scholarly journals Comparative Analysis of Machine Learning Algorithms Based on Variable Importance Evaluation

2021 ◽  
Author(s):  
Hasan YILDIRIM
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Learning Algorithms ◽  
Variable Importance ◽  
Machine Learning Algorithms

scholarly journals Comparative analysis of machine learning algorithms for prediction of smart grid stability †

2021 ◽  
Author(s):  
Ali Kashif Bashir ◽  
Suleman Khan ◽  
B Prabadevi ◽  
N Deepa ◽  
Waleed S. Alnumay ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Smart Grid ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals A Comparative Analysis of Machine Learning Algorithms Modeled from Machine Vision-Based Lettuce Growth Stage Classification in Smart Aquaponics

2020 ◽  
Vol 11 (9) ◽  
pp. 442-449 ◽  
Author(s):  
Sandy C. Lauguico ◽  
◽  
Ronnie S. Concepcion II ◽  
Jonnel D. Alejandrino ◽  
Rogelio Ruzcko Tobias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Machine Vision ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Urban Farming ◽  
K Nearest Neighbor ◽  
Lettuce Growth

The arising problem on food scarcity drives the innovation of urban farming. One of the methods in urban farming is the smart aquaponics. However, for a smart aquaponics to yield crops successfully, it needs intensive monitoring, control, and automation. An efficient way of implementing this is the utilization of vision systems and machine learning algorithms to optimize the capabilities of the farming technique. To realize this, a comparative analysis of three machine learning estimators: Logistic Regression (LR), K-Nearest Neighbor (KNN), and Linear Support Vector Machine (L-SVM) was conducted. This was done by modeling each algorithm from the machine vision-feature extracted images of lettuce which were raised in a smart aquaponics setup. Each of the model was optimized to increase cross and hold-out validations. The results showed that KNN having the tuned hyperparameters of n_neighbors=24, weights='distance', algorithm='auto', leaf_size = 10 was the most effective model for the given dataset, yielding a cross-validation mean accuracy of 87.06% and a classification accuracy of 91.67%.

Sign in / Sign up

Export Citation Format

Share Document