Prediction Analysis using Random Forest Algorithms to Forecast the Air Pollution Level in a Particular Location

Reddy, Puli Dilliswar; Parvathy, L. Rama

doi:10.1109/icosec54921.2022.9952138

Cited by 9 publications

(2 citation statements)

References 9 publications

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“…Random Forest in Figure 1 is a machine learning algorithm with an ensemble method that can be used for classification and regression [9][10][11][12]. A Random Forest consists of a collection of decision trees associated with a bootstrap sample from a dataset [4].…”

Section: Random Forestmentioning

confidence: 99%

Detection of Bruteforce Attacks on the MQTT Protocol Using Random Forest Algorithm

Akbar,

Hariyadi,

Hanani

2023

IOTA

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Bruteforce is a hacking technique that launches an attack by guessing the username and password of the system that is the target of the attack. The Bruteforce attack on the MQTT protocol is an attack that often occurs on the IoT, so it is necessary to detect attacks on the MQTT protocol to find out normal traffic and brute force traffic. Random Forest was chosen because this method can classify a lot of data in a relatively short time, and the results from Random Forest can improve accuracy and prevent overfitting in the data classification process. This study uses two types of data: primary data from the hacking environment lab and secondary data from the IEEE Data Port MQTT-IOT-IDS2020 dataset. Trials on primary data and the results obtained are accuracy of 99.55%, precision of 100%, recall of 99.54%, and f-measure of 99.77%, the duration needed to get these results with 1796 data lines, i.e., for 0 seconds. As for the secondary data, the researcher obtained an accuracy of 99.77%, a precision of 100%, a recall of 99.43%, and an f-measure of 98.71%, the duration required to obtain these results with 85002 data lines, i.e., for 62 seconds.

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Section: Random Forestmentioning

confidence: 99%

Detection of Bruteforce Attacks on the MQTT Protocol Using Random Forest Algorithm

Akbar,

Hariyadi,

Hanani

2023

IOTA

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“…A prominent feature selection strategy, Recursive Feature Elimination (RFE), gradually eliminates less important characteristics from the dataset until the most informative subset is achieved. This approach increases model performance while simultaneously lowering computational overhead [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Youtube Comment Feature Selection And Classification Using Fused Machine Learning

R. Rooba,

2023

tjjpt

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The exponential rise of internet platforms, notably YouTube, has resulted in a massive volume of user-generated material, including video comments. Understanding audience input and enhancing user experience require analyzing and forecasting the mood of YouTube comments. This work provides a comprehensive method for YouTube comment prediction and sentiment classification that combines feature selection using Recursive Feature Elimination (RFE), Elastic Net Random Forest with Logistic Regression (RF with LR), and Principal Component Analysis (PCA). The first stage is to choose the most informative features from a given dataset using RFE, a common approach for doing so. RFE aids in the elimination of unnecessary or redundant features, resulting in enhanced model performance and decreased computing complexity. The Elastic Net Random Forest with Logistic Regression (RF with LR) technique is then used to construct a robust sentiment classification model. Elastic Net regularization combines the advantages of both L1 (Lasso) and L2 (Ridge) regularization, allowing for improved feature selection and management of multicollinearity concerns. By integrating many decision trees, the Random Forest ensemble approach improves the model's predictive potential even more. We employ Principal Component Analysis (PCA) to increase the classification model's effectiveness and solve possible difficulties created by high-dimensional data. PCA decreases the dataset's complexity while retaining its fundamental qualities, resulting in a more manageable and efficient feature space for classification. Finally, we compare the performance of three prominent classifiers on the preprocessed dataset: Linear Support Vector Machine (LSVM), Gaussian Naive Bayes (GNB), Logistic Regression (LR), and Decision Tree (DT). We can select the best-performing model for YouTube comment categorization by comparing these classifiers.

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Recycling: An Optimization with Machine Learning

Teixeira,

Lourenço

2024

Springer Proceedings in Earth and Environmental Sciences

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Prediction Analysis using Random Forest Algorithms to Forecast the Air Pollution Level in a Particular Location

Cited by 9 publications

References 9 publications

Detection of Bruteforce Attacks on the MQTT Protocol Using Random Forest Algorithm

Detection of Bruteforce Attacks on the MQTT Protocol Using Random Forest Algorithm

Youtube Comment Feature Selection And Classification Using Fused Machine Learning

Recycling: An Optimization with Machine Learning

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