relation: https://khub.utp.edu.my/scholars/9091/
title: Predicting dengue cases by aggregation of climate variable using manifold learning
creator: Mathulamuthu, S.S.
creator: Asirvadam, V.S.
creator: Dass, S.C.
creator: Gill, B.S.
description: Recently, Malaysia has been reported with dengue epidemic, that could rise up to 120, 000 cases recorded per year. This serious issue needs a vital look to prevent the dengue occurrences as it has no medicine yet to be found. Therefore, studies need to be done in order to prevent the dengue occurrences. This paper presents a high accuracy dengue occurrences prediction model which could forecast the dengue occurrences accurately. Manifold learning theorem has been performed to reduce the dimension into one by maintaining the geodesic distances between all points. Next machine learning theorem such as clustering (K-means technique) and linear regression has been done to model the data. Averaged silhouette width method was used to determine the number of K for K-means technique. Each cluster the regression model is built and SSE was shown in table. Overall, it's shown that there is low SSE achieved after applying dimension reduction and cluster based regression. The regression fit is improved and bring out better fit. © 2017 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2017
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Mathulamuthu, S.S. and Asirvadam, V.S. and Dass, S.C. and Gill, B.S.  (2017) Predicting dengue cases by aggregation of climate variable using manifold learning.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041404718&doi=10.1109%2fICSIPA.2017.8120670&partnerID=40&md5=4d7e83fceb5dfc2290e20beff61032f5
relation: 10.1109/ICSIPA.2017.8120670
identifier: 10.1109/ICSIPA.2017.8120670