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Machine learning model for random forest acute oral toxicity prediction
A. M. Elsayad
,
M. Zeghid
, K. A. Elsayad
, A. N. Khan
, A. K.M. Baareh
, A. Sadiq
, S. A. Mukhtar
, H. F. Ali
, S. Abd El-kader
Electrical Engineering
College of Engineering
Cairo University
University of Engineering and Technology, Peshawar
Al-Balqa Applied University
Electronics Research Institute of Cairo
Research output
:
Contribution to journal
›
Article
›
peer-review
9
Scopus citations
Overview
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Computer Science
Machine Learning
100%
Learning System
100%
Random Decision Forest
100%
Class Imbalance
33%
Neural Network
16%
Superior Performance
16%
Feature Extraction
16%
Feature Selection
16%
Decision Tree
16%
Extreme Gradient Boosting
16%
Interpretability
16%
Characteristic Curve
16%
Ensemble Method
16%
Explainable Artificial Intelligence
16%
Gradient Boosting
16%
Chemical Engineering
Neural Network
100%
Learning System
100%
Artificial Intelligence
100%
Earth and Planetary Sciences
Machine Learning
100%
Sustainable Development Goals
100%
Artificial Neural Network
20%
Artificial Intelligence
20%
Surface Area
20%
Pharmacology, Toxicology and Pharmaceutical Science
Van Der Waals Surface
100%