Arithmetic Optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn Prediction
Companies in a wide variety of industries use the customer churn prediction (CCP) process to keep their current clientele happy. Insurance companies need to be able to forecast churn to enhance the potency and functionality of deep learning methods. Deep learning techniques have a significant impact...
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10216284/ |
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| author | Nagaraju Jajam Nagendra Panini Challa Kamepalli S. L. Prasanna C H Venkata Sasi Deepthi |
| author_facet | Nagaraju Jajam Nagendra Panini Challa Kamepalli S. L. Prasanna C H Venkata Sasi Deepthi |
| author_sort | Nagaraju Jajam |
| collection | DOAJ |
| description | Companies in a wide variety of industries use the customer churn prediction (CCP) process to keep their current clientele happy. Insurance companies need to be able to forecast churn to enhance the potency and functionality of deep learning methods. Deep learning techniques have a significant impact on improving and forecasting customer retention. Numerous studies employ standard machine learning and Deep Learning strategies to enhance customer retention, despite the fact that these strategies have a number of accuracy issues. In light of this need, this piece is dedicated to the development of a stacked bidirectional long short-term memory (SBLSTM) and RNN model for Arithmetic Optimisation Algorithm (AOA) in CCP. The proposed AOA-SBLSTM-RNN model intends to proficiently forecast the occurrence of Customer Churn in the Insurance industry. Initially, the AOA model performs pre-processing to transform the original data into a useful format. In addition, the SBLSTM-RNN model is used to distinguish between churning and non-churning customers. To improve the CCP outcomes of the SBLSTM-RNN model, an optimal Hyperparameters tuning process using Improved Gravitational Search Optimization Algorithm (IGSA) is used in this study. In this work, Three Health Insurance datasets were used to evaluate performance, and four sets of experiments were conducted. The Measures of true churn, false churn, specificity, precision, and accuracy are employed to assess the efficacy of the proposed approach. Experimental result shows that the Ensemble Deep Learning model AOA-SBLSTM-RNN with IGSA produces accuracy value of 97.89 and 97.67 on dataset 2 and dataset 1. which is better and had higher predictability levels in compared with all other models. |
| first_indexed | 2024-03-12T02:34:54Z |
| format | Article |
| id | doaj.art-73cda04e82a44310b9f700e50489e59d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T02:34:54Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-73cda04e82a44310b9f700e50489e59d2023-09-04T23:02:16ZengIEEEIEEE Access2169-35362023-01-0111931119312810.1109/ACCESS.2023.330466910216284Arithmetic Optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn PredictionNagaraju Jajam0Nagendra Panini Challa1https://orcid.org/0000-0002-8894-6436Kamepalli S. L. Prasanna2C H Venkata Sasi Deepthi3School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, IndiaSchool of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, IndiaSchool of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, IndiaDepartment of Computer Science and Engineering, Shri Vishnu Engineering College for Women (Autonomous), Bhimavaram, IndiaCompanies in a wide variety of industries use the customer churn prediction (CCP) process to keep their current clientele happy. Insurance companies need to be able to forecast churn to enhance the potency and functionality of deep learning methods. Deep learning techniques have a significant impact on improving and forecasting customer retention. Numerous studies employ standard machine learning and Deep Learning strategies to enhance customer retention, despite the fact that these strategies have a number of accuracy issues. In light of this need, this piece is dedicated to the development of a stacked bidirectional long short-term memory (SBLSTM) and RNN model for Arithmetic Optimisation Algorithm (AOA) in CCP. The proposed AOA-SBLSTM-RNN model intends to proficiently forecast the occurrence of Customer Churn in the Insurance industry. Initially, the AOA model performs pre-processing to transform the original data into a useful format. In addition, the SBLSTM-RNN model is used to distinguish between churning and non-churning customers. To improve the CCP outcomes of the SBLSTM-RNN model, an optimal Hyperparameters tuning process using Improved Gravitational Search Optimization Algorithm (IGSA) is used in this study. In this work, Three Health Insurance datasets were used to evaluate performance, and four sets of experiments were conducted. The Measures of true churn, false churn, specificity, precision, and accuracy are employed to assess the efficacy of the proposed approach. Experimental result shows that the Ensemble Deep Learning model AOA-SBLSTM-RNN with IGSA produces accuracy value of 97.89 and 97.67 on dataset 2 and dataset 1. which is better and had higher predictability levels in compared with all other models.https://ieeexplore.ieee.org/document/10216284/Customer churninsurance industryensemble deep learningarithmetic optimization algorithmfeature extractionstacked bidirectional long short-term memory |
| spellingShingle | Nagaraju Jajam Nagendra Panini Challa Kamepalli S. L. Prasanna C H Venkata Sasi Deepthi Arithmetic Optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn Prediction IEEE Access Customer churn insurance industry ensemble deep learning arithmetic optimization algorithm feature extraction stacked bidirectional long short-term memory |
| title | Arithmetic Optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn Prediction |
| title_full | Arithmetic Optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn Prediction |
| title_fullStr | Arithmetic Optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn Prediction |
| title_full_unstemmed | Arithmetic Optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn Prediction |
| title_short | Arithmetic Optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn Prediction |
| title_sort | arithmetic optimization with ensemble deep learning sblstm rnn igsa model for customer churn prediction |
| topic | Customer churn insurance industry ensemble deep learning arithmetic optimization algorithm feature extraction stacked bidirectional long short-term memory |
| url | https://ieeexplore.ieee.org/document/10216284/ |
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