ashion items are hard to classify since there are a million variations in style, texture, and pattern. Image classification is among the noted strengths of convolutional neural networks. This research introduces an improved CNN architecture for fashion classification, utilizing image augmentation and batch normalization to improve model performance and generalization. To make the model more robust, image augmentation techniques like rotation, width and height shift, zoom, and flips were employed. In addition, a Batch Normalization layer is added in the middle, which can help on stabilizing the learning process and accelerating convergence. The proposed model was trained on an augmented dataset, achieving a satisfactory improvement in test accuracy of 91.97% compared to a baseline CNN model, which obtained 88.5% accuracy. According to the results, the image augmentation with the application of Batch Normalization improves the CNN architecture for better effectiveness in fashion classification tasks. Keywords: fashion classification, convolutional neural networks, batch normalization, image augmentation

This study proposes an ensemble learning approach to enhance sentiment classification accuracy on the IMDB movie reviews dataset. We combined three diverse models: Logistic Regression, Random Forest, and a Bidirectional Long Short-Term Memory (LSTM) neural network. Text data was vectorized using bigram term frequency, optimizing it for traditional classifiers, while the LSTM captured sequential dependencies via an embedding layer. By aggregating predictions through majority voting, the ensemble preserves the interpretability of traditional models while leveraging the deep learning capabilities of the neural network. Experimental results demonstrate that our proposed ensemble method achieves an accuracy of 89.2%, outperforming the individual models. This highlights the effectiveness of integrating traditional machine learning with deep learning for robust sentiment analysis.