Titlebook: Deep Learning Applications, Volume 2; M. Arif Wani,Taghi M. Khoshgoftaar,Vasile Palade Book 2021 The Editor(s) (if applicable) and The Aut

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Operational Environment for the HDSLr Motor Current Signal (MCS) is also gaining popularity. This paper uses MCS for the diagnosis of bearing inner raceway and outer raceway fault. Diagnosis using MCS is difficult as the fault signatures are buried beneath the noise in the current signal. Hence, signal-processing techniques are employ

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https://doi.org/10.1007/978-1-4615-6291-7r panel arrays from satellite imagery. The networks are tested on real data and augmented data. Results indicate that deep learning segmentation networks work well for automatic solar panel detection from high-resolution orthoimagery.

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José Daniel García-Castro,Josefa Mulaing techniques are the typically used for analyzing past observations and to predict the future occurrences of events in a given RF environment. Machine learning (ML) techniques, having already proven useful in various domains, are also being sought for characterizing and understanding the RF enviro

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H. Kayapinar,H.-C. Möhring,B. Denkena Several deep learning algorithms have been employed to learn the error drift for a better positioning prediction. We therefore investigate in this chapter the performance of Long Short-Term Memory (LSTM), Input Delay Neural Network (IDNN), Multi-Layer Neural Network (MLNN) and Kalman Filter (KF) fo

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Wear Behavior in Microactuator Interfacesd training data from the computer vision and medical imaging domains demonstrate performance competitive to state-of-the-art semi-supervised models in simultaneous image generation and classification tasks.

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Deep Learning-Based Recommender Systems,inent parts of information in order to make better recommendations. The learned features are then incorporated into the learning process of MF. Comprehensive experiments on three real-world datasets have shown our method performs better than other state-of-the-art methods according to various evalua