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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H. Kayapinar,H.-C. M?hring,B. Denkenaal GNSS receivers usually sample at 1?Hz, which is not sufficient to robustly and accurately track a vehicle in certain scenarios, such as driving on the highway, where the vehicle could travel at medium to high speeds, or in safety-critical scenarios. In addition, the GNSS relies on a number of sat

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Wear Behavior in Microactuator Interfaceseep generative models can learn to generate realistic images approximating real-world distributions. In particular, the proper training of Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs) enables them to perform semi-supervised image classification. Combining the power of t

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H. Kayapinar,H.-C. M?hring,B. Denkenand Mathematical analysis such as bifurcation study of dynamical systems. However, as far as we know, such efficient methods have seen relatively limited use in the optimization of neural networks. In this chapter, we propose a novel training method for deep neural networks based on the ideas from pa

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Syed V. Ahamed,Victor B. Lawrencee 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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Operational Environment for the HDSLnce 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