Deep Learning for Real-Time Diagnostics of Cold Atmospheric Plasma

Raghavendra M. Devadas; Preethi  Preethi; R. Sapna

doi:10.1007/978-981-96-1747-0_47

Deep Learning for Real-Time Diagnostics of Cold Atmospheric Plasma

Raghavendra M. Devadas
, Preethi^*
, R. Sapna

^*Corresponding author for this work

School of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This study investigates applying convolutional neural networks (CNNs) for real-time analysis of cold atmospheric plasma, introducing a novel architecture with inception modules, residual blocks, and attention mechanisms to improve feature extraction and accuracy. The model is trained on synthetic plasma diagnostic data, showing a decrease in training mean absolute error (MAE) from 0.255 to 0.175 over 10 epochs, and a slight decrease in validation MAE from 0.295 to 0.275, indicating effective learning and generalization. The proposed CNN achieved a Test MAE of 0.27 and Test Loss of 0.10.

Original language	English
Title of host publication	Information Systems for Intelligent Systems - Proceedings of ISBM 2024
Editors	Andres Iglesias, Jungpil Shin, Bharat Patel, Amit Joshi
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	575-586
Number of pages	12
ISBN (Print)	9789819617463
DOIs	https://doi.org/10.1007/978-981-96-1747-0_47
Publication status	Published - 2025
Event	3rd World Conference on Information Systems for Business Management, ISBM 2024 - Bangkok, Thailand Duration: 12-09-2024 → 13-09-2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1255
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	3rd World Conference on Information Systems for Business Management, ISBM 2024
Country/Territory	Thailand
City	Bangkok
Period	12-09-24 → 13-09-24

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-96-1747-0_47

Cite this

Devadas, R. M., Preethi, & Sapna, R. (2025). Deep Learning for Real-Time Diagnostics of Cold Atmospheric Plasma. In A. Iglesias, J. Shin, B. Patel, & A. Joshi (Eds.), Information Systems for Intelligent Systems - Proceedings of ISBM 2024 (pp. 575-586). (Lecture Notes in Networks and Systems; Vol. 1255). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-1747-0_47

Devadas, Raghavendra M. ; Preethi ; Sapna, R. / Deep Learning for Real-Time Diagnostics of Cold Atmospheric Plasma. Information Systems for Intelligent Systems - Proceedings of ISBM 2024. editor / Andres Iglesias ; Jungpil Shin ; Bharat Patel ; Amit Joshi. Springer Science and Business Media Deutschland GmbH, 2025. pp. 575-586 (Lecture Notes in Networks and Systems).

@inproceedings{e910ca90ff8d42cf86b78e8f8e0bc976,

title = "Deep Learning for Real-Time Diagnostics of Cold Atmospheric Plasma",

abstract = "This study investigates applying convolutional neural networks (CNNs) for real-time analysis of cold atmospheric plasma, introducing a novel architecture with inception modules, residual blocks, and attention mechanisms to improve feature extraction and accuracy. The model is trained on synthetic plasma diagnostic data, showing a decrease in training mean absolute error (MAE) from 0.255 to 0.175 over 10 epochs, and a slight decrease in validation MAE from 0.295 to 0.275, indicating effective learning and generalization. The proposed CNN achieved a Test MAE of 0.27 and Test Loss of 0.10.",

author = "Devadas, \{Raghavendra M.\} and Preethi and R. Sapna",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 3rd World Conference on Information Systems for Business Management, ISBM 2024 ; Conference date: 12-09-2024 Through 13-09-2024",

year = "2025",

doi = "10.1007/978-981-96-1747-0\_47",

language = "English",

isbn = "9789819617463",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "575--586",

editor = "Andres Iglesias and Jungpil Shin and Bharat Patel and Amit Joshi",

booktitle = "Information Systems for Intelligent Systems - Proceedings of ISBM 2024",

address = "Germany",

}

Devadas, RM, Preethi & Sapna, R 2025, Deep Learning for Real-Time Diagnostics of Cold Atmospheric Plasma. in A Iglesias, J Shin, B Patel & A Joshi (eds), Information Systems for Intelligent Systems - Proceedings of ISBM 2024. Lecture Notes in Networks and Systems, vol. 1255, Springer Science and Business Media Deutschland GmbH, pp. 575-586, 3rd World Conference on Information Systems for Business Management, ISBM 2024, Bangkok, Thailand, 12-09-24. https://doi.org/10.1007/978-981-96-1747-0_47

Deep Learning for Real-Time Diagnostics of Cold Atmospheric Plasma. / Devadas, Raghavendra M.; Preethi; Sapna, R.
Information Systems for Intelligent Systems - Proceedings of ISBM 2024. ed. / Andres Iglesias; Jungpil Shin; Bharat Patel; Amit Joshi. Springer Science and Business Media Deutschland GmbH, 2025. p. 575-586 (Lecture Notes in Networks and Systems; Vol. 1255).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Deep Learning for Real-Time Diagnostics of Cold Atmospheric Plasma

AU - Devadas, Raghavendra M.

AU - Preethi, null

AU - Sapna, R.

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

PY - 2025

Y1 - 2025

N2 - This study investigates applying convolutional neural networks (CNNs) for real-time analysis of cold atmospheric plasma, introducing a novel architecture with inception modules, residual blocks, and attention mechanisms to improve feature extraction and accuracy. The model is trained on synthetic plasma diagnostic data, showing a decrease in training mean absolute error (MAE) from 0.255 to 0.175 over 10 epochs, and a slight decrease in validation MAE from 0.295 to 0.275, indicating effective learning and generalization. The proposed CNN achieved a Test MAE of 0.27 and Test Loss of 0.10.

AB - This study investigates applying convolutional neural networks (CNNs) for real-time analysis of cold atmospheric plasma, introducing a novel architecture with inception modules, residual blocks, and attention mechanisms to improve feature extraction and accuracy. The model is trained on synthetic plasma diagnostic data, showing a decrease in training mean absolute error (MAE) from 0.255 to 0.175 over 10 epochs, and a slight decrease in validation MAE from 0.295 to 0.275, indicating effective learning and generalization. The proposed CNN achieved a Test MAE of 0.27 and Test Loss of 0.10.

UR - https://www.scopus.com/pages/publications/105008002138

UR - https://www.scopus.com/pages/publications/105008002138#tab=citedBy

U2 - 10.1007/978-981-96-1747-0_47

DO - 10.1007/978-981-96-1747-0_47

M3 - Conference contribution

AN - SCOPUS:105008002138

SN - 9789819617463

T3 - Lecture Notes in Networks and Systems

SP - 575

EP - 586

BT - Information Systems for Intelligent Systems - Proceedings of ISBM 2024

A2 - Iglesias, Andres

A2 - Shin, Jungpil

A2 - Patel, Bharat

A2 - Joshi, Amit

PB - Springer Science and Business Media Deutschland GmbH

T2 - 3rd World Conference on Information Systems for Business Management, ISBM 2024

Y2 - 12 September 2024 through 13 September 2024

ER -

Deep Learning for Real-Time Diagnostics of Cold Atmospheric Plasma

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this