What should the specialist consider to fix this issue?

Explanations:

Longer training time may further increase training accuracy but is unlikely to address the overfitting issue, as indicated by the high discrepancy between training and validation accuracy. This could lead to worse generalization to unseen data.

Making the network larger could potentially capture more complex patterns, but it may exacerbate the overfitting problem, as the model might learn to memorize the training data rather than generalize from it.

Using a different optimizer might improve the convergence speed or fine-tuning of the model, but it does not directly address the overfitting issue indicated by the training and validation accuracy discrepancy.

Using some form of regularization, such as dropout or L2 regularization, would help reduce overfitting by discouraging the model from fitting too closely to the training data, thus improving its generalization to the validation set.