(Purposes) This paper shows the importance and necessity of intelligent identification technology of fruit detection. (Methods) We enumerate several state-of-the-art methods and illustrate the specific application in the process of recognition, by selecting eleven highly related literature. (Results) On this basis, we make an analysis and comparison on the advantages and disadvantages of each approaches. (Conclusion) This summary can be beneficial to researchers who are interested in fruit identification.

Abstract. In order to detect and identify fruit category more efficiently, this study presents a novel method based on Haar wavelet entropy, multilayer perceptron, and standard genetic algorithm. The Haar wavelet entropy extracted features from a given fruit image. The multilayer perceptron received the features and acted as a classifier. Finally, genetic algorithm was used to train the classifier. The experiment was performed over a 10x12-fold cross validation. The overall accuracy was 81.11±4.23%., better than the result of back propagation gradient descent algorithm of 74.17± 4.98%, and the result of simulated annealing of 78.10± 3.57%.

Abstract-This survey paper describes a focused literature survey of machine learning methods in order to detect pathological brain. Based on the published time and emerging methods, this paper introduces in details the methods used in each documents. Because of the requirement to select a good approach in the process of pathological brain analysis, we compare the classification results of different methods and present a promising future.