Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285263305> ?p ?o ?g. }
- W4285263305 endingPage "290" @default.
- W4285263305 startingPage "273" @default.
- W4285263305 abstract "Machine learning has a special place in data science among researchers and scientists nowadays. Machine learning contains algorithms and models which permit computer systems to explore patterns in data. It is quite challenging and difficult for traditional machine learning techniques to obtain information and pattern from big and complex data. As a subset of machine learning or even artificial intelligence, deep learning focuses on developing large network architectures to make suitable and accurate data-driven decisions. Deep learning architectures contain multiple hidden layers (deep network) to learn different features from complex and extensive datasets. In such datasets, deep learning algorithms explore the unknown datasets and structures to identify valuable relationships. Deep learning has shown its capability in different water and environmental sectors and represents itself as an appropriate model, particularly in modeling large (big) and complex datasets. This chapter provides a review of deep learning concepts, introduce some of the developed deep learning structures, and their application in water and environmental studies." @default.
- W4285263305 created "2022-07-14" @default.
- W4285263305 creator A5004675565 @default.
- W4285263305 creator A5011600518 @default.
- W4285263305 creator A5015132867 @default.
- W4285263305 creator A5031008154 @default.
- W4285263305 date "2022-01-01" @default.
- W4285263305 modified "2023-10-18" @default.
- W4285263305 title "Deep Learning Application in Water and Environmental Sciences" @default.
- W4285263305 cites W1984020445 @default.
- W4285263305 cites W2040263621 @default.
- W4285263305 cites W2064675550 @default.
- W4285263305 cites W2107878631 @default.
- W4285263305 cites W2109574129 @default.
- W4285263305 cites W2136922672 @default.
- W4285263305 cites W2157331557 @default.
- W4285263305 cites W2165698076 @default.
- W4285263305 cites W2285252321 @default.
- W4285263305 cites W2311607323 @default.
- W4285263305 cites W2565516711 @default.
- W4285263305 cites W2767547957 @default.
- W4285263305 cites W2789367970 @default.
- W4285263305 cites W2790979755 @default.
- W4285263305 cites W2802436364 @default.
- W4285263305 cites W2802942478 @default.
- W4285263305 cites W2884273733 @default.
- W4285263305 cites W2889678009 @default.
- W4285263305 cites W2896556344 @default.
- W4285263305 cites W2897842591 @default.
- W4285263305 cites W2898791292 @default.
- W4285263305 cites W2919115771 @default.
- W4285263305 cites W2919358988 @default.
- W4285263305 cites W2921254630 @default.
- W4285263305 cites W2935631455 @default.
- W4285263305 cites W2936399818 @default.
- W4285263305 cites W2940726923 @default.
- W4285263305 cites W2942231644 @default.
- W4285263305 cites W2942300526 @default.
- W4285263305 cites W2947411064 @default.
- W4285263305 cites W2953582187 @default.
- W4285263305 cites W2960560113 @default.
- W4285263305 cites W2971689172 @default.
- W4285263305 cites W2972721601 @default.
- W4285263305 cites W2973053290 @default.
- W4285263305 cites W2996139122 @default.
- W4285263305 cites W3005619874 @default.
- W4285263305 cites W3008325040 @default.
- W4285263305 cites W3008439211 @default.
- W4285263305 cites W3011988525 @default.
- W4285263305 cites W3015368251 @default.
- W4285263305 cites W3017246359 @default.
- W4285263305 cites W3020472141 @default.
- W4285263305 cites W3026717820 @default.
- W4285263305 cites W3032621852 @default.
- W4285263305 cites W3036299832 @default.
- W4285263305 cites W3036426692 @default.
- W4285263305 cites W3036845691 @default.
- W4285263305 cites W3039452827 @default.
- W4285263305 cites W3041909991 @default.
- W4285263305 cites W3047057933 @default.
- W4285263305 cites W3047335959 @default.
- W4285263305 cites W3080705895 @default.
- W4285263305 cites W3083014107 @default.
- W4285263305 cites W3089275591 @default.
- W4285263305 cites W3093442198 @default.
- W4285263305 cites W3106370744 @default.
- W4285263305 cites W3109365969 @default.
- W4285263305 cites W3110015110 @default.
- W4285263305 cites W3113930266 @default.
- W4285263305 cites W3115724051 @default.
- W4285263305 cites W3118690163 @default.
- W4285263305 cites W3124635350 @default.
- W4285263305 cites W3127269894 @default.
- W4285263305 cites W3128444610 @default.
- W4285263305 cites W3131138274 @default.
- W4285263305 cites W3131324180 @default.
- W4285263305 cites W3136180508 @default.
- W4285263305 cites W3138725786 @default.
- W4285263305 cites W3149578452 @default.
- W4285263305 cites W3158254415 @default.
- W4285263305 cites W3158774913 @default.
- W4285263305 cites W3162510967 @default.
- W4285263305 cites W3162826256 @default.
- W4285263305 cites W3164419852 @default.
- W4285263305 cites W3165374651 @default.
- W4285263305 cites W3181693116 @default.
- W4285263305 cites W3193828983 @default.
- W4285263305 cites W3194796466 @default.
- W4285263305 cites W3196366948 @default.
- W4285263305 cites W4242572347 @default.
- W4285263305 cites W44815768 @default.
- W4285263305 doi "https://doi.org/10.1007/978-981-19-2519-1_13" @default.
- W4285263305 hasPublicationYear "2022" @default.
- W4285263305 type Work @default.
- W4285263305 citedByCount "0" @default.
- W4285263305 crossrefType "book-chapter" @default.
- W4285263305 hasAuthorship W4285263305A5004675565 @default.
- W4285263305 hasAuthorship W4285263305A5011600518 @default.