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Vertical handoff (VHO) decision services play an essential role in the current wireless networks. Many algorithms designed aim at quality of service while granting seamless roaming amongst a number of heterogeneous access networks. In this study, the authors proposed an intelligent VHO decision strategy targeting to maximise user satisfaction without compromising on quality of service for non-real time mobile based services. The proposed strategy applies consumer surplus value (CSV)-based pricing scheme and also a prediction system to approximate network performances at various intervals. Finally, a fuzzy rule-based behavioral system is proposed to find optimal network by considering both CSV as well as network performance as stimulus parameters. Simulation is carried out using OM Net++ and MATLAB, and the results are acknowledged with discussion and analysis.

In this study, we compare the performance of four different imputation strategies ranging from the commonly used Listwise Deletion to model based approaches such as the Max- 19imum Likelihood on enhancing completeness in incomplete software project data sets. We evaluate the impact of each of these methods by implementing them on six different 21 real-time software project data sets which are classified into different categories based on their inherent properties. The reliability of the constructed data sets using these 23 techniques are further tested by building prediction models using stepwise regression. The experimental results are noted and the findings are finally discussed. 25

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