Comparative Analysis of Supervised Classification Algorithms for Residential Water End Uses by Zahra Heydari, Ashlynn S. Stillwell

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Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method by Behnam Seyedi, Octavian Postolache

“…In the final phase, an ensemble classifier combines the strengths of the Decision Tree, Random Forest, and XGBoost algorithms to achieve the accurate and robust detection of anomalous behaviors. …”
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Predicting Movie Production Years through Facial Recognition of Actors with Machine Learning by Asraa Muayed Abdalah, Noor Redha Alkazaz

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Improving Surgical Site Infection Prediction Using Machine Learning: Addressing Challenges of Highly Imbalanced Data by Salha Al-Ahmari, Farrukh Nadeem

“…Seven machine learning algorithms were created and tested: Decision Tree (DT), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Stochastic Gradient Boosting (SGB), and K-Nearest Neighbors (KNN). …”
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Path planning algorithm based on the improved Informed-RRT* using the sea-horse optimizer by YAN Guiseng, YANG Jie

“…ObjectiveIn order to solve the problems of random sampling, inefficient search, and difficulty in providing optimal paths in complex environments faced by traditional Informed-RRT* algorithms, an improved Informed-RRT* path planning algorithm based on the sea-horse optimizer (SHO) was proposed.MethodsThis algorithm combined the strengths of Informed-RRT* and SHO. …”
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Reducing bias in coronary heart disease prediction using Smote-ENN and PCA. by Xinyi Wei, Boyu Shi

“…To address the data imbalance issue, SMOTE-ENN is utilized, and five machine learning algorithms-Decision Trees, KNN, SVM, XGBoost, and Random Forest-are applied for classification tasks. …”
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