Histogram Equalization (HE) is a very popular algorithm in the field of image enhancement. Its theory is very simple but effective and easy to implement. However, this algorithm cannot get good result in some special cases. Furthermore, it will change the mean brightness of original image significantly. According to these drawbacks of HE, some novel algorithms have been proposed. The main target of these algorithms is trying to preserve the brightness and entropy of original image better. But they also decrease the enhancement efforts at the same time. In this paper, a novel algorithm, Normal Matching Histogram Equalization (NMHE), is proposed. Experimental results show that this algorithm can not only preserve the mean brightness and entropy of original image but also keep the enhancement efforts simultaneously.

The suggested method for extracting Functional requirement from the SRS by using concept of clustering with neural genetic approach. Extracting functional requirement is a different task for judgment, a pattern in the functional requirement that is communicated as regular expression in the contribution of SRS. We proposed and implement pre-processing of SRS by text mining and classified by cluster based supervised leaning. We reduce the complexity and increase the accuracy functional requirement extraction by using text from SRS and then clustered then classified by cluster based supervised learning. It targets at providing an automatic supervised Functional requirement content mining based on mode based structure that divides the SRS documents when a shared pattern is found.

Environmental pollution as a result of greenhouse gas emissions is becoming a major problem within developing nations experiencing significant growth in their industrial activities. This increase in industrial activities is deemed to be in correlation with economic growth as seen in countries like China and India