Depending on your application RNN might or might not be the best model for you, but it is possible. For example, if your input is a time series, it might make sense to use RNN. On the other hand, if your input has no sequential relationships RNN is probably not the best model to use.

very interested

Dear Muhammad Karam Shehzad ,

Look the link, maybe useful.

https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/

Regards,

Shafagat

Hi. The use of a single Sigmoid/Logistic neuron in the output layer is the mainstay of a binary classification neural network. This is because the output of a Sigmoid/Logistic function can be conveniently interpreted as the estimated probability(p̂, pronounced p-hat) that the given input belongs to the “positive” class.

The use of a single Sigmoid/Logistic neuron in the output layer is the mainstay of a binary classification neural network. This is because the output of a Sigmoid/Logistic function can be conveniently interpreted as the estimated probability(p̂, pronounced p-hat) that the given input belongs to the “positive” class. https://towardsdatascience.com/nothing-but-numpy-understanding-creating-binary-classification-neural-networks-with-e746423c8d5c

Check https://medium.com/@jklein694/deep-dive-in-recurrent-neural-networks-for-binary-classification-project-cd15d89694da

check this sir

Conference Paper Time Series Classification with Recurrent Neural Networks

Dear Muhammad Karam Shehzad

Yes, you can use the RNN, but it seems more complex than other algorithms such as HMM.