A Social Cloud Computing: Employing a Bee Colony Algorithm for Sharing and Allocating Tourism Resources
Bibliographic record
Abstract
With the growth and development of social networks and emerging cloud computing networks, social network users will be able to share their intended data, in data centers from various places all over the worlds which belong to cloud, with one another and additionally use others data simultaneously. Since with the passage of time, sharing the services and data sources through social networks in Tourism industry would be more complex for clients, it could be possible to facilitate the usage and sharing the Tourism resources with designing a social cloud platform. Initially, social cloud in Tourism area collects clients’ information their inter-connected relationships from social networks. Afterwards, with the utilization of artificial bee colony algorithm, it can make an adjustment and balance in Tourism resources that have been shared as of yet and the comments and friendly relationships obtained from social networks. Finally, implementing such a social cloud in Tourism field can lead to an increase in the level of satisfaction of users when they want to have an access to useful documents and can achieve progress in the Tourism industry and develop it within the country.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".