In this paper Cubic Bezier curve-based image retrieval system is proposed. This system evaluates similarity of each image in its database to a query image in terms of shape characteristics. Then, returns those images within a desired range of similarity. The proposed system determines nonlinear relationship between image's features for more accurate similarity comparison between query image and existing database images. Among existing approaches to shape feature analysis, statistical approach to extract shape features is adopted here. It works in form of control points of spline curves from both given query image and images of available database. These control points are further used to find out the Fourier Descriptors. Control points and Fourier descriptors are used for image retrieval in proposed system. With the vast number of images available on-line, quality CBIR systems are critical. The performance and results obtained by proposed system are compared to other CBIR systems. Comparison reveals proposed system performance is state of the art. In many parameters it outperforms other CBIR systems.
In banking sector credit score plays a very important factor. It is important to find which customer is valid and which is not valid for loan. Now to classify customer’s credit score is used. Based on this credit score of customers the bank will decide whether to approve loan or not. In banks there are major failures due to credit risks. We can automate this by using various Machine learning algorithms to identify loan defaulters. To classify and predict the customers here various Machine learning techniques like gradient boosting, random forest and Feature Selection technique along with Decision Tree are used. Using these algorithms we accurately classify valid and invalid customers for loan. Designed model can classify their customers into good and bad applicants and train the model for getting the better accuracy of the customer data.
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