Facial Emotion Images Recognition Based On Binarized Genetic Algorithm-Random Forest by Murad Ibrahim Husin Alzawali, Yusliza Yusoff, Razana Alwee, Zuriahati Mohd Yunos, Mohamad Shukor Talib, Haswadi Hassan, Fahad Taha AL-Dhief, Musatafa Abbas Abbood Albadr, Majid Razaq Mohamed Alsemawi, Sharifah Zarith Rahmah Syed Ahmad

Get full text

Optimal Economic Modelling of Hybrid Combined Cooling, Heating, and Energy Storage System Based on Gravitational Search Algorithm-Random Forest Regression by Muhammad Shahzad Nazir, Sami ud Din, Wahab Ali Shah, Majid Ali, Ali Yousaf Kharal, Ahmad N. Abdalla, Padmanaban Sanjeevikumar

Get full text

Modified Firefly Algorithm by Surafel Luleseged Tilahun, Hong Choon Ong

“…The algorithm is inspired by the flashing behavior of fireflies. In the algorithm, randomly generated solutions will be considered as fireflies, and brightness is assigned depending on their performance on the objective function. …”
Get full text

Optimizing Imbalanced Data Classification: Under Sampling Algorithm Strategy with Classification Combination by Nauval Dwi Primadya, Adhitya Nugraha, Sahrul Yudha Fahrezi, Ardytha Luthfiarta

“…Then, modeling is done using the KNN algorithm, Random Forest, Logistic Regression, Adaboost, And Perceptron. …”
Get full text

Improving earthquake prediction accuracy in Los Angeles with machine learning by Cemil Emre Yavas, Lei Chen, Christopher Kadlec, Yiming Ji

“…Among sixteen evaluated machine learning algorithms, Random Forest proved to be the most effective. …”
Get full text

PREDICTION OF TBM OIL TEMPERATURE BY IMPROVED CAMEL ALGORITHM ASSISTED RANDOM FOREST MODEL (MT) by REN JianJi, ZHAO RunQiu, WANG ZhenXi, LIU YuMing, YUAN YongLiang

Get full text

Enhanced Network Traffic Classification Using Bayesian-Optimized Logistic Regression and Random Forest Algorithm by Manisankar Sannigrahi, R. Thandeeswaran

“…The research evaluates four key machine learning algorithms: Random forest, logistic regression, support vector machine (SVM), and K-nearest neighbors (K-NN). …”
Get full text

Gravity Predictions in Data-Missing Areas Using Machine Learning Methods by Yubin Liu, Yi Zhang, Qipei Pang, Sulan Liu, Shaobo Li, Xuguo Shi, Shaofeng Bian, Yunlong Wu

“…In this study, utilizing the EGM2008 satellite gravity model, we conducted a comprehensive analysis of three machine learning algorithms—random forest, support vector machine, and recurrent neural network—and compared their performances against the traditional Kriging interpolation method. …”
Get full text

Assessing Climate and Land-Use Change Scenarios on Future Desertification in Northeast Iran: A Data Mining and Google Earth Engine-Based Approach by Weibo Yin, Qingfeng Hu, Jinping Liu, Peipei He, Dantong Zhu, Abdolhossein Boali

“…Six remote sensing indices were selected to model desertification using four machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Generalized Linear Models (GLM). …”
Get full text

Modeling Rural Labor Responses to Digital Finance: A Hybrid IGSA-Random Forest Approach by Zhiru Lin, Yishuai Tian

“…This paper investigates how digital inclusive finance affects RLE by integrating the Improved Gravitational Search Algorithm Random Forest (IGSA-RF) with the Gini coefficient, Out-of-Bag (OOB) coefficient, and the Gini-OOB coupling coefficient. …”
Get full text

Machine learning based QSAR and Molecular Dynamics simulations in the structural design and mechanism of action of imidazole derivatives with anti-melanoma activity by Rosalvo Ferreira de Oliveira Neto, Sérgio Ruschi Bergamachi Silva, Cintia Emi Yanaguibashi Leal, Edilson Beserra de Alencar Filho

Get full text

Bandwidth-Aware Scheduling of Workflow Application on Multiple Grid Sites by Harshadkumar B. Prajapati, Vipul A. Shah

“…The results of the performed experiments indicate that the bandwidth-aware workflow scheduling algorithms perform better than bandwidth-unaware algorithms: Random and Heft of Pegasus WMS. Moreover, our proposed workflow scheduling algorithm performs better than the bandwidth-aware Heft algorithms. …”
Get full text

Research and application of optimization of physical education training model based on multi-objective differential evolutionary algorithm by Man Wu

Get full text

Optimization and prediction of corporate credit rating through advanced feature selection based on AI and deep learning by Jumanah Ahmed Darwish

“…This study offers a comprehensive evaluation of six machine learning algorithms—Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine One-vs-One (SVM OVO), Support Vector Machine One-vs-All (SVM OVA), and Multi-Layer Perceptron (MLP)—in the context of corporate credit rating classification. …”
Get full text

Fine-scale carbon stocks mapping in the mangrove forests of Tumaco, Colombia using machine learning and remote sensing approaches by Laura Lozano-Arias, Bryan Ernesto Gallego-Pérez, John Josephraj Selvaraj

“…This study presents an innovative approach that integrates remote sensing with field data, utilizing high-resolution imagery and evaluating two machine learning algorithms: Random Forest and Support Vector Regression. …”
Get full text

An interpretable stacking ensemble model for high-entropy alloy mechanical property prediction by Songpeng Zhao, Zeyuan Li, Changshuai Yin, Zhaofu Zhang, Teng Long, Jingjing Yang, Ruyue Cao, Yuzheng Guo

“…Three machine learning algorithms-Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (Gradient Boosting)-were integrated into a multi-level stacking ensemble, with Support Vector Regression serving as the meta-learner. …”
Get full text

Machine learning-based prediction of torsional behavior for ultra-high-performance concrete beams with variable cross-sectional shapes by Elhabyb Khaoula, Baina Amine, Bellafkih Mostafa, A. Deifalla, Amr El-Said, Mohamed Salama, Ahmed Awad

“…Three powerful algorithms, Random Forest, Gradient Boosting Regressor, and Long Short-Term Memory (LSTM), were trained and assessed on a dataset of 113 UHPC specimens. …”
Get full text

Machine learning approaches for mapping and predicting landslide-prone areas in São Sebastião (Southeast Brazil) by Enner Alcântara, Cheila Flávia Baião, Yasmim Carvalho Guimarães, José Roberto Mantovani, José Antonio Marengo

“…We compared five algorithms: Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and k-Nearest Neighbors, using various environmental factors as inputs. …”
Get full text

An Innovative Proposal for Developing a Dynamic Urban Growth Model Through Adaptive Vector Cellular Automata by Ahmet Emir Yakup, Ismail Ercument Ayazli

“…During the calibration phase, the model was trained using three machine learning algorithms: Random forest, support vector machine, and multi-layer perceptron. …”
Get full text

Predicting patient risk of leaving without being seen using machine learning: a retrospective study in a single overcrowded emergency department by Arianna Scala, Teresa Angela Trunfio, Massimo Majolo, Michelangelo Chiacchio, Giuseppe Russo, Paolo Montuori, Giovanni Improta

“…Four ML classification algorithms—Random Forest, Naïve Bayes, Decision Tree, and Logistic Regression—were evaluated for their predictive capabilities. …”
Get full text