Article: Specific K-mean clustering-based perceptron for dengue prediction Journal: International Journal of Intelligent Information and Database Systems (IJIIDS) 2017 Vol.10 No.3/4 pp.269 - 288 Abstract: Traditional neural networks come up with drawback relating to choosing the number of nodes in each layer. This paper proposes a novel adaptive network fuzzy inference system (ANFIS) to overcome the aforementioned problem. In particular, we use incremental k-mean to pre-identify the number of nodes in the adaptive network. Each node includes a set of samples in a training set. For each sample, we identify a fuzzy value of the particular sample data belonging to each node in the network. The learning perceptron algorithm also investigates to adjust weights by learning from real output data. In this study, the novel ANFIS model is employed to the dengue prediction application as well as evaluates performance execution by a real dataset of dengue disease in Tien Giang, Vietnam. The result shows that our proposed model of ANFIS gets better accuracy in comparison with linear regression, multiple linear regression, time series and neural network. Inderscience Publishers - linking academia, business and industry through research

Title: Specific K-mean clustering-based perceptron for dengue prediction

Authors: Hoang Long Nguyen; Trong Hai Duong; Cuong Phan Nguyen; Duc Cuong Nguyen; Thach Phat Chiem; Manh Hung Nguyen; Thi Nhu Mai Nguyen; Hung Vi Nguyen

Addresses: International University, Vietnam National University HCMC, Vietnam ' International University, Vietnam National University HCMC, Vietnam ' Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam ' Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam ' International University, Vietnam National University HCMC, Vietnam ' Institute of Science and Technology of Industry 4.0, Nguyen Tat Thanh University, Vietnam ' Tien Giang Preventive Health Center, Vietnam ' Tien Giang Health Department, Vietnam

Abstract: Traditional neural networks come up with drawback relating to choosing the number of nodes in each layer. This paper proposes a novel adaptive network fuzzy inference system (ANFIS) to overcome the aforementioned problem. In particular, we use incremental k-mean to pre-identify the number of nodes in the adaptive network. Each node includes a set of samples in a training set. For each sample, we identify a fuzzy value of the particular sample data belonging to each node in the network. The learning perceptron algorithm also investigates to adjust weights by learning from real output data. In this study, the novel ANFIS model is employed to the dengue prediction application as well as evaluates performance execution by a real dataset of dengue disease in Tien Giang, Vietnam. The result shows that our proposed model of ANFIS gets better accuracy in comparison with linear regression, multiple linear regression, time series and neural network.

Keywords: perceptron; adaptive network fuzzy inference system; ANFIS; K-mean clustering; neural network; epidemic prediction.

DOI: 10.1504/IJIIDS.2017.087242

International Journal of Intelligent Information and Database Systems, 2017 Vol.10 No.3/4, pp.269 - 288

Received: 08 Aug 2016
Accepted: 08 Aug 2016
Published online: 11 Oct 2017 *

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Title: Specific K-mean clustering-based perceptron for dengue prediction

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