Matches in SemOpenAlex for { <https://semopenalex.org/work/W3210178995> ?p ?o ?g. }
Showing items 1 to 71 of
71
with 100 items per page.
- W3210178995 abstract "In recent years, due to the vigorous development of artificial intelligence in various fields, many various applications have appeared. However, due to the biological uncertainty, only a few research apply artificial intelligence to manage the biological production process. The fermentation process of lactic acid bacteria has biologically uncertain, and the parameters in the fermentation process are difficult to set with fixed values to be automatically executed. Therefore, the current fermentation process is carried out manually. Due to the uncertainty in the production process, once human error occurs, it often causes hundreds of thousands or even millions dollars of losses. Therefore, if the fermentation effect can be improved, the subsequent production efficiency can be directly improved. In order to automate the fermentation process, in this project, we hope that by combining artificial intelligence (AI) with the background of lactic acid bacteria cultivation, the current complicated manual fermentation process can be transformed into automation as the goal of Industry 4.0. Based on the logs of the experiments of Lactobacillus fermentation, we use Long Shorten-Memory (LSTM) to predict the output amount of fermentation results. In the experimental results, we collects 9 trials of experimental results (4 case for over 3*109, 5 cases for approaching 3*109 and 7 cases for 0 output). And then, all the results are randomly separated into training and testing datasets for 20 different runs. The training dataset average accuracy of 20 runs is 100%. And the testing dataset average accuracy of 20 runs is 95%. Hence, according to the experimental results, we can know the proposed methods really can predicted the amount of the fermentation products." @default.
- W3210178995 created "2021-11-08" @default.
- W3210178995 creator A5003359245 @default.
- W3210178995 creator A5038115794 @default.
- W3210178995 creator A5089216695 @default.
- W3210178995 date "2021-09-24" @default.
- W3210178995 modified "2023-09-23" @default.
- W3210178995 title "Novel Lactobacillus Fermentation Prediction Using Deep Learning" @default.
- W3210178995 cites W3022604663 @default.
- W3210178995 doi "https://doi.org/10.1109/icasi52993.2021.9568412" @default.
- W3210178995 hasPublicationYear "2021" @default.
- W3210178995 type Work @default.
- W3210178995 sameAs 3210178995 @default.
- W3210178995 citedByCount "0" @default.
- W3210178995 crossrefType "proceedings-article" @default.
- W3210178995 hasAuthorship W3210178995A5003359245 @default.
- W3210178995 hasAuthorship W3210178995A5038115794 @default.
- W3210178995 hasAuthorship W3210178995A5089216695 @default.
- W3210178995 hasConcept C100544194 @default.
- W3210178995 hasConcept C111919701 @default.
- W3210178995 hasConcept C115901376 @default.
- W3210178995 hasConcept C119857082 @default.
- W3210178995 hasConcept C127413603 @default.
- W3210178995 hasConcept C139719470 @default.
- W3210178995 hasConcept C154945302 @default.
- W3210178995 hasConcept C162324750 @default.
- W3210178995 hasConcept C183696295 @default.
- W3210178995 hasConcept C2775920511 @default.
- W3210178995 hasConcept C2778348673 @default.
- W3210178995 hasConcept C31903555 @default.
- W3210178995 hasConcept C41008148 @default.
- W3210178995 hasConcept C523546767 @default.
- W3210178995 hasConcept C54355233 @default.
- W3210178995 hasConcept C78519656 @default.
- W3210178995 hasConcept C86803240 @default.
- W3210178995 hasConcept C98045186 @default.
- W3210178995 hasConceptScore W3210178995C100544194 @default.
- W3210178995 hasConceptScore W3210178995C111919701 @default.
- W3210178995 hasConceptScore W3210178995C115901376 @default.
- W3210178995 hasConceptScore W3210178995C119857082 @default.
- W3210178995 hasConceptScore W3210178995C127413603 @default.
- W3210178995 hasConceptScore W3210178995C139719470 @default.
- W3210178995 hasConceptScore W3210178995C154945302 @default.
- W3210178995 hasConceptScore W3210178995C162324750 @default.
- W3210178995 hasConceptScore W3210178995C183696295 @default.
- W3210178995 hasConceptScore W3210178995C2775920511 @default.
- W3210178995 hasConceptScore W3210178995C2778348673 @default.
- W3210178995 hasConceptScore W3210178995C31903555 @default.
- W3210178995 hasConceptScore W3210178995C41008148 @default.
- W3210178995 hasConceptScore W3210178995C523546767 @default.
- W3210178995 hasConceptScore W3210178995C54355233 @default.
- W3210178995 hasConceptScore W3210178995C78519656 @default.
- W3210178995 hasConceptScore W3210178995C86803240 @default.
- W3210178995 hasConceptScore W3210178995C98045186 @default.
- W3210178995 hasLocation W32101789951 @default.
- W3210178995 hasOpenAccess W3210178995 @default.
- W3210178995 hasPrimaryLocation W32101789951 @default.
- W3210178995 hasRelatedWork W154817399 @default.
- W3210178995 hasRelatedWork W1971712740 @default.
- W3210178995 hasRelatedWork W2349154821 @default.
- W3210178995 hasRelatedWork W2365466992 @default.
- W3210178995 hasRelatedWork W2386939488 @default.
- W3210178995 hasRelatedWork W2387391827 @default.
- W3210178995 hasRelatedWork W2389868263 @default.
- W3210178995 hasRelatedWork W2391944248 @default.
- W3210178995 hasRelatedWork W250821284 @default.
- W3210178995 hasRelatedWork W3137125847 @default.
- W3210178995 isParatext "false" @default.
- W3210178995 isRetracted "false" @default.
- W3210178995 magId "3210178995" @default.
- W3210178995 workType "article" @default.