2023
DOI: 10.1016/j.saa.2023.122809
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Research on highly sensitive quantitative detection of aflatoxin B2 solution based on THz metamaterial enhancement

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Cited by 7 publications
(1 citation statement)
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References 27 publications
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“…Application field: Electromagnetics Sakurai, Yada, Simomura, et al [327] 2019 Bayesian optimization optimization framework Pita Ruiz, Amad, Gabrielli, et al [101] 2019 Gradient-descent and Opological optimization framework -derivative-based optimization Kurniawati, Putri, and Ningsih [123] 2020 Random forest regression optimization framework Zhang, Wang, Xu, et al [328] 2022 Bayesian Optimization Optimization framework Chuma and Rasmussen [329] 2022 KNN,SVM,Bayesian Optimization Optimization framework Jian, Alexandropoulos, Basar, et al [330] 2022 KNN,SVM,Bayesian Optimization Optimization framework Alharbi, Abdelhamid, Ibrahim, et al [331] 2023 KNN,SVM,Gradient-based Optimization Optimization framework Lin, Zheng, Hu, et al [332] 2023 Bayesian Optimization Optimization framework Application field: Mechanical Morris and Seepersad [333] 2018 Spectral clustering Classification and clustering Bessa, Glowacki, and Houlder [130] 2019 Bayesian ML Classification and clustering Singleton, Cheer, and Daley [100] 2019 Gradient-descent optimization framework Dong, Chen, Zeng, et al [129] 2019 Nelder-Mead optimization optimization framework Stern, Arinze, Perez, et al [334] 2020 Nonlinear programming optimization framework Liu, Ye, Silva Izquierdo, et al [335] 2022 SVM classification Prasanna, Shantha, Pradeep, et al [336] 2022 SVM ,KNN Optimization Framework Zhai and Yeo [337] 2023 Bayesian Learning inverse design Hu, Wang, Du, et al [338] 2023 Bayesian Optimization inverse design Hu, Zhan, Wang, et al [339] 2023 LS-SVM Optimization Framework…”
Section: The Causal Relationship Problemmentioning
confidence: 99%
“…Application field: Electromagnetics Sakurai, Yada, Simomura, et al [327] 2019 Bayesian optimization optimization framework Pita Ruiz, Amad, Gabrielli, et al [101] 2019 Gradient-descent and Opological optimization framework -derivative-based optimization Kurniawati, Putri, and Ningsih [123] 2020 Random forest regression optimization framework Zhang, Wang, Xu, et al [328] 2022 Bayesian Optimization Optimization framework Chuma and Rasmussen [329] 2022 KNN,SVM,Bayesian Optimization Optimization framework Jian, Alexandropoulos, Basar, et al [330] 2022 KNN,SVM,Bayesian Optimization Optimization framework Alharbi, Abdelhamid, Ibrahim, et al [331] 2023 KNN,SVM,Gradient-based Optimization Optimization framework Lin, Zheng, Hu, et al [332] 2023 Bayesian Optimization Optimization framework Application field: Mechanical Morris and Seepersad [333] 2018 Spectral clustering Classification and clustering Bessa, Glowacki, and Houlder [130] 2019 Bayesian ML Classification and clustering Singleton, Cheer, and Daley [100] 2019 Gradient-descent optimization framework Dong, Chen, Zeng, et al [129] 2019 Nelder-Mead optimization optimization framework Stern, Arinze, Perez, et al [334] 2020 Nonlinear programming optimization framework Liu, Ye, Silva Izquierdo, et al [335] 2022 SVM classification Prasanna, Shantha, Pradeep, et al [336] 2022 SVM ,KNN Optimization Framework Zhai and Yeo [337] 2023 Bayesian Learning inverse design Hu, Wang, Du, et al [338] 2023 Bayesian Optimization inverse design Hu, Zhan, Wang, et al [339] 2023 LS-SVM Optimization Framework…”
Section: The Causal Relationship Problemmentioning
confidence: 99%