Titlebook: Visual Knowledge Discovery and Machine Learning; Boris Kovalerchuk Book 2018 Springer International Publishing AG 2018 Intelligent Systems

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into account the entire image. The functionality of the RC component is differentiable. Thus, it can be merged to the deep neural network, and trained end-to-end with other sub-networks. We achieve identification rates of 85.32% and 52.28% for sagittal and coronal views and localization distance of

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Boris Kovalerchuk each axial slice. Second, the abdominal wall and psoas muscle are segmented by combining MALF with active shape models and deformable models. Third, skeletal muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) are measured to assess the areas of muscle and fat tissue. The pr

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Boris Kovalerchukrt of the process..The hydrodynamic characteristics of the entire flow field around different obstacles were investigated by a Laser-Doppler-Velocimeter (LDV) in order to determine a “critical pressure history“ P(t) for a single cavitation nucleus. Hydrodynamic scaling-up of both pressure and time a

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Boris Kovalerchuka “ready reference” of the key equations for the application of one very widely used theoretical strategy—the eigenfunction-expansion or “close-coupling“ method— to one very important class of problems: quantum scattering (at incident energies less than about 10 eV) from a closed-shell diatomic mole

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Boris Kovalerchukarent. The first is that the primitive-variable formulation is preferable to the stream-function vorticity approach in terms of efficiency and ease of application. For inviscid flows the reason is clear because of the lower order of differentiation required in the primitive-variable formulation comp