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* [[energy industry|energy]] for predicting the [[Electric energy consumption|electricity consumption]];<ref name="Rif'an et al. (2019)">{{cite journal |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 |page=033092 |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><ref name="Hagenauer et al. (2022)" />
==See also==
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