Arithmetic Optimization with Deep Learning Enabled Churn Prediction Model for Telecommunication Industries  

作　　者：Vani Haridasan Kavitha Muthukumaran K.Hariharanath 

机构地区：[1]SSN School of Management,Kalavakkam,Chennai,603110,India

出　　处：《Intelligent Automation & Soft Computing》2023年第3期3531-3544,共14页智能自动化与软计算（英文）

摘　　要：Customer retention is one of the challenging issues in different business sectors,and variousfirms utilize customer churn prediction(CCP)process to retain existing customers.Because of the direct impact on the company revenues,particularly in the telecommunication sector,firms are needed to design effective CCP models.The recent advances in machine learning(ML)and deep learning(DL)models enable researchers to introduce accurate CCP models in the telecom-munication sector.CCP can be considered as a classification problem,which aims to classify the customer into churners and non-churners.With this motivation,this article focuses on designing an arithmetic optimization algorithm(AOA)with stacked bidirectional long short-term memory(SBLSTM)model for CCP.The proposed AOA-SBLSTM model intends to proficiently forecast the occurrence of CC in the telecommunication industry.Initially,the AOA-SBLSTM model per-forms pre-processing to transform the original data into a useful format.Besides,the SBLSTM model is employed to categorize data into churners and non-chur-ners.To improve the CCP outcomes of the SBLSTM model,an optimal hyper-parameter tuning process using AOA is developed.A widespread simulation analysis of the AOA-SBLSTM model is tested using a benchmark dataset with 3333 samples and 21 features.The experimental outcomes reported the promising performance of the AOA-SBLSTM model over the recent approaches.

关 键 词：Customer churn prediction business intelligence telecommunication industry decision making deep learning 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]