Search engine for discovering works of Art, research articles, and books related to Art and Culture
Javascript must be enabled to continue!
Influence of Missing Values on Artificial Neural Network Performance
View through CrossRef
The problem of databases containing missing values is a common one in the medical environment. Researchers must find a way to incorporate the incomplete data into the data set to use those cases in their experiments. Artificial neural networks (ANNs) cannot interpret missing values, and when a database is highly skewed, ANNs have difficulty identjing the factors leading to a rare outcome. This study investigates the impact on ANN performance when predicting neonatal mortality of increasing the number of cases with missing values in the data sets.
Title: Influence of Missing Values on Artificial Neural Network Performance
Description:
The problem of databases containing missing values is a common one in the medical environment.
Researchers must find a way to incorporate the incomplete data into the data set to use those cases in their experiments.
Artificial neural networks (ANNs) cannot interpret missing values, and when a database is highly skewed, ANNs have difficulty identjing the factors leading to a rare outcome.
This study investigates the impact on ANN performance when predicting neonatal mortality of increasing the number of cases with missing values in the data sets.
Related Results
La luz: de herramienta a lenguaje. Una nueva metodología de iluminación artificial en el proyecto arquitectónico.
La luz: de herramienta a lenguaje. Una nueva metodología de iluminación artificial en el proyecto arquitectónico.
The constant development of artificial lighting throughout the twentieth century helped to
develop architecture to the current situation in which a new methodology is needed for
...
Comparison of Methods for Imputing Social Network Data
Comparison of Methods for Imputing Social Network Data
Social network data often contain missing values because of the sensitive nature of the information collected and the dependency among the network actors. As a response, network im...
Long-range superharmonic Josephson current and spin-triplet pairing correlations in a junction with ferromagnetic bilayers
Long-range superharmonic Josephson current and spin-triplet pairing correlations in a junction with ferromagnetic bilayers
AbstractThe long-range spin-triplet supercurrent transport is an interesting phenomenon in the superconductor/ferromagnet ("Equation missing") heterostructure containing noncolline...
Modified neural networks for rapid recovery of tokamak plasma parameters for real time control
Modified neural networks for rapid recovery of tokamak plasma parameters for real time control
Two modified neural network techniques are used for the identification of the equilibrium plasma parameters of the Superconducting Steady State Tokamak I from external magnetic mea...
PARAMETRIC STUDY OF WARREN STEEL TRUSS BRIDGE USING ARTIFICIAL NEURAL NETWORKS
PARAMETRIC STUDY OF WARREN STEEL TRUSS BRIDGE USING ARTIFICIAL NEURAL NETWORKS
Abstract
Steel truss bridges are a popular type of amongst several other standard bridges in Indonesia due to their lightweight yet robust and strong structure. In this study A...
Missing Person Identification System
Missing Person Identification System
One main objective of the project is to develop a missing youngster (people) realize application that may be useful for the folk whose beloved ones are kidnapped lost, or saved by ...
Self-Supervised Multi-Level Generative Adversarial Network Data Imputation Algorithm
Self-Supervised Multi-Level Generative Adversarial Network Data Imputation Algorithm
Abstract
Data missing has always been a challenging problem in machine learning. The Generative Adversarial Imputation Networks (GAIN) have been shown to outperform many ex...
Handling Missing Data in COVID-19 Incidence Estimation: Secondary Data Analysis
Handling Missing Data in COVID-19 Incidence Estimation: Secondary Data Analysis
Abstract
Background
The COVID-19 pandemic has revealed significant challenges in disease forecasting and in developing a public health response, ...