Network Congestion Tracking and Detection in Banking Industry Using Machine Learning Models
The escalating threat of congestion in wireless networks on a global scale prompts the need for effective detection and management techniques. This study investigates the tracking and detection of congestion in wireless networks, particularly within the banking industry, where digital transactions...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria
2024-09-01
|
| Series: | ABUAD Journal of Engineering Research and Development |
| Subjects: | |
| Online Access: | https://journals.abuad.edu.ng/index.php/ajerd/article/view/783 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846099686209880064 |
|---|---|
| author | Kingsley Ifeanyi Chibueze Nwamaka Georgenia Ezeji Nnenna Harmony Nwobodo-Nzeribe |
| author_facet | Kingsley Ifeanyi Chibueze Nwamaka Georgenia Ezeji Nnenna Harmony Nwobodo-Nzeribe |
| author_sort | Kingsley Ifeanyi Chibueze |
| collection | DOAJ |
| description |
The escalating threat of congestion in wireless networks on a global scale prompts the need for effective detection and management techniques. This study investigates the tracking and detection of congestion in wireless networks, particularly within the banking industry, where digital transactions are rapidly increasing. It addresses the challenge of congestion management through machine learning (ML) models, aiming to enhance network performance and service quality. This research evaluates various ML algorithms, including Support Vector Machines, Decision Trees, and Random Forests, to identify the most effective approach for congestion detection. This research utilizes a dataset sourced from MainOne Limited, which covered August 18th, 20th, 22nd, 23rd, and 24th, 2023, and included banking operation hours from 7 AM to 4 PM each day. Preprocessing of data is conducted to optimize model training. Following training, various performance metrics including accuracy, precision, recall, F1 score, response time, and confusion matrix are assessed. Results demonstrate that Random Forest outperforms other models in accuracy, precision, recall, F1 score, and response time, with an accuracy of 98.90%. This research discusses the importance of continuous innovation in banking network analytics to tackle evolving congestion challenges. Future recommendations include leveraging advanced ML techniques like deep learning and reinforcement learning and exploring ensemble learning methods to enhance congestion detection models further.
|
| format | Article |
| id | doaj-art-e4410d0c633b4bd49139e734d67ffe04 |
| institution | Kabale University |
| issn | 2756-6811 2645-2685 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria |
| record_format | Article |
| series | ABUAD Journal of Engineering Research and Development |
| spelling | doaj-art-e4410d0c633b4bd49139e734d67ffe042024-12-31T10:18:59ZengCollege of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, NigeriaABUAD Journal of Engineering Research and Development2756-68112645-26852024-09-017210.53982/ajerd.2024.0702.24-j655Network Congestion Tracking and Detection in Banking Industry Using Machine Learning ModelsKingsley Ifeanyi Chibueze0Nwamaka Georgenia Ezeji1Nnenna Harmony Nwobodo-Nzeribe2Department of Computer Science and Mathematics, Godfrey Okoye University, Enugu State, NigeriaDepartment of Computer Engineering, Enugu State University of Science and Technology, Enugu, NigeriaDepartment of Computer Engineering, Enugu State University of Science and Technology, Enugu, Nigeria The escalating threat of congestion in wireless networks on a global scale prompts the need for effective detection and management techniques. This study investigates the tracking and detection of congestion in wireless networks, particularly within the banking industry, where digital transactions are rapidly increasing. It addresses the challenge of congestion management through machine learning (ML) models, aiming to enhance network performance and service quality. This research evaluates various ML algorithms, including Support Vector Machines, Decision Trees, and Random Forests, to identify the most effective approach for congestion detection. This research utilizes a dataset sourced from MainOne Limited, which covered August 18th, 20th, 22nd, 23rd, and 24th, 2023, and included banking operation hours from 7 AM to 4 PM each day. Preprocessing of data is conducted to optimize model training. Following training, various performance metrics including accuracy, precision, recall, F1 score, response time, and confusion matrix are assessed. Results demonstrate that Random Forest outperforms other models in accuracy, precision, recall, F1 score, and response time, with an accuracy of 98.90%. This research discusses the importance of continuous innovation in banking network analytics to tackle evolving congestion challenges. Future recommendations include leveraging advanced ML techniques like deep learning and reinforcement learning and exploring ensemble learning methods to enhance congestion detection models further. https://journals.abuad.edu.ng/index.php/ajerd/article/view/783Support Vector MachineDecision TreesRandom ForestsCongestion |
| spellingShingle | Kingsley Ifeanyi Chibueze Nwamaka Georgenia Ezeji Nnenna Harmony Nwobodo-Nzeribe Network Congestion Tracking and Detection in Banking Industry Using Machine Learning Models ABUAD Journal of Engineering Research and Development Support Vector Machine Decision Trees Random Forests Congestion |
| title | Network Congestion Tracking and Detection in Banking Industry Using Machine Learning Models |
| title_full | Network Congestion Tracking and Detection in Banking Industry Using Machine Learning Models |
| title_fullStr | Network Congestion Tracking and Detection in Banking Industry Using Machine Learning Models |
| title_full_unstemmed | Network Congestion Tracking and Detection in Banking Industry Using Machine Learning Models |
| title_short | Network Congestion Tracking and Detection in Banking Industry Using Machine Learning Models |
| title_sort | network congestion tracking and detection in banking industry using machine learning models |
| topic | Support Vector Machine Decision Trees Random Forests Congestion |
| url | https://journals.abuad.edu.ng/index.php/ajerd/article/view/783 |
| work_keys_str_mv | AT kingsleyifeanyichibueze networkcongestiontrackinganddetectioninbankingindustryusingmachinelearningmodels AT nwamakageorgeniaezeji networkcongestiontrackinganddetectioninbankingindustryusingmachinelearningmodels AT nnennaharmonynwobodonzeribe networkcongestiontrackinganddetectioninbankingindustryusingmachinelearningmodels |