Generating Road Accident Prediction Set with Road Accident Data Analysis Using Enhanced Expectation-Maximization Clustering Algorithm and Improved Association Rule Mining
Bibliographic record
Abstract
Prediction of Road Accidents has gained importance over the years however road accidents may not be stopped but rather can be controlled. Driver feelings, for example, tragic, sad, and anger can be one purpose behind accidents. In the meantime, weather conditions, for example, climate, traffic conditions, sort of road, health of driver, and speed can likewise be the purposes behind accidents. Big data is a term utilized for vast and complex informational collections for handling as the traditional data mining techniques are incomplete for preparing them. In this paper an Enhanced Expectation-Maximization (EEM) Algorithm is utilized which works dependent on the Gaussian dissemination. In the proposed work the entire dataset is divided into different clusters based on vehicle type and again these groups are separated into sub groups dependent on parameter on each vehicle type. Strong Association Rules using Improved Association Rule Mining (IARM) algorithm are designed for every vehicle class and for each parameter. The Congestion control using Machine Framework (CCMF) and Traffic Congestion Analyzer using Map Reduce TCAMP () algorithms are used for training the machine and to apply each and every association rule on the dataset and accurate prediction set is generated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".