Proceedings of the 2016 International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2016)

Research on the Biological Data Visualization Algorithm based on Self-Organizing Network

Authors
Xiaohan Mei, Da Liu
Corresponding Author
Xiaohan Mei
Available Online September 2016.
DOI
https://doi.org/10.2991/iccia-16.2016.72How to use a DOI?
Keywords
Artificial neural network; Self-organizing feature map (SOFM); Grist; Maize yield; Unified distance matrix.
Abstract
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.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2016)
Part of series
Advances in Computer Science Research
Publication Date
September 2016
ISBN
978-94-6252-240-4
ISSN
2352-538X
DOI
https://doi.org/10.2991/iccia-16.2016.72How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

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  - 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  - https://doi.org/10.2991/iccia-16.2016.72
ID  - Mei2016/09
ER  -