Code for Improved SMO algorithm for cancer classification with deep learning

[Rabiaaziz2023]

Sure, here's a simplified outline of an improved SMO algorithm where the exploitation phase is replaced with cuckoo search algorithm for a deep learning classification task using Convolutional Neural Networks (CNNs):

import numpy as np
import tensorflow as tf
from tensorflow.keras import layers, models
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from cuckoo_search import cuckoo_search_algorithm

# Load and preprocess your microarray data
# X, y = load_microarray_data()  # Load your microarray data here
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  # Split data into train and test sets
# scaler = StandardScaler()  # Initialize scaler
# X_train_scaled = scaler.fit_transform(X_train)  # Scale the training data
# X_test_scaled = scaler.transform(X_test)  # Scale the test data

# Define CNN architecture
def create_cnn_model(input_shape, num_classes):
    model = models.Sequential([
        layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(64, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(64, (3, 3), activation='relu'),
        layers.Flatten(),
        layers.Dense(64, activation='relu'),
        layers.Dense(num_classes, activation='softmax')
    ])
    return model

# Define the improved SMO algorithm
def improved_smo(X_train, y_train, X_test, y_test, cuckoo_search_params, cnn_params):
    input_shape = X_train.shape[1:]
    num_classes = len(np.unique(y_train))

    # Step 1: Apply cuckoo search algorithm to optimize CNN hyperparameters
    best_cnn_params = cuckoo_search_algorithm(X_train, y_train, cnn_params, cuckoo_search_params)

    # Step 2: Create and compile CNN model with the optimized hyperparameters
    cnn_model = create_cnn_model(input_shape, num_classes)
    cnn_model.compile(optimizer=best_cnn_params['optimizer'],
                      loss=best_cnn_params['loss'],
                      metrics=['accuracy'])

    # Step 3: Train CNN model
    cnn_model.fit(X_train, y_train, epochs=best_cnn_params['epochs'], batch_size=best_cnn_params['batch_size'], verbose=0)

    # Step 4: Evaluate CNN model
    train_loss, train_accuracy = cnn_model.evaluate(X_train, y_train, verbose=0)
    test_loss, test_accuracy = cnn_model.evaluate(X_test, y_test, verbose=0)

    return cnn_model, train_accuracy, test_accuracy

# Define CNN and cuckoo search parameters
cnn_params = {
    'optimizer': 'adam',  # Optimizer
    'loss': 'sparse_categorical_crossentropy',  # Loss function
    'epochs': 10,  # Number of epochs
    'batch_size': 32  # Batch size
}
cuckoo_search_params = {
    'population_size': 20,  # Number of nests (cuckoos)
    'max_generations': 50,  # Maximum number of iterations
    # Other cuckoo search parameters can be added here
}

# Call the improved SMO algorithm
# cnn_model, train_accuracy, test_accuracy = improved_smo(X_train_scaled, y_train, X_test_scaled, y_test, cuckoo_search_params, cnn_params)
# print("Training Accuracy:", train_accuracy)
# print("Test Accuracy:", test_accuracy)

In this example, create_cnn_model() defines the architecture of the CNN model, and improved_smo() implements the improved SMO algorithm with cuckoo search for optimizing CNN hyperparameters. Adjust the code according to your specific requirements and data.

[Rabiaaziz2023]

In this example, create_cnn_model() defines the architecture of the CNN model, and improved_smo() implements the improved SMO algorithm with cuckoo search for optimizing CNN hyperparameters. Adjust the code according to your specific requirements and data.