| Title |
Application Of Intelligent Analysis Based On Engineering Management And Decision Making For Economic Development Of Regional Enterprise |
| ID_Doc |
73301 |
| Authors |
Song, QZ; Yao, T; Dai, YH |
| Title |
Application Of Intelligent Analysis Based On Engineering Management And Decision Making For Economic Development Of Regional Enterprise |
| Year |
2024 |
| Published |
Scalable Computing-Practice And Experience, 25, 5 |
| DOI |
10.12694/scpe.v25i5.3027 |
| Abstract |
The convergence of advanced detection mechanisms, engineering management, and intelligence analysis presents a disruptive model for local companies pursuing economic growth. The paper presents a thorough strategy meant to improve regional processes for making decisions to promote long-term economic growth by utilizing modern technology. Using deep learning techniques, such as neural networks and deep neural architectures, to examine large datasets that are pertinent to local businesses. This makes data-driven decision-making easier and empowers stakeholders to choose wisely and strategically for the best possible economic results. incorporating management of engineering concepts to optimize resource allocation, improve operational efficiency, and streamline operations. To guarantee the successful implementation of economic development programs, management of projects, quality control, and methods for optimization must be applied. The research's findings have great potential to further regional businesses' goals for economic development. Through the integration of robust engineering management concepts and the analytical capacity of deep learning, this framework aims to equip decision-makers with the essential skills to navigate the intricacies of local economic environments, propel sustainable expansion, and promote equitable prosperity. |
| Author Keywords |
Intelligent Analysis; Engineering Management and Detection; Economic Development; Regional Enterprise; Decision Making |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:001293357300064 |
| WoS Category |
Computer Science, Software Engineering |
| Research Area |
Computer Science |
| PDF |
https://www.scpe.org/index.php/scpe/article/download/3027/1203
|