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# Output output = multimodal_dense This example demonstrates a simplified architecture for generating deep features for Indonesian entertainment and popular videos. You may need to adapt and modify the code to suit your specific requirements.

multimodal_features = concatenate([text_dense, image_dense, video_dense]) multimodal_dense = Dense(512, activation='relu')(multimodal_features)

# Video features (e.g., using YouTube-8M) video_features = np.load('youtube8m_features.npy') bokep malay daisy bae nungging kena entot di tangga

import pandas as pd import numpy as np from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.applications import VGG16 from tensorflow.keras.layers import Dense, concatenate

# Multimodal fusion text_dense = Dense(128, activation='relu')(text_features) image_dense = Dense(128, activation='relu')(image_features) video_dense = Dense(256, activation='relu')(video_features) # Output output = multimodal_dense This example demonstrates

# Load data df = pd.read_csv('video_data.csv')

# Text preprocessing tokenizer = Tokenizer(num_words=5000) tokenizer.fit_on_texts(df['title'] + ' ' + df['description']) sequences = tokenizer.texts_to_sequences(df['title'] + ' ' + df['description']) text_features = np.array([np.mean([word_embedding(word) for word in sequence], axis=0) for sequence in sequences]) video_dense]) multimodal_dense = Dense(512

Here's a simplified code example using Python, TensorFlow, and Keras: