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  1. PSO-ELM with Modified Acceleration Coefficients for Classifying the Active Compound
  2. Comparison of activation function on extreme learning machine (ELM) performance for classifying the active compound
  3. Features Selection for Classification of SMILES Codes Based on Their Function
  4. Prediction of active compounds from SMILES codes using backpropagation algorithm