main.py

# Import required libraries
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import SGDClassifier

# DewCrypt Project
# A state-of-the-art model for detecting vulnerabilities in smart contracts within the Dew-Cloud framework.

def data_preprocessing():
    """
    Function to perform data preprocessing.
    This includes efficient data preprocessing algorithms as mentioned in the paper.
    """
    # Placeholder for data preprocessing code
    pass

def feature_selection():
    """
    Function to perform feature selection.
    Utilizes Recursive Feature Elimination and Principal Component Analysis as mentioned in the paper.
    """
    # Placeholder for feature selection code
    pass

def classification():
    """
    Function to perform classification.
    Utilizes a Mini-Batch SGD Classifier and a Random Forest Classifier as mentioned in the paper.
    """
    # Placeholder for classification code
    clf1 = SGDClassifier()
    clf2 = RandomForestClassifier()
    # Placeholder for training and prediction code
    pass

def weighted_voting_scheme():
    """
    Function to implement a weighted voting scheme.
    Optimizes the confidence levels of both classifiers.
    """
    # Placeholder for weighted voting scheme code
    pass

def main():
    """
    Main function to run the DewCrypt model.
    """
    print("Running DewCrypt model...")
    
    # Data Preprocessing
    print("Performing data preprocessing...")
    data_preprocessing()
    
    # Feature Selection
    print("Performing feature selection...")
    feature_selection()
    
    # Classification
    print("Performing classification...")
    classification()
    
    # Weighted Voting Scheme
    print("Implementing weighted voting scheme...")
    weighted_voting_scheme()
    
    print("DewCrypt model has been successfully executed.")

if __name__ == "__main__":
    main()