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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Vydáno v:Electronic research archive Ročník 31; číslo 8; s. 4443 - 4458
Hlavní autoři: Abdullaev, Ilyоs, Prodanova, Natalia, Altaf Ahmed, Mohammed, Laxmi Lydia, E., Shrestha, Bhanu, Prasad Joshi, Gyanendra, Cho, Woong
Médium: Journal Article
Jazyk:angličtina
Vydáno: AIMS Press 01.01.2023
Témata:
artificial intelligence
data exploration
deep learning
jaya optimization algorithm
ISSN:2688-1594, 2688-1594
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Shrnutí: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