Neural Networks and Their Applications

By Simranjeet Kaur, Baljeet Kaur. 2012

381 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9eb1ae010d0a0a7c29f6a6b90eb03b5b72cb3c3a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. G. Clifford
2. Leo Anthony Celi
3. Lionel Tarassenko
4. Louis Mayaud
5. Luca Pulina
6. Armando Tacchella
7. Peggy S. Lai
8. Djillali Annane

Citations in the corpus

• Dynamic data during hypotensive episode improves mortality predictions among patients with sepsis and hypotension (2013, background): “deaths) in a sample divided by ten (24).”
• Interactive Character Animation Using Simulated Physics: A State‐of‐the‐Art Review (2012, background): “The most common type of stimulus–response network is the artificial neural network (ANN), in which each node outputs a value based on the weighted sum of its input nodes and a threshold function [Bis94].”
• Predictive modelling of brewing fermentation: from knowledge-based to black-box models (2001, background): “Bishop [12] proposed a concise introduction to the field.”
• Challenging SMT solvers to verify neural networks (2012, background): “, [5]) are sigmoidal non-linearities such as the hyperbolic tangent (tanh) and the logistic (logi) functions defined as follows:”