Open AccessArtificial Recurrent Neural Network Architecture in Customer Consumption Prediction for Business Development
Author(s) -
P. Karuppusamy
Publication year - 2020
Publication title -
journal of artificial intelligence and copsule networks
Language(s) - English
Resource type - Journals
ISSN - 2582-2012
DOI - 10.36548/jaicn.2020.2.004
Subject(s) - computer science , artificial neural network , frame (networking) , consumption (sociology) , artificial intelligence , recurrent neural network , association rule learning , association (psychology) , architecture , prefix , span (engineering) , machine learning , data mining , engineering , telecommunications , art , social science , philosophy , linguistics , civil engineering , epistemology , sociology , visual arts
The customer consumption pattern prediction has become one of a significant role in developing the business and taking it to a competitive edge. For forecasting the behaviors of the consumers the paper engages an artificial recurrent neural network architecture the long short-term memory an improvement of recurrent neural network. The mechanism laid out to predict the pattern of the consumption, uses the information’s about the consumption of products based on the age and the gender. The information essential are extracted and described with the prefix-span procedure based association rule. Utilizing the information about the day to day products purchase pattern as input a frame work to predict the customer daily essentials was designed, the designed frame was capable enough to learn the dissimilarities across the predicted and the original miscalculation rates. The frame work devised was tested using real life applications and the results observed demonstrated that the proposed LSTM based prediction with the prefix span association rule to acquire the day today consumption details is compatible for forecasting the customer consumption over time accurately.