A Voting-Based Ensemble Deep Learning Method Focused on Multi-Step Prediction of Food Safety Risk Levels: Applications in Hazard Analysis of Heavy Metals in Grain Processing Products

Wang, Zuzheng; Wu, Zhixiang; Zou, Minke; Xu, Wen; Wang, Zheng; Li, Yuanzhang; Zhang, Qingchuan

doi:10.3390/foods11060823

Cited by 16 publications

(11 citation statements)

References 50 publications

(67 reference statements)

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“…Remarkably, several studies have demonstrated the high level of accuracy of the machine learning methods [67][68][69][70], and they used these models for improving many problems. On the other hand, From the literature, most of the studies have investigated the relationship between oil prices and macroeconomic factors, but such studies seldom focus to validate the agreement on how much these macroeconomic factors influence oil prices [71].…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

Fathalla¹,

Alameer²,

Abbas³

et al. 2023

Computer Systems Science and Engineering

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The oil industries are an important part of a country's economy. The crude oil's price is influenced by a wide range of variables. Therefore, how accurately can countries predict its behavior and what predictors to employ are two main questions. In this view, we propose utilizing deep learning and ensemble learning techniques to boost crude oil's price forecasting performance. The suggested method is based on a deep learning snapshot ensemble method of the Transformer model. To examine the superiority of the proposed model, this paper compares the proposed deep learning ensemble model against different machine learning and statistical models for daily Organization of the Petroleum Exporting Countries (OPEC) oil price forecasting. Experimental results demonstrated the outperformance of the proposed method over statistical and machine learning methods. More precisely, the proposed snapshot ensemble of Transformer method achieved relative improvement in the forecasting performance compared to autoregressive integrated moving average ARIMA (1,1,1), ARIMA (0,1,1), autoregressive moving average (ARMA) (0,1), vector autoregression (VAR), random walk (RW), support vector machine (SVM), and random forests (RF) models by 99.94%, 99.62%, 99.87%, 99.65%, 7.55%, 98.38%, and 99.35%, respectively, according to mean square error metric.

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Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

Fathalla¹,

Alameer²,

Abbas³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Geng et al propose both [ 25 , 26 ] used the AHP-EW algorithm to generate a combined risk value for each sample and then combined it with a machine learning model for risk prediction. On this basis, Wang et al [ 27 ] used integrated learning techniques to improve the accuracy of the prediction models. However, existing research methods require the introduction of external expert knowledge, slow convergence, or preprocessing of food data to calculate the desired output of the model.…”

Section: Related Workmentioning

confidence: 99%

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

Zuo

Aysa

et al. 2022

Foods

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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.

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“…Quantitative assessment methods are data-driven models [ 9 ]. They are based on data to establish a mathematical model and use the mathematical model to calculate the risk value of the index [ 10 ]; these models include the random forest algorithm [ 11 ], support vector machine (SVM) [ 12 ], and back-propagation (BP) network [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

A Rice Hazards Risk Assessment Method for a Rice Processing Chain Based on a Multidimensional Trapezoidal Cloud Model

Chen

Zhang

et al. 2023

Foods

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Rice is common in the human diet, making rice safety issues important. Moreover, rice processing safety is key for rice security, so rice processing chain risk assessment is critical. However, methods proposed to assess the rice processing chain risk have issues, such as the use of unreasonable thresholds for the rice processing chain and fixed weight. To solve these problems, we propose a risk assessment method for the rice processing chain based on a multidimensional trapezoidal cloud model. First, an evaluation model based on a multidimensional trapezoidal cloud model was established. Based on the historical evaluation results, Atanassov’s interval-valued intuition language numbers (AIVILNs) were introduced to determine the cloud model’s parameters. Second, the concept of dynamic weight was introduced to integrate the static and dynamic weights. An exponential function was used to construct dynamic weighting mechanisms, and the analytic hierarchy stage (AHP) was used to construct a static weight. The proposed method was validated by 104 sets of rice processing chain data, and the results show that the method could evaluate the risk level of the rice processing chain more accurately and reasonably than other methods, indicating that it can provide a sound decision-making basis for food safety supervision authorities.

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A Voting-Based Ensemble Deep Learning Method Focused on Multi-Step Prediction of Food Safety Risk Levels: Applications in Hazard Analysis of Heavy Metals in Grain Processing Products

Cited by 16 publications

References 50 publications

A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

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

A Rice Hazards Risk Assessment Method for a Rice Processing Chain Based on a Multidimensional Trapezoidal Cloud Model

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