بدائل البحث:
learning algorithm » learning algorithms (توسيع البحث)
method algorithm » network algorithm (توسيع البحث), means algorithm (توسيع البحث), mean algorithm (توسيع البحث)
elements method » element method (توسيع البحث)
using algorithm » using algorithms (توسيع البحث), routing algorithm (توسيع البحث), fusion algorithm (توسيع البحث)
a learning » _ learning (توسيع البحث), e learning (توسيع البحث), q learning (توسيع البحث)
learning algorithm » learning algorithms (توسيع البحث)
method algorithm » network algorithm (توسيع البحث), means algorithm (توسيع البحث), mean algorithm (توسيع البحث)
elements method » element method (توسيع البحث)
using algorithm » using algorithms (توسيع البحث), routing algorithm (توسيع البحث), fusion algorithm (توسيع البحث)
a learning » _ learning (توسيع البحث), e learning (توسيع البحث), q learning (توسيع البحث)
-
1
Algorithmic experimental parameter design.
منشور في 2024"…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …"
-
2
Spatial spectrum estimation for three algorithms.
منشور في 2024"…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …"
-
3
-
4
-
5
-
6
ML algorithms used in this study.
منشور في 2025"…Six machine learning algorithms, including Random Forest, were applied and their performance was investigated in balanced and unbalanced data sets with respect to binary and multiclass classification scenarios. …"
-
7
List of the time used by each algorithm.
منشور في 2024"…Finally, to solve the issue of concept drift, EDAC designs and implements an ensemble classifier that uses a self-feedback strategy to determine the initial weight of the classifier by adjusting the weight of the sub-classifier according to the performance on the arrived data chunks. …"
-
8
Types of machine learning algorithms.
منشور في 2024"…Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.</p><p>Materials and methods</p><p>This study used the latest nationally representative cross-sectional Bangladesh demographic health survey (BDHS), 2017–18 data. …"
-
9
-
10
Data used in "Material Classification System using Inductive Tactile Sensors and Machine Learning Algorithms"
منشور في 2024"…<p dir="ltr">This study presents an innovative material classification system involving an inductive tactile sensor and machine learning algorithms. A simple-structured sensor based on the principle of electromagnetic induction was developed to capture varying inductance signals induced by different materials with distinct magnetic properties, facilitating material detection and distinction. …"
-
11
-
12
Supporting data for "Using AI-based Deep Learning Algorithms for Nowcasting Cloud Evolution"
منشور في 2025"…To this end, this study, ''Using AI-based Deep Learning Algorithms for Nowcasting Cloud Evolution'', focuses on using AI-based deep learning algorithms for accurate nowcasting of cloud evolution. …"
-
13
-
14
Credit Card Fraud Classification Using Applied Machine Learning – A Comparative Study of 24 ML Algorithms
منشور في 2025"…<p dir="ltr">Credit Card Fraud Classification Using Applied Machine Learning – A Comparative Study of 24 ML Algorithms</p><p dir="ltr">This study describes an empirical evaluation of 24 machine learning models, including Logistic Regression, Decision Trees, Random Forests, Support Vector Machines and Neural Networks using a highly imbalanced fraud dataset that reflects the real-world where the data was culled from. …"
-
15
Decision tree algorithms.
منشور في 2025"…Ensemble learning in supervised machine learning is also a common technique for handling imbalanced data. …"
-
16
-
17
-
18
-
19
A new approach in soil organic carbon estimation using machine learning algorithms: a study in a tropical forest in Vietnam
منشور في 2024"…This study aimed to evaluate the ability of SOC estimation using a multiple linear regression model (MLR) and four machine learning algorithms: artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) with satellite data sources and soil nutrient indicator data to find the optimal method. …"
-
20
Feature selection using Boruta algorithm.
منشور في 2025"…</p><p>Methods</p><p>Multiple machine learning (ML) algorithms were applied to data from the 2022 Bangladesh Demographic Health Survey, including Random Forest, Decision Tree, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, XGBoost, LightGBM and Neural Networks. …"