scholarly journals A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars

Agronomy ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 117 ◽  
Author(s):  
Yousef Abbaspour-Gilandeh ◽  
Amir Molaee ◽  
Sajad Sabzi ◽  
Narjes Nabipur ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Brown Rice ◽  
Principal Component ◽  
Rice Cultivar ◽  
Rice Cultivars ◽  
Texture Properties ◽  
Effective Components ◽  
Significant Difference ◽  
Artificial Neural

Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2%, 87.7%, and 83.1%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars.

Non-invasive prediction of bloodstain age using the principal component and a back propagation artificial neural network

2017 ◽  
Vol 14 (9) ◽  
pp. 095601 ◽  
Author(s):  
Huimin Sun ◽  
Yaoyong Meng ◽  
Pingli Zhang ◽  
Yajing Li ◽  
Nan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Principal Component ◽  
Non Invasive ◽  
Artificial Neural

scholarly journals P055 THE USE OF ARTIFICIAL NEURAL NETWORK TO PREDICT THE RISK OF INCISIONAL HERNIA AFTER MIDLINE LAPAROTOMY

2021 ◽  
Vol 108 (Supplement_8) ◽  
Author(s):  
Edgard Efren Lozada Hernandez ◽  
Tania Aglae Ramírez del Real ◽  
Dagoberto Armenta Medina ◽  
Jose Francisco Molina Rodriguez ◽  
Juan ramon Varela Reynoso
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Incisional Hernia ◽  
False Negative ◽  
Wound Dehiscence ◽  
Midline Laparotomy ◽  
Significant Difference ◽  
Positive Rate ◽  
Artificial Neural

Abstract Aim “Incisional Hernia (IH) has an incidence of 10-23%, which can increase to 38% in specific risk groups. The objective of this study was developed and validated an artificial neural network (ANN) model for the prediction of IH after midline laparotomy (ML) and this model can be used by surgeons to help judge a patient’s risk for IH.” Material and Methods “A retrospective, single arm, observational cohort trial was conducted from January 2016 to December 2020. Study participants were recruited from patients undergoing ML for elective or urgent surgical indication. Using logistic regression and ANN models, we evaluated surgical treated IH, wound dehiscence, morbidity, readmission, and mortality using the area under the receiver operating characteristic curves, true-positive rate, true-negative rate, false-positive rate, and false-negative rates.” Results “There was no significant difference in the power of the ANN and logistic regression for predicting IH, wound dehiscence, mortality, readmission, and all morbidities after ML. The resulting model consisted of 4 variables: surgical site infection, emergency surgery, previous laparotomy, and BMI(Kg/m2) > 26. The patient with the four positive factors has a 73% risk of developing incisional hernia. The area under the curve was 0.82 (95% IC 0.76-0.87). Conclusions “ANNs perform comparably to logistic regression models in the prediction of IH. ANNs may be a useful tool in risk factor analysis of IH and clinical applications.”

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