• Harness the power of R for statistical computing and data science
• Use R to apply common machine learning algorithms with real-world applications
• Prepare,...

• Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization
• Learn effective strategies and best practices to...

The 16 revised full papers presented together with 4 invited papers were carefully reviewed and selected for inclusion in the book. The papers are devoted to all aspects of...

This book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The text emphasizes an organized model identification process by which to discover models that generalize and...

Stroke is the leading cause of serious, long term disability. This book proposes different cellular therapies under investigation to promote neural regeneration after stroke. Authored by an international panel of scientists and clinicians, this volume is a vital, one-of-a-kind resource for all scientists interested in regenerative...

This textbook provides a comprehensive introduction to the concepts and idea of multisensor data fusion.
It is an extensively revised second edition of the author's successful book: "Multi-Sensor Data Fusion:
An Introduction" which was originally published by Springer-Verlag in 2007.
The main changes in...

This book models  an idealized neuron as being driven by basic electrical elements, the goal being to systematically characterize the logical properties of neural pulses.  In order to constitute a system, neurons as pulsating devices may be represented using novel circuit elements as delineated in this book.  A plausible brain...