Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385698857> ?p ?o ?g. }
- W4385698857 endingPage "122358" @default.
- W4385698857 startingPage "122358" @default.
- W4385698857 abstract "Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecology and health (EEH). Considering the rapid growth and increasing complexity of data in EEH, it is of significance to summarize recent advances and applications of ML and DL in EEH. This review summarized the basic processes and fundamental algorithms of the ML and DL modeling, and indicated the urgent needs of ML and DL in EEH. Recent research hotspots such as environmental ecology and restoration, environmental fate of new pollutants, chemical exposures and risks, chemical hazard identification and control were highlighted. Various applications of ML and DL in EEH demonstrate their versatility and technological revolution, and present some challenges. The perspective of ML and DL in EEH were further outlined to promote the innovative analysis and cultivation of the ML-driven research paradigm." @default.
- W4385698857 created "2023-08-10" @default.
- W4385698857 creator A5024587677 @default.
- W4385698857 creator A5028786072 @default.
- W4385698857 creator A5031331014 @default.
- W4385698857 creator A5033000844 @default.
- W4385698857 creator A5036091798 @default.
- W4385698857 creator A5046926838 @default.
- W4385698857 creator A5070397169 @default.
- W4385698857 date "2023-10-01" @default.
- W4385698857 modified "2023-10-17" @default.
- W4385698857 title "Advances and applications of machine learning and deep learning in environmental ecology and health" @default.
- W4385698857 cites W1983478747 @default.
- W4385698857 cites W1986688691 @default.
- W4385698857 cites W2000089305 @default.
- W4385698857 cites W2040324575 @default.
- W4385698857 cites W2071318022 @default.
- W4385698857 cites W2072421553 @default.
- W4385698857 cites W2073097316 @default.
- W4385698857 cites W2125253492 @default.
- W4385698857 cites W2129434099 @default.
- W4385698857 cites W2171659470 @default.
- W4385698857 cites W2253609413 @default.
- W4385698857 cites W2560477544 @default.
- W4385698857 cites W2563115000 @default.
- W4385698857 cites W2769514070 @default.
- W4385698857 cites W2792919287 @default.
- W4385698857 cites W2800000557 @default.
- W4385698857 cites W2811290790 @default.
- W4385698857 cites W2886544065 @default.
- W4385698857 cites W2888936354 @default.
- W4385698857 cites W2897597313 @default.
- W4385698857 cites W2897702508 @default.
- W4385698857 cites W2899179560 @default.
- W4385698857 cites W2905810301 @default.
- W4385698857 cites W2913323966 @default.
- W4385698857 cites W2920795827 @default.
- W4385698857 cites W2922283117 @default.
- W4385698857 cites W2926763475 @default.
- W4385698857 cites W2951572348 @default.
- W4385698857 cites W2951934944 @default.
- W4385698857 cites W2952935243 @default.
- W4385698857 cites W2954932437 @default.
- W4385698857 cites W2964850315 @default.
- W4385698857 cites W2969990469 @default.
- W4385698857 cites W2971296471 @default.
- W4385698857 cites W2971653617 @default.
- W4385698857 cites W2972050955 @default.
- W4385698857 cites W2991722393 @default.
- W4385698857 cites W2995191368 @default.
- W4385698857 cites W2999347613 @default.
- W4385698857 cites W3000877276 @default.
- W4385698857 cites W3026511954 @default.
- W4385698857 cites W3034548713 @default.
- W4385698857 cites W3037428236 @default.
- W4385698857 cites W3040129451 @default.
- W4385698857 cites W3043480203 @default.
- W4385698857 cites W3043872269 @default.
- W4385698857 cites W3045792597 @default.
- W4385698857 cites W3084731547 @default.
- W4385698857 cites W3093016721 @default.
- W4385698857 cites W3098916164 @default.
- W4385698857 cites W3099252273 @default.
- W4385698857 cites W3104008342 @default.
- W4385698857 cites W3105212678 @default.
- W4385698857 cites W3109353507 @default.
- W4385698857 cites W3110035474 @default.
- W4385698857 cites W3110911372 @default.
- W4385698857 cites W3111017134 @default.
- W4385698857 cites W3122085937 @default.
- W4385698857 cites W3128027065 @default.
- W4385698857 cites W3139528877 @default.
- W4385698857 cites W3157796038 @default.
- W4385698857 cites W3174911655 @default.
- W4385698857 cites W3182207208 @default.
- W4385698857 cites W3184855018 @default.
- W4385698857 cites W3185151041 @default.
- W4385698857 cites W3186265534 @default.
- W4385698857 cites W3196386002 @default.
- W4385698857 cites W3196409416 @default.
- W4385698857 cites W3200542697 @default.
- W4385698857 cites W3206221987 @default.
- W4385698857 cites W3209674990 @default.
- W4385698857 cites W3214790664 @default.
- W4385698857 cites W3216511624 @default.
- W4385698857 cites W4200229959 @default.
- W4385698857 cites W4206973253 @default.
- W4385698857 cites W4210538803 @default.
- W4385698857 cites W4210943698 @default.
- W4385698857 cites W4220920502 @default.
- W4385698857 cites W4223413603 @default.
- W4385698857 cites W4223458449 @default.
- W4385698857 cites W4223979191 @default.
- W4385698857 cites W4224786608 @default.
- W4385698857 cites W4224982784 @default.
- W4385698857 cites W4226086528 @default.
- W4385698857 cites W4281383946 @default.
- W4385698857 cites W4281759084 @default.