Cognitive Computational Model Using Machine Learning Algorithm in Artificial Intelligence Environment

Simple item page
dc.contributor.authorLiu, Shangyi
dc.contributor.authorSpiridonidis, Constantin-Viktor
dc.contributor.authorAbdulrazzqa, Mohammed
dc.contributor.authorLiu, Shangyi
dc.contributor.authorSpiridonidis, Constantin-Viktor
dc.contributor.authorAbdulrazzqa, Mohammed
dc.date.accessioned2022-02-14T16:10:55Z
dc.date.available2022-02-14T16:10:55Z
dc.date.copyright© 2021
dc.date.issued2022
dc.descriptionThis article is licensed under Creative Commons License and full text is openly accessible in CUD Digital Repository. The version of the scholarly record of this article is published in Applied Mathematics and Nonlinear Sciences (2022), available online at: https://doi.org/10.2478/amns.2021.2.00112
dc.description.abstractIn order to explore the application of machine learning algorithm to intelligent analysis of big data in an artificial intelligence (AI) environment, make cognitive computing meet the requirements of AI and better assist humans to carry out data analysis, first, the theoretical basis of machine learning algorithm is elaborated. Then, a cognitive computational model based on the machine learning algorithm is proposed, including the essence, principle, function, training method of deep belief network (DBN) algorithm, as well as the joint use of DBN algorithm and multilayer perceptron. Finally, the proposed algorithm is simulated. The results show that under the same parameter conditions, the accuracy rate of the DBN algorithm combined with multilayer perceptron is higher than that of the DBN algorithm; when the number of units is >40, the accuracy rate of the DBN algorithm combined with multilayer perceptron is significantly higher than that of the DBN algorithm; when the number of units is 30, the best effect can be obtained, and the error rate is <0.05, but the DBN algorithm cannot achieve this effect alone; when the number of network layers is specified as four, the error rate of the DBN algorithm combined with multilayer perceptron is <0.05, forming the optimal level. In the AI environment, the performance of the cognitive computational model based on the DBN algorithm and multilayer perceptron can reach the highest level, which makes the computer become a handy intelligent auxiliary tool for human beings. © 2021 Liu et al., published by Sciendo.
dc.identifier.citationLiu, S., Spiridonidis, C. -., & Abdulrazzqa, M. (2022). Cognitive computational model using machine learning algorithm in artificial intelligence environment. Applied Mathematics and Nonlinear Sciences, https://doi.org/10.2478/amns.2021.2.00065
dc.identifier.issn24448656
dc.identifier.urihttps://doi.org/10.2478/amns.2021.2.00065
dc.identifier.urihttp://hdl.handle.net/20.500.12519/511
dc.language.isoen
dc.publisherSciendo
dc.relationAuthors Affiliations : Liu, S., University of Science and Technology Liaoning, Anshan, 114051, China; Spiridonidis, C.-V., Department of Architecture, Faculty of Architecture and Interior Design, Canadian University Dubai, Dubai, United Arab Emirates; Abdulrazzqa, M., Department of Business Administration, Faculty of Administration Sciences, Applied Science University, Al Eker, Bahrain
dc.relation.ispartofseriesApplied Mathematics and Nonlinear Sciences;
dc.rightsCreative Commons Attribution 4.0 International License.
dc.rights.holderCopyright : © 2021 Liu et al., published by Sciendo.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject92B20
dc.subjectartificial intelligence
dc.subjectcognitive computational model
dc.subjectDBN algorithm
dc.subjectmachine learning
dc.subjectmultilayer perceptron
dc.titleCognitive Computational Model Using Machine Learning Algorithm in Artificial Intelligence Environment
dc.typeArticle

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Access Instruction
Size:
1.18 MB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Department of Architecture