Titlebook: Data Science and Predictive Analytics; Biomedical and Healt Ivo D. Dinov Textbook 2023Latest edition The Editor(s) (if applicable) and The

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https://doi.org/10.1007/978-3-642-17413-1 emphasize the importance of their ethical, responsible, and reproducible practical use. This chapter also covers the foundations of R, contrasts R against other languages and computational data science platforms, and introduces basic functions and data objects, formats, and simulation.

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Computational Approach to Riemann Surfacesniques, such as principal and independent component analyses, factor analysis, matrix singular value decomposition, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection. These methods will be applied to hand-written digit recognition imaging data and Parkinson’s disease clinical data.

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The Geometric Attributes of Paintingss analysis, autoregressive integrated moving average (ARIMA) models, structural equation models (SEM), linear mixed models, generalized estimating equations (GEE), recurrent neural networks (RNN), and long short-term memory (LSTM) networks.

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Function Optimization,anual and automated function optimization. We demonstrate a number of examples including a generic healthcare manufacturer product optimization problem, data denoising, and optimization of specific multivariate test functions.

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