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Integrated transportation system planning with gravitational search algorithm approach based on fuzzy mutant controller
Published 2025-03-01“…In these relations, <strong>3r </strong>is a uniform random variable in the interval [1,0], which is used to create the random property of the speed of the particle population optimization algorithm and the acceleration of the gravitational search algorithm in the gravitational particle population algorithm, and <strong>3C </strong>and <strong>4C</strong> are two constants to determine the degree of the speed of the particle population optimizer algorithm and the acceleration of the gravitational search algorithm in the gravitational particle population algorithm the values of which are considered 3C and 4C. …”
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A stacked ensemble machine learning model for the prediction of pentavalent 3 vaccination dropout in East Africa
Published 2025-04-01“…The objective is to identify predictors of dropout and enhance intervention strategies.MethodsThe study utilized seven base machine learning algorithms to create a stacked ensemble model with three meta-learners: Random Forest (RF), Generalized Linear Model (GLM), and Extreme Gradient Boosting (XGBoost). …”
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Comparative Analysis of Supervised Classification Algorithms for Residential Water End Uses
Published 2024-06-01Get full text
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Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method
Published 2025-06-01“…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
Published 2024-12-01Get full text
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Improving Surgical Site Infection Prediction Using Machine Learning: Addressing Challenges of Highly Imbalanced Data
Published 2025-02-01“…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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Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations
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Universal Simulation Model of Battery Degradation with Optimization of Parameters by Genetic Algorithm
Published 2022-12-01Get full text
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Path planning algorithm based on the improved Informed-RRT* using the sea-horse optimizer
Published 2025-02-01“…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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A novel method based on improved SFLA for IP information extraction from TEM signals
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Finding high posterior density phylogenies by systematically extending a directed acyclic graph
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Generation of a Social Network Graph by Using Apache Spark
Published 2016-12-01Get full text
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Reducing bias in coronary heart disease prediction using Smote-ENN and PCA.
Published 2025-01-01“…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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Novel neoadjuvant therapies for muscle‐invasive bladder cancer: Systematic review and meta‐analysis
Published 2025-05-01Get full text
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A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data
Published 2025-06-01“…Despite advancements in satellite-based radiometry such as NASA’s Soil Moisture Active Passive (SMAP), significant challenges persist in coastal SSS retrieval due to radio frequency interference (RFI), land-sea contamination, and complex interactions of nearshore dynamic processes.MethodThis study proposes a deep neural network (DNN) framework that integrates SMAP L-band brightness temperature data with ancillary oceanographic and geographic parameters such as sea surface temperature, the shortest distance to the coastline (dis) to enhance SSS estimation accuracy in the Yellow and East China Seas. The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
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