Analysis Of Mobile Users’ Activities Using 2 Mean-Normalization Method

Sandhya  B S; Rohini Deshpande Deshpande

doi:10.6180/jase.202402_27(2).0012

Analysis Of Mobile Users’ Activities Using 2 Mean-Normalization Method

Computer Science and Information Engineering

Sandhya B SThis email address is being protected from spambots. You need JavaScript enabled to view it., Rohini Deshpande

Reva University, India

Received: October 6, 2022
Accepted: May 25, 2023
Publication Date: July 5, 2023

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202402_27(2).0012

In recent decades, with the attracting features of mobiles including 4G and 5G, world is getting more connected to mobile communications. This results in the accumulation of large amount of data in the mobile network. The analysis of the network data is very complex but is essential in terms of resource and cost management. The network data analytics include detection of unusual network behaviour due to traffic created by the mobile users and Short Message Service (SMS) spammers. Research to an approach with the same impulsion is creating a new interest in the field of mobile network data analytics using machine learning tools. To attain this, Call Detail Record (CDR) provided by the telecom network industry is utilized. The timely analysis of CDR helps to understand the behaviour of the network due to various activities of mobile users. To analyse CDR, it has to be pre-processed to convert it from the raw data into machine understandable form. The proposed method is mean-normalization pre-processing which is suitable in understanding the behaviour of mobile users’ individual activities like incoming-outgoing calls, incoming-outgoing SMS and internet activity. Later, machine learning tools can be applied to analyse and predict the network anomalies like network traffic and Short

Keywords: Call Detail Record, machine learning tool, mobile user activity, network anomalies, pre-processing

[1] N. Ghotekar, (2016) “Analysis and Data Mining of Call Detail Records using Big Data Technology" IJARCCE 5(12): 280–283.
[2] C. Xu, K. Wang, Y. Sun, S. Guo, and A. Y. Zomaya, (2018) “Redundancy avoidance for big data in data centers: A conventional neural network approach" IEEE Transactions on Network Science and Engineering 7(1): 104–114.
[3] K. Sultan, H. Ali, and Z. Zhang, (2018) “Call detail records driven anomaly detection and traffic prediction in mobile cellular networks" IEEE Access 6: 41728–41737.
[4] M. S. Parwez, D. B. Rawat, and M. Garuba, (2017) “Big data analytics for user-activity analysis and useranomaly detection in mobile wireless network" IEEE Transactions on Industrial Informatics 13(4): 2058–2065.
[5] B. Hussain, Q. Du, and P. Ren, (2018) “Semi-supervised learning based big data-driven anomaly detection in mobile wireless networks" China Communications 15(4): 41–57.
[6] R. Sharifi, M. M. Majdabadi, and V. T. Vakili. “Mobile user-activity prediction utilizing LSTM recurrent neural network”. In: 2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM). IEEE. 2019, 1–7.
[7] K. Sultan, H. Ali, A. Ahmad, and Z. Zhang, (2019) “Call details record analysis: A spatiotemporal exploration toward mobile traffic classification and optimization" Information 10(6): 192.
[8] M. Al-Saadi, B. V. Ghita, S. Shiaeles, and P. Sarigiannidis. “A novel approach for performance-based clustering and management of network traffic flows”. In: 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC). IEEE. 2019, 2025–2030.
[9] L. Kesheng, N. Yikun, L. Zihan, and D. Bin. “Data mining and feature analysis of college students’ campus network behavior”. In: 2020 5th IEEE International Conference on Big Data Analytics (ICBDA). IEEE. 2020, 231–237.
[10] S. Qin, Y. Zuo, Y. Wang, X. Sun, and H. Dong. “Travel trajectories analysis based on call detail record data”. In: 2017 29th Chinese Control And Decision Conference (CCDC). IEEE. 2017, 7051–7056.
[11] P. Pandey. Data Preprocessing: Concepts. 2019. URL: https : //towardsdatascience.com/data-preprocessing-concepts-fa946d11c825 (visited on 11/25/2019).
[12] M. S. Mahmud, J. Z. Huang, S. Salloum, T. Z. Emara, and K. Sadatdiynov, (2020) “A survey of data partitioning and sampling methods to support big data analysis" Big Data Mining and Analytics 3(2): 85–101.
[13] S. Salloum, J. Z. Huang, and Y. He, (2019) “Random sample partition: a distributed data model for big data analysis" IEEE Transactions on Industrial Informatics 15(11): 5846–5854.
[14] M. Li, H. Wang, and J. Li, (2019) “Mining conditional functional dependency rules on big data" Big Data Mining and Analytics 3(1): 68–84.
[15] A. Alim and D. Shukla. “A Parameter Estimation Model of Big Data Setup Based on Sampling Technique”. In: 2nd International Conference on Data, Engineering and Applications (IDEA). IEEE. 2020, 1–5.
[16] R. Jony et al. “Preprocessing solutions for telecommunication specific big data use cases". (mathesis). 2014.