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A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning

Computers, Materials & Continua 2022年第9期72卷 4283-4301页

作者： Soban Arshad Khalid Iqbal sheneela naz Sadaf Yasmin Zobia Rehman Department of Computer Science COMSATS UniversityIslamabadAttock CampusPakistan Department of Computer Science COMSATS UniversityIslamabadIslamabad CampusPakistan

Telecom industry relies on churn prediction models to retain their customers.These prediction models help in precise and right time recognition of future switching by a group of customers to other service providers.Retention not only contributes to the profit of an organization,but it is also important for upholding a position in the competitive market.In the past,numerous churn prediction models have been proposed,but the current models have a number of flaws that prevent them from being used in real-world largescale telecom datasets.These schemes,fail to incorporate frequently changing requirements.Data sparsity,noisy data,and the imbalanced nature of the dataset are the other main challenges for an accurate prediction.In this paper,we propose a hybrid model,name as“A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning(HCPRs)”that used Synthetic Minority Over-Sampling Technique(SMOTE)and Particle Swarm Optimization(PSO)to address the issue of imbalance class data and feature selection.Data cleaning and normalization has been done on big Orange dataset contains 15000 features along with 50000 entities.Substantial experiments are performed to test and validate the model on Random Forest(RF),Linear Regression(LR),Naïve Bayes(NB)and XG-Boost.Results show that the proposed model when used with XGBoost classifier,has greater Accuracy Under Curve(AUC)of 98%as compared with other methods.

关键词： Telecom churn prediction data sparsity class imbalance big data particle swarm optimization

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A Smart Heart Disease Diagnostic System Using Deep Vanilla LSTM

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Computers, Materials & Continua 2023年第10期77卷 1251-1279页

作者： Maryam Bukhari Sadaf Yasmin sheneela naz Mehr Yahya Durrani Mubashir Javaid Jihoon Moon Seungmin Rho Department of Computer Science COMSATS University IslamabadAttock CampusAttockPakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Department of Computer Science UET TaxilaTaxilaPakistan Department of AI and Big Data Soonchunhyang UniversityAsanKorea Department of Industrial Security Chung-Ang UniversitySeoul06974Korea

Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics,which aid in the prevention of several diseases including heart-related abnormalities.In this context,regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram(ECG)signals has the potential to save many lives.In existing studies,several heart disease diagnostic systems are proposed by employing different state-of-the-art methods,however,improving such methods is always an intriguing area of research.Hence,in this research,a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals.The proposed framework extracts both linear and time-series information on the ECG signals and fuses them into a single framework concurrently.The linear characteristics of ECG signals are extracted by convolution layers followed by Gaussian Error Linear Units(GeLu)and time series characteristics of ECG beats are extracted by Vanilla Long Short-Term Memory Networks(LSTM).Following on,the feature reduction of linear information is done with the help of ID Generalized Gated Pooling(GGP).In addition,data misbalancing issues are also addressed with the help of the Synthetic Minority Oversampling Technique(SMOTE).The performance assessment of the proposed model is done over the two publicly available datasets named MIT-BIH arrhythmia database(MITDB)and PTB Diagnostic ECG database(PTBDB).The proposed framework achieves an average accuracy performance of 99.14%along with a 95%recall value.

关键词： Smart systems deep learning ECG signals heart disease concurrent learning LSTM generalized gated pooling

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