An artificial neural network approach for the language learning model
Abstract The current study provides the numerical solutions of the language-based model through the artificial intelligence (AI) procedure based on the scale conjugate gradient neural network (SCJGNN). The mathematical learning language differential model is characterized into three classes, named a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-50219-9 |
| _version_ | 1797377031794589696 |
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| author | Zulqurnain Sabir Salem Ben Said Qasem Al-Mdallal |
| author_facet | Zulqurnain Sabir Salem Ben Said Qasem Al-Mdallal |
| author_sort | Zulqurnain Sabir |
| collection | DOAJ |
| description | Abstract The current study provides the numerical solutions of the language-based model through the artificial intelligence (AI) procedure based on the scale conjugate gradient neural network (SCJGNN). The mathematical learning language differential model is characterized into three classes, named as unknown, familiar, and mastered. A dataset is generalized by using the performance of the Adam scheme, which is used to reduce to mean square error. The AI based SCJGNN procedure works by taking the data with the ratio of testing (12%), validation (13%), and training (75%). An activation log-sigmoid function, twelve numbers of neurons, SCJG optimization, hidden and output layers are presented in this stochastic computing work for solving the learning language model. The correctness of AI based SCJGNN is noted through the overlapping of the results along with the small calculated absolute error that are around 10–06 to 10–08 for each class of the model. Moreover, the regression performances for each case of the model is performed as one that shows the perfect model. Additionally, the dependability of AI based SCJGNN is approved using the histogram, and function fitness. |
| first_indexed | 2024-03-08T19:47:11Z |
| format | Article |
| id | doaj.art-36350ee8f5be4ae1bf0112cefd2a7816 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T19:47:11Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-36350ee8f5be4ae1bf0112cefd2a78162023-12-24T12:16:33ZengNature PortfolioScientific Reports2045-23222023-12-0113111410.1038/s41598-023-50219-9An artificial neural network approach for the language learning modelZulqurnain Sabir0Salem Ben Said1Qasem Al-Mdallal2Department of Computer Science and Mathematics, Lebanese American UniversityDepartment of Mathematical Sciences, College of Science, United Arab Emirates UniversityDepartment of Mathematical Sciences, College of Science, United Arab Emirates UniversityAbstract The current study provides the numerical solutions of the language-based model through the artificial intelligence (AI) procedure based on the scale conjugate gradient neural network (SCJGNN). The mathematical learning language differential model is characterized into three classes, named as unknown, familiar, and mastered. A dataset is generalized by using the performance of the Adam scheme, which is used to reduce to mean square error. The AI based SCJGNN procedure works by taking the data with the ratio of testing (12%), validation (13%), and training (75%). An activation log-sigmoid function, twelve numbers of neurons, SCJG optimization, hidden and output layers are presented in this stochastic computing work for solving the learning language model. The correctness of AI based SCJGNN is noted through the overlapping of the results along with the small calculated absolute error that are around 10–06 to 10–08 for each class of the model. Moreover, the regression performances for each case of the model is performed as one that shows the perfect model. Additionally, the dependability of AI based SCJGNN is approved using the histogram, and function fitness.https://doi.org/10.1038/s41598-023-50219-9 |
| spellingShingle | Zulqurnain Sabir Salem Ben Said Qasem Al-Mdallal An artificial neural network approach for the language learning model Scientific Reports |
| title | An artificial neural network approach for the language learning model |
| title_full | An artificial neural network approach for the language learning model |
| title_fullStr | An artificial neural network approach for the language learning model |
| title_full_unstemmed | An artificial neural network approach for the language learning model |
| title_short | An artificial neural network approach for the language learning model |
| title_sort | artificial neural network approach for the language learning model |
| url | https://doi.org/10.1038/s41598-023-50219-9 |
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