Grey wolf optimization (GWO) with the convolution neural network (CNN)-based pattern recognition system

The dynamic video frame dataset’s automated feature analysis addresses the complexity of intensity mapping with normal and abnormal classes. Iterative modelling is needed to learn component a in several patterns for various data types threshold-based clustering analysis. GWO optimises Convoluted Pattern Wavelet Transform (CPWT) vectors employed this paper's CNN technique. A median filter reduces noise smooths before normalising it. Edge information represents frame's bright spot boundary. Neural network based classification clusters pixels using recurrent learning minimal dataset training. filtered features were evaluated complex wavelet transformation extraction algorithms. These demonstrate spatial textural classifications. classifiers help analyse instances classify action labels. Categorization improves fewest training datasets. This strategy may be beneficial if compared optimal practises.