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All Journal International Journal of Electrical and Computer Engineering
Abderrazak Ben Salah
Imam Abdulrahman Bin Faisal University
Computer Science & IT Electrical & Electronics Engineering

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Learning trends in customer churn with rule-based and kernel methods Nahier Aldhafferi; Abdullah Alqahtani; Fatema Sabeen Shaikh; Sunday Olusanya Olatunji; Abdullah Almurayh; Fahad A. Alghamdi; Ghalib H. Alshammri; Amani K. Samha; Mutasem Khalil Alsmadi; Hayat Alfagham; Abderrazak Ben Salah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5364-5374

Abstract

In the present article an attempt has been made to predict the occurrences of customers leaving or ‘churning’ a business enterprise and explain the possible causes for the customer churning. Three different algorithms are used to predict churn, viz. decision tree, support vector machine and rough set theory. While two are rule-based learning methods which lead to more interpretable results that might help the marketing division to retain or hasten cross-sell of customers, one of them is a kernel-based classification that separates the customers on a feature hyperplane. The nature of predictions and rules obtained from them are able to provide a choice between a more focused or more extensive program the company may wish to implement as part of its customer retention program.
A genetic algorithm for shortest path with real constraints in computer networks Fahad. A. Alghamdi; Ahmed Younes Hamed; Abdullah M. Alghamdi; Abderrazak Ben Salah; Tamer Hashem Farag; Walaa Hassan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp435-442

Abstract

The shortest path problem has many different versions. In this manuscript, we proposed a muti-constrained optimization method to find the shortest path in a computer network. In general, a genetic algorithm is one of the common heuristic algorithms. In this paper, we employed the genetic algorithm to find the solution of the shortest path multi-constrained problem. The proposed algorithm finds the best route for network packets with minimum total cost, delay, and hop count constrained with limited bandwidth. The new algorithm was implemented on four different capacity networks with random network parameters, the results showed that the shortest path under constraints can be found in a reasonable time. The experimental results showed that the algorithm always found the shortest path with minimal constraints.
Co-Authors Abdullah Almurayh Abdullah Alqahtani Abdullah M. Alghamdi Ahmed Younes Hamed Amani K. Samha Fahad A. Alghamdi Fahad. A. Alghamdi Fatema Sabeen Shaikh Ghalib H. Alshammri Hayat Alfagham Mutasem Khalil Alsmadi Nahier Aldhafferi Sunday Olusanya Olatunji Tamer Hashem Farag Walaa Hassan