Deep Learning-based Classification and Prediction Modeling for Big Data
Bibliographic record
Abstract
In today's rapid development of information technology, the big data industry has ushered in explosive growth, and big data analysis has become an important research topic in the cross-cutting field of computing.This study constructs a big data prediction base model based on deep learning, and uses the improved butterfly optimization algorithm with OGRU model to realize feature selection and classification processing of big data.Then the Adam algorithm is used to optimize the parameters of the model, and finally the classification and prediction model of big data based on deep learning is constructed.Simulation and empirical analysis results show that the model proposed in this paper has excellent classification and prediction performance, and can meet the efficiency requirements of big data classification and prediction.The prediction errors of distribution network load data and smart charging pile operation data are lower than 9% and 16%, respectively, which have high practical application value.This study is of great significance to the research related to big data classification and prediction in different fields, and provides an effective method for data prediction in complex scenarios such as industrial as well as power grid scheduling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 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".