High correlation-based banknote gradient assessment of ensemble classifier
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research presents a client and server-based mobile application for recognition and authentication of banknotes; the system extracted the shape context (SC), Scale Invariant Feature Transform (SIFT), gradient location and orientation histogram (GLOH), and Histogram of Gradient (HOG). It then reduces the feature set using Principal Component Analysis (PCA), Bag of Words and proposed two-dimension reduction approach based on low variance and high correlation filter. The classification was done using a 2-fold Weighted Majority Average (WMA) Ensemble technique with MPLNN and MCSVM as base classifiers. The application was built using Unity 3D; it was tested on Naira, USD, CAD and Euro banknotes and the experimental results proved that the implemented feature vector and the proposed feature reduction and classification technique presented the best results and with promising recognition accuracy, detection rate, and processing time.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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 it