Research on adaptive conversion of AI language based on rough set
by Yuping Fang; Da Fang
International Journal of Biometrics (IJBM), Vol. 14, No. 3/4, 2022

Abstract: In order to solve the problems of high complexity and low computational efficiency in traditional artificial intelligence (AI) language conversion methods, an adaptive AI language conversion method based on rough set is proposed. AI language preprocessing is realised by pre-emphasising, adding window, frame processing and endpoint detection. The attribute reduction algorithm based on rough set theory is used to select the features of AI language. The dimension of input feature vector is reduced. The experimental results show that after feature extraction, the computational efficiency is obviously improved, and the efficiency of the proposed method is the highest, averaging close to 100%. Compared with the traditional method, the complexity of the proposed method is lower, and the average complexity is 1.68% during the ten experimental iterations. This method simplifies the adaptive conversion process of AI language and has high computational efficiency.

Online publication date: Fri, 05-Aug-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biometrics (IJBM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com