Hopfield Neural Network Love It or Leave It for Classification

Classification problems play an important role in machine learning and data mining. It is used in many areas such as classification problems, disease diagnosis, customer forecasting in companies, drug research, emotional evaluation. Its applications span various fields and contribute to advancements in both theory and practice. For tasks like character recognition, postal code sorting, and handwriting recognition in optical character recognition (OCR) systems, it may be necessary to achieve higher classification accuracy with less processing. In this study, the classification of two distinct classification features-handwritten digits and skin cancer lesions-is done in order to evaluate the effectiveness of the Hopfield neural network (HNN). HNN is the one of the recurrent neural network consists of single layer where every neuron is connected to all other neurons except itself. The HNN architecture's simplicity causes the complexity to decrease. The HNN model conducts the investigation using the MNIST database. The dataset contains handwritten digits from 0 to 9 in 60,000 training photos and 10,000 testing order to classify skin cancer lesions, an ISIC combination database made up of seven different classes produced an overall average accuracy of the training and validation accuracy of 16.3 percent and 15.1 percent, respectively because it captures the pattern's structure, the performance study of the HNN model yields greater accuracy for digit classification than it does for skin cancer, which lacks any predetermined patterns. The study of HNN is expanded using this technique in order to produce better categorization methods and outcomes.