Titlebook: Deep Learning and Missing Data in Engineering Systems; Collins Achepsah Leke,Tshilidzi Marwala Book 2019 Springer Nature Switzerland AG 20

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Studies in Big Datahttp://image.papertrans.cn/d/image/264595.jpg

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Industrial Process Emission Policiesy a discussion of the classical missing data techniques ensued by a presentation of machine learning approaches to address the missing data problem. Subsequently, machine learning optimization techniques are presented for missing data estimation tasks.

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https://doi.org/10.1007/978-3-030-00317-3wing number of studies in the deep learning area warrants a closer look at its possible application in the domain. Missing data being an unavoidable scenario in present-day datasets results in different challenges, which are nontrivial for existing techniques that constitute narrow artificial intell

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Networking Humans and Non-Humansing data is a recurrent issue in day-to-day datasets, resulting in a variety of setbacks which are often difficult for existing techniques which constitute narrow artificial intelligence architectures and computational intelligence methods. This is normally aligned with dimensionality and the number