A Smart Shopping System for Modern E-Commerce Applications
Bibliographic record
Abstract
In a modern E-commerce system, exact and efficient classification of products is one of the major challenge. The user experience of online purchasing is heavily relies on product classification accuracy as well as. Given the large number of products and viable categories, have developing a new framework automatically for assigning the products to suitable categories at scale is desirable. However, inadequate item descriptions, poor data quality and adversarial noise in training data, can lead to low prediction accuracy when applying the Machine Learning (ML) algorithms. In addition, the various influx of new products uploaded daily and the dynamic nature of categories highlight the need for novel intelligent classification models that can control the cost and time required by human editors. To overcome these kind of issues, Squirrel search War Strategy Optimization (SSWSO)_LeNet replica is introduced. SSWSO is a revolutionary nature-inspired optimization algorithm developed for unconstrained optimization issues. The foraging behavior of southern flying squirrels is examined and quantitatively simulated, considering every aspect of their food hunt to achieve the desired optimum. Squirrel search Algorithm (SSA) provides global optimum solutions with excellent convergence behavior. Meanwhile, War Strategy Optimization (WSA) algorithm includes an adaptive weight mechanism that varies from one solution (soldier) to another and is updated weights depends on the soldier's rank during the update phase. Thus, the combined optimization strategies ensure an efficient balance among the exploration and exploitation stages. By integrating WSO and SSA provides better product classification accuracy and superior performance. Furthermore, the experimental findings showed that the SSWSO_LeNet performed better in accuracy, sensitivity, and specificity, with values of 0.976, 0.877, and 0.857, which is impressive compared to state-of-the-art results.
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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 | 0.000 | 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 | 0.000 | 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 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".