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Iwata K.,Aichi University | Nakashima T.,Sugiyama Jogakuen University | Anan Y.,Omron Software Co Ltd | Ishii N.,Aichi Institute of Technology
Studies in Computational Intelligence | Year: 2012

In this paper, we cluster and analyze data from the past embedded software development projects using self-organizing maps (SOMs)[9] that are a type of artificial neural networks that rely on unsupervised learning. The purpose of the clustering and analysis is to improve the accuracy of predicting the number of errors. A SOMproduces a low-dimensional, discretized representation of the input space of training samples; these representations are called maps. SOMs are useful for visualizing low-dimensional views of high-dimensional data, a multidimensional scaling technique. The advantages of SOMs for statistical applications are as follows: (1) data visualization, (2) information processing on association and recollection, (3) summarizing large-scale data, and (4) creating nonlinear models. To verify our approach, we perform an evaluation experiment that compares SOM classification to product type classification using Welch's t-test for Akaike's Information Criterion (AIC). The results indicate that the SOM classification method is more contributive than product type classification in creating estimation models, because the mean AIC of SOM classification is statistically significantly lower. Source


Iwata K.,Aichi University | Nakashima T.,Sugiyama Jogakuen University | Anan Y.,Omron Software Co Ltd | Ishii N.,Aichi Institute of Technology
Studies in Computational Intelligence | Year: 2010

In this paper we propose a method for reducing the margin of error in effort and error prediction models for embedded software development projects using artificial neural networks(ANNs). In addition, we perform an evaluation experiment that uses Welch's t-test to compare the accuracy of the proposed ANN method with that of our original ANN model. The results show that the proposed ANN model is more accurate than the original one in predicting the number of errors for new projects, since the means of the errors in the proposed ANN are statistically significantly lower. © 2010 Springer-Verlag Berlin Heidelberg. Source


Ohmura R.,Toyohashi University of Technology | Uchida R.,Omron Software Co Ltd
UbiComp 2014 - Adjunct Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing | Year: 2014

Disrupting the transmission of sensor data due to sensor failure or connection loss significantly decrease accuracy in existing activity recognition techniques. We introduce an approach towards managing missing sensor data which operates at each step of the standard activity recognition, beginning with raw sensor data, feature calculation, classification, and result, as well as their combination methods. Our evaluation showed that the F1-score increased from 0.61 in the case of sensor data loss to 0.68 with the combination of all methods. Moreover, by selecting the combination of methods according to the failed sensor position, the F1-score increased to 0.69. Copyright 2014 ACM. Source


Anan Y.,Omron Software Co Ltd | Nakashima T.,Nagoya University | Iwata K.,Aichi University | Yonemitsu H.,Omron Software Kyusyu Co. | And 2 more authors.
IEEJ Transactions on Electronics, Information and Systems | Year: 2010

In this paper, we propose an errors estimation model for embedded software development projects and implement a model visual tool for processing projects with upper and lower limits from estimated errors. The models are derived by multiple regression analysis, because this way is well known and easily used by developers. In addition, we perform evaluate to conform the models effectiveness. © 2010 The Institute of Electrical Engineers of Japan. Source


Iwata K.,Aichi University | Nakashima T.,Sugiyama Jogakuen University | Anan Y.,Omron Software Co Ltd | Ishii N.,Aichi Institute of Technology
IEEJ Transactions on Electronics, Information and Systems | Year: 2010

In this paper, we establish effort and error prediction models using an artificial neural networks (ANNs). We propose the normalizing method to reduce the margin of errors for ANN models. In addition, we perform an evaluation experiment to compare the accuracy of the ANN models with that of the regression analysis (RA) model and that of two ANN models using Steel-Dwass's multiple comparison test. The results show that each ANN model is more accurate than the RA model and the proposed method can reduce the errors for some cases, since the mean errors of the ANN models are statistically significantly lower. © 2010 The Institute of Electrical Engineers of Japan. Source

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