The P feature encoder values are determined for every Q frame in a video clip through images that are transformed for every frame. They also perform computations of a specific succession of convolution and pooling layers of an CNN based deep learning model, followed by operations using a nested invariance pooling layer. Each feature encoded value in a video clip is converted from a real number to an integer value within the range of the color intensity, as defined by the quantization scheme. The 2-D graphical symbol N.times.N pixels are created by placing the respective colors in the N.times.N pixels according to the pattern of data arrangement which represents all the frames of the videoclip in the form of P.times.Q feature encode values. This guarantees that the symbol in 2-D has an underlying meaning that could be used in image classification with another CNN-based deep learning model.

Machine learning is an application of artificial intelligence. Machine learning is the process of programming a computer or computing device to think and behave as humans do to enable it to think and behave in its own way. Neuralnetworks is a key component of teaching computers to think and perceive the world like human beings.

A video stream is still images or a sequence of frames. As an example, there are 30 frames in a second. A still image is a image that captures an event. A video stream shows the actual action. A snapshot of someone swimming in a pool is one illustration, while a stream shows the person doing freestyle strokes. To detect the activity in a video stream, it must be done by video classification technique. Machine learning is needed to effectively recognize the action in the video stream.

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