By Wei (ed.)

This lawsuits quantity includes chosen papers awarded on the 2014 AASRI overseas convention on utilized Engineering Sciences, held in Hollywood, l. a., united states. Contributions disguise the newest advancements and advances within the box of utilized Engineering Sciences.

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1997). Since then, more proposals with improved characteristics based on the traditional sharpening have been presented, for example by Dolecek & Mitra (2005) and Zaimin et al. (2011). However, the traditional sharpening method was improved by Hartnett & Boudreaux (1995) (this method will be referred to here as improved sharpening). The first application of the improved sharpening technique to a comb decimator filter was recently introduced by Jimenez & Dolecek (2013). This approach was based on a two-stage comb decimator filter.

The first application of the improved sharpening technique to a comb decimator filter was recently introduced by Jimenez & Dolecek (2013). This approach was based on a two-stage comb decimator filter. Then, the improved sharpening was applied by Jimenez et al. (2013) to the non-recursive comb filters with a decimation factor that is a power of two. This paper is an extension of our previous works (Jimenez & Dolecek 2013, Jimenez et al. 2013). The contributions of this paper are the following: The comb filter is popular due to its simplicity and is usually used in the first stage of a decimation process.

In this case, classifiers tend to produce high classification accuracy on the majority class but very poor accuracy on the minority class. Imbalanced data learning is of great importance and a challenge in many real applications, such as fraud detection [1], medical diagnostics [2], image recognition [3] and oil spill detection [4], etc. In general, imbalanced data learning involves two main aspects [5]. The first uses various sampling techniques to create an artificially balanced distribution of class examples for training.

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