A Novel Autoregressive Co-Variance Matrix and Gabor Filter Ensemble Convolutional Neural Network (ARCM-GF-E-CNN) Model for E-Commerce Product Classification
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
With the advancement of technologies intelligent and automated environments are rapidly evolved with deep learning and transfer learning techniques. However, the existing technique exhibits different difficulties due to increases in processing data complexity. This research developed an Artificial Intelligence (AI) framework for e-commerce product classification. The data for analysis is collected from different website sources and images are classified. The proposed AI framework is stated as Autoregressive Co-Variance Matrix and Gabor Filter Ensemble Convolutional Neural Network (ARCM-GF-E-CNN). The ARCM-GF-E-CNN incorporates an auto-regressive Co-variance matrix for the classification of online product images. The collected database is categorized into class based on features of the image. The simulation results expressed that the proposed ARCM-GF-E-CNN exhibits higher accuracy for the validation and testing dataset. Further, the analysis of ARCM-GF-E-CNN with existing technique expressed that the proposed classifier increases accuracy, precision, recall, and F1-score.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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