Hybrid Global Sensitivity Analysis Based Optimal Attribute Selection Using Classification Techniques by Machine Learning Algorithm

2021 ◽  
Author(s):  
G. Saranya ◽  
A. Pravin
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Learning Algorithm ◽  
Attribute Selection ◽  
Machine Learning Algorithm ◽  
Classification Techniques ◽  
Global Sensitivity

scholarly journals Designing bioinspired green nanosilicas using statistical and machine learning approaches

2021 ◽  
Vol 6 (4) ◽  
pp. 293-307
Author(s):  
Luc Dewulf ◽  
Mauro Chiacchia ◽  
Aaron S. Yeardley ◽  
Robert A. Milton ◽  
Solomon F. Brown ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Design Of Experiments ◽  
Process Design ◽  
Global Sensitivity Analysis ◽  
Sequential Design ◽  
Learning Approaches ◽  
Global Sensitivity ◽  
Sustainable Product ◽  
Product And Process

This is a first comparison of the sequential design of experiments strategy and global sensitivity analysis for nanomaterials, thus enabling sustainable product and process design in future.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Sign in / Sign up

Export Citation Format

Share Document