Predicting Usability of Library Websites: Fuzzy Inference System and Artificial Neural Network based Approach
R. R. Kamat, R. S. Kamath, R. K. Kamat, S. M. Pujar
Journal of Networking Technology, Vol. 11, No. 2, 2020.
DOI: https://doi.org/10.6025/jnt/2020/11/2/59-66
Link: http://www.dline.info/jnt/fulltext/v11n2/jntv11n2_2.pdf
Abstract:
We report soft computing approach for predicting usability of library websites using Fuzzy Inference system (FIS) and Artificial Neural Network (ANN). Proposed model is the fusion of these two computing paradigms to create a successful synergic effect. The website usability dataset is derived from doctoral thesis on Usability Evaluation of Library Websites [1]. Usability index (UI) determinants such as visibility, SR_world, user control, consistency, error, recognition, flexibility, aesthetic, recovery, documentation, effectiveness, efficiency, memorability, learnability, satisfaction and motivation are considered here for computing. The reported investigations depicts optimum ANN architecture achieved by tuning the parameters viz. network type, training function, transfer function and number of neurons in hidden neurons. ANN architecture, thus derived entails nonlinear sigmoid activation function for hidden layer and Levenberg-Marquardt back propagation method for training the model. Moreover the performance of the model is evaluated with reference to Mean Squared Error (MSE), Pearson Correlation Coefficient (r) and Gradient (g). Validation of the model has portrayed reasonably
good prediction accuracy.