{"id":3032,"date":"2026-04-09T23:24:18","date_gmt":"2026-04-09T15:24:18","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=3032"},"modified":"2026-06-09T21:42:17","modified_gmt":"2026-06-09T13:42:17","slug":"analysis-of-mobile-users-activities-using-2-mean-normalization-method","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=analysis-of-mobile-users-activities-using-2-mean-normalization-method","title":{"rendered":"Analysis Of Mobile Users\u2019 Activities Using 2 Mean-Normalization Method"},"content":{"rendered":"\n
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<\/i> 2024<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 27, Issue 2<\/a><\/p>\n<\/div>\n\n\n\n

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Sandhya B S<\/i><\/a> and Rohini Deshpande<\/p>\n\n\n\n

Reva University, India<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0October 6, 2022
Accepted:\u00a0May 25, 2023
Publication Date:\u00a0April 9, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Incoming calls activity on 05\/11\/2013.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202402_27(2).0012<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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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\u2019 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<\/p>\n\n\n\n

Keywords:\u00a0Call Detail Record, machine learning tool, mobile user activity, network anomalies, pre-processing<\/em><\/p>\n\n\n\n

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