Quality Assessment of Web Services Using Soft Computing Techniques - Patro, V Mohan; Khuntia, Bonomali; Patra, Manas Ranjan (Discoveries in Agriculture and Food Sciences, 2015)
With the ever growing number of web services in the Internet the selection of a suitable web service has become a mind boggling task. Several quality parameters are being considered for a judicious selection of a web service in a given service context. One of the popular measures for classifying web services is the use of Web Service Relevance Function (WSRF). A number of classifiers have been used which have yielded accuracy up to 99% considering WSRF as one of the attributes of QWS dataset. But, the maximum accuracy achieved by any classifier without WSRF is only 89.99%. In this paper, a feed-forward backpropagation neural network (BPNN) with adaptive momentum factor has been employed to further improve the accuracy. Furthermore, a similar neural network is modelled for determining the WSRF of the web services included in the QWS dataset. As an alternative, Genetic Algorithm is used to find the weight factors associated with each parameter to calculate WSRF, assuming it to be a linear function of those parameters. The average errors for WSRF per pattern obtained in the two approaches are 1.0447 and 1.176 respectively. These calculated WSRF values can be used for classification to enhance the accuracy of a classifier.