Mining large‐scale high utility patterns in vehicular ad hoc network environments
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
Abstract One well‐known type of mobile ad hoc network is known as a vehicular ad hoc network (VANET). The functions of such a network are integrated into a new generation of wireless networks for vehicles, which has established a robust self‐organizing network that exists between roadside units and mobile vehicles. In this article, we research the comfort applications in VANET with a new proposed algorithm, EHUM, short form for efficient high utility itemset mining, to mine patterns of the more popular Points of Interest (POIs). This algorithm is based on the traditional high‐utility itemset mining (HUIM) algorithm, and we propose a more reasonable pruning strategy. Concurrently, for solving the problem of excessive data volume in VANET, we applied this algorithm to the MapReduce architecture used for improving the feasibility in practical applications. Our in‐depth work in this article culminates with some experimental results that clearly show that our proposed algorithm can perform well to mine the POIs pattern in a big data data set and shows great performance in a Hadoop computing cluster.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it