Recommendation of Business Models for Agriculture-Related Platforms Based on Deep Learning. (11th July 2022)
- Record Type:
- Journal Article
- Title:
- Recommendation of Business Models for Agriculture-Related Platforms Based on Deep Learning. (11th July 2022)
- Main Title:
- Recommendation of Business Models for Agriculture-Related Platforms Based on Deep Learning
- Authors:
- Zhou, Yufei
Hua, Sha - Other Names:
- Ding Baiyuan Academic Editor.
- Abstract:
- Abstract : Agriculture is a basic and pillar industry. With the integration and development of Internet+, platform economy, and various industries, the business model of agriculture-related platforms is also constantly innovating. In this context, it is necessary to recommend suitable business models for different types of agriculture-related platforms. Based on the characteristics of agriculture-related platforms and various business models, this paper proposes a business model recommendation algorithm based on radial basis function neural network (RBFNN). This method trains the RBFNN model with the goal of maximizing the correlation between agricultural-related platforms and business models. In the application stage, for a specific agriculture-related platform, after inputting its characteristic parameters, a suitable business model can be recommended. In the experiment, the proposed method is tested and verified with relevant data, and the results show the effectiveness of the method.
- Is Part Of:
- Computational intelligence and neuroscience. Volume 2022(2022)
- Journal:
- Computational intelligence and neuroscience
- Issue:
- Volume 2022(2022)
- Issue Display:
- Volume 2022, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 2022
- Issue:
- 2022
- Issue Sort Value:
- 2022-2022-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-07-11
- Subjects:
- Neurosciences -- Data processing -- Periodicals
Computational intelligence -- Periodicals
Computational neuroscience -- Periodicals
612.80285 - Journal URLs:
- https://www.hindawi.com/journals/cin/ ↗
- DOI:
- 10.1155/2022/7330078 ↗
- Languages:
- English
- ISSNs:
- 1687-5265
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 22697.xml