Adagrad Optimizer with Elephant Herding Optimization based Hyper Parameter Tuned Bidirectional LSTM for Customer Churn Prediction in IoT Enabled Cloud Environment

At recent times, customer churn is an important activity in quickly developing industries such as telecom, banking, e-commerce, etc. Earlier studies revealed that the cost of getting a new customer is considerably higher than the cost of retaining the existing ones. Therefore, it becomes essential to predict the nature of customer churn for retaining the customers to a greater extent. The advent of deep learning (DL) models have begun to be employed for efficient CCP. This paper presents a new Adagrad Optimizer with Elephant Herding Optimization (EHO) based Hyper-parameter Tuned Bidirectional Long Short Term Memory (AG-EHO-BiLSTM) for CCP in Internet of Things (IoT) enabled Cloud Environment. The proposed AG-EHO-BiLSTM model initially acquires the customer data using its devices like smart phones, laptop, smart watch, etc. Next, the gathered data will be classified by the use of Bi-LSTM model, which determines the customers as churner or non-churner. The efficiency of the Bi-LSTM model can be increased through hyper parameter tuning techniques, namely Adagrad optimizer and EHO algorithm to optimally select the parameter values namely learning rate, number of hidden layer and epochs. The performance validation of the AG-EHO-BiLSTM model takes place on benchmark dataset and the simulation outcome reported the supremacy of the AG-EHO-BiLSTM model over the comparative methods.