• Compared two final Pipelines, one with an 85% lower computational cost than the other
• Pipeline 1 outperformed Pipeline 2 when provided with equal computational resources
• Obtained 99% accuracy and achieved superior results with less training generations (5 vs 15)
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Cyber-Security project on “Forecasting Zero-Day vulnerabilities using ML” (Prep, Samp, GA)
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