhan, Hubei, ChinaArtificial Intelligence, Software, and Information Systems Engineering Departments, Research Center for AI and IoT, AI and Robotics Institute, Near East University, Nicosia, TurkeyPurpose This study aims to address the limitations of traditional data processing methods in predicting agricultural product prices, which is essential for advancing rural informatization to enhance agricultural efficiency and support rural economic growth. Methodology The RL-CNN-GRU framework combines reinforcement learning (RL), convolutional neural network (CNN), and gated recurrent unit (GRU) to improve agricultural price predictions using multidimensional time series data, including historical prices, weather, soil conditions, and other influencing factors. Initially, the model employs a 1D-CNN for feature extraction, followed by GRUs to capture temporal patterns in the data. Reinforcement learning further optimizes the model, enhancing the analysis and accuracy of multidimensional data inputs for more reliable price predictions. Results Testing on public and proprietary datasets shows that the RL-CNN-GRU framework significantly outperforms traditional models in predicting prices, with lower mean squared error (MSE) and mean absolute error (MAE) metrics. Conclusion The RL-CNN-GRU framework contributes to rural informatization by offering a more accurate prediction tool, thereby supporting improved decision-making in agricultural processes and fostering rural economic development.https://peerj.com/articles/cs-2547.pdfCloud computingRural economyIntelligent agricultureDeep learning |
| spellingShingle | Haohao Chen Fadi Al-Turjman Cloud-based configurable data stream processing architecture in rural economic development PeerJ Computer Science Cloud computing Rural economy Intelligent agriculture Deep learning |
| title | Cloud-based configurable data stream processing architecture in rural economic development |
| title_full | Cloud-based configurable data stream processing architecture in rural economic development |
| title_fullStr | Cloud-based configurable data stream processing architecture in rural economic development |
| title_full_unstemmed | Cloud-based configurable data stream processing architecture in rural economic development |
| title_short | Cloud-based configurable data stream processing architecture in rural economic development |
| title_sort | cloud based configurable data stream processing architecture in rural economic development |
| topic | Cloud computing Rural economy Intelligent agriculture Deep learning |
| url | https://peerj.com/articles/cs-2547.pdf |
| work_keys_str_mv | AT haohaochen cloudbasedconfigurabledatastreamprocessingarchitectureinruraleconomicdevelopment AT fadialturjman cloudbasedconfigurabledatastreamprocessingarchitectureinruraleconomicdevelopment |