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Adaptive filter for detection outlier data on electronic nose signal
Doni Putra Purbawa
, Riyanarto Sarno
,
Malikhah
, M. Syauqi Hanif Ardani
, Shoffi Izza Sabilla
, Kelly Rossa Sungkono
, Chastine Fatichah
, Dwi Sunaryono
, Indra Sampe Parimba
,
Arief Bakhtiar
Pulmonary and Respiratory UNAIR Teaching Hospital
Pulmonologi
Department of Pulmonology and Respiratory Medicine
Faculty of Medicine
Pulmonology and Respiratory Medicine
Research output
:
Contribution to journal
›
Article
›
peer-review
17
Citations (Scopus)
Overview
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Keyphrases
Electronic Nose (E-nose)
100%
Outlier Data
100%
Adaptive Filter
100%
Feature Extraction
50%
Armpit
50%
Deep Neural Network
50%
Invalid Data
50%
Outlier Detection
50%
Diabetes Patients
25%
Nave Bayes
25%
Support Vector Machine
25%
Lung Cancer
25%
Balanced Accuracy
25%
Beef
25%
Ensemble Model
25%
K-nearest
25%
Work System
25%
Pork
25%
Random Forest
25%
Detection Performance
25%
Score Combination
25%
Damage Data
25%
Detection Task
25%
Clinical Detection
25%
Human Nose
25%
Data Outliers
25%
Neighborhood Model
25%
Data Quality
25%
Respiratory Infectious Diseases
25%
XGBoost
25%
Engineering
Electronic Nose
100%
Data Outlier
100%
Outlier Detection
100%
Adaptive Filter
100%
Feature Extraction
50%
Deep Neural Network
50%
Conventional Method
25%
Random Forest
25%
Feasibility Study
25%
Detection Task
25%
Quality Data
25%
Extreme Case
25%
Detection Performance
25%
Support Vector Machine
25%
Nearest Neighbor
25%
Computer Science
Outlier Detection
100%
Adaptive Filter
100%
Deep Neural Network
66%
Feature Extraction
66%
Support Vector Machine
33%
Nave Bayes
33%
Conventional Method
33%
Scientific Field
33%
Extreme Gradient Boosting
33%
Detection Performance
33%
Random Decision Forest
33%