A Smart Car Parking System Based on IoT with Gray Wolf Optimization-Probability Correlated Neural Network Recognition Methods
Bibliographic record
Abstract
The use of vehicles is increasing every day because of the growing industrialization. Hence, parking the vehicles in the metropolitan cities could create the traffic congestion, which is one of the major problem need to be resolved in the smart city systems. For this purpose, this research work intends to develop a smart car parking system with proper controlling and monitoring units. The main motive of this work was to avoid the traffic congestion by developing an advanced car parking system with the help of Internet of Things (IoT) technology. Also, an image processing technique is utilized in this framework for identifying whether the car is present or not in the parking area. In which, an Anisotropic Diffusion Gaussian Filtering (ADGF) technique is utilized to preprocess the given image for improving the quality and reducing the noise effects. After that, the Grey Level Co-occurrence Matrix (GLCM) is employed to extract the contrast, correlation, energy and homogeneity features. After that, the suitable number of features are optimally selected by using the Grey Wolf Optimization (GWO) technique, which efficiently improves the speed of operation. Finally, the Probability Correlated Neural Network (PCNN) technique deployed for accurately recognizing that whether the car is present or not. For validation, the performance of this scheme is evaluated and compared by using various measures.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 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 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".