Name
Abstract | ||
|---|---|---|
Leveraging machine learning (ML) for the detection of network problems dates back to handling call-dropping issues in telephony. However, troubleshooting cellular networks is still a manual task, assigned to experts who monitor the network around the clock. We present here TTrees (from Troubleshooting Trees), a practical and interpretable ML software tool that implements a methodology we have desi... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/WoWMoM51794.2021.00033 | 2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) |
Keywords | DocType | ISBN |
Cellular networks,Wireless communication,Training,Software algorithms,Redundancy,Tools,Telephony | Conference | 978-1-6654-2263-5 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed Moulay | 1 | 2 | 1.05 |
| Rafael García Leiva | 2 | 8 | 1.50 |
| Vincenzo Mancuso | 3 | 1249 | 76.65 |
| Pablo J. Rojo Maroni | 4 | 1 | 0.70 |
| Antonio Fernández Anta | 5 | 1 | 1.37 |