1Department of Computer Engineering, Federal University of Technology Minna, Nigeria
2Main Campus, Gidan Kwanu, Along Minna - Bida Road; PMB 65 Minna, Niger State, Nigeria, Nigeria
3Department of Telecommunications Engineering, Federal University of Technology Minna, Nigeria
4 Department of Electrical and Electronics Engineering, Nigerian Turkish Nile University State, Nigeria
5 Cadastral Zone, Plot 681 Airport Rd, Jabi, Abuja, Nigeria, Nigeria
BibTex Citation Data :
@article{JTSISKOM13118, author = {Olaonipekun Oluwafemi Erunkulu and Elizabeth Nnonye Onwuka and Okechukwu Ugweje and Lukman Adewale Ajao}, title = {Prediction of Call Drops in GSM Network using Artificial Neural Network}, journal = {Jurnal Teknologi dan Sistem Komputer}, volume = {7}, number = {1}, year = {2019}, keywords = {Artificial Neural Network; call drop rate; Global System for Mobile communication; performance indicator; Quality of Service}, abstract = { Global System for Mobile communication is a digital mobile system that is widely used in the world. Over the years, the number of subscribers has tremendously increased, the quality of service (Call Drop Rate) became an issue to consider as many subscribers were not satisfied with the services rendered. In this paper, we present the Artificial Neural Network approach to predict call drop during an initiated call. GSM parameters data for the prediction were acquired using TEMS Investigations software. The measurements were carried out over a period of three months. Post analysis and training of the parameters was done using the Artificial Neural Network to have an output of “0” for no-drop calls and “1” for drop calls. The developed model has an accuracy of 87.5% prediction of drop call. The developed model is both useful to operators and end users for optimizing the network. }, issn = {2338-0403}, pages = {38--46} doi = {10.14710/jtsiskom.7.1.2019.38-46}, url = {https://jtsiskom.undip.ac.id/article/view/13118} }
Refworks Citation Data :
Note: This article has supplementary file(s).
Article Metrics:
Last update:
Smart Contextual Modeling for Customer Data Interchange
5G Mobile Communication Applications: A Survey and Comparison of Use Cases
A comparative analysis of alpha-beta-gamma and close-in path loss models based on measured data for 5G mobile networks
A data‐driven assessment of mobile operator service quality in Ghana
An Optimized Half Wave Dipole Antenna for the Transmission of WiFi and Broadband Networks
Cellular Communications Coverage Prediction Techniques: A Survey and Comparison
A Deep Auto Imputation Integrated Bayes Optimized Transfer Learning Model with Hybrid Skill-Levy Search Algorithm (DAI-BOTS) for Call Drop Prediction in Mobile Networks
Application of Machine Learning in Predicting Call Quality of Telecom Service Providers
Last update: 2024-10-06 07:41:13
Data mining for mobile internet traffic flow forecasting
Starting from 2021, the author(s) whose article is published in the JTSiskom journal attain the copyright for their article and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By submitting the manuscript to JTSiskom, the author(s) agree with this policy. No special document approval is required.
The author(s) guarantee that:
The author(s) retain all rights to the published work, such as (but not limited to) the following rights:
Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. JTSiskom will not be held responsible for anything arising because of the writer's internal dispute. JTSiskom will only communicate with correspondence authors.
Authors should also understand that their articles (and any additional files, including data sets and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. JTSiskom allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and JTSiskom to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published.