Aplicação de redes neurais recorrentes e self-organizing maps em dados reais de operador de telecomunicações para predição de tráfego de interface de bng e análise de relatório de falha de rede GPON
Bibliographic record
Abstract
This work consists of the application of two Artificial Intelligence algorithms in two real situations of a telecommunications operator. The first application is the concept of recurrent neural network or recurrent neural network (RNN). A special type of neural network, which has great applicability in scenarios with temporal data and which was applied for traffic forecast in an interface of a broadband remote access server (also called broadband network gateway). The second situation of applied artificial intelligence was the evaluation of the use of the self-organizing maps (SOM) neural network model. This was used to classify and detect inconsistent data from a field failure report from a GPON broadband access network. The use of SOM for anomaly detection and classification was able to reduce the dimensionality of the data. It was possible to extract the list of these distant data (or outliers) and make an observation. The study seeks to demonstrate that neural network applications can be used as a tool for automating network analysis in telecommunications, generating benefits such as cost reduction and greater agility, which provides better quality to users, more optimized networks and evolution of telecommunications services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".