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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2197-6503 es new paradigms of machine learning and computational intel.Deep Learning and Missing Data in Engineering Systems. uses deep learning and swarm intelligence methods to cover missing data estimation in engineering systems. The missing data estimation processes proposed in the book can be applied in

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Networking Humans and Non-Humansitute narrow artificial intelligence architectures and computational intelligence methods. This is normally aligned with dimensionality and the number of rows. We propose a framework for the imputation procedure that uses a deep learning method with a swarm intelligence algorithm called deep learning-invasive weed optimization (DL-IWO) approach.

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Networking Individuals and Groupsained from the bottleneck layer of the deep autoencoder network; in this case, the number of reduced features is 30. The aim is to observe whether this approach preserves accuracy while minimizing execution time.

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Engineering for Children Curriculumization algorithm and deep learning with cuckoo search algorithm, to name a few. Also presented in this book are experiments that show the impact of using lower dimensions and different numbers of hidden layers in the deep autoencoder networks.

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https://doi.org/10.1007/978-3-030-00317-3ates for the missing data entries surpasses that of existing methods, but this is considered a worthy bargain when the accuracy of the said estimates in a high-dimensional setting is taken into consideration.

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Missing Data Estimation Using Cuckoo Search Algorithm,ates for the missing data entries surpasses that of existing methods, but this is considered a worthy bargain when the accuracy of the said estimates in a high-dimensional setting is taken into consideration.

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