MicroRNA (miRNA) plays vital roles in biological processes like RNA splicing and regulation of gene expression. Studies have revealed that there might be possible links between oncogenesis and expression profiles of some miRNAs, due to their differential expression between normal and tumor tissues. However, the automatic classification of miRNAs into different categories by considering the similarity of their expression values has rarely been addressed. This article proposes a solution framework for solving some real-life classification problems related to cancer, miRNA, and mRNA expression datasets. In the first stage, a multiobjective optimization based framework, non-dominated sorting genetic algorithm II, is proposed to automatically determine the appropriate classifier type, along with its suitable parameter and feature combinations, pertinent for classifying a given dataset. In the second page, a stack-based ensemble technique is employed to get a single combinatorial solution from the set of solutions obtained in the first stage. The performance of the proposed two-stage approach is evaluated on several cancer and RNA expression profile datasets. Compared to several state-of-the-art approaches for classifying different datasets, our method shows supremacy in the accuracy of classification.

No abstract

No abstract

Only a few works has been done for printed devanagari text in the area of optical character recognition. In this paper there is describing about a simple and fast algorithm for detection of italic and bold character in Devanagari script, without recognition of actual character. Here present an automatic information which tells us about the font type phase in the way of weight and slope. The process of identification and classification of italic and bold character can be used for making an accuracy of the text recognition system in the OCR. This simple and fast algorithm gives high accuracy and very easy to implement.