Matilda: A Machine Learning Software Application to Virtually Assist with Skincare for Visually Acute and Impaired—A Capstone Design Project
Bibliographic record
Abstract
This paper details a two-semester senior software capstone project spanning a timeframe of approximately seven months, in which students are tasked to design an innovative project, supervised by a faculty member.The authors hope that by detailing their experience through this report, they can provide insight into the design of a software capstone project to aspiring engineers and soon-to-be senior engineering students.Skincare has become an increasingly popular industry with a global reach in the e-commerce space.With a saturated skincare industry, companies have developed technology to customize recommendations but provide limited and potentially biased choices.Currently, the industry lacks an all-inclusive application that generates user-customized recommendations to allow consumers to focus on suitable products.Hence, the project seeks to fill this gap by building a web application that helps all users find skincare products tailored to their needs and skin conditions via a multi-part recommendation algorithm.Utilizing machine learning predictions with personalized user profiles, the web application solution efficiently compiles relevant and necessary product information for consumers to decide between products in a centralized location.The user interface of the web application has also been designed with usability in mind to serve a broader audience.The application specifically considers individuals who are visually acute and impaired through font size, color contrast, screen reader compatibility, and keyboard accessibility.Through conducting user surveys, the team found that 79.2% of users found Matilda to be user-friendly and 87.5% of users were satisfied with Matilda's recommendations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".