An enhanced intelligent model: To protect marine IoT sensor environment using ensemble machine learning approach

2021 ◽  
Vol 242 ◽  
pp. 110180
Author(s):  
Dimple Tiwari ◽  
Bhoopesh Singh Bhati ◽  
Bharti Nagpal ◽  
Shweta Sankhwar ◽  
Fadi Al-Turjman
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Ensemble Machine Learning ◽  
Machine Learning Approach ◽  
Intelligent Model

scholarly journals Advanced chromatographic technique for performance simulation of anti-Alzheimer agent: an ensemble machine learning approach

2020 ◽  
Vol 2 (11) ◽  
Author(s):  
U. M. Ghali ◽  
Abdullahi Garba Usman ◽  
Z. M. Chellube ◽  
Mohamed Alhosen Ali Degm ◽  
Kujtesa Hoti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Chromatographic Technique ◽  
Performance Simulation ◽  
Ensemble Machine Learning ◽  
Machine Learning Approach

scholarly journals Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project

PLoS ONE ◽  
2017 ◽  
Vol 12 (7) ◽  
pp. e0179805 ◽  
Author(s):  
Manal Alghamdi ◽  
Mouaz Al-Mallah ◽  
Steven Keteyian ◽  
Clinton Brawner ◽  
Jonathan Ehrman ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Exercise Testing ◽  
Learning Approach ◽  
Ensemble Machine Learning ◽  
Machine Learning Approach ◽  
Henry Ford

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.

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