| Title |
Application of Compound Neural Networks to Classifying Corporate Green Technology Investments |
| ID_Doc |
30846 |
| Authors |
Dong, ZL; Asif, M |
| Title |
Application of Compound Neural Networks to Classifying Corporate Green Technology Investments |
| Year |
2024 |
| Published |
Journal Of Organizational And End User Computing, 36, 1 |
| DOI |
10.4018/JOEUC.348654 |
| Abstract |
In the current context of sustainable development and environmental protection issues, enterprises are paying more and more attention to green technology innovation. For this purpose, we introduced a composite neural network model, including the Siamese Network, Temporal Convolutional Networks (TCN) and Random Forests technology. First, the Siamese Network is used to measure the green technology investment similarities between enterprises to better understand the connections between them. Second, Temporal Convolutional Networks (TCN) are applied to process time series data to capture the time evolution trend of green technology investment. Finally, we use Random Forests technology to integrate the output of the Siamese Network and TCN to classify enterprises. Experimental results show that our method is effective in green technology investment classification and financial performance prediction, can more accurately assess the financial performance of enterprises, and can also help enterprises better understand the effects of their green technology investments. |
| Author Keywords |
Deep Learning; Compound Neural Network; Green Technology Innovation; Corporate Finance; Model Fusion; Performance Evaluation |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:001293789600001 |
| WoS Category |
Computer Science, Information Systems; Information Science & Library Science; Management |
| Research Area |
Computer Science; Information Science & Library Science; Business & Economics |
| PDF |
https://www.igi-global.com/ViewTitle.aspx?TitleId=348654&isxn=9798369324530
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