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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書目名稱Deep Learning and Missing Data in Engineering Systems影響因子(影響力)




書目名稱Deep Learning and Missing Data in Engineering Systems影響因子(影響力)學(xué)科排名




書目名稱Deep Learning and Missing Data in Engineering Systems網(wǎng)絡(luò)公開度




書目名稱Deep Learning and Missing Data in Engineering Systems網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Deep Learning and Missing Data in Engineering Systems被引頻次




書目名稱Deep Learning and Missing Data in Engineering Systems被引頻次學(xué)科排名




書目名稱Deep Learning and Missing Data in Engineering Systems年度引用




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書目名稱Deep Learning and Missing Data in Engineering Systems讀者反饋




書目名稱Deep Learning and Missing Data in Engineering Systems讀者反饋學(xué)科排名

GNAT

發(fā)源

Missing Data Estimation Using Invasive Weed Optimization Algorithm,itute 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.

浮夸

Missing Data Estimation Using Swarm Intelligence Algorithms from Reduced Dimensions,ained 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.

Muffle

運(yùn)氣

運(yùn)氣

Outshine

無脊椎

Missing Data Estimation Using Firefly Algorithm,izing an error function based on the interrelationship and correlation between features in the dataset. The proposed methodology in this chapter, therefore, has longer running times, however, the promising potential outcomes justify the trade-off. Also, basic knowledge of statistics is presumed.

molest

Book 2019ing systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate the imputation of missing data, several artificial intelligence approaches are presented, including:.deep autoencoder neural networks;.deep denoising autoenco