Unlike other BPNN variants that rely on specific activation functions to process numerical values, ANFIS inherently normalizes inputs and converts them into fuzzy values using IF-THEN rules, allowing approximation of non-linear functions. The integration of fuzzy logic and neural networks enhances ANFIS's capability to model intricate relationships, especially in cases of vague or uncertain information. However, similar to BPNN, ANFIS is susceptible to overfitting when addressing complex datasets that require a substantial number of neurons.