On the effects of pseudorandom and quantum-random number generators in soft computing

Keyphrases

• Soft Computing 100%
• Random Forest 100%
• Pseudorandomness 100%
• Quantum Random number Generator 100%
• Random Tree 85%
• EEG Classification 71%
• Accent Identification 57%
• Classification Problem 28%
• Random number 28%
• Convolutional Neural Network 28%
• MNIST 28%
• CIFAR-10 28%
• Dense Neural Network 28%
• Machine Learning Techniques 14%
• Learning Patterns 14%
• Mental States 14%
• Tree Model 14%
• Machine Learning Models 14%
• Random Inputs 14%
• Classification Accuracy 14%
• Data Dependency 14%
• Functional Behavior 14%
• Classification Data 14%
• Tree Ensembles 14%
• Weight Distribution 14%
• Multiple Machines 14%
• Random Splitting 14%

Mathematics

• Classification Problem 100%
• Convolutional Neural Network 100%
• Random Number Generator 100%
• Neural Network 50%
• Random Input 50%
• Weight Distribution 50%
• Generate Random Number 50%
• Dependent Data 50%
• Random Number 50%

Computer Science

• Soft Computing 100%
• Random Decision Forest 100%
• Random Number Generator 100%
• Classification Problem 28%
• Random Number 28%
• Convolutional Neural Network 28%
• Neural Network 14%
• Machine Learning Technique 14%
• Machine Learning 14%
• Learning System 14%
• Data Classification 14%
• Multiple Machine 14%