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Forecasting the Fuel Consumption of Passenger Ships with a Combination of Shallow and Deep Learning
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Marine Navigation and Safety of Sea Transportation
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inferred← citeshttps://doi.org/10.1016/j.tre.2024.103426openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/jmse13061140openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/jmse8090625openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/jmse9030273openalex-snapshot
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inferred← citeshttps://doi.org/10.3846/transport.2021.15952openalex-snapshot
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