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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A Comprehensive Set of Novel Residual Blocks for Deep Learning Architectures for Diagnosis of Retine deep residual architectures. The technique proposed in this chapter achieves better accuracy compared to the state of the art for two separately hosted Retinal OCT image data-sets. Furthermore, we illustrate a real-time prediction system that by exploiting this deep residual architecture, consisti

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Three-Stream Convolutional Neural Network for Human Fall Detection,nce of the individual, diminishing their independence. In this work, we propose a method capable of detecting human falls in video sequences using multi-channel convolutional neural networks (CNN). Our method makes use of a 3D CNN fed with features previously extracted from each frame to generate a

Torrid

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Automatic Solar Panel Detection from High-Resolution Orthoimagery Using Deep Learning Segmentation r 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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Training Deep Learning Sequence Models to Understand Driver Behavior,twork and the encoder–decoder model with attention were built and trained to analyze the effect of memory and attention on the computational expense and performance of the model. We compare the performance of these two complex networks to that of the MLP in estimating driver behavior. We show that o

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Exploiting Spatio-Temporal Correlation in RF Data Using Deep Learning,ing 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

斗爭

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Canyon

Vehicular Localisation at High and Low Estimation Rates During GNSS Outages: A Deep Learning Approa 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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