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Stock market prediction has always been a difficult process, most of the prediction rely solely on the data of the corresponding stock market. Relationship of gold and oil price with stock market performance has been proven significant in some major world stock index. Prediction of stock market price index using machine learning methods is expected to perform well, with the ability of machine learning method to predict using nonlinear inputs. The methods were commonly able to predict relatively well in predicting the values of Japanese Nikkei 225 and Japanese Nikkei 400 indexes.

This paper presents an approach for automatic recognition of vehicle make from its logo in a front-view image using SIFT descriptor of interior structure and back-propagation neural network. The proposed method focuses on recognition of automobile make by integrating Top-Hat transformation with shape descriptor to locate the logo of an automobile from an image then uses back-propagation neural network to recognize an automobile make from the SIFT (Scale-Invariant Feature Transform) descriptor of inner structure of the logo. The training set contains eighteen images of six different logos, whereas the test set contains 220 images of automobile. The recognition results from the proposed method were compared with the results from other existing methods and the results reveal that it can recognize automobile makes regardless of illumination condition or position and the accuracy rate is over 50%.

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