Classification system for rain fed wheat grain cultivars using artificial neural network

Artificial neural network (ANN) models have found wide applications, including prediction, classification, system modeling and image processing. Image analysis based on texture, morphology and color features of grains is essential for various applications as wheat grain industry and cultivation. In order to classify the rain fed wheat cultivars using artificial neural network with different neurons number of hidden layers, this study was done in Islamic Azad University, Shahr-e-Rey Branch, during 2010 on 6 main rain fed wheat cultivars grown in different environments of Iran. Firstly, data on 6 colors, 11 morphological features and 4 shape factors were extracted, then these candidated features fed Multilayer Perceptron (MLP) neural network. The topological structure of this MLP model consisted of 21 neurons in the input layer, 6 neurons (Sardari, Sardari 39, Zardak, Azar 2, ABR1 and Ohadi) in the output layer and two hidden layers with different neurons number (21-30-10-6, 21-30-20-6 and 21-30-30-6). Finally, accuracy average for classification of rain fed wheat grains cultivars computed 86.48% and after feature selection application with UTA algorithm increased to 87.22% in 21-30-20-6 structure. The results indicate that the combination of ANN, image analysis and the optimum model architecture 21-30-20-6 had excellent potential for cultivars classification.Key words: Rain fed wheat, grain, artificial neural networks (ANNs),  multilayer perceptron (MLP), feature selection

CLASSIFICATION OF RICE GRAIN VARIETIES USING TWO ARTIFICIAL NEURAL NETWORKS (MLP AND NEURO-FUZZY)

ABSTRACT Artificial neural networks (ANNs) have many applications in various scientific areas such as identification, prediction and image processing. This research was done at the Islamic Azad University, Shahr-e-Rey Branch, during 2011 for classification of 5 main rice grain varieties grown in different environments in Iran. Classification was made in terms of 24 color features, 11 morphological features and 4 shape factors that were extracted from color images of each grain of rice. The rice grains were then classified according to variety by multi layer perceptron (MLP) and neuro-fuzzy neural networks. The topological structure of the MLP model contained 39 neurons in the input layer, 5 neurons (Khazar, Gharib, Ghasrdashti, Gerdeh and Mohammadi) in the output layer and two hidden layers; neuro-fuzzy classifier applied the same structure in input and output layers with 60 rules. Average accuracy amounts for classification of rice grain varieties computed 99.46% and 99.73% by MLP and neuro-fuzzy classifiers alternatively. The accuracy of MLP and neuro-fuzzy networks changed after feature selections were 98.40% and 99.73 % alternatively

A Re-Parameterized and Improved Nonlinear Muskingum Model for Flood Routing

The nonlinear form of the Muskingum model has been widely applied to river flood routing. There are four variants of the nonlinear Muskingum model based on alternative formulations of the nonlinear storage equation. This paper proposes a new Muskingum model with an improved, seven-parameter, nonlinear storage equation. The proposed model provides more degrees of freedom in fitting observed hydraulic data than other nonlinear Muskingum models. The proper estimation of the proposed Muskingum nonlinear model’s parameters is essential to achieve accurate flood-routing predictions. This paper introduces a hybrid method for the estimation of Muskingum parameters. The parameter-estimation method combines the shuffled frog leaping algorithm (SFLA) and the Nelder-Mead simplex (NMS). The proposed Muskingum model and parameter estimation method were applied to the routing of several hydrographs. Our results indicate improved performance of the methodology described in this work when compared with those of other Muskingum models

Application of a new hybrid nonlinear Muskingum model to flood routing

This paper introduces a hybrid non-linear Muskingum model for flood routing. The proposed hybrid model has more degrees of freedom for fitting observed data than other non-linear Muskingum models. The main goal of this work is to develop a comprehensive model for outflow routing. The proposed hybrid model's predictive skill is evaluated with experimental, real and multimodal hydrograph-routing problems. The results confirm the predictive skill of the hybrid model based on the minimisation of the sum of the square deviation (SSD) between observed and routed outflows, the sum of the absolute value of the deviations (SAD) between the observed outflow and the computed outflow, and the deviations between the peak of the routed and actual outflows (DPO). Results from this study show the hybrid model improved the SSD by 79, 15 and 5%, SAD by 50, 2 and 5%, and the DPO by 77, 4 and 34% compared with the best alternative Muskingum model in solving the experimental, real and multimodal example problems, respectively

Atorvastatin treatment softens human red blood cells: An optical tweezers study

Optical tweezers are proven indispensable single-cell micro-manipulation and mechanical phenotyping tools. In this study, we have used optical tweezers for measuring the viscoelastic properties of human red blood cells (RBCs). Comparison of the viscoelastic features of the healthy fresh and atorvastatin treated cells revealed that the drug softens the cells. Using a simple modeling approach, we proposed a molecular model that explains the drug-induced softening of the RBC membrane. Our results suggest that direct interactions between the drug and cytoskeletal components underlie the drug-induced softening of the cells. © 2018 Optical Society of America