Nadhmi Gazem

Recent Trends in Information and Communication Technology

Recently, several evolutionary algorithms have been proposed on the basis of preference in literature. Most of multi-objective evolutionary algorithms used NSGA-II due to a good performance in comparison with other multi-objective evolutionary algorithms. Our research is focused on enhancement of a well-known evolutionary algorithm NSGA-II by combining a local search method for solving Breast cancer classification problem based on Backpropagation neural network. The use of local search within the enhanced NSGA II operating can accelerate the convergence speed towards the non-dominated front and ensures the solutions attained are well spread over it. The proposed hybrid method has been experimentally evaluated by applying to the Breast cancer classification problem. It has been experimentally shown that the combination of the local search method has a positive impact to the final solution and thus increased the classification accuracy of the results.

Using TRIZ Systematic Innovation Methods for Redesign Services in Small and Medium Enterprises

Small and Medium Enterprises (SMEs) in service sector take the lion share of the market compared to other sectors such as agriculture and manufacturing. The increasing number of SMEs in the service sector brought the competition in the market to its peak, which forced SMEs to invest in improving their services. However, solving service problems requires more resources such as skilled labor or research and development funds that might be limited for many SMEs. In this paper, we review the systematic innovation methods, TRIZ, for redesign services in Small and Medium Enterprises.