TY  - CONF
Y1  - 2017///
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781509055593
A1  - Mathulamuthu, S.S.
A1  - Asirvadam, V.S.
A1  - Dass, S.C.
A1  - Gill, B.S.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041404718&doi=10.1109%2fICSIPA.2017.8120670&partnerID=40&md5=4d7e83fceb5dfc2290e20beff61032f5
EP  - 540
AV  - none
N1  - cited By 0; Conference of 5th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017 ; Conference Date: 12 September 2017 Through 14 September 2017; Conference Code:132915
N2  - 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.
TI  - Predicting dengue cases by aggregation of climate variable using manifold learning
ID  - scholars9091
SP  - 535
KW  - Artificial intelligence; Forecasting; Learning systems; Regression analysis
KW  -  Averaged silhouettes; Dengue Incidences; ISOMAP; K-means clusters; Manifold learning; Regression
KW  -  Image processing
ER  -