A multilayer perceptron is a feedforward artificial neural network model that maps sets of input data onto a set of appropriate output. It is a modification of the standard linear perceptron in that it uses three or more layers of neurons (nodes) with nonlinear activation functions, and is more powerful than the perceptron in that it can distinguish data that is not linearly separable, or separable by a hyper plane. MLP networks are general-purpose, flexible, nonlinear models consisting of a number of units organised into multiple layers. The complexity of the MLP network can be changed by varying the number of layers and the number of units in each layer. Given enough hidden units and enough data, it has been shown that MLPs can approximate virtually any function to any desired accuracy. This paper presents the performance comparison between Multi-layer Perceptron (back propagation, delta rule and perceptron). Perceptron is a steepest descent type algorithm that normally has slow convergence rate and the search for the global minimum often becomes trapped at poor local minima. The current study investigates the performance of three algorithms to train MLP networks. Its was found that the Perceptron algorithm are much better than others algorithms.

With the evolution of mobile technologies and its applications, more and more government agencies are putting forth a huge effort to encourage citizens to use mobile government services. Hence, the citizens’ opinion is important to enhance the services and improve their engagement in the government services. Hence, this study has critically reviewed and explored the existing technology acceptance models and theories in order to develop an evaluation framework. The proposed framework is empirically tested by structural equation modeling using data collected from a structured questionnaire. Furthermore, the validated research model contains important variables to ensure the acceptance of mobile services. We hope that the implications of this study will help the public organizations and governments to gain more understanding about what drives the development and acceptance of mobile government services