محفوظ في:
| المؤلفون الرئيسيون: | , , , , |
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| التنسيق: | Recurso digital |
| اللغة: | الإنجليزية |
| منشور في: |
Zenodo
2026
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://doi.org/10.5281/zenodo.20032212 |
| الوسوم: |
إضافة وسم
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| _version_ | 1866901386719920128 |
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| author | Maitri More Vaishnavi Rajput Snehal Naik Shruti Wawage Prof. P. J. Jambhulkar |
| author_facet | Maitri More Vaishnavi Rajput Snehal Naik Shruti Wawage Prof. P. J. Jambhulkar |
| contents | <p>This project presents an AI-based system for analyzing the safety of cosmetic products using ingredient information extracted from product labels. The system leverages Optical Character Recognition (OCR) to extract ingredient text from uploaded images and applies machine learning techniques to evaluate toxicity risks.</p> <p>The extracted ingredients are processed using fuzzy matching to correct OCR errors and mapped to a structured toxicological dataset containing scientific attributes such as carcinogenicity (IARC, NTP, ECHA), reproductive toxicity, endocrine disruption, sensitization potential, allergen classification, and regulatory restrictions.</p> <p>A trained machine learning model predicts toxicity scores for each ingredient, and a product-level safety score is computed based on the highest-risk ingredient, following established toxicological principles. The system provides explainable outputs and visual representations including bar charts, pie charts, and ingredient-level analysis through an interactive React-based dashboard.</p> <p>The methodology is inspired by the Think Dirty® rating system and is grounded in the principle:<br>Risk = Hazard × Exposure.</p> <p>The system aims to improve consumer awareness by providing transparent, scientific, and real-time cosmetic safety insights.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20032212 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A TSP Framework for Verifying Cosmetic Sustainability Claims on ONDC Maitri More Vaishnavi Rajput Snehal Naik Shruti Wawage Prof. P. J. Jambhulkar Cosmetic Safety Machine Learning OCR Toxicity Analysis Ingredient Analysis AI in Healthcare EasyOCR FastAPI ONDC <p>This project presents an AI-based system for analyzing the safety of cosmetic products using ingredient information extracted from product labels. The system leverages Optical Character Recognition (OCR) to extract ingredient text from uploaded images and applies machine learning techniques to evaluate toxicity risks.</p> <p>The extracted ingredients are processed using fuzzy matching to correct OCR errors and mapped to a structured toxicological dataset containing scientific attributes such as carcinogenicity (IARC, NTP, ECHA), reproductive toxicity, endocrine disruption, sensitization potential, allergen classification, and regulatory restrictions.</p> <p>A trained machine learning model predicts toxicity scores for each ingredient, and a product-level safety score is computed based on the highest-risk ingredient, following established toxicological principles. The system provides explainable outputs and visual representations including bar charts, pie charts, and ingredient-level analysis through an interactive React-based dashboard.</p> <p>The methodology is inspired by the Think Dirty® rating system and is grounded in the principle:<br>Risk = Hazard × Exposure.</p> <p>The system aims to improve consumer awareness by providing transparent, scientific, and real-time cosmetic safety insights.</p> |
| title | A TSP Framework for Verifying Cosmetic Sustainability Claims on ONDC |
| topic | Cosmetic Safety Machine Learning OCR Toxicity Analysis Ingredient Analysis AI in Healthcare EasyOCR FastAPI ONDC |
| url | https://doi.org/10.5281/zenodo.20032212 |