KFU. Card publication. Intelligent System for Customer Churn Prediction using Dipper Throat Optimization with Deep Learning on Telecom Industries

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- 中文
- ES

INTELLIGENT SYSTEM FOR CUSTOMER CHURN PREDICTION USING DIPPER THROAT OPTIMIZATION WITH DEEP LEARNING ON TELECOM INDUSTRIES

Form of presentation

Articles in international journals and collections

Year of publication

2024

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Authors, employees of KFU

Bakhvalov Sergey Yurevich, author

Osadchiy Eduard Aleksandrovich, author

Bibliographic description in the original language

Bakhvalov S, Osadchy E, Bogdanova I, Intelligent System for Customer Churn Prediction using Dipper Throat Optimization with Deep Learning on Telecom Industries//Fusion: Practice and Applications. - 2024. - Vol.14, Is.2. - P.172-185.

Annotation

Intelligent System for Customer Churn Prediction (CCP) relates to a system or application that controls advanced artificial intelligence (AI), data analysis, and machine learning (ML) methods for anticipating and predicting customer churn in business or service. CCP approach utilizes various data sources comprising customer behavior and historical data, to create predictive method able of categorizing customers who are potential to leave or stop their engagement. By employing intelligent method, this system supports businesses in proactively addressing customer retention and executing manners to decrease churn, ultimately enhancing revenue retention and customer satisfaction. It connects wide data sources, comprising customer behavior and historical information, to progress difficult methods that can identify customers at risk of leaving or discontinuing their service or subscription. By leveraging deep learning (DL) method, this intelligent system enhances the efficiency and accuracy of customer churn prediction, allowing businesses to take proactive measures to maintain customers, maintain revenue, and develop customer satisfaction. This article presents an Intelligent System for Customer Churn Prediction using Dipper Throat Optimization with Deep Learning (ISCCP-DTODL) methodology in Telecom Industries. The purpose of the ISCCP-DTODL system focuses on the design of intelligent systems for the effective prediction of customer churners and non-churners. To accomplish this, the ISCCP-DTODL system performs Z-score data normalization to preprocess the data. For feature selection and to reduce high dimensionality of features, the ISCCP-DTODL technique uses DTO algorithm. Besides, the ISCCP-DTODL technique makes use of hybrid CNN-BiLSTM model for churn prediction. At last, jellyfish optimization (JFO) based hyperparameter tuning approach can be employed to pick hyperparameters connected to CNN-BiLSTM technique. To display enhanced performance of ISCCP-DTODL technique, a widespread set of simulations was performed. The extensive results stated that ISCCP-DTODL model illustrates improved results than its current techniques in terms of dissimilar measures

Keywords

Intelligent System

The name of the journal

Fusion: Practice and Applications

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186206302&doi=10.54216%2fFPA.140214&partnerID=40&md5=1d2e5f0726697af203755852237449d4

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=301482&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Bakhvalov Sergey Yurevich	ru_RU
dc.contributor.author	Osadchiy Eduard Aleksandrovich	ru_RU
dc.date.accessioned	2024-01-01T00:00:00Z	ru_RU
dc.date.available	2024-01-01T00:00:00Z	ru_RU
dc.date.issued	2024	ru_RU
dc.identifier.citation	Bakhvalov S, Osadchy E, Bogdanova I, Intelligent System for Customer Churn Prediction using Dipper Throat Optimization with Deep Learning on Telecom Industries//Fusion: Practice and Applications. - 2024. - Vol.14, Is.2. - P.172-185.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=301482&p_lang=2	ru_RU
dc.description.abstract	Fusion: Practice and Applications	ru_RU
dc.description.abstract	Intelligent System for Customer Churn Prediction (CCP) relates to a system or application that controls advanced artificial intelligence (AI), data analysis, and machine learning (ML) methods for anticipating and predicting customer churn in business or service. CCP approach utilizes various data sources comprising customer behavior and historical data, to create predictive method able of categorizing customers who are potential to leave or stop their engagement. By employing intelligent method, this system supports businesses in proactively addressing customer retention and executing manners to decrease churn, ultimately enhancing revenue retention and customer satisfaction. It connects wide data sources, comprising customer behavior and historical information, to progress difficult methods that can identify customers at risk of leaving or discontinuing their service or subscription. By leveraging deep learning (DL) method, this intelligent system enhances the efficiency and accuracy of customer churn prediction, allowing businesses to take proactive measures to maintain customers, maintain revenue, and develop customer satisfaction. This article presents an Intelligent System for Customer Churn Prediction using Dipper Throat Optimization with Deep Learning (ISCCP-DTODL) methodology in Telecom Industries. The purpose of the ISCCP-DTODL system focuses on the design of intelligent systems for the effective prediction of customer churners and non-churners. To accomplish this, the ISCCP-DTODL system performs Z-score data normalization to preprocess the data. For feature selection and to reduce high dimensionality of features, the ISCCP-DTODL technique uses DTO algorithm. Besides, the ISCCP-DTODL technique makes use of hybrid CNN-BiLSTM model for churn prediction. At last, jellyfish optimization (JFO) based hyperparameter tuning approach can be employed to pick hyperparameters connected to CNN-BiLSTM technique. To display enhanced performance of ISCCP-DTODL technique, a widespread set of simulations was performed. The extensive results stated that ISCCP-DTODL model illustrates improved results than its current techniques in terms of dissimilar measures	ru_RU
dc.language.iso	ru	ru_RU
dc.subject		ru_RU
dc.title	Intelligent System for Customer Churn Prediction using Dipper Throat Optimization with Deep Learning on Telecom Industries	ru_RU
dc.type	Articles in international journals and collections	ru_RU