Performance of HAF in Attention-BiLSTM for predicting the quality of automobile ancillary suppliers

Abstract

Deep learning plays an important role in Machine learning and Artificial Neural Networks. Deep learning resembles the working of human brain in data processing for detecting objects and making decisions. Training and predicting data could happen through various deep learning algorithms available. One such algorithm is Bidirectional LSTM which could be used for classification and prediction of data in machine learning. Attention layer is a cognitive process in neural networks that gives attention and weights towards certain important features. The activation functions are mathematical equations used in neural networks to learn a complex problem which determines the output. Activation functions decide which feature to be activated and define the output. There are 7 types of Activation functions, they are tanh, sigmoid, relu,Leaky ReLU, Parametric ReLU and softmax. The proposed novel work uses Modified tanH with ReLU activation functions to analyze the performance of Auto Ancillary suppliers. Automobile Ancillary spare parts are one of the core industries in Indian Economy. Suppliers of Auto Ancillary Manufacturers provide the raw materials to the product Manufacturers which should be good in quality with reasonable cost and delivered on time.