Churn prediction in telecom industry using machine learning ensembles with class balancing
- Authors: Chowdhury, Abdullahi , Kaisar, Shahriar , Rashid, Md Mamunur , Shafin, Sakib , Kamruzzaman, Joarder
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021, Brisbane, 8-10 December 2021
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- Description: Telecommunication service providers are going through a very competitive and challenging time to retain existing customers by offering new and attractive services (e.g., unlimited local and international calls, high-speed internet, new phones). It is therefore imperative to analyse and predict customer churn behaviour more accurately. One of the major challenges to analyse churn data and build better prediction model is the imbalance nature of the data. Customer behaviour for churn and non-churn scenarios may contain resembling features. Using a single classifier or simple oversampling method to handle data imbalance often struggles to identify the minority (churn) class data. To overcome the issue, we introduce a model that uses sophisticated oversampling technique in conjunction with ensemble methods, namely Random Forest, Gradient Boost, Extreme Gradient Boost, and AdaBoost. The hyperparameters of the baseline ensemble methods and the oversampling methods were tuned in several ways to investigate their impact on prediction performances. Using a widely used publicly available customer churn dataset, prediction performance of the proposed model was evaluated in term of various metrics, namely, accuracy, precision, recall, F-1 score, AUC under ROC curve. Our model outperformed the existing models and significantly reduced both false positive and false negative prediction. © IEEE 2022.
Decentralized content sharing in mobile ad-hoc networks : a survey
- Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Rashid, Md Mamunur
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Digital Communications and Networks Vol. 9, no. 6 (2023), p. 1363-1398
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- Description: The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic. To address this issue and take advantage of the short-range communication capabilities of smart mobile devices, the decentralized content sharing approach has emerged as a suitable and promising alternative. Decentralized content sharing uses a peer-to-peer network among co-located smart mobile device users to fulfil content requests. Several articles have been published to date to address its different aspects including group management, interest extraction, message forwarding, participation incentive, and content replication. This survey paper summarizes and critically analyzes recent advancements in decentralized content sharing and highlights potential research issues that need further consideration. © 2022 Chongqing University of Posts and Telecommunications