An Application of ANN Trained by ABC Algorithm for Classification of Wheat Grains

Abstract

Artificial Neural Networks (ANNs) have emerged as an important tool for classification problem. This paper presents an application of ANN model trained by artificial bee colony (ABC) optimization algorithm for classification the wheat grains into bread and durum. ABC algorithm is used to optimize the weights and biases of three-layer multilayer perceptron (MLP) based ANN. The classification is carried out through data of wheat grains (#200) acquired using image-processing techniques (IPTs). The data set includes five grain's geometric parameters: length, width, area, perimeter and fullness. The ANN-ABC model input with the geometric parameters are trained through 170 wheat grain data and their accuracies are tested via 30 data. The ANN-ABC model numerically calculate the outputs with mean absolute error (MAE) of 0.0034 and classify the grains with accuracy of 100% for the testing process. The results of ANN-ABC model are compared with other ANN models trained by 4 different learning algorithms. These results point out that the ANN trained by ABC optimization algorithm can be successfully applied to classification of wheat grains.

Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data

“…When the Gauss-Newton method is used to express the backpropagation of neural network, the algorithm has a higher probability to reach an optimal solution [29]. In addition, the LM algorithm has faster convergence in backpropagation and therefore, widely used [30]. e Hessian calculation approximation (H) and gradient calculation (g) in LM algorithm are shown in equations (1) and (2), respectively:…”

“…More information of LM algorithm can be found in Ramadasan et al [31]. [30]. More information on the BR algorithm can be found in Bueden and Winkler (2008).…”

Recurrent Neural Network Training using ABC Algorithm For Traffic Volume Prediction

“…In this case, the ABC algorithm is used to find the precise weights that enable the network connections to make accurate decisions. The algorithm uses a cost function as a measure for our progress in determining the right weights [19], [25].…”

“…This process as represented by the ratios from the three gates is denoted by equation (14) and also depicted diagrammatically in the figure 3 [20]. Therefore, the algorithm below outlines the optimization process for the deep neural network using the ABC algorithm [10], [12], [19], [ is the fitness value of ; indicates a neighbor solution of ; is the probability value of ;…”

“…An employed bee produces a modification on the solution in its memory depending on the probability and fitness tests. Thereby, generating optimal weights that serve to minimize the cost function and with each cycle the RNN is adequately trained with varying parameters using the ABC algorithm until optimal conditions are met [10], [18], [19].…”