Application of Gist SVM in Cancer Detection

In this paper, we study the application of GIST SVM in disease prediction (detection of cancer). Pattern classification problems can be effectively solved by Support vector machines. Here we propose a classifier which can differentiate patients having benign and malignant cancer cells. To improve the accuracy of classification, we propose to determine the optimal size of the training set and perform feature selection. To find the optimal size of the training set, different sizes of training sets are experimented and the one with highest classification rate is selected. The optimal features are selected through their F-Scores.

The Expert System Designed to Improve Customer Satisfaction

Customer Relationship Management becomes a leading business strategy in highly competitive business environment. It aims to enhance the performance of the businesses by improving the customer satisfaction and loyalty. The objective of this paper is to improve customer satisfaction on product's colors and design with the help of the expert system developed by using Artificial Neural Networks. The expert system's role is to capture the knowledge of the experts and the data from the customer requirements, and then, process the collected data and form the appropriate rules for choosing product's colors and design. In order to identify the hidden pattern of the customer's needs, the Artificial Neural Networks technique has been applied to classify the colors and design based upon a list of selected information. Moreover, the expert system has the capability to make decisions in ranking the scores of the colors and design presented in the selection. In addition, the expert system has been validated with a different customer types.