Watermelon Ripeness Detection via Extreme Learning Machine with Kernel Principal Component Analysis Based on Acoustic Signals
Bibliographic record
Abstract
Many investigations have proved that the acoustics method is intuitive and effective for determining watermelon ripeness. The objective of this work is to drive a new robust acoustics classification scheme KPCA-ELM, which is based on the kernel principal component analysis (KPCA) and extreme learning machine (ELM). Acoustic signals are sampled by a microphone from unripe, ripe and over-ripe watermelon samples, which are randomly divided into two sample sets for training and testing. A set of basic signals is first obtained via KPCA of the training sample. Thus, any given signal can be represented as a linear combination of basis signals, and the coefficients of linear combination are extracted as the features of a signal. Corresponding to the unripe, ripe and over-ripe watermelons, a three-class ELM identification model is constructed based on the training data. The scheme presented in this paper is tested with the testing sample and an accuracy of 92% is achieved. To further evaluate the scheme performance, a comparison of ELM and SVM is conducted in terms of the classification results. The results reveal that the proposed scheme can classify faster than SVM, while ELM is better than SVM in accuracy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".