Deep Belief Network Architecture and Their Applications – A Survey - Sornam, M; Radhika, A (International Journal of Computer Sciences and Engineering, 2025)
Deep learning has proven to be beneficial for complex tasks such as classifying the image, pattern recognition, speech recognition, natural language processing, and recommendation systems. Autoencoder, Restricted Boltzmann Machine, Deep belief Network and Convolutional Neural network are four different types of architecture used in deep learning. Deep Belief Network is now the new the state of the art for many fields of machine learning research. The main aim of this survey is to widely cover deep belief network architecture and their practical applications such as computer-aided diagnosis for the dreadful diseases, pattern recognition and also in the field of industry. The proposed work helps to improve the classification performance for breast cancer to a certain extent, which provides a good direction for the future classification of breast cancer. At last, the limitations of Deep Belief network and list of future research information has been given