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

Mathematics

• Soft Computing 100%
• Random number Generator 90%
• Random Forest 68%
• Random Trees 51%
• Electroencephalogram (EEG) 44%
• Neural Networks 19%
• Random number 15%
• Classification Problems 14%
• Machine Learning 14%
• Data Classification 10%
• Weight Distribution 8%
• Dependent Data 7%
• High Accuracy 6%
• Ensemble 6%
• Likely 6%
• Exceed 6%
• Learning 6%
• Model 4%
• Performance 4%
• Standards 3%

Engineering & Materials Science

• Soft computing 80%
• Random forests 62%
• Electroencephalography 40%
• Convolutional neural networks 15%
• Machine learning 12%
• Program processors 7%
• Neural networks 5%