A Fuzzy-Neural Network Approach to Market Supervision and Product Recall Prediction

The paper suggests a fuzzy-neural network market monitoring and product recall prediction method. This method uses fuzzy logic and neural networks to handle complex and ambiguous input. The fuzzy logic component fuzzes product quality, customer complaint, and market trend index input variables. The neural network component learns fuzzified data patterns to predict product recalls. Online information is used for product recalls. Customer complaint rate, product quality rating, and market trend index are in this dataset. Fuzzy sets and membership functions finish input variable fuzzying. A neural network trained on fuzzified data predicts product recalls. We assess the proposed method's accuracy, precision, recall, and F1-score. After testing, the suggested technique had an accuracy of 0.863, precision of 0.854, recall of 0.872, F1-score of 0.863, and MSE of 0.123. The fuzzy-neural network technology improves market monitoring and product recall predictions. Fuzzy logic and neural networks analyze complicated and unexpected data, improving prediction accuracy. This strategy may assist market supervisors and manufacturers decide on product recalls.