ANALYSIS OF PERFORMANCE PREDICTION MODELS IN PREDICTING DENGUE FEVER PATIENTS NUMBER IN EACH GROUP OF MALANG, INDONESIA
Published In: 5TH INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, CONTROL AND NETWORKING
Author(s): EDWIN RIKSAKOMARA , EKA MULYA A. LULUS CONDRO T. , FEBRILIYAN SAMOPA , PUJIADI , RADITYO P.W , WIWIK ANGGRAENI
Abstract: Dengue Fever is one of acute and deadly diseases that commonly happens in tropical area. The spread of it is also influenced by geographical condition. Indonesia, Particularly in Malang that is a tropical area with a geographical condition supports the development of this disease. It needs a fat-moving action to the early step precaution so that the number of patients can be reduced. As the primary decision for an early prevention, it needs predictions about several cases of dengue fever of some period in the future. The result of this prediction is needed by Public Health Office of Malang as one of instances that responsible of dengue fever cases. This research analyses performance as a prediction model in getting the predictions in a number of dengue fever cases in Malang, Indonesia for some different group of data. The models suggested are Multiplicative Holt-Winters, Additive Holt-Winters, Multiplicative Decomposition and Autoregressive Moving Average (ARIMA). Those models are appl
- Publication Date: 26-Sep-2016
- DOI: 10.15224/978-1-63248-104-7-16
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SPORTS SKILL ANALYSIS USING MOTION FREQUENCY
Published In: 5TH INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, CONTROL AND NETWORKING
Author(s): MASUMI YAJIMA , TAKESHI MATSUDA , TOSHIYUKI MAEDA
Abstract: This paper addresses sports skill discrimination using motion picture data, focused on volleyball attack skill. We attempt to certify the hypothesis that expert skills have relatively low frequency motions rather than novice skills as the similarity of human postural control. For this purpose we proceed experiments and analyze sports skills as for frequency of motion using time series motion pictures of volleyball attacks. In this paper, volleyball play is analyzed with motion picture data recorded by hi-speed cam-coder, where we do not use physical information such as body skeleton model, and so on. Time series data are obtained from the motion picture data with four marking points, and analyzed using Fast Fourier Transform (FFT) and clustering data mining method. As the experiment results, we have found that y-axes of novice data may have more highfrequency data, and that implies novice motions may have high frequency motions, and that may support our hypothesis.
- Publication Date: 26-Sep-2016
- DOI: 10.15224/978-1-63248-104-7-17
- Views: 0
- Downloads: 0