Research on the Biological Data Visualization Algorithm based on Self-Organizing Network
- 10.2991/iccia-16.2016.72How to use a DOI?
- Artificial neural network; Self-organizing feature map (SOFM); Grist; Maize yield; Unified distance matrix.
Among different strategies for Artificial Neural Networks and learning calculations, Self-Organizing Feature Map (SOFM) is a standout amongst the most well-known models. The point of this study is to order features affecting the natural yield and yield of Maize utilizing Self-Organizing Feature Map calculation. In Self-Organizing Feature Map, as indicated by subjective information, the grouping propensity of yield and natural yield of Maize were researched utilizing 1000 information from 12 features. Information was gathered from the writings on the subject of Maize in google.com. Results demonstrated that when natural yield was as yield, S with pH of soil, and natural substance with grain were identified with each other nearly. Besides, grist and natural substance had nearer relationship to organic yield. At the point when Maize grain yield was yield of Self-Organizing Feature Map model, S with pH of soil, and OC with natural substance were identified with each other nearly. Generally, grist and natural substance were much nearer identified with harvest yield than different parameters. Like organic yield, marks map demonstrated that information ordered in three classes for Maize yield and the main four lines of Unified distance matrix were set in Group X. An unmistakable partition was seen among Group X with Y and Z. Our outcomes demonstrated that among the yield parts, grist was the most imperative features adding to grain yield and 500-bit weight utilizing Self-Organizing Feature Map.
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xiaohan Mei AU - Da Liu PY - 2016/09 DA - 2016/09 TI - Research on the Biological Data Visualization Algorithm based on Self-Organizing Network BT - Proceedings of the 2016 International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2016) PB - Atlantis Press SP - 393 EP - 399 SN - 2352-538X UR - https://doi.org/10.2991/iccia-16.2016.72 DO - 10.2991/iccia-16.2016.72 ID - Mei2016/09 ER -