Exploratory study of Kohonen network for human health state classification

Kohonen Network is an unsupervised learning which forms clusters from patterns that share common features and group similar patterns together. This network are commonly uses grids of artificial neurons which connected to all the inputs. This paper presents an exploratory study of Kohonen Neural Netw...

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Main Authors: Mohd Rahman, Hamijah, Arbaiy, Nureize, Che Lah, Muhammad Shukeri, Hassan, Norlida Hassan
Format: Article
Language:English
Published: Politeknik Negeri Padang 2018
Subjects:
Online Access:http://eprints.uthm.edu.my/5668/
http://eprints.uthm.edu.my/5668/1/AJ%202018%20%28290%29.pdf
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author Mohd Rahman, Hamijah
Arbaiy, Nureize
Che Lah, Muhammad Shukeri
Hassan, Norlida Hassan
author_facet Mohd Rahman, Hamijah
Arbaiy, Nureize
Che Lah, Muhammad Shukeri
Hassan, Norlida Hassan
author_sort Mohd Rahman, Hamijah
building UTHM Institutional Repository
collection Online Access
description Kohonen Network is an unsupervised learning which forms clusters from patterns that share common features and group similar patterns together. This network are commonly uses grids of artificial neurons which connected to all the inputs. This paper presents an exploratory study of Kohonen Neural Network to classify human health state. Neural Connection tool is used to generate the result based on Kohonen learning algorithm. Procedural steps are provided to assist the implementation of the Kohonen Network. The result shows that side 2 is more appropriate for this problem with efficient learning rate 1.0. It gives good distribution for training and test patterns. Study to the variation of dataset’s size will be considered in the near future to evaluate the performance of the network.
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publisher Politeknik Negeri Padang
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spelling uthm-56682022-01-20T02:46:29Z http://eprints.uthm.edu.my/5668/ Exploratory study of Kohonen network for human health state classification Mohd Rahman, Hamijah Arbaiy, Nureize Che Lah, Muhammad Shukeri Hassan, Norlida Hassan R855-855.5 Medical technology Kohonen Network is an unsupervised learning which forms clusters from patterns that share common features and group similar patterns together. This network are commonly uses grids of artificial neurons which connected to all the inputs. This paper presents an exploratory study of Kohonen Neural Network to classify human health state. Neural Connection tool is used to generate the result based on Kohonen learning algorithm. Procedural steps are provided to assist the implementation of the Kohonen Network. The result shows that side 2 is more appropriate for this problem with efficient learning rate 1.0. It gives good distribution for training and test patterns. Study to the variation of dataset’s size will be considered in the near future to evaluate the performance of the network. Politeknik Negeri Padang 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/5668/1/AJ%202018%20%28290%29.pdf Mohd Rahman, Hamijah and Arbaiy, Nureize and Che Lah, Muhammad Shukeri and Hassan, Norlida Hassan (2018) Exploratory study of Kohonen network for human health state classification. International Journal on Informatics Visualization, 2 (3). pp. 209-214. ISSN 2549-9610
spellingShingle R855-855.5 Medical technology
Mohd Rahman, Hamijah
Arbaiy, Nureize
Che Lah, Muhammad Shukeri
Hassan, Norlida Hassan
Exploratory study of Kohonen network for human health state classification
title Exploratory study of Kohonen network for human health state classification
title_full Exploratory study of Kohonen network for human health state classification
title_fullStr Exploratory study of Kohonen network for human health state classification
title_full_unstemmed Exploratory study of Kohonen network for human health state classification
title_short Exploratory study of Kohonen network for human health state classification
title_sort exploratory study of kohonen network for human health state classification
topic R855-855.5 Medical technology
url http://eprints.uthm.edu.my/5668/
http://eprints.uthm.edu.my/5668/1/AJ%202018%20%28290%29.pdf