Leveraging metaheuristics with artificial intelligence for customer churn prediction in telecom industries-Reference-Cited by-同舟云学术

Leveraging metaheuristics with artificial intelligence for customer churn prediction in telecom industries

Published:2023 Issue:8 Volume:31 Page:4443-4458
ISSN:2688-1594
Container-title:Electronic Research Archive
language:
Short-container-title:era

Author:

Abdullaev Ilyоs¹,Prodanova Natalia²,Altaf Ahmed Mohammed³,Laxmi Lydia E.⁴,Shrestha Bhanu⁵,Prasad Joshi Gyanendra⁶,Cho Woong⁷

Affiliation:

1. Department of Management and Marketing, Urgench State University, Urgench 220100, Uzbekistan

2. Basic Department Financial Control, Analysis and Audit of Moscow Main Control Department, Plekhanov Russian University of Economics, Moscow 117997, Russia

3. Department of Computer Engineering, College of Computer Engineering & Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia

4. Department of Computer Science and Engineering, Vignan's Institute of Information Technology, Visakhapatnam 530049, India

5. Department of Electronic Engineering, Kwangwoon University, Seoul 01897, Korea

6. Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea

7. Department of Electronics, Information and Communication Engineering, Kangwon National University, Gangwon-do, Samcheok-si 25913, Korea

Abstract

<abstract> <p>Customer churn prediction (CCP) is among the greatest challenges faced in the telecommunication sector. With progress in the fields of machine learning (ML) and artificial intelligence (AI), the possibility of CCP has dramatically increased. Therefore, this study presents an artificial intelligence with Jaya optimization algorithm based churn prediction for data exploration (AIJOA-CPDE) technique for human-computer interaction (HCI) application. The major aim of the AIJOA-CPDE technique is the determination of churned and non-churned customers. In the AIJOA-CPDE technique, an initial stage of feature selection using the JOA named the JOA-FS technique is presented to choose feature subsets. For churn prediction, the AIJOA-CPDE technique employs a bidirectional long short-term memory (BDLSTM) model. Lastly, the chicken swarm optimization (CSO) algorithm is enforced as a hyperparameter optimizer of the BDLSTM model. A detailed experimental validation of the AIJOA-CPDE technique ensured its superior performance over other existing approaches.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

General Mathematics

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