Food fraud detection using explainable artificial intelligence
<div><p>Recently, the global food supply chain has become increasingly complex, and its scalability has grown. From farm to fork, the performance of food‐producing systems is influenced by significant changes in the environment, population and economy. These changes may cause an increase...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2023
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| _version_ | 1864513526951837696 |
|---|---|
| author | Okan Buyuktepe (17991493) |
| author2 | Cagatay Catal (6897842) Gorkem Kar (17788499) Yamine Bouzembrak (3693754) Hans Marvin (8849543) Anand Gavai (5290655) |
| author2_role | author author author author author |
| author_facet | Okan Buyuktepe (17991493) Cagatay Catal (6897842) Gorkem Kar (17788499) Yamine Bouzembrak (3693754) Hans Marvin (8849543) Anand Gavai (5290655) |
| author_role | author |
| dc.creator.none.fl_str_mv | Okan Buyuktepe (17991493) Cagatay Catal (6897842) Gorkem Kar (17788499) Yamine Bouzembrak (3693754) Hans Marvin (8849543) Anand Gavai (5290655) |
| dc.date.none.fl_str_mv | 2023-06-25T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1111/exsy.13387 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Food_fraud_detection_using_explainable_artificial_intelligence/25249141 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Mathematical sciences Numerical and computational mathematics deep learning explainable artificial intelligence food safety interpretable machine learning machine learning |
| dc.title.none.fl_str_mv | Food fraud detection using explainable artificial intelligence |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Recently, the global food supply chain has become increasingly complex, and its scalability has grown. From farm to fork, the performance of food‐producing systems is influenced by significant changes in the environment, population and economy. These changes may cause an increase in food fraud and safety hazards and hence, harm human health. Adopting artificial intelligence (AI) technology in the food supply chain is one strategy to reduce these hazards. Although the use of AI has been rising in numerous industries, such as precision nutrition, self‐driving cars, precision agriculture, precision medicine and food safety, much of what AI systems do is a black box due to its poor explainability. This study covers numerous use cases of food fraud risk prediction using explainable artificial intelligence (XAI) techniques, such as LIME, SHAP and WIT. We aimed to interpret the predictions of a machine learning model with the aid of these technologies. The case study was performed on a food fraud dataset using adulteration/fraud notifications retrieved from the Rapid Alert System for Food and Feed system and economically motivated adulteration database. A deep learning model was built based on this dataset and XAI tools have been investigated on the proposed deep learning model. Both features and shortcomings of the current XAI tools in the food fraud area have been presented.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Expert Systems<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1111/exsy.13387" target="_blank">https://dx.doi.org/10.1111/exsy.13387</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_24dc42a889f62a31e58761cd3c757357 |
| identifier_str_mv | 10.1111/exsy.13387 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25249141 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Food fraud detection using explainable artificial intelligenceOkan Buyuktepe (17991493)Cagatay Catal (6897842)Gorkem Kar (17788499)Yamine Bouzembrak (3693754)Hans Marvin (8849543)Anand Gavai (5290655)EngineeringControl engineering, mechatronics and roboticsInformation and computing sciencesArtificial intelligenceMathematical sciencesNumerical and computational mathematicsdeep learningexplainable artificial intelligencefood safetyinterpretable machine learningmachine learning<div><p>Recently, the global food supply chain has become increasingly complex, and its scalability has grown. From farm to fork, the performance of food‐producing systems is influenced by significant changes in the environment, population and economy. These changes may cause an increase in food fraud and safety hazards and hence, harm human health. Adopting artificial intelligence (AI) technology in the food supply chain is one strategy to reduce these hazards. Although the use of AI has been rising in numerous industries, such as precision nutrition, self‐driving cars, precision agriculture, precision medicine and food safety, much of what AI systems do is a black box due to its poor explainability. This study covers numerous use cases of food fraud risk prediction using explainable artificial intelligence (XAI) techniques, such as LIME, SHAP and WIT. We aimed to interpret the predictions of a machine learning model with the aid of these technologies. The case study was performed on a food fraud dataset using adulteration/fraud notifications retrieved from the Rapid Alert System for Food and Feed system and economically motivated adulteration database. A deep learning model was built based on this dataset and XAI tools have been investigated on the proposed deep learning model. Both features and shortcomings of the current XAI tools in the food fraud area have been presented.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Expert Systems<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1111/exsy.13387" target="_blank">https://dx.doi.org/10.1111/exsy.13387</a></p>2023-06-25T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1111/exsy.13387https://figshare.com/articles/journal_contribution/Food_fraud_detection_using_explainable_artificial_intelligence/25249141CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252491412023-06-25T03:00:00Z |
| spellingShingle | Food fraud detection using explainable artificial intelligence Okan Buyuktepe (17991493) Engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Mathematical sciences Numerical and computational mathematics deep learning explainable artificial intelligence food safety interpretable machine learning machine learning |
| status_str | publishedVersion |
| title | Food fraud detection using explainable artificial intelligence |
| title_full | Food fraud detection using explainable artificial intelligence |
| title_fullStr | Food fraud detection using explainable artificial intelligence |
| title_full_unstemmed | Food fraud detection using explainable artificial intelligence |
| title_short | Food fraud detection using explainable artificial intelligence |
| title_sort | Food fraud detection using explainable artificial intelligence |
| topic | Engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Mathematical sciences Numerical and computational mathematics deep learning explainable artificial intelligence food safety interpretable machine learning machine learning |