Language transliteration is one of the important area in natural language processing. Accurate transliteration of named entities plays an important role in the performance of machine translation and cross-language information retrieval processes. The transliteration model must be design in such a way that the phonetic structure of words should be preserve as closely as possible. We have developed hybrid (statistical +rules) approach based transliteration system of person names; from a person name written in Punjabi (Gurumukhi Script), the system produces its English (Roman Script) transliteration. Experiments have shown that the performance is sufficiently high. The overall accuracy of system comes out to be 95.23%.

In real life, information available on situations/issues/problems is vague, inexact, or insufficient and so the parameters involved therein are grasped in an uncertain way by the decision maker. But in real life such uncertainty is unavoidable. One possible way out is to consider the knowledge of experts about the parameters involved as fuzzy data. In a network, the arc length may represent time or cost. In Relevant literature reports there are several methods to solve such problems in network-flow. This paper proposes an optimized path for use in networks, using trapezoidal intuitionistic fuzzy numbers, assigned to each arc length in a fuzzy environment. It proposes a new algorithm to find the optimized path and implied distance from source node to destination node.

Gene Selection from gene expression data for Cancer prediction has been an area of intensive research, aiming at identifying the minimal and optimal set of candidate genes that could generate accurate predictive performance. The two major problems encountered in this process are the high dimensionality of data with comparatively few instances and the need to categorize records under multiple classes. In this paper we propose a novel approach called Rank-Weight Feature Selection that utilizes the filtering capacity of more than one feature selection algorithm to detect the minimal set of predictive genes that generate higher predictor performance in categorizing and predicting diverse oncogenic gene expression data. The filtered features (genes) are weighted based on the number of feature relevance algorithms reporting them to be significant. The ranked genes are then used to validate the proposed method by utilizing ten classifiers over five diverse gene expression datasets. The results proved that the proposed approach generated higher predictive performance with fewer features than previously reported results with the most relevant and minimal set of genes and commend classifiers based on their accuracy and reliability in predicting cancer data.

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In this paper, we introduce product intuitionistic fuzzy graphs and prove several results which are analogous to intuitionistic fuzzy graphs. We conclude by giving properties for a product partial intuitionistic fuzzy sub graph.