Fruit classification is quite difficult because of the various categories and similar shapes and features of fruit. In this work, we proposed two novel machine-learning based classification methods. The developed system consists of wavelet entropy (WE), principal component analysis (PCA), feedforward neural network (FNN) trained by fitness-scaled chaotic artificial bee colony (FSCABC) and biogeography-based optimization (BBO), respectively. The K-fold stratified cross validation (SCV) was utilized for statistical analysis. The classification performance for 1653 fruit images from 18 categories showed that the proposed “WE + PCA + FSCABC-FNN” and “WE + PCA + BBO-FNN” methods achieve the same accuracy of 89.5%, higher than state-of-the-art approaches: “(CH + MP + US) + PCA + GA-FNN ” of 84.8%, “(CH + MP + US) + PCA + PSO-FNN” of 87.9%, “(CH + MP + US) + PCA + ABC-FNN” of 85.4%, “(CH + MP + US) + PCA + kSVM” of 88.2%, and “(CH + MP + US) + PCA + FSCABC-FNN” of 89.1%. Besides, our methods used only 12 features, less than the number of features used by other methods. Therefore, the proposed methods are effective for fruit classification.

Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization

Accurate fruit classification is difficult to accomplish because of the similarities among the various categories. In this paper, we proposed a novel fruit-classification system, with the goal of recognizing fruits in a more efficient way. Our methodology included the following steps. First, a four-step pre-processing was employed. Second, the features colour, shape, and texture were extracted. Third, we utilized principal component analysis to remove excessive features. Fourth, a novel fruit-classification system based on biogeography-based optimization BBO and feedforward neural network FNN was proposed, with the short name of BBO-FNN. The experiment employed over 1653 chromatic fruit images 18 categories by fivefold stratified cross-validation. The results showed that the proposed BBO-FNN yielded an overall accuracy of 89.11%, which was higher than the five state-of-the-art methods: genetic algorithm-FNN, artificial bee colony-FNN, particle swarm optimization-FNN, kernel support vector machine, and ant colony optimization-FNN. Also, the BBO-FNN achieved the same accuracy as fitness-scaling chaotic artificial bee colony-FNN, but it performed much faster than the latter. The proposed BBO-FNN was effective in fruit-classification in terms of classification accuracy and computation time. This indicated that it can be applied in credible use.

Fruit classification by biogeography-based optimization and feedforward neural network

Background) We proposed a novel computer-aided diagnosis (CAD) system based on the hybridization of biogeography-based optimization (BBO) and particle swarm optimization (PSO), with the goal of detecting pathological brains in MRI scanning. (Method) The proposed method used wavelet entropy (WE) to extract features from MR brain images, followed by feed-forward neural network (FNN) with training method of a Hybridization of BBO and PSO (HBP), which combined the exploration ability of BBO and exploitation ability of PSO. (Results) The 10 repetition of k-fold cross validation result showed that the proposed HBP outperformed existing FNN training methods and that the proposed WE + HBP-FNN outperformed fourteen state-of-the-art CAD systems of MR brain classification in terms of classification accuracy. The proposed method achieved accuracy of 100%, 100%, and 99.49% over Dataset-66, Dataset-160, and Dataset-255, respectively. The offline learning cost 208.2510 s for Dataset-255, and merely 0.053s for online prediction. (Conclusion) The proposed WE + HBP-FNN method achieves nearly perfect detection pathological brains in MRI scanning.

/pdf/pathological-brain-detection-in-magnetic-resonance-imaging-105mw356cw.pdf

Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-based Optimization and Particle Swarm Optimization

(Aim) Abnormal breast can be diagnosed using the digital mammography. Traditional manual interpretation method cannot yield high accuracy. (Method) In this study, we proposed a novel computer-aided diagnosis system for detecting abnormal breasts in mammogram images. First, we segmented the region-of-interest. Next, the weighted-type fractional Fourier transform (WFRFT) was employed to obtain the unified time-frequency spectrum. Third, principal component analysis (PCA) was introduced and used to reduce the spectrum to only 18 principal components. Fourth, feed-forward neural network (FNN) was utilized to generate the classifier. Finally, a novel algorithm-specific parameter free approach, Jaya, was employed to train the classifier. (Results) Our proposed WFRFT + PCA + Jaya-FNN achieved sensitivity of 92.26%±3.44%, specificity of 92.28%±3.58%, and accuracy of 92.27%±3.49%. (Conclusions) The proposed CAD system is effective in detecting abnormal breasts and performs better than 5 state-of-the-art systems. Besides, Jaya is more effective in training FNN than BP, MBP, GA, SA, and PSO. ∗Address for correspondence: Jiangsu Key Laboratory of 3D, Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042, China 192 S. Wang et al. / Abnormal Breast Detection in Mammogram Images by Feed-forward Neural Network Trained

Abnormal Breast Detection in Mammogram Images by Feed-forward Neural Network Trained by Jaya Algorithm

This work proposes a tea-category identification (TCI) system, which can automatically determine tea category from images captured by a 3 charge-coupled device (CCD) digital camera. Three-hundred tea images were acquired as the dataset. Apart from the 64 traditional color histogram features that were extracted, we also introduced a relatively new feature as fractional Fourier entropy (FRFE) and extracted 25 FRFE features from each tea image. Furthermore, the kernel principal component analysis (KPCA) was harnessed to reduce 64 + 25 = 89 features. The four reduced features were fed into a feedforward neural network (FNN). Its optimal weights were obtained by Jaya algorithm. The 10 × 10-fold stratified cross-validation (SCV) showed that our TCI system obtains an overall average sensitivity rate of 97.9%, which was higher than seven existing approaches. In addition, we used only four features less than or equal to state-of-the-art approaches. Our proposed system is efficient in terms of tea-category identification.

https://www.mdpi.com/1099-4300/18/3/77/pdf

Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm

The utilization of back-propagation neural network in identification of internal dynamics of chaotic motion is found appropriate. However, during its training through Rumelhart algorithm, it is found that, a high learning rate ( ) leads to rapid learning but the weights may oscillate, while a lower value of ` ' leads to slower learning process in weight updating formula   Momentum factor ( ) is to accelerate the convergence of error during the training in the equation   and   while transfer function sigmoid  . It is the most complicated and experimental task to identify optimum value of ` ' and ` ' during the training. To identify optimum value of   ` ' and  ` ' , firstly the network is trained with 103 epochs under different values of  ` ' in the close interval   and   At   the convergence of initial weights and minimization of error (i.e., mean square error) process is found appropriate. Afterwards to find optimum value of  , the network was trained again with   = 0.3 (fixed) and with different values of   in the close interval   for 103 epochs. It was observed that the convergence of initial weights and minimization of error was appropriate with  = 0.3 and   = 0.9. On this optimum value of   and   the network was trained successfully from local minima of error = 1.67029292416874E-03 at 103 epochs to global minima of error = 4.99180426869658E-04 at 15   105 epochs. At the global minima, the network has exhibited excellent performance in identification of internal dynamics of chaotic motion and in prediction of future values by past recorded data series. These essentials are presented through this research paper.

https://journalskuwait.org/kjs/index.php/KJS/article/download/394/44

G. Shrivastava

Papers

Impact of learning rate and momentum factor in the performance of back-propagation neural network to identify internal dynamics of chaotic motion