Mobile User-Activity Prediction Utilizing LSTM Recurrent Neural Network
Bibliographic record
Abstract
The demand for mobile services is increasing exponentially and mobile telecommunication operators are facing new challenges such as bandwidth choking and resource allocation issues consequently. One of the most promising methods for overcoming these challenges is using Big Data Analytics on Call Detail Record (CDR). In this paper, a Long-Short Term Memory neural network is utilized for user activity prediction for the first time. The real CDR data is preprocessed and used for training the neural network. The accuracy of this prediction is evaluated in normal values and anomalies. The proposed system can predict user activity accurately using a limited amount of training data and its performance in anomalous behaviors is promising. The networks ability to be generalized is also evaluated with the cross-dataset test. The neural network is trained with one cells data and predict another cells activity properly. The proposed system is a step forward toward designing a practical and efficient CDR anomaly prediction system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one teacher head, not a consensus.
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".