Segiru Omatu

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Fatigued bills have harmful influence on daily operation of Automated Teller Machine (ATM). To make the fatigued bills classification more efficient, development of an automatic fatigued bill classification method is desired. We proposed a new method to estimate fatigue levels of bills from acoustic energy feature of banking machines by using the supervised(More)
Microarray technology has been increasingly used in cancer research because of its potential for measuring expression levels of thousands of genes simultaneously in tissue samples. It is used to collect the information from tissue samples regarding gene expression differences that could be useful for cancer classification. However, this classification task(More)
In this paper, an intelligent electronic nose (EN)system designed using cheap metal oxide gas sensors (MOGS) is designed to detect fires at an early stage. The time series signals obtained from the same source of fire are highly correlated, and different sources of fire exhibit unique patterns in the time series data. Therefore, the error back propagation(More)
In the practical use of automated teller machines (ATM's), dealing with much fatigued bills causes serious trouble. To avoid this problem, rapid development of automatic classification methods that can be implemented on banking machines is desired. We propose a new automatic classification method of fatigued bill based on acoustic signal feature of a(More)
This paper addresses the reliability of neuro-classifiers for bank note recognition. A local principal component analysis (PCA) method is applied to remove nonlinear dependencies among variables and extract the main principal features of data. At first the data space is partitioned into regions by using a self-organizing map (SOM) model and then the PCA is(More)
In this paper, a reliable electronic nose (EN) system designed from the combination of various metal oxide gas sensors (MOGS) is applied to detect the early stage of fire from various sources. The time series signals of the same source of fire in every repetition data are highly correlated and each source of fire has a unique pattern of time series data.(More)
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