Improving Thermal Camera Performance in Fever Detection during COVID-19 Protocol with Random Forest Classification

Putrada, Aji Gautama; Perdana, Doan

doi:10.1109/icadeis52521.2021.9702045

Cited by 13 publications

(2 citation statements)

References 19 publications

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“…Random forest is also a bootstrap aggregating (bagging) type of ensemble learning, where the bootstrapping process generates a sub-sample of the dataset and builds a weak learner based on the sub-sample [21]. The weak learner used is usually a decision tree [22]. Then aggregating is taking a decision based on these decision trees by majority voting.…”

Section: Ensemble Votingmentioning

confidence: 99%

Enhancing Intrusion Detection System on Nodemcu With Ensemble Voting

Alamsyah

2023

JETT

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As part of the Internet, a NodeMCU becomes a vulnerability to cyber-attack, so the intrusion detection system (IDS) against cyber-attack on the NodeMCU becomes a poignant research challenge. Several studies have applied several machine learning techniques for IDS on NodeMCU; however, there is a research opportunity to improve the performance of existing models. This research aims to increase the IDS performance on NodeMCU with ensemble voting. We hypothesize the realization of how the implementation of an IDS in NodeMCU. Then we obtain and observe a dataset from Kaggle. The dataset comprises five attacks: misconfiguration, DDoS, probe, scanning, and MiTM. Then we design the detection with ensemble voting consisting of a decision tree and a random forest. We benchmark our proposed solution with decision trees and random forest performance. This study uses several test metrics, including , , , and .The test results show that the decision tree has better Precision in predicting misconfiguration attacks and scan attacks than random forests. On the other hand, the random forest has better in predicting normal data, DDoS attacks, probe attacks, and MiTM attacks. In terms of , ensemble voting has the best performance, which is , compared to the decision tree and random forest, which are and , respectively. We conclude that by assembling a decision tree in the random forest with ensemble voting, random forest performance can improve.

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Section: Ensemble Votingmentioning

confidence: 99%

Enhancing Intrusion Detection System on Nodemcu With Ensemble Voting

Alamsyah

2023

JETT

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“…We benchmark this study's four machine learning models: random forest, SVM, MLP, and logistic regression. Random forest is part of the ensemble learning method using bootstrap and aggregating (bagging) [23]. The essence of the random forest is to conduct majority voting on several decision trees called weak learners for generalization purposes.…”

Section: Airplane Failure Detection By Bird Strikementioning

confidence: 99%

ImbGAFS: GA Feature Selection for AUC in Bird Strike Prediction

Putrada,

Prabowo

2023

Machine Learning Techniques and NLP

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Several studies discuss airplane failure prediction due to bird strikes. However, these studies need to analyze further the imbalance in their dataset. Our research aim is to create an airplane failure prediction by bird strike using a machine learning method optimized using GA feature selection. GA feature selection uses AUC maximization as the objective function to tackle imbalance problems in the bird strike dataset. First, we obtained the airplane bird strike dataset from Kaggle. We carry out preprocessing on the dataset.We then compared and chose one of four stateof-the-art machine learning methods: SVM, MLP, logistic regression, and random forest. The selection process involves oversampling methods, synthetic minority oversampling technique (SMOTE), and optimum threshold selection, which involves geometric mean (g- mean) and area under curve (AUC) values. Finally, we optimize airplane failure prediction by performing AUC maximization using GA feature selection. Our test results show that random forest is the best machinelearning method in airplane failure prediction compared to SVM, logistic regression, and MLP. SMOTE can increase random forest AUC from 0.845 to 0.878. Finally, the random forest model from ImbGAFS is better than the conventional method without feature selection. The increase in the AUC value is from 0.878 to 0.889. Then, after carrying out optimal threshold selection, ImbGAFS+random forest also has better sensitivity, specificity, and g-mean than conventional methods. The increase is from 0.7737, 0.8350, and 0.8037 to 0.8033, 0.8301, and 0.8166, respectively.

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