Automated Detection of Diabetes From Exhaled Human Breath Using Deep Hybrid Architecture

Author:

Bhaskar Navaneeth1ORCID,Bairagi Vinayak2ORCID,Boonchieng Ekkarat3ORCID,Munot Mousami V.4ORCID

Affiliation:

1. Faculty of Computer Science and Engineering (Data Science), Sahyadri College of Engineering and Management, Mangaluru, India

2. Department of Electronics and Telecommunication Engineering, AISSMS Institute of Information Technology, Pune, India

3. Center of Excellence in Community Health Informatics, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand

4. Department of Electronics and Telecommunication Engineering, Pune Institute of Computer Technology, Pune, India

Funder

Chiang Mai University and National Science, Research and Innovation Fund (NSRF) via the Program Management Unit for Human Resources and Institutional Development, Research and Innovation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Advancing Paddy Crop Productivity using Deep Learning Ensemble Approach;2024 Second International Conference on Data Science and Information System (ICDSIS);2024-05-17

2. Metal Oxide Semiconductor Sensors for Acetone Detection with Hot Wire Type Structure;2024 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN);2024-05-12

3. A Deep Learning Hybrid Approach for Automated Leaf Disease Identification in Paddy Crops;2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT);2024-03-15

4. Diabetes Detection Based on Health Conditions Using Advanced Learning Algorithm;Information Systems Engineering and Management;2024

5. Automated COVID-19 Detection From Exhaled Human Breath Using CNN-CatBoost Ensemble Model;IEEE Sensors Letters;2023-10

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