Answer: (ii) high, low

In an Artificial Neural Network (ANN), the training time is high while the testing (application) time is low:

• Training (high): learning uses the backpropagation algorithm, which requires many forward-backward passes over the training data, repeated over many epochs, adjusting weights by gradient descent until the error converges. This is computationally expensive and slow — especially for large datasets and deep networks.

• Testing (low): once the network is trained, classifying a new input is just a single forward pass through the fixed-weight network (a sequence of weighted sums and activation functions), which is very fast.

Training phase (slow):  data ──▶ [weight initialization] ──▶ [forward pass]
                                            ▲                  │
                                            └── backward pass / weight update ◀── error
Testing phase (fast):   single forward pass ──▶ output class

Why the others are wrong:

• (i) low, high — the reverse of the truth.
• (iii) low, low — training of ANNs is definitely not fast.
• (iv) high, high — testing is fast, not high.

Hence training time is high and testing time is low for ANN — option (ii).