(untitled) (Journal of Advances and Scholarly Researches in Allied Education, 2023)
Neural networks, particularly deep learning models, emerge as a powerful tool for addressingthe challenges posed by unstructured data. The paper investigates the application of convolutionalneural networks (CNNs) for image and text data, recurrent neural networks (RNNs) for sequential data,and transformer models for natural language processing tasks. The effectiveness of these models incapturing complex relationships within unstructured data is thoroughly evaluated. The integration ofdata mining and neural networks is the core innovation proposed in this study. We present a novelframework that combines data preprocessing, feature extraction, and model training to facilitatestructured insights from unstructured data. This framework is designed to adapt to various data types,making it versatile for a wide range of applications, including image recognition, text sentiment analysis,and anomaly detection.