The classification of multiple intelligence of people with epilepsy using fuzzy inverse model
One of the most challenging problems faced by people with epilepsy (PWE) is employment. But, from human resource managers’ point of view, they need reliable information before they can hire the PWE. A fuzzy model is developed to meet the need for both parties. The model is to help PWE identify their...
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my.utm.408642017-08-16T04:11:21Z http://eprints.utm.my/id/eprint/40864/ The classification of multiple intelligence of people with epilepsy using fuzzy inverse model Awang, Siti Rahmah Aripin, Rasimah Rafia, Md. Hanip Ahmad, Tahir HD28 Management. Industrial Management One of the most challenging problems faced by people with epilepsy (PWE) is employment. But, from human resource managers’ point of view, they need reliable information before they can hire the PWE. A fuzzy model is developed to meet the need for both parties. The model is to help PWE identify their intelligence strengths and weaknesses in order to improve the probability of being employed. This paper presents a new fuzzy algorithm, namely Fuzzy Inverse ATIE (FIA) which is integrated to a crisp Logistic regression model to obtain a fuzzy model. Then based on the model, an ideal combination of eight intelligences which were based on Howard Gardner’s Multiple Intelligence was determined to improve the probability of PWE to be employed. The results show that with the suggested combinations, the probability, P(Y=1), is closed to 1. It can be concluded that the fuzzy model developed using the FIA algorithms has successfully improved the probability of PWE to be hired based on the best parameters of the eight intelligences. 2013 Article PeerReviewed Awang, Siti Rahmah and Aripin, Rasimah and Rafia, Md. Hanip and Ahmad, Tahir (2013) The classification of multiple intelligence of people with epilepsy using fuzzy inverse model. Malaysian Journal of Fundamental and Applied Sciences, 9 (2). pp. 86-92. ISSN 1823-626X |
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HD28 Management. Industrial Management Awang, Siti Rahmah Aripin, Rasimah Rafia, Md. Hanip Ahmad, Tahir The classification of multiple intelligence of people with epilepsy using fuzzy inverse model |
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One of the most challenging problems faced by people with epilepsy (PWE) is employment. But, from human resource managers’ point of view, they need reliable information before they can hire the PWE. A fuzzy model is developed to meet the need for both parties. The model is to help PWE identify their intelligence strengths and weaknesses in order to improve the probability of being employed. This paper presents a new fuzzy algorithm, namely Fuzzy Inverse ATIE (FIA) which is integrated to a crisp Logistic regression model to obtain a fuzzy model. Then based on the model, an ideal combination of eight intelligences which were based on Howard Gardner’s Multiple Intelligence was determined to improve the probability of PWE to be employed. The results show that with the suggested combinations, the probability, P(Y=1), is closed to 1. It can be concluded that the fuzzy model developed using the FIA algorithms has successfully improved the probability of PWE to be hired based on the best parameters of the eight intelligences. |
format |
Article |
author |
Awang, Siti Rahmah Aripin, Rasimah Rafia, Md. Hanip Ahmad, Tahir |
author_facet |
Awang, Siti Rahmah Aripin, Rasimah Rafia, Md. Hanip Ahmad, Tahir |
author_sort |
Awang, Siti Rahmah |
title |
The classification of multiple intelligence of people with epilepsy using fuzzy inverse model |
title_short |
The classification of multiple intelligence of people with epilepsy using fuzzy inverse model |
title_full |
The classification of multiple intelligence of people with epilepsy using fuzzy inverse model |
title_fullStr |
The classification of multiple intelligence of people with epilepsy using fuzzy inverse model |
title_full_unstemmed |
The classification of multiple intelligence of people with epilepsy using fuzzy inverse model |
title_sort |
classification of multiple intelligence of people with epilepsy using fuzzy inverse model |
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2013 |
url |
http://eprints.utm.my/id/eprint/40864/ |
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1643650578326224896 |