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improve: completeness – i.e. specify a case study in the 'energy industry' field within the section on: SNNs' applications; ii.consistency – i.e. add 1 bibliographic reference for supporting the proposition/statement |
improve: meaningfulness – i.e. add a Wikipedia's internal link for facilitating the understanding of my previous edit/contribution, in the section on: "SNNs' applications" |
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<!-- thematic section within a methodological entry should remain a list, so that readers can quickly find examples of applications in their thematic fields/subfields; illustrative syntheses of case studies should appear within a separate/new Wikipedia’s entry (e.g. on 'applications of spatial models'); a section: 'software' reviewing the 'application software' and 'libraries/packages' with snippets of source codes would be really informative -->
There exist [[case-study]] applications of SNNs in:
* [[energy industry|energy]] for predicting the [[Electricity generation|electricity consumption]]s;<ref name="Rif'an et al. (2019)">{{cite conference |vauthors= Rif'an M, Daryanto D, Agung A |date=2019 |title=Spatial neural network for forecasting energy consumption of Palembang area |journal=Journal of physics: conference series |volume=1402 |issue=3 |doi=10.1088/1742-6596/1402/3/033092}}</ref>
* agriculture for classifying the [[vegetation]];<ref name="Podlipnov et al. (2023)">{{cite conference |vauthors=Podlipnov V, Firsov N, Ivliev N, Mashkov S, Ishkin P, Skidanov R, Nikonorov A |date=2023 |title=Spectral-spatial neural network classification of hyperspectral vegetation images |book-title= IOP conference series: earth and environmental science |volume=1138 |doi=10.1088/1755-1315/1138/1/012040}}</ref>
* real estate for appraising the [[premises]].<ref name="Lin et al. (2021)">{{cite journal |vauthors=Lin R, Ou C, Tseng K, Bowen D, Yung K, Ip W |date=2021 |title=The Spatial neural network model with disruptive technology for property appraisal in real estate industry |journal=Technological Forecasting and Social Change |volume=177 |page=121067 |doi=10.1016/j.techfore.2021.121067}}</ref>
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