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Article ; Online: Fluctuation-based outlier detection.

Du, Xusheng / Zuo, Enguang / Chu, Zheng / He, Zhenzhen / Yu, Jiong

2023 Volume 13, Issue 1, Page(s) 2408

Abstract: Outlier detection is an important topic in machine learning and has been used in a wide range of applications. Outliers are objects that are few in number and deviate from the majority of objects. As a result of these two properties, we show that ... ...

Abstract	Outlier detection is an important topic in machine learning and has been used in a wide range of applications. Outliers are objects that are few in number and deviate from the majority of objects. As a result of these two properties, we show that outliers are susceptible to a mechanism called fluctuation. This article proposes a method called fluctuation-based outlier detection (FBOD) that achieves a low linear time complexity and detects outliers purely based on the concept of fluctuation without employing any distance, density or isolation measure. Fundamentally different from all existing methods. FBOD first converts the Euclidean structure datasets into graphs by using random links, then propagates the feature value according to the connection of the graph. Finally, by comparing the difference between the fluctuation of an object and its neighbors, FBOD determines the object with a larger difference as an outlier. The results of experiments comparing FBOD with eight state-of-the-art algorithms on eight real-worlds tabular datasets and three video datasets show that FBOD outperforms its competitors in the majority of cases and that FBOD has only 5% of the execution time of the fastest algorithm. The experiment codes are available at: https://github.com/FluctuationOD/Fluctuation-based-Outlier-Detection .
Language	English
Publishing date	2023-02-10
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-29549-1
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: CSGNN: Contamination Warning and Control of Food Quality via Contrastive Self-Supervised Learning-Based Graph Neural Network

Yan, Junyi / Li, Hongyi / Zuo, Enguang / Li, Tianle / Chen, Chen / Chen, Cheng / Lv, Xiaoyi

Foods. 2023 Mar. 01, v. 12, no. 5

2023

Abstract: Effective contamination warning and control of food quality can significantly reduce the likelihood of food quality safety incidents. Existing food contamination warning models for food quality rely on supervised learning, do not model the complex ... ...

Abstract	Effective contamination warning and control of food quality can significantly reduce the likelihood of food quality safety incidents. Existing food contamination warning models for food quality rely on supervised learning, do not model the complex feature associations between detection samples, and do not consider the unevenness of detection data categories. In this paper, To overcome these limitations, we propose a Contrastive Self-supervised learning-based Graph Neural Network framework (CSGNN) for contamination warning of food quality. Specifically, we structure the graph for detecting correlations between samples and then define the positive and negative instance pairs for contrastive learning based on attribute networks. Further, we use a self-supervised approach to capture the complex relationships between detection samples. Finally, we assessed each sample’s contamination level based on the absolute value of the subtraction of the prediction scores from multiple rounds of positive and negative instances obtained by the CSGNN. Moreover, we conducted a sample study on a batch of dairy product detection data in a Chinese province. The experimental results show that CSGNN outperforms other baseline models in contamination assessment of food quality, with AUC and recall of unqualified samples reaching 0.9188 and 1.0000, respectively. Meanwhile, our framework provides interpretable contamination classification for food detection. This study provides an efficient early warning method with precise and hierarchical contamination classification for contamination warning of food quality work.
Keywords	dairy products ; food contamination ; food quality ; prediction
Language	English
Dates of publication	2023-0301
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2704223-6
ISSN	2304-8158
ISSN	2304-8158
DOI	10.3390/foods12051048
Database	NAL-Catalogue (AGRICOLA)

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Article: CSGNN: Contamination Warning and Control of Food Quality via Contrastive Self-Supervised Learning-Based Graph Neural Network.

Yan, Junyi / Li, Hongyi / Zuo, Enguang / Li, Tianle / Chen, Chen / Chen, Cheng / Lv, Xiaoyi

Foods (Basel, Switzerland)

2023 Volume 12, Issue 5

Abstract	Effective contamination warning and control of food quality can significantly reduce the likelihood of food quality safety incidents. Existing food contamination warning models for food quality rely on supervised learning, do not model the complex feature associations between detection samples, and do not consider the unevenness of detection data categories. In this paper, To overcome these limitations, we propose a Contrastive Self-supervised learning-based Graph Neural Network framework (CSGNN) for contamination warning of food quality. Specifically, we structure the graph for detecting correlations between samples and then define the positive and negative instance pairs for contrastive learning based on attribute networks. Further, we use a self-supervised approach to capture the complex relationships between detection samples. Finally, we assessed each sample's contamination level based on the absolute value of the subtraction of the prediction scores from multiple rounds of positive and negative instances obtained by the CSGNN. Moreover, we conducted a sample study on a batch of dairy product detection data in a Chinese province. The experimental results show that CSGNN outperforms other baseline models in contamination assessment of food quality, with AUC and recall of unqualified samples reaching 0.9188 and 1.0000, respectively. Meanwhile, our framework provides interpretable contamination classification for food detection. This study provides an efficient early warning method with precise and hierarchical contamination classification for contamination warning of food quality work.
Language	English
Publishing date	2023-03-01
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2704223-6
ISSN	2304-8158
ISSN	2304-8158
DOI	10.3390/foods12051048
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: DIEANet: an attention model for histopathological image grading of lung adenocarcinoma based on dimensional information embedding.

Wang, Zexin / Gao, Jing / Li, Min / Zuo, Enguang / Chen, Chen / Chen, Cheng / Liang, Fei / Lv, Xiaoyi / Ma, Yuhua

Scientific reports

2024 Volume 14, Issue 1, Page(s) 6209

Abstract: Efficient and rapid auxiliary diagnosis of different grades of lung adenocarcinoma is conducive to helping doctors accelerate individualized diagnosis and treatment processes, thus improving patient prognosis. Currently, there is often a problem of large ...

Abstract	Efficient and rapid auxiliary diagnosis of different grades of lung adenocarcinoma is conducive to helping doctors accelerate individualized diagnosis and treatment processes, thus improving patient prognosis. Currently, there is often a problem of large intra-class differences and small inter-class differences between pathological images of lung adenocarcinoma tissues under different grades. If attention mechanisms such as Coordinate Attention (CA) are directly used for lung adenocarcinoma grading tasks, it is prone to excessive compression of feature information and overlooking the issue of information dependency within the same dimension. Therefore, we propose a Dimension Information Embedding Attention Network (DIEANet) for the task of lung adenocarcinoma grading. Specifically, we combine different pooling methods to automatically select local regions of key growth patterns such as lung adenocarcinoma cells, enhancing the model's focus on local information. Additionally, we employ an interactive fusion approach to concentrate feature information within the same dimension and across dimensions, thereby improving model performance. Extensive experiments have shown that under the condition of maintaining equal computational expenses, the accuracy of DIEANet with ResNet34 as the backbone reaches 88.19%, with an AUC of 96.61%, MCC of 81.71%, and Kappa of 81.16%. Compared to seven other attention mechanisms, it achieves state-of-the-art objective metrics. Additionally, it aligns more closely with the visual attention of pathology experts under subjective visual assessment.
MeSH term(s)	Humans ; Adenocarcinoma of Lung ; Adenocarcinoma ; Benchmarking ; Data Compression ; Lung Neoplasms/diagnosis
Language	English
Publishing date	2024-03-14
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-56355-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: An explainable unsupervised risk early warning framework based on the empirical cumulative distribution function: Application to dairy safety.

Yan, Junyi / Sun, Lei / Zuo, Enguang / Zhong, Jie / Li, Tianle / Chen, Chen / Chen, Cheng / Lv, Xiaoyi

Food research international (Ottawa, Ont.)

2024 Volume 178, Page(s) 113933

Abstract: Efficient food safety risk assessment significantly affects food safety supervision. However, food detection data of different types and batches show different feature distributions, resulting in unstable detection results of most risk assessment models, ...

Abstract	Efficient food safety risk assessment significantly affects food safety supervision. However, food detection data of different types and batches show different feature distributions, resulting in unstable detection results of most risk assessment models, lack of interpretability of risk classification, and insufficient risk traceability. This study aims to explore an efficient food safety risk assessment model that takes into account robustness, interpretability and traceability. Therefore, the Explainable unsupervised risk Warning Framework based on the Empirical cumulative Distribution function (EWFED) was proposed. Firstly, the detection data's underlying distribution is estimated as non-parametric by calculating each testing indicator's empirical cumulative distribution. Next, the tail probabilities of each testing indicator are estimated based on these distributions and summarized to obtain the sample risk value. Finally, the "3σ Rule" is used to achieve explainable risk classification of qualified samples, and the reasons for unqualified samples are tracked according to the risk score of each testing indicator. The experiments of the EWFED model on two types of dairy product detection data in actual application scenarios have verified its effectiveness, achieving interpretable risk division and risk tracing of unqualified samples. Therefore, this study provides a more robust and systematic food safety risk assessment method to promote precise management and control of food safety risks effectively.
MeSH term(s)	Food Safety/methods ; Risk Factors ; Risk Assessment ; Food
Language	English
Publishing date	2024-01-11
Publishing country	Canada
Document type	Journal Article
ZDB-ID	1111695-x
ISSN	1873-7145 ; 0963-9969
ISSN (online)	1873-7145
ISSN	0963-9969
DOI	10.1016/j.foodres.2024.113933
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Fluctuation-based Outlier Detection

Du, Xusheng / Zuo, Enguang / He, Zhenzhen / Yu, Jiong

2022

Abstract	Outlier detection is an important topic in machine learning and has been used in a wide range of applications. Outliers are objects that are few in number and deviate from the majority of objects. As a result of these two properties, we show that outliers are susceptible to a mechanism called fluctuation. This article proposes a method called fluctuation-based outlier detection (FBOD) that achieves a low linear time complexity and detects outliers purely based on the concept of fluctuation without employing any distance, density or isolation measure. Fundamentally different from all existing methods. FBOD first converts the Euclidean structure datasets into graphs by using random links, then propagates the feature value according to the connection of the graph. Finally, by comparing the difference between the fluctuation of an object and its neighbors, FBOD determines the object with a larger difference as an outlier. The results of experiments comparing FBOD with seven state-of-the-art algorithms on eight real-world tabular datasets and three video datasets show that FBOD outperforms its competitors in the majority of cases and that FBOD has only 5% of the execution time of the fastest algorithm. The experiment codes are available at: https://github.com/FluctuationOD/Fluctuation-based-Outlier-Detection. Comment: 20pages,11figures
Keywords	Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2022-04-21
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Anomaly Score-Based Risk Early Warning System for Rapidly Controlling Food Safety Risk

Zuo, Enguang / Du, Xusheng / Aysa, Alimjan / Lv, Xiaoyi / Muhammat, Mahpirat / Zhao, Yuxia / Ubul, Kurban

Foods. 2022 July 13, v. 11, no. 14

2022

Abstract: Food safety is a high-priority issue for all countries. Early warning analysis and risk control are essential for food safety management practices. This paper innovatively proposes an anomaly score-based risk early warning system (ASRWS) via an ... ...

Abstract	Food safety is a high-priority issue for all countries. Early warning analysis and risk control are essential for food safety management practices. This paper innovatively proposes an anomaly score-based risk early warning system (ASRWS) via an unsupervised auto-encoder (AE) for the effective early warning of detection products, which classifies qualified and unqualified products by reconstructing errors. The early warning analysis of qualified samples is carried out by early warning thresholds. The proposed method is applied to a batch of dairy product testing data from a Chinese province. Extensive experimental results show that the unsupervised anomaly detection model AE can effectively analyze the dairy product testing data, with a prediction accuracy and fault detection rate of 0.9954 and 0.9024, respectively, within only 0.54 s. We provided an early warning threshold-based method to conduct the risk analysis, and then a panel of food safety experts performed a risk revision on the prediction results produced by the proposed method. In this way, AI improves the panel’s efficiency, whereas the panel enhances the model’s reliability. This study provides a fast and cost-effective, food safety early warning method for detection data and assists market supervision departments in controlling food safety risk.
Keywords	cost effectiveness ; dairy products ; food safety ; markets ; models ; prediction ; risk ; risk reduction
Language	English
Dates of publication	2022-0713
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2704223-6
ISSN	2304-8158
ISSN	2304-8158
DOI	10.3390/foods11142076
Database	NAL-Catalogue (AGRICOLA)

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Article: A food safety prescreening method with domain-specific information using online reviews

Zuo, Enguang / Aysa, Alimjan / Muhammat, Mahpirat / Zhao, Yuxia / Chen, Bing / Ubul, Kurban

Journal für Verbraucherschutz und Lebensmittelsicherheit. 2022 June, v. 17, no. 2

2022

Abstract: Food contamination and food poisoning are presenting a substantial safety risk to consumers worldwide. In the era of information quantity and availability, the potential of social-media data has attracted increasing attention from relevant government ... ...

Abstract	Food contamination and food poisoning are presenting a substantial safety risk to consumers worldwide. In the era of information quantity and availability, the potential of social-media data has attracted increasing attention from relevant government regulatory agencies, food companies, and consumers. This paper takes text data from online media as a research object and innovatively proposes a new type of food text-mining technology based on the associated attention mechanism to quickly screen for potential food safety issues. First, we used the mutual information between each review Chinese word segment(CWS) and label to calculate the correlation score between each word and food safety hazards. Then, the attention score in supervised deep learning was combined in order to assess whether foods sold online may be unsafe for consumers. We compared the method in this paper with existing text-mining methods on food-safety-related datasets and found that the proposed method performs markedly better than the benchmark model, achieving an accuracy rate of 96.95[Formula: see text]. A team of food safety experts also performed a food risk assessment on the prediction results produced by the proposed model, and experimental results showed that the proposed tool can markedly reduce the time required to screen for food safety risks. This study provides a fast and cost-effective food-safety screening method and helps reduce consumer dietary safety hazards.
Keywords	cost effectiveness ; data collection ; food contamination ; models ; prediction ; risk ; risk assessment
Language	English
Dates of publication	2022-06
Size	p. 163-175.
Publishing place	Springer International Publishing
Document type	Article
ZDB-ID	2232114-7
ISSN	1661-5867 ; 1661-5751
ISSN (online)	1661-5867
ISSN	1661-5751
DOI	10.1007/s00003-022-01367-z
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Context aware semantic adaptation network for cross domain implicit sentiment classification.

Zuo, Enguang / Aysa, Alimjan / Muhammat, Mahpirat / Zhao, Yuxia / Ubul, Kurban

Scientific reports

2021 Volume 11, Issue 1, Page(s) 22038

Abstract: Cross-domain sentiment classification could be attributed to two steps. The first step is used to extract the text representation, and the other is to reduce domain discrepancy. Existing methods mostly focus on learning the domain-invariant information, ... ...

Abstract	Cross-domain sentiment classification could be attributed to two steps. The first step is used to extract the text representation, and the other is to reduce domain discrepancy. Existing methods mostly focus on learning the domain-invariant information, rarely consider using the domain-specific semantic information, which could help cross-domain sentiment classification; traditional adversarial-based models merely focus on aligning the global distribution ignore maximizing the class-specific decision boundaries. To solve these problems, we propose a context-aware semantic adaptation (CASA) network for cross-domain implicit sentiment classification (ISC). CASA can provide more semantic relationships and an accurate understanding of the emotion-changing process for ISC tasks lacking explicit emotion words. (1) To obtain inter- and intrasentence semantic associations, our model builds a context-aware heterogeneous graph (CAHG), which can aggregate the intrasentence dependency information and the intersentence node interaction information, followed by an attention mechanism that remains high-level domain-specific features. (2) Moreover, we conduct a new multigrain discriminator (MGD) to effectively reduce the interdomain distribution discrepancy and improve intradomain class discrimination. Experimental results demonstrate the effectiveness of different modules compared with existing models on the Chinese implicit emotion dataset and four public explicit datasets.
Language	English
Publishing date	2021-11-11
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-01385-1
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Anomaly Score-Based Risk Early Warning System for Rapidly Controlling Food Safety Risk.

Zuo, Enguang / Du, Xusheng / Aysa, Alimjan / Lv, Xiaoyi / Muhammat, Mahpirat / Zhao, Yuxia / Ubul, Kurban

Foods (Basel, Switzerland)

2022 Volume 11, Issue 14

Abstract	Food safety is a high-priority issue for all countries. Early warning analysis and risk control are essential for food safety management practices. This paper innovatively proposes an anomaly score-based risk early warning system (ASRWS) via an unsupervised auto-encoder (AE) for the effective early warning of detection products, which classifies qualified and unqualified products by reconstructing errors. The early warning analysis of qualified samples is carried out by early warning thresholds. The proposed method is applied to a batch of dairy product testing data from a Chinese province. Extensive experimental results show that the unsupervised anomaly detection model AE can effectively analyze the dairy product testing data, with a prediction accuracy and fault detection rate of 0.9954 and 0.9024, respectively, within only 0.54 s. We provided an early warning threshold-based method to conduct the risk analysis, and then a panel of food safety experts performed a risk revision on the prediction results produced by the proposed method. In this way, AI improves the panel's efficiency, whereas the panel enhances the model's reliability. This study provides a fast and cost-effective, food safety early warning method for detection data and assists market supervision departments in controlling food safety risk.
Language	English
Publishing date	2022-07-13
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2704223-6
ISSN	2304-8158
ISSN	2304-8158
DOI	10.3390/foods11142076
Database	MEDical Literature Analysis and Retrieval System OnLINE

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