Search the World's Largest Database of Information Science & Technology Terms & Definitions
InfInfoScipedia LogoScipedia
A Free Service of IGI Global Publishing House
Below please find a list of definitions for the term that
you selected from multiple scholarly research resources.

What is Activation Functions

Chapter 5
Artificial Intelligence Transformations for Healthcare Applications: Medical Diagnosis, Treatment, and Patient Care
Deep neural networks rely heavily on activation functions, which provide non-linearity to the model and allow it to recognize and interpret complicated patterns in the data.
Published in Chapter:
Time Series Models for an Exposure-Response Relationship Problem in Mental Healthcare: Impact of Environmental Factors on Schizophrenia
Jobin Thomas (Presidency University, India) and Murali Parameswaran (Presidency University, India)
Copyright: © 2024 |Pages: 25
DOI: 10.4018/979-8-3693-7462-7.ch005
Abstract
The problem of the exposure-response relationship between environmental factors and mental illness is gaining attraction in recent years. This chapter explores the various time series approaches that can be applied to solve the exposure-response relationship. In the problem of predicting psychiatric facility admissions based on environmental factors, there is a lagged association between the daily concentration of environmental variables and hospital admission, which is non-linear. The Poisson generalized linear regression in conjunction with the distributed lag non-linear model is utilized to explore this non-linear and lagged effect. The various deep learning approaches employed for addressing the exposure-response relationship are discussed in this chapter. The performance of various time series techniques is illustrated with the help of a dataset based in Bangalore City, India.
Full Text Chapter Download: US $37.50 Add to Cart
More Results
Chapter 9
Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network
Activation function assists neural network to learn from training data. It transforms values from the previous layer into the next layer, with adjusted weights on parameters and adjusted bias values.
Published in Chapter: Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network; From: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 3
Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays
In neural network architecture, these are the pre-determined functions at each node or neuron, determining the output value of the node or neuron. These inputs to these functions are the linear combinations of the values at each node in the previous layer. Activation functions are often continuous functions. However, some activation functions are not differentiable everywhere, such as the Rectified Linear Units (ReLU) function, which is not differentiable at 0.
Published in Chapter: Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays; From: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
Full Text Chapter Download: US $37.50 Add to Cart
eContent Pro Discount Banner
InfoSci OnDemandECP Editorial ServicesAGOSR