A Technique Based on Ensemble Machine Learning for The Analysis of Electronic Nose Signals-Reference-Cited by-同舟云学术

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A Technique Based on Ensemble Machine Learning for The Analysis of Electronic Nose Signals

Published:2023-05-12 Issue: Volume: Page:
ISSN:
Container-title:2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)
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Author:

Taneja Amit¹,Rajamani Santhosh Kumar²,Shekhawat Deepika³,Chanti Yerrolla⁴,Joshi Kapil⁵,Garg Ashish⁶

Affiliation:

1. Chandigarh University,AIT-CSE

2. MAEER MIT Pune’s MIMER Medical College and Dr BSTR Hospital TalegaonDabhade,Department of E.N.T,Pune,Maharashtra,India

3. Manipal University,Department of Computer Science & Engineering,Jaipur,Rajasthan

4. SR University,School of Computer Science and Artificial Intelligent,Warangal,Telangana,India

5. Uttaranchal University,Uttaranchal Institute of Technology

6. Graphic Era Deemed to be University,Department of Computer Science & Engineering,Dehradun,Uttarakhand,India,248002

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10182388/10182370/10182894.pdf?arnumber=10182894

Reference22 articles.

1. An active method of online drift-calibration-sample formation for an electronic nose

2. Network mining techniques to analyze the risk of the occupational accident via bayesian network

3. Development of a Low-Cost Electronic Nose for Detection of Pathogenic Fungi and Applying It to Fusarium oxysporum and Rhizoctonia solani

4. Application of a Low-Cost Electronic Nose for Differentiation between Pathogenic Oomycetes Pythium intermedium and Phytophthora plurivora

5. Data Analytics and Cloud-Based Platform for Internet of Things Applications in Smart Cities

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