Predicting Students' Behavioral Patterns in University Networks for Efficient Bandwidth Allocation: A Hybrid Data Mining Method (Application Paper)
Bibliographic record
Abstract
The effective bandwidth management in multi-service computer networks such as university networks has become a challenge in recent years. The growth of internet traffic and limitation of bandwidth resources persuade the information technology (IT) managers to focus on effective bandwidth allocation policies. One of the important issues discussed in this domain is how to assign the bandwidth fairly based on the priority levels. In this paper, focusing on the "priority-based bandwidth allocation", a hybrid data mining method is developed to manage the limited bandwidth in a university network more effectively. This method is composed of two main steps and uses the clustering and classification techniques. The main purpose is to detect, analyze and predict students' behavioral patterns in a university network and identify the main factors that affect their tendency in using internet. The proposed method is applied on a real data of a network university. The results indicate that "degree level" and "age" are the most important factors that influence students' tendency to use internet. The results would be also useful for prediction purposes. It helps the IT managers to predict a new student's tendency to use internet given his/her characteristics. By analyzing the results, the IT managers can make better decisions to optimize the allocation of bandwidth resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".