Which of the following statements is true?

I. Convolution involves the mathematical fusion of two functions to generate a third function.

II$.$ In a Convolutional Neural Network $($CNN$)$ context, information integration occurs through convolution, where an input dataset undergoes convolution with a filter or kernel $($these terms are used interchangeably$)$ to produce a feature map.

III. A crucial component within a CNN is the pooling layer, which plays a role in progressively diminishing the spatial dimensions of the representation. This reduction helps reduce the network's parameters and computational load, and each feature map undergoes pooling independently.

IV$.$ The commonly utilised activation function for the outputs of CNN neurons is known as ReLU, short for Rectifier Unit. ReLU functions to confine values within the range of $-1$ to $1.$

V$.$ To form a single elongated feature vector, the outputs of the convolutional layers are flattened, and this vector is subsequently connected to the final classification model, referred to as a fully$-$connected layer.

Question $25$ options:

I, II$,$ III, IV

I, II$,$ III, V

II$,$ III, IV$,$ V

None of the above

All of the above