Regularized discriminant analysis for multi-sensor decision fusion and damage detection with Lamb waves

Mishra, Spandan; Vanli, O. Arda; Huffer, Fred W.; Jung, Sungmoon

doi:10.1117/12.2217959

Cited by 6 publications

(6 citation statements)

References 17 publications

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“…Overall, the DT technique outperforms the MNB method for air quality prediction when both ABH and MBH discretization is used, and MBH is implemented better in each method than ABH. The accuracy and specificity of air quality prediction utilizing the DT-MBH, DT-ADH, and MNB-MBH methods in this work are rated as very good (Mishra et al, 2016), with the accuracy and specificity of this prediction exceeding 93%. Simultaneously, the F1 score is greater than 86%.…”

Section: Methodsmentioning

confidence: 74%

Implementation of a Breakpoint Halfway Discretization to Predict Jakarta's Air Quality

2022

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Despite the pandemic, Jakarta is one of the most polluted cities in the world. Knowing the daily air quality forecast aids the community, particularly Jakarta residents. Among these is the ability to protect oneself from dangerous air. The multinomial naive Bayes and the decision tree-ID3 methods are popular and perform well. Both of these strategies, however, require categorical variables. This need necessitates the implementation of a discretization technique for numerical variables. The purpose of this study is to predict Jakarta's air quality using the multinomial naive Bayes and decision tree method based on Particulate Matter 10 µg (PM10), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), Ozone (O3), and Carbon Monoxide (CO). These continuous variables are discretized in two ways: using all midway breakpoints or halfway mixture breakpoints. The results indicated that the decision tree method with the mixture breakpoints halfway approach performed better than the multinomial nave Bayes method, with an accuracy of 98.90%, a specificity of 98.92%, a sensitivity of 75.00%, a precision of 75.00%, and an F1 score of 97.81%.

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Section: Methodsmentioning

confidence: 74%

Implementation of a Breakpoint Halfway Discretization to Predict Jakarta's Air Quality

2022

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“…In addition, referring to Ramasubramanian and Singh ( 2017) , the performance of the decision tree and the proposed models was categorized as good (kappa 60-80%) and very good (kappa more than 80%), respectively. Referring to Mishra et al (2016) , both models' performance was categorized as excellent (AUC more than 90%). Compared to Panigrahi et al (2020) , who also proposed the decision tree model to identify corn plant disease, the result of this work is better.…”

Section: Resultsmentioning

confidence: 99%

Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests

Resti

Irsan

Amini

et al. 2022

Sci. Technol. Indones

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Corn is an essential agricultural commodity since it is used in animal feed, biofuel, industrial processing, and the manufacture of non-food industrial commodities such as starch, acid, and alcohol. Early detection of diseases and pests of corn aims to reduce the possibility of crop failure and maintain the quality and quantity of crop yields. A decision tree is a nonparametric classification model in statistical machine learning that predicts target variables using tree-structured decisions. The performance of this model can increase significantly if the continuous predictor variables are discretized into valid categories. However, in some cases, the result does not provide satisfactory performance. The possible cause is the ambiguity in discretizing predictor variables. The incorporation of fuzzy membership functions into the model to resolve discretization ambiguity issues. This work aims to classify diseases and pests of corn plants using the decision tree model and improve the model’s performance by implementing fuzzy membership functions. The main contribution of this work is that we have shown a significant improvement in the decision tree model performance by implementing fuzzy membership functions; S-growth, triangle, and S-shrinkage curves. The proposed fuzzy model is better than the decision tree model, with an average performance increase from the largest to the smallest; kappa (12.16%), recall (11.8%), F-score (9.71%), precision (5.08%), accuracy (3.23%), specificity (1.94%), and AUC (0.49%). The combination of bias and variance generated by the proposed model is quite small, indicating that the model is able to capture data trends well.

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“…Referring to Mishra et al (2016) , the performance of the KNN method is better than the performance of the MNB method. The performance of the MNB method in 2 categories was categorized as fair (AUC 70-80%), but the performance of the KNN method for all categories was categorized as excellent (AUC 90%).…”

Section: Resultsmentioning

confidence: 99%

“…Next, the evaluation of methods' performance to predict the class of corn plant disease and pest use measures accuracy, precision, recall, kappa, and AUC (Dinesh and Dash, 2016;Mishra et al, 2016;Karthik and Abhishek, 2019;Sokolova and Lapalme, 2009) based on the confusion matrix in Table 1 for the rst class of disease and pest corn plant. For another class, the measures are similar.…”

Section: Methodsmentioning

confidence: 99%

Identification of Corn Plant Diseases and Pests Based on Digital Images using Multinomial Naïve Bayes and K-Nearest Neighbor

Resti

Irsan

Putri

et al. 2022

Sci.Technol.Indonesia

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Statistical machine learning has developed into integral components of contemporary scientific methodology. This integration provides automated procedures for predicting phenomena, case diagnosis, or object identification based on previous observations, uncovering patterns underlying data, and providing insights into the problem. Identification of corn plant diseases and pests using it has become popular recently. Corn (Zea mays L) is one of the essential carbohydrate-producing foodstuffs besides wheat and rice. Corn plants are sensitive to pests and diseases, resulting in a decrease in the quantity and quality of the production. Eradicate pests and diseases according to their type is a solution to overcome the problem of disease in corn plants. This research aims to identify corn plant diseases and pests based on the digital image using the Multinomial Naïve Bayes and K-Nearest Neighbor methods. The data used consisted of 761 digital images with six classes of corn plants disease and pest. The investigation shows that the K-Nearest Neighbor method has a better predictive performance than the Multinomial Naïve Bayes (MNB) method. The MNB method with two categories has an accuracy level of 92.72%, a precision level of 79.88%, a recall level of 79.24%, F1-score 78.17%, kappa 72.44%, and AUC 71.91%. Simultaneously, the K-Nearest Neighbor approach with k=3 has an accuracy of 99.54 %, a precision of 88.57%, recall 94.38%, F1-score 93.59%, kappa 94.30%, and AUC 95.45%.

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Regularized discriminant analysis for multi-sensor decision fusion and damage detection with Lamb waves

Cited by 6 publications

References 17 publications

Implementation of a Breakpoint Halfway Discretization to Predict Jakarta's Air Quality

Implementation of a Breakpoint Halfway Discretization to Predict Jakarta's Air Quality

Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests

Identification of Corn Plant Diseases and Pests Based on Digital Images using Multinomial Naïve Bayes and K-Nearest Neighbor

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