Linear regression on a set of selected templates from a pool of randomly generated templates

Taraba, Peter

doi:10.1016/j.mlwa.2021.100126

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…This research gives a clear-cut insight into applying accurate (Harrington 2012) analysis of numerical data of customers (Siddig, Ibrahim, and Elkatatny 2021) by using the Novel Decision tree over the logistic regression algorithm. The applications are building an effective predictive model in banking and computation of accuracy for the loan prediction and calculation of the classification accuracy for loans (Taraba 2021).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Accuracy for Approving Bank Loan using Numerical Data of Customer by Comparing Decision Tree over Logistic Regression Algorithm

2022

pnr

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To predict the loan of the person using a Novel Decision Tree (DT) over the Logistic Regression algorithm (LR). Materials and Methods:The existing model uses a Logistic Regression algorithm and in the proposed Novel Decision Tree. The 20 sample values are used to find out the mean. Std. deviation, std. error means. The sigmoid function is calculated using Levene's Test for Equality of Variances both assumed and non-assumed. Decision trees use different algorithms to decide to split a node into two or more sub-nodes. The purity of the node depends upon the target variable. The prediction of the loan can be done only after knowing all the factors. The sample size was measured as 40 for both groups using G power (80%). Results: The graph explains the comparison of the mean accuracy value with algorithms Novel Decision Tree and Logistic Regression where the mean accuracy is about 88% and 70%. The statistical significant value obtained is 0.78 (p>0.05), it shows insignificance between the groups based on an independent sample T-Test. Conclusion:The mean accuracy rate of the Novel Decision tree algorithm has been improved to 88% compared to Logistic Regression which is having around 70% mean accuracy.

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Section: Introductionmentioning

confidence: 99%

Analysis of Accuracy for Approving Bank Loan using Numerical Data of Customer by Comparing Decision Tree over Logistic Regression Algorithm

2022

pnr

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Hybrid Feature-Based Disease Detection in Plant Leaf Using Convolutional Neural Network, Bayesian Optimized SVM, and Random Forest Classifier

Singh

Sreenivasu²,

Mahalaxmi

et al. 2022

Journal of Food Quality

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Plant diseases are unfavourable factors that cause a significant decrease in the quality and quantity of crops. Experienced biologists or farmers often observe plants with the naked eye for disease, but this method is often imprecise and can take a long time. In this study, we use artificial intelligence and computer vision techniques to achieve the goal of designing and developing an intelligent classification mechanism for leaf diseases. This paper follows two methodologies and their simulation outcomes are compared for performance evaluation. In the first part, data augmentation is performed on the PlantVillage data set images (for apple, corn, potato, tomato, and rice plants), and their deep features are extracted using convolutional neural network (CNN). These features are classified by a Bayesian optimized support vector machine classifier and the results attained in terms of precision, sensitivity, f-score, and accuracy. The above-said methodologies will enable farmers all over the world to take early action to prevent their crops from becoming irreversibly damaged, thereby saving the world and themselves from a potential economic crisis. The second part of the methodology starts with the preprocessing of data set images, and their texture and color features are extracted by histogram of oriented gradient (HoG), GLCM, and color moments. Here, the three types of features, that is, color, texture, and deep features, are combined to form hybrid features. The binary particle swarm optimization is applied for the selection of these hybrid features followed by the classification with random forest classifier to get the simulation results. Binary particle swarm optimization plays a crucial role in hybrid feature selection; the purpose of this Algorithm is to obtain the suitable output with the least features. The comparative analysis of both techniques is presented with the use of the above-mentioned evaluation parameters.

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Optimal blending of multiple independent prediction models

Taraba

2023

Front. Artif. Intell.

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We derive blending coefficients for the optimal blend of multiple independent prediction models with normal (Gaussian) distribution as well as the variance of the final blend. We also provide lower and upper bound estimation for the final variance and we compare these results with machine learning with counts, where only binary information (feature says yes or no only) is used for every feature and the majority of features agreeing together make the decision.

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Linear regression on a set of selected templates from a pool of randomly generated templates

Cited by 3 publications

References 8 publications

Analysis of Accuracy for Approving Bank Loan using Numerical Data of Customer by Comparing Decision Tree over Logistic Regression Algorithm

Analysis of Accuracy for Approving Bank Loan using Numerical Data of Customer by Comparing Decision Tree over Logistic Regression Algorithm

Hybrid Feature-Based Disease Detection in Plant Leaf Using Convolutional Neural Network, Bayesian Optimized SVM, and Random Forest Classifier

Optimal blending of multiple independent prediction models

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