Results 51 to 60 of about 6,285,017 (364)

Weather prediction using random forest machine learning model

Indonesian Journal of Electrical Engineering and Computer Science, 2021
The complex numerical climate models pose a big challenge for scientists in weather predictions, especially for tropical system. This paper is focused on presenting the importance of weather prediction using machine learning (ML) technique. Recently many
R. Meenal, P. A. Michael, D. Pamela, E. Rajasekaran   +3 more
semanticscholar   +1 more source

A COMPARISON OF RANDOM FOREST AND DOUBLE RANDOM FOREST: DROPOUT RATES OF MADRASAH STUDENTS IN INDONESIA

Barekeng
Random forest algorithm allows for building better CART models. However, the disadvantage of this method is often underfitting, especially for small node sizes. Therefore, the double random forest method was developed to overcome this problem.
Arie Purwanto, Bagus Sartono, Khairil Anwar Notodiputro   +2 more
doaj   +1 more source

COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm

Frontiers in Public Health, 2020
Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature.
C. Iwendi, A. Bashir, Atharva Peshkar, R. Sujatha, J. Chatterjee, Swetha Pasupuleti, R. Mishra, S. Pillai, Ohyun Jo   +8 more
semanticscholar   +1 more source

Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes

JURNAL MEDIA INFORMATIKA BUDIDARMA, 2021
In this study aims to compare the performance of several classification algorithms namely C4.5, Random Forest, SVM, and naive bayes. Research data in the form of JISC participant data amounting to 200 data. Training data amounted to 140 (70%) and testing
M. Azhari, Zakarias Situmorang, Rika Rosnelly   +2 more
semanticscholar   +1 more source

Investigation of the possibility of landslide hazard mapping using the Random Forest algorithm (Case study: Sardarabad Watershed, Lorestan Province) [PDF]

مخاطرات محیط طبیعی, 2018
With respect to the ability of data analysis techniques, their applications in various engineering and geosciences disciplines have been expanded. In this study, the random forest algorithm has been used for landslide susceptibility mapping in the ...
Ali Talebi, Sahar Goudarzi, Hamid Reza Pourghsemi Pourghsemi   +2 more
doaj   +1 more source

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

, 2020
In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the ...
E. Y. Boateng, Joseph Otoo., D. Abaye
semanticscholar   +1 more source

Enriched Random Forest for High Dimensional Genomic Data

IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2021
Ensemble methods such as random forest works well on high-dimensional datasets. However, when the number of features is extremely large compared to the number of samples and the percentage of truly informative feature is very small, performance of ...
Debopriya Ghosh, Javier Cabrera
semanticscholar   +1 more source

SMOTE and Weighted Random Forest for Classification of Areas Based on Health Problems in Java

Journal of Applied Informatics and Computing
Random Forest (RF) is a popular Machine Learning (ML) approach extensively employed for addressing classification issues. Nevertheless, the RF method for classification problems demonstrates suboptimal performance in cases of data imbalance.
Erwan Setiawan, Bagus Sartono, Khairil Anwar Notodiputro   +2 more
doaj   +1 more source

Fecal source identification using random forest

Microbiome, 2018
Background Clostridiales and Bacteroidales are uniquely adapted to the gut environment and have co-evolved with their hosts resulting in convergent microbiome patterns within mammalian species.
Adélaïde Roguet, A. Murat Eren, Ryan J Newton, Sandra L McLellan   +3 more
doaj   +1 more source

Predicting Epithelial Ovarian Cancer first recurrence with Random Survival Forest: Comparison Parametric, Semi-Parametric, and Random Survival Forest Methods

Journal of Biostatistics and Epidemiology, 2021
Objective: Rapid technological advances in the last century and the large amount of information have made it difficult to analyze a large number of independent variables.
Maryam Deldar, Robab Anbiaee, Kourosh Sayehmiri   +2 more
doaj   +1 more source