دورية أكاديمية
Leveraging metaheuristics with artificial intelligence for customer churn prediction in telecom industries
| العنوان: | Leveraging metaheuristics with artificial intelligence for customer churn prediction in telecom industries |
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| المؤلفون: | Ilyоs Abdullaev, Natalia Prodanova, Mohammed Altaf Ahmed, E. Laxmi Lydia, Bhanu Shrestha, Gyanendra Prasad Joshi, Woong Cho |
| المصدر: | Electronic Research Archive, Vol 31, Iss 8, Pp 4443-4458 (2023) |
| بيانات النشر: | AIMS Press, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Mathematics LCC:Applied mathematics. Quantitative methods |
| مصطلحات موضوعية: | artificial intelligence, jaya optimization algorithm, deep learning, data exploration, Mathematics, QA1-939, Applied mathematics. Quantitative methods, T57-57.97 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2688-1594 |
| Relation: | https://doaj.org/toc/2688-1594 |
| DOI: | 10.3934/era.2023227?viewType=HTML |
| DOI: | 10.3934/era.2023227 |
| URL الوصول: | https://doaj.org/article/327c29576d534dc39a37fc77298e27ce |
| رقم الأكسشن: | edsdoj.327c29576d534dc39a37fc77298e27ce |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 26881594 |
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| DOI: | 10.3934/era.2023227?viewType=HTML |