FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2552433453> ?p ?o ?g. }

• W2552433453 endingPage "22" @default.
• W2552433453 startingPage "16" @default.
• W2552433453 abstract "A majority of gastrointestinal infectious diseases are caused by food contamination, and prediction of morbidity can be very useful for etiological factor controlling and medical resource utilization. However, an accurate prediction is often very difficult not only because there are various types of food and contaminants, but also because the relationship between the diseases and the contaminants is highly complex and probabilistic. In this study, we use the deep denoising autoencoder (DDAE) to model the effect of food contamination on gastrointestinal infections, and thus provide a valuable tool for morbidity prediction. For effectively training the model with high-dimensional input data, we propose an evolutionary learning algorithm based on ecogeography-based optimization (EBO) in order to avoid premature convergence. Experimental results show that our evolutionary deep learning model obtains a much higher prediction accuracy than the shallow artificial neural network (ANN) model and the DDAE with other learning algorithms on a real-world dataset." @default.
• W2552433453 created "2016-11-30" @default.
• W2552433453 creator A5008139007 @default.
• W2552433453 creator A5012475436 @default.
• W2552433453 creator A5019515293 @default.
• W2552433453 creator A5019915591 @default.
• W2552433453 creator A5083870757 @default.
• W2552433453 date "2017-02-01" @default.
• W2552433453 modified "2023-10-17" @default.
• W2552433453 title "An evolutionary deep neural network for predicting morbidity of gastrointestinal infections by food contamination" @default.
• W2552433453 cites W1617447347 @default.
• W2552433453 cites W1668207500 @default.
• W2552433453 cites W1801780804 @default.
• W2552433453 cites W1832003755 @default.
• W2552433453 cites W1936687626 @default.
• W2552433453 cites W1966124573 @default.
• W2552433453 cites W1985402541 @default.
• W2552433453 cites W1993741516 @default.
• W2552433453 cites W2000753909 @default.
• W2552433453 cites W2009062596 @default.
• W2552433453 cites W2016517940 @default.
• W2552433453 cites W2019153133 @default.
• W2552433453 cites W2020093619 @default.
• W2552433453 cites W2024550526 @default.
• W2552433453 cites W2025768430 @default.
• W2552433453 cites W2028416619 @default.
• W2552433453 cites W2035553146 @default.
• W2552433453 cites W2042663758 @default.
• W2552433453 cites W2045338291 @default.
• W2552433453 cites W2048299340 @default.
• W2552433453 cites W2048685544 @default.
• W2552433453 cites W2050186755 @default.
• W2552433453 cites W2057137111 @default.
• W2552433453 cites W2059136964 @default.
• W2552433453 cites W2066597741 @default.
• W2552433453 cites W2069084725 @default.
• W2552433453 cites W2069767684 @default.
• W2552433453 cites W2070878875 @default.
• W2552433453 cites W2073964173 @default.
• W2552433453 cites W2077456032 @default.
• W2552433453 cites W2086755414 @default.
• W2552433453 cites W2091564852 @default.
• W2552433453 cites W2100495367 @default.
• W2552433453 cites W2103748993 @default.
• W2552433453 cites W2111116447 @default.
• W2552433453 cites W2126018595 @default.
• W2552433453 cites W2132107213 @default.
• W2552433453 cites W2133295230 @default.
• W2552433453 cites W2133569576 @default.
• W2552433453 cites W2136922672 @default.
• W2552433453 cites W2153125595 @default.
• W2552433453 cites W2157098397 @default.
• W2552433453 cites W2168081761 @default.
• W2552433453 cites W2170410701 @default.
• W2552433453 cites W2192655208 @default.
• W2552433453 cites W2267583898 @default.
• W2552433453 cites W2284937233 @default.
• W2552433453 cites W2300976231 @default.
• W2552433453 cites W2336484677 @default.
• W2552433453 cites W2342594933 @default.
• W2552433453 cites W2344725271 @default.
• W2552433453 cites W2520642424 @default.
• W2552433453 cites W2525305541 @default.
• W2552433453 cites W3022436500 @default.
• W2552433453 cites W2081265317 @default.
• W2552433453 doi "https://doi.org/10.1016/j.neucom.2016.11.018" @default.
• W2552433453 hasPublicationYear "2017" @default.
• W2552433453 type Work @default.
• W2552433453 sameAs 2552433453 @default.
• W2552433453 citedByCount "63" @default.
• W2552433453 countsByYear W25524334532017 @default.
• W2552433453 countsByYear W25524334532018 @default.
• W2552433453 countsByYear W25524334532019 @default.
• W2552433453 countsByYear W25524334532020 @default.
• W2552433453 countsByYear W25524334532021 @default.
• W2552433453 countsByYear W25524334532022 @default.
• W2552433453 countsByYear W25524334532023 @default.
• W2552433453 crossrefType "journal-article" @default.
• W2552433453 hasAuthorship W2552433453A5008139007 @default.
• W2552433453 hasAuthorship W2552433453A5012475436 @default.
• W2552433453 hasAuthorship W2552433453A5019515293 @default.
• W2552433453 hasAuthorship W2552433453A5019915591 @default.
• W2552433453 hasAuthorship W2552433453A5083870757 @default.
• W2552433453 hasConcept C101738243 @default.
• W2552433453 hasConcept C108583219 @default.
• W2552433453 hasConcept C112570922 @default.
• W2552433453 hasConcept C119857082 @default.
• W2552433453 hasConcept C154945302 @default.
• W2552433453 hasConcept C18903297 @default.
• W2552433453 hasConcept C41008148 @default.
• W2552433453 hasConcept C49937458 @default.
• W2552433453 hasConcept C50644808 @default.
• W2552433453 hasConcept C86803240 @default.
• W2552433453 hasConceptScore W2552433453C101738243 @default.
• W2552433453 hasConceptScore W2552433453C108583219 @default.
• W2552433453 hasConceptScore W2552433453C112570922 @default.
• W2552433453 hasConceptScore W2552433453C119857082 @default.
• W2552433453 hasConceptScore W2552433453C154945302 @default.

• next