The use of computational intelligence systems such as neural networks, fuzzy set, genetic algorithms, etc., for stock market predictions has been widely established. This paper presents a generic stock pricing prediction model based on a rough set approach. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. The rough set reduction technique is applied to find all the reducts of the data which contains the minimal subset of attributes that are associated with a class label for prediction. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. Using a data set consisting of the daily movements of a stock traded in Kuwait Stock Exchange, a preliminary assessment indicates that rough sets are shown to be applicable and is an effective tool to achieve this goal. For comparison, the results obtained using the rough set approach were compared to that of the neural networks algorithm and it was shown that the Rough set approach has a higher overall accuracy rate and generates more compact and fewer rules than the neural networks.
The pulse-coupled neural network (PCNN) is a neural network that has the ability to extract edges, image segments and texture information from images. Only a few changes to the PCNN parameters are necessary to effective operating on different types of data. This is an advantage over the published image segmentation algorithms which generally require information about the target before they are effective.
This paper introduces the PCNN algorithm to provide an accurate segmentation of potential masses in mammogram images to assist radiologists in making their decisions. The fuzzy histogram hyperbolization algorithm is first applied to increase the contrast of the mammogram image before reasonable segmentation. It is followed by the PCNN algorithm to extract the region of interest to arrive at the final result. To test the effectiveness of the introduces algorithm on high quality images, a set of mammogram images was chosen and obtained from the Digital Databases for Mammography Image Analysis Society (MIAS). Four measures of quantifying enhancement have been adapted in this work. Each measure is based on the statistical information obtained from the labeled region of interest and a border area surrounding it. A comparison with the fuzzy c-mean clustering algorithm has been made.