Enhancing Intercultural Business English Communication Factors Evaluation System Using the Termite Life Cycle Optimization Algorithm and Dynamically Stabilized Recurrent Neural Network
| Autor: | Yandong Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: |
Intercultural business English communication
Termite life cycle optimization algorithm Dynamically spatial recurrent neural network Intelligent weight decreasing firefly–particle filtering Entropy based spatial fuzzy C-means clustering Invariant improved feature extraction method Electronic computers. Computer science QA75.5-76.95 |
| Zdroj: | International Journal of Computational Intelligence Systems, Vol 17, Iss 1, Pp 1-16 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1875-6883 |
| DOI: | 10.1007/s44196-024-00564-y |
| Popis: | Abstract In today's globalized business environment, effective intercultural communication in English is paramount for successful collaboration among professionals from diverse backgrounds. To enhance the accuracy of the evaluation system, enhancing intercultural business English communication factors evaluation system using the termite life cycle optimization algorithm and dynamically stabilized recurrent neural network (IBEC–DSRNN–TLCOA) is proposed in this manuscript. The input image is captured from mobile camera. Then the input images are preprocessed using intelligent weight decreasing firefly–particle filtering (IWDFPF) to remove noise and enhance the input images. Afterwards, the preprocessed image is fed to the entropy-founded spatial fuzzy C-means clustering approach for segmenting the image. Then the contrast, correlation, energy and homogeneousness features are extracted by using force-invariant improved feature extraction technique. The extracted features are given to dynamically stabilized recurrent neural network (DSRNN) to image target detection and English description generation. Termite life cycle optimization algorithm (TLCOA) is employed to enhance the weight parameters of DSRNN. The proposed IBEC–DSRNN–TLCOA method is implemented. The proposed IBEC–DSRNN–TLCOA method provides 32.53%, 31.86%, and 35.72% higher accuracy; 35.58%, 32.16%, and 37.72% higher F-measure when compared with the existing methods, such as exploration of intelligent translation with evaluation systems for business English (IBEC–RCNN), E-learning engagement with convolution neural networks on business education (IBEC–CNN), and deep neural network-based research on scoring business English oral training (IBEC–DNN), respectively. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|
Full text from SpringerLink