Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/7964
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dc.contributor.authorJena, P.R.
dc.contributor.authorMajhi, R.
dc.date.accessioned2020-03-30T10:03:12Z-
dc.date.available2020-03-30T10:03:12Z-
dc.date.issued2016
dc.identifier.citationInternational Conference on Electrical, Electronics, and Optimization Techniques, ICEEOT 2016, 2016, Vol., , pp.4164-4169en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/7964-
dc.description.abstractThis paper develops and employs a novel artificial neural network (ANN) model to study farmers' behaviour towards decision making on maize production in Kenya. The paper has compared the accuracy level of ANN based model and the statistical model and found out that the ANN model has achieved higher accuracy and efficiency. The findings from the study reveal that the farmers are mostly influenced by their demographic and food security for decision making. Further to examine the relative importance of different demographic and food security characteristics, an ANOVA test is undertaken. The results found that education and food security indices are instrumental in influencing farmers' decision making. � 2016 IEEE.en_US
dc.titleClassifying behavioural traits of small-scale farmers: Use of a novel artificial neural network (ANN) classifieren_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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