HIGHLIGHTS
SUMMARY
This study proposes an "End-to-End" model using a Convolution Neural_Network and a Recurrent Neural to learn useful features from the sequences of data samples effectively. Cyclic Neural_Networks, where there are both forward, and backward connections between neurons, have gradually developed into Long Short-Term Memory (LSTM) and a Bidirectional (Bi) BiLSTM with a capacity to remember longer data sequences. The contribution in this paper is a development of a DL model consisting of Active Learning (AL), Convolutional Neural_Network (CNN), BiGRU, and Multi-Attention, which is henceforth named (ACBMA). The algorithm initially extracts . . .
If you want to have access to all the content you need to log in!
Thanks :)
If you don't have an account, you can create one here.