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

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...

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Bibliographic Details
Published in:Electronic research archive Vol. 31; no. 8; pp. 4443 - 4458
Main Authors: Abdullaev, Ilyоs, Prodanova, Natalia, Altaf Ahmed, Mohammed, Laxmi Lydia, E., Shrestha, Bhanu, Prasad Joshi, Gyanendra, Cho, Woong
Format: Journal Article
Language:English
Published: AIMS Press 01.01.2023
Subjects:
artificial intelligence
data exploration
deep learning
jaya optimization algorithm
ISSN:2688-1594, 2688-1594
Online Access:Get full text
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Summary: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.
ISSN:2688-1594
2688-1594
DOI:10.3934/era.2023227