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• W3204037232 abstract "This study is aimed at modeling biodigestion systems as a function of the most influencing parameters to generate two robust algorithms on the basis of the machine learning algorithms, including adaptive network-based fuzzy inference system (ANFIS) and least square support vector machine (LSSVM). The models are assessed utilizing multiple statistical analyses for the actual values and model outcomes. Results from the suggested models indicate their great capability of predicting biogas production from vegetable food, fruits, and wastes for a variety of ranges of input parameters. The values that are calculated for the mean relative error (MRE %) and mean squared error (MSE) were 29.318 and 0.0039 for ANFIS, and 2.951 and 0.0001 for LSSVM which shows that the latter model has a better ability to predict the target data. Finally, in order to have additional certainty, two analyses of outlier identification and sensitivity were performed on the input parameter data that proved the proposed model in this paper has higher reliability in assessing output values compared with the previous model." @default.
• W3204037232 created "2021-10-11" @default.
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• W3204037232 date "2021-09-24" @default.
• W3204037232 modified "2023-10-03" @default.
• W3204037232 title "On the Prediction of Biogas Production from Vegetables, Fruits, and Food Wastes by ANFIS- and LSSVM-Based Models" @default.
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• W3204037232 doi "https://doi.org/10.1155/2021/9202127" @default.
• W3204037232 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8486538" @default.
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