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Predictions of financial time series often show a characteristic one step shift relative to the original data as in a random walk. This has been the cause for opposing views whether such time series do contain information that can be extracted for predictions, or are simply random walks. In this case study, we show that NNs that are capable of extracting(More)
We present a method for optimizing software testing efficiency by identifying the most error prone path clusters in a program. We do this by developing variable length genetic algorithms that optimize and select the software path clusters which are weighted with sources of error indexes. Although various methods have been applied to detecting and reducing(More)
This paper is the continuation of previously published work in which we have been analysing different methods – traditionally used in speech recognition – for their suitability to be applied to Environmental Sound Recognition. While current research devotes much effort to speech and speaker recognition, Environmental Sound Recognition is an area where(More)
Visualizations are a crucial component in CAD systems, typically displaying design layout or physical behavior. In this paper we introduce in an innovative way visualizations, that combine both, the display of physical dimension and abstract concepts. These types of visualizations are part of MAGDA a CAD system for Micro Electro Mechanical Systems (MEMS).(More)
In earlier work we have demonstrated that GA can successfully identify error prone paths that have been weighted according to our weighting scheme. In this paper we investigate whether the depth of strata in the software affects the performance of the GA. Our experiments show that the GA performance changes throughout the paths. It performs better in the(More)