HG-SSA-ChurnNet: A Hybrid Gradient-Guided Salp Swarm Optimized Deep Learning Framework for Telecom Analytics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this era, the precise analysis of user behavior and network performance in tele communications is essential for demonstrating effective service quality and reducing customer churn. Specifically, when compared to deep learningbased models like ChurnNet, which provided good prediction accuracy for telecom data, their performance is very sensitive to the choice of hyper-parameters that are usually selected through soft or heuristic trial and error methods. To address this limitation, this research proposes a Hybrid Gradient Guided Salp Swarm Algorithm with ChurnNet (HG-SSAChurnNet) for churn prediction and modeling of DNNs. In particular, the proposed HG-SSA-ChurnNet framework incorporates chaotic initialization for search diversity improvement, validation-loss gradient-based guiding for fast convergence and diversity-preserving follower dynamics that helps prevent early stagnation. Therefore, these mechanisms allow the optimizer to effectively explore the ChurnNet hyperparameter space, which includes learning rate, convolutional filters size, kernel sizes, dropout probability, mini-batch size, and optimizer type. Subsequently, the optimization is determined by a telecom-aware multi-objective fitness function that includes accuracy, F1-score and training time. Thus, the experimental analysis demonstrates that the proposed HG-SSA-ChurnNet outperforms the existing ChurnNet model with$\mathbf{8 1. 9 6 \%}$accuracy and$\mathbf{7 8. 8 4 \%}$F1-score.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it