Diagnosis the dependence of revenue sources of communication service companies on specific services using machine learning

Document Type : conference paper


1 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

2 Strategic & business development, Telecommunication Infrastructure Company, Tehran, Iran

3 Tehran University, Tehran, Iran


Nowadays, Telecommunication has a vital role in both developed and emerging economies countries. Especially after coronavirus epidemic, the importance of telecommunication service like internet in education, research, economy and other areas is evident. Due to the alluring market of providing internet services to the main customers of IT industry and its significant profit, the demand of the other services has decreased sharply. Hence, a large part of the revenues of the IT industry be related to internet services. In this study, balancing of revenue sources has investigated as one of the important diagnosis facing the IT industry. In order to overcome this problem, introducing low-demand services along with internet service in the form of a package to the main customers is analyzed with a best-known machine learning algorithm, Generalized Linear Model. In order to validate the applicability of our study, a case study of a company providing telecommunication infrastructure and internet network bandwidth in Iran, is presented.


Main Subjects

Agresti, A. (2015) Foundations of linear and generalized linear models. John Wiley & Sons.
Badri Ahmadi, H., Hashemi Petrudi, S. and Wang, X. (2017) ‘Integrating sustainability into supplier selection with analytical hierarchy process and improved grey relational analysis: A case of telecom industry.’, International Journal of Advanced Manufacturing Technology, 90.
Bandyoapdhyay, P. S. et al. (2011) ‘The logic of Simpson’s paradox’, Synthese, 181(2), pp. 185–208.
Büyüközkan, G. and ┼čakir Ersoy, M. (2009) ‘Applying fuzzy decision making approach to IT outsourcing supplier selection’, system, 2, p. 2.
Carrera, Á. et al. (2014) ‘A real-life application of multi-agent systems for fault diagnosis in the provision of an Internet business service’, Journal of Network and Computer Applications, 37, pp. 146–154.
Chen, X. et al. (2020) ‘A Novel Fault Diagnosis Method for High-Speed Railway Turnout Based On DCAE-Logistic Regression’, in 2020 3rd International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 318–323.
Covas, M. T., Silva, C. A. and Dias, L. C. (2013) ‘Multicriteria decision analysis for sustainable data centers location’, International Transactions in Operational Research, 20(3), pp. 269–299. doi: 10.1111/j.1475-3995.2012.00874.x.
Daim, T. U., Bhatla, A. and Mansour, M. (2013) ‘Site selection for a data centre--a multi-criteria decision-making model’, International Journal of Sustainable Engineering, 6(1), pp. 10–22.
Echraibi, A. et al. (2020) ‘An Infinite Multivariate Categorical Mixture Model for Self-Diagnosis of Telecommunication Networks’, in 2020 23rd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN), pp. 258–265.
Emami, M. et al. (2020) ‘Generalization error of generalized linear models in high dimensions’, in International Conference on Machine Learning, pp. 2892–2901.
Félix, A., Garc’ia, N. and Vera, R. (2020) ‘Participatory diagnosis of the tourism sector in managing the crisis caused by the pandemic (COVID-19)’, Revista Interamericana de Ambiente y Turismo, 16(1), pp. 66–78.
Hossain, M. L., Abu-Siada, A. and Muyeen, S. M. (2018) ‘Methods for advanced wind turbine condition monitoring and early diagnosis: A literature review’, Energies, 11(5), p. 1309.
Khatib, E. J. et al. (2015) ‘Data mining for fuzzy diagnosis systems in LTE networks’, Expert Systems with Applications, 42(21), pp. 7549–7559.
Kim, J. S. et al. (2018) ‘Development of data-driven in-situ monitoring and diagnosis system of fused deposition modeling (FDM) process based on support vector machine algorithm’, International Journal of Precision Engineering and Manufacturing-Green Technology, 5(4), pp. 479–486.
Ko, R. K. L., Lee, S. S. G. and Lee, E. W. (2009) ‘Business process management (BPM) standards: a survey’, Business Process Management Journal.
Kohlbacher, M. (2010) ‘The effects of process orientation: a literature review’, Business process management journal.
Lei, Y. et al. (2020) ‘Applications of machine learning to machine fault diagnosis: A review and roadmap’, Mechanical Systems and Signal Processing, 138, p. 106587.
Liu, P. et al. (2020) ‘Optimization of Edge-PLC-Based Fault Diagnosis With Random Forest in Industrial Internet of Things’, IEEE Internet of Things Journal, 7(10), pp. 9664–9674.
Nelder, J. A. and Wedderburn, R. W. M. (1972) ‘Generalized linear models’, Journal of the Royal Statistical Society: Series A (General), 135(3), pp. 370–384.
Quiñones-Grueiro, M., Llanes-Santiago, O. and Neto, A. J. S. (2021) ‘Fault Diagnosis in Industrial Systems’, in Monitoring Multimode Continuous Processes. Springer, pp. 1–14.
De Ramon Fernandez, A., Ruiz Fernandez, D. and Sabuco Garcia, Y. (2020) ‘Business Process Management for optimizing clinical processes: A systematic literature review’, Health informatics journal, 26(2), pp. 1305–1320.
Shahabi, H. et al. (2020) ‘Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier’, Remote Sensing, 12(2), p. 266.
Simpson, E. H. (1949) ‘Measurement of diversity’, nature, 163(4148), p. 688.
Sorrentino, M. et al. (2019) ‘A Novel Energy Efficiency Metric for Model-Based Fault Diagnosis of Telecommunication Central Offices’, Energy Procedia, 158, pp. 3901–3907.
Soualhi, A. and Razik, H. (2020) ‘Diagnostic Methods for the Health Monitoring of Gearboxes’, Electrical Systems 1: From Diagnosis to Prognosis, pp. 1–43.
Sun, Y. et al. (2020) ‘A new convolutional neural network with random forest method for hydrogen sensor fault diagnosis’, IEEE Access, 8, pp. 85421–85430.
Tetteh, V. K. (2012) Organisational Diagnosis--A Management Tool for Change in the Telecommunication Industry.
Varela-Vaca, Á. J. et al. (2019) ‘Automatic verification and diagnosis of security risk assessments in business process models’, IEEE Access, 7, pp. 26448–26465.
Wang, F. et al. (2020) ‘Neural cognitive diagnosis for intelligent education systems’, in Proceedings of the AAAI Conference on Artificial Intelligence, pp. 6153–6161.
Wardani, S., Sihombing, P. and others (2020) ‘Hybrid of Support Vector Machine Algorithm and K-Nearest Neighbor Algorithm to Optimize the Diagnosis of Eye Disease’, in 2020 3rd International Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT), pp. 321–326.
Wu, J.-Y. and Hsiao, H.-I. (2021) ‘Food quality and safety risk diagnosis in the food cold chain through failure mode and effect analysis’, Food Control, 120, p. 107501.
Yao, J. and Ye, Y. (2020) ‘The effect of image recognition traffic prediction method under deep learning and naive Bayes algorithm on freeway traffic safety’, Image and Vision Computing, 103, p. 103971.