A NOVEL ENTROPY-BASED CASCADED CAPSULE NEURAL NETWORK WITH AN OPTIMIZED LSTM FOR ANOMALY SEGMENTATION AND CLASSIFICATION

The topic at hand of Anomaly Detection (AD) is important and well-researched. But creating successful AD techniques for complex and high-dimensional data is still difficult. There exist various methods for the detection of anomalies. Though, there are various existing methods, to improve the efficiency and to bring more advantages a method called Entropy based cascaded capsule neural network (CCNN) with an optimized LSTM (OLSTM) for segmentation and classification is suggested for the AD in the videos. Initially, pre-processing is done using the Gaussian filter followed by the segmentation using the Cascaded Capsule Neural network. Using this segmented data along with the dataset classified using entropy, Feature extraction (FE) is done by which the features are extracted. Finally, classification is done using the Optimized LSTM using ROCO algorithm method. The suggested approach has an accuracy of 98%, specificity of 99.8%, sensitivity of 92.9%, Precision of 92.9, F measure of 92.9%, FNR of 10%, FPR of 0.3%and Matthew Correlation Coefficient (MCC) of 89%. Thus, from the results, it is seen that our suggested approach performed more effectively than the other methods that are currently in use.