No abstract

Handwritten character and numeral recognition have gained interest in the research community as part of the big picture of Machine Learning. Writer independent recognition systems are still in the working and the research is geared towards an optimized technique that can achieve this. In this paper, we propose a numeral recognition system that forms fuzzy sets of the features extracted using modified structural features for English, Arabic, Persian, and Devanagari Numerals. The structural features extract the geometrical primitives that distinguish each image. After the feature extraction phase, the results are input into a classifier, we test two different classifiers namely Neural Network and Naïve Base. To further enhance the recognition process with low overhead the erroneously recognized numerals (confusion matrix) are processed through the fuzzy set-based decision mechanism to enhance the numeral recognition process. Results indicate that recognition is enhanced by applying the fuzzy set-based decision mechanism for both classifer.

Soft consensus is a relevant topic in group decision making problems. Soft consensus measures are utilized to reflect the different agreement degrees between the experts leading the consensus reaching process. This may determine the final decision and the time needed to reach it. The concept of coincidence has led to two main approaches to calculating the soft consensus measures, namely, concordance among expert preferences and concordance among individual solutions. In the first approach the coincidence is obtained by evaluating the similarity among the expert preferences, while in the second one the concordance is derived from the measurement of the similarity among the solutions proposed by these experts. This paper performs a comparative study of consensus approaches based on both coincidence approaches. We obtain significant differences between both approaches by comparing several distance functions for measuring expert preferences and a consensus measure over the set of alternatives for measuring the solutions provided by experts. To do so, we use the nonparametric Wilcoxon signed-ranks test. Finally, these outcomes are analyzed using Friedman mean ranks in order to obtain a quantitative classification of the considered measurements according to the convergence criterion considered in the consensus reaching process.

No abstract

No abstract