Classification of broadband network devices using text mining technique
The Broadband Internet industry is highly competitive, with service providers investing heavily in network development to meet customer demands and competing on pricing. Effective cost management is crucial for profitability in this market. This work proposes a model for classifying broadband networ...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016123003424 |
| _version_ | 1797405476124622848 |
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| author | Mahasak Ketcham Thittaporn Ganokratanaa Nattapat Sridoung |
| author_facet | Mahasak Ketcham Thittaporn Ganokratanaa Nattapat Sridoung |
| author_sort | Mahasak Ketcham |
| collection | DOAJ |
| description | The Broadband Internet industry is highly competitive, with service providers investing heavily in network development to meet customer demands and competing on pricing. Effective cost management is crucial for profitability in this market. This work proposes a model for classifying broadband network devices based on text mining techniques applied to a device list from a leading broadband network company in Thailand. The device descriptions are used to generate a feature vector, which is then employed by a classification algorithm to categorize devices into core, access, and last mile hierarchies. Various algorithms including decision tree, naïve Bayes, Bayesian network, k-nearest neighbor, support vector machine, and deep neural network are compared, with support vector machine achieving the highest accuracy of 90.35%. The results are visualized to provide insights into network hierarchy, device replacement dates, and budget requirements, enabling support for cost management, budget planning, maintenance, and investment decision-making. The methodology outline includes, • Obtaining a device list from a major broadband network company and extracting device descriptions through text mining and generating a feature vector. • Using a support vector machine for classification and comparing algorithm performances. • Visualizing the results for actionable insights in cost management, budget planning, and investment decisions. |
| first_indexed | 2024-03-09T03:10:30Z |
| format | Article |
| id | doaj.art-f43f01c118d74c21a0b4bbc4195e6f68 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-03-09T03:10:30Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-f43f01c118d74c21a0b4bbc4195e6f682023-12-04T05:22:25ZengElsevierMethodsX2215-01612023-12-0111102346Classification of broadband network devices using text mining techniqueMahasak Ketcham0Thittaporn Ganokratanaa1Nattapat Sridoung2Department of Information Technology Management, King Mongkut's University of Technology North Bangkok, Bangkok, ThailandApplied Computer Science Programme, Department of Mathematics, King Mongkut's University of Technology Thonburi, Bangkok, Thailand; Corresponding author.Department of Information Technology Management, King Mongkut's University of Technology North Bangkok, Bangkok, ThailandThe Broadband Internet industry is highly competitive, with service providers investing heavily in network development to meet customer demands and competing on pricing. Effective cost management is crucial for profitability in this market. This work proposes a model for classifying broadband network devices based on text mining techniques applied to a device list from a leading broadband network company in Thailand. The device descriptions are used to generate a feature vector, which is then employed by a classification algorithm to categorize devices into core, access, and last mile hierarchies. Various algorithms including decision tree, naïve Bayes, Bayesian network, k-nearest neighbor, support vector machine, and deep neural network are compared, with support vector machine achieving the highest accuracy of 90.35%. The results are visualized to provide insights into network hierarchy, device replacement dates, and budget requirements, enabling support for cost management, budget planning, maintenance, and investment decision-making. The methodology outline includes, • Obtaining a device list from a major broadband network company and extracting device descriptions through text mining and generating a feature vector. • Using a support vector machine for classification and comparing algorithm performances. • Visualizing the results for actionable insights in cost management, budget planning, and investment decisions.http://www.sciencedirect.com/science/article/pii/S2215016123003424Classification modelText miningBroadband networkSupport vector machineDecision support system |
| spellingShingle | Mahasak Ketcham Thittaporn Ganokratanaa Nattapat Sridoung Classification of broadband network devices using text mining technique MethodsX Classification model Text mining Broadband network Support vector machine Decision support system |
| title | Classification of broadband network devices using text mining technique |
| title_full | Classification of broadband network devices using text mining technique |
| title_fullStr | Classification of broadband network devices using text mining technique |
| title_full_unstemmed | Classification of broadband network devices using text mining technique |
| title_short | Classification of broadband network devices using text mining technique |
| title_sort | classification of broadband network devices using text mining technique |
| topic | Classification model Text mining Broadband network Support vector machine Decision support system |
| url | http://www.sciencedirect.com/science/article/pii/S2215016123003424 |
| work_keys_str_mv | AT mahasakketcham classificationofbroadbandnetworkdevicesusingtextminingtechnique AT thittapornganokratanaa classificationofbroadbandnetworkdevicesusingtextminingtechnique AT nattapatsridoung classificationofbroadbandnetworkdevicesusingtextminingtechnique |