Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety
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| الحاوية / القاعدة: | Foods vol. 14, no. 2 (2025), p. 247 |
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| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , , , , |
| منشور في: |
MDPI AG
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| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| الوسوم: |
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| 024 | 7 | |a 10.3390/foods14020247 |2 doi | |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Ding, Haohan |u Science Center for Future Foods, Jiangnan University, Wuxi 214122, China; <email>dinghaohan@jiangnan.edu.cn</email>; School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China; <email>6243110027@stu.jiangnan.edu.cn</email> (H.H.); <email>6233115022@stu.jiangnan.edu.cn</email> (L.W.) | |
| 245 | 1 | |a Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This review explores the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in food safety detection and risk prediction. This paper highlights the advantages of CNNs in image processing and feature recognition, as well as the powerful capabilities of RNNs (especially their variant LSTM) in time series data modeling. This paper also makes a comparative analysis in many aspects: Firstly, the advantages and disadvantages of traditional food safety detection and risk prediction methods are compared with deep learning technologies such as CNNs and RNNs. Secondly, the similarities and differences between CNNs and fully connected neural networks in processing image data are analyzed. Furthermore, the advantages and disadvantages of RNNs and traditional statistical modeling methods in processing time series data are discussed. Finally, the application directions of CNNs in food safety detection and RNNs in food safety risk prediction are compared. This paper also discusses combining these deep learning models with technologies such as the Internet of Things (IoT), blockchain, and federated learning to improve the accuracy and efficiency of food safety detection and risk warning. Finally, this paper mentions the limitations of RNNs and CNNs in the field of food safety, as well as the challenges in the interpretability of the model, and suggests the use of interpretable artificial intelligence (XAI) technology to improve the transparency of the model. | |
| 653 | |a Food safety | ||
| 653 | |a Humidity | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Internet of Things | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Defective products | ||
| 653 | |a Image processing | ||
| 653 | |a Statistical models | ||
| 653 | |a Chromatography | ||
| 653 | |a Food processing | ||
| 653 | |a Machine learning | ||
| 653 | |a Time series | ||
| 653 | |a Suppliers | ||
| 653 | |a Feature recognition | ||
| 653 | |a Efficiency | ||
| 653 | |a Data analysis | ||
| 653 | |a Raw materials | ||
| 653 | |a Analytical chemistry | ||
| 653 | |a Predictions | ||
| 653 | |a Traditional foods | ||
| 653 | |a Neural networks | ||
| 653 | |a Recurrent neural networks | ||
| 653 | |a Public health | ||
| 653 | |a Statistical methods | ||
| 653 | |a Supply chains | ||
| 653 | |a Information processing | ||
| 653 | |a Safety | ||
| 653 | |a Federated learning | ||
| 653 | |a Blockchain | ||
| 700 | 1 | |a Hou, Haoke |u School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China; <email>6243110027@stu.jiangnan.edu.cn</email> (H.H.); <email>6233115022@stu.jiangnan.edu.cn</email> (L.W.) | |
| 700 | 1 | |a Wang, Long |u School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China; <email>6243110027@stu.jiangnan.edu.cn</email> (H.H.); <email>6233115022@stu.jiangnan.edu.cn</email> (L.W.) | |
| 700 | 1 | |a Cui, Xiaohui |u Science Center for Future Foods, Jiangnan University, Wuxi 214122, China; <email>dinghaohan@jiangnan.edu.cn</email>; School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China | |
| 700 | 1 | |a Yu, Wei |u Department of Chemical & Materials Engineering, University of Auckland, Auckland 1010, New Zealand; <email>w.yu@auckland.ac.nz</email> | |
| 700 | 1 | |a Wilson, David I |u Electrical and Electronic Engineering Department, Auckland University of Technology, Auckland 1010, New Zealand; <email>diwilson@aut.ac.nz</email> | |
| 773 | 0 | |t Foods |g vol. 14, no. 2 (2025), p. 247 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3159465973/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3159465973/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3159465973/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |