Mohammad Arshad , Neutrosophic logic, which models truth (T), indeterminacy (I), and falsity (F), is increasingly being explored for enhancing neural networks in uncertain or ambiguous environments. Rather than using neutrosophic logic only at the preprocessing stage, some recent approaches embed it directly into neural network architectures.

In these models, neurons and activation functions are modified to handle triplet outputs (T, I, F) instead of a single scalar. Each component may have its own set of weights and biases, and custom activation functions are used to process these values. During training, backpropagation is adapted to compute gradients for each component individually. Indeterminacy (I) is often regularized—either penalized in the loss function or used to control confidence—helping the network manage uncertainty more effectively.

This integration has shown benefits in terms of robustness to noise, better generalization on ambiguous data, and improved interpretability. Because the indeterminacy score is explicit, it's easier to explain or assess the model’s confidence, which is particularly valuable in sensitive fields like medical imaging or decision-making systems.

Applications include brain tumor segmentation, retinal image classification, remote sensing image analysis, and intelligent decision support. While not yet widespread in mainstream frameworks like TensorFlow or PyTorch, some research prototypes and custom implementations exist, particularly in academic settings.