scholarly journals An adaptive fault detection scheme using optimized self-healing ensemble machine learning algorithm

2021 ◽  
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Self Healing ◽  
Detection Scheme ◽  
Ensemble Machine Learning

Fall detection system for elderly people using IoT and ensemble machine learning algorithm

2019 ◽  
Vol 23 (5-6) ◽  
pp. 801-817 ◽  
Author(s):  
Diana Yacchirema ◽  
Jara Suárez de Puga ◽  
Carlos Palau ◽  
Manuel Esteve
Keyword(s):  
Machine Learning ◽  
Elderly People ◽  
Learning Algorithm ◽  
Detection System ◽  
Fall Detection ◽  
Machine Learning Algorithm ◽  
Ensemble Machine Learning

scholarly journals FastEmbed: Predicting vulnerability exploitation possibility based on ensemble machine learning algorithm

PLoS ONE ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. e0228439 ◽  
Author(s):  
Yong Fang ◽  
Yongcheng Liu ◽  
Cheng Huang ◽  
Liang Liu
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Ensemble Machine Learning

Sentiment Analysis for Iraqis Dialect in Social Media

2018 ◽  
Vol 1 (2) ◽  
pp. 24-32
Author(s):  
Lamiaa Abd Habeeb
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
K Nearest Neighbor ◽  
Multinomial Models ◽  
Ensemble Machine Learning ◽  
Machine Learning Approach

In this paper, we designed a system that extract citizens opinion about Iraqis government and Iraqis politicians through analyze their comments from Facebook (social media network). Since the data is random and contains noise, we cleaned the text and builds a stemmer to stem the words as much as possible, cleaning and stemming reduced the number of vocabulary from 28968 to 17083, these reductions caused reduction in memory size from 382858 bytes to 197102 bytes. Generally, there are two approaches to extract users opinion; namely, lexicon-based approach and machine learning approach. In our work, machine learning approach is applied with three machine learning algorithm which are; Naïve base, K-Nearest neighbor and AdaBoost ensemble machine learning algorithm. For Naïve base, we apply two models; Bernoulli and Multinomial models. We found that, Naïve base with Multinomial models give highest accuracy.

P125. Development of a novel ensemble machine learning algorithm for prediction of complications and readmission after anterior cervical spinal fusion

2021 ◽  
Vol 21 (9) ◽  
pp. S201-S202
Author(s):  
Akash A. Shah ◽  
Sai Devana ◽  
Changhee Lee ◽  
Amador Bugarin ◽  
Alexander Upfill-Brown ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spinal Fusion ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Ensemble Machine Learning ◽  
Prediction Of Complications
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