Harnessing Machine Learning for EMI Shielding Materials: State-of-the-Art Advances and Prospects Ahead

Reenu Elizabeth John; M Suresh Babu; Aagii Mariam Thomas; O. K Prajina; G Anusree P; Steffy Sara Varghese

doi:10.1109/ICCIKE67021.2025.11318156

Harnessing Machine Learning for EMI Shielding Materials: State-of-the-Art Advances and Prospects Ahead

Reenu Elizabeth John, M Suresh Babu, Aagii Mariam Thomas, O. K Prajina, G Anusree P, Steffy Sara Varghese

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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Abstract

The field of EMI shielding materials is undergoing rapid transformation, driven by escalating demands for high-performance shielding in modern electronics. However, the design and development of advanced shielding materials depend on empirical, trial and error methodologies. This dependance on iterative experiments not only brings up the cost but also results in longer developmental cycles presenting a significant bottleneck for innovations. The integration of machine learning (ML) in the field of EMI shielding materials drastically reduces the cost and delay in the conventional experimental method, and effectively establishes a rapid prediction model for shielding effectiveness and proper analysis and optimization of new and advanced shielding materials. This review critically examines the progress in applying ML to the field of EMI shielding materials. Furthermore, the article discusses the key challenges and emerging trends in ML to EMI shielding materials.

Original language	English
Title of host publication	2025 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	6
ISBN (Electronic)	9798331562472
ISBN (Print)	9798331562489
DOIs	https://doi.org/10.1109/ICCIKE67021.2025.11318156
Publication status	Published - 12 Jan 2026
Event	2025 International Conference on Computational Intelligence and Knowledge Economy - Dubai, United Arab Emirates, Dubai, United Arab Emirates Duration: 27 Nov 2025 → 28 Nov 2025 https://amityuniversity.ae/ICCIKE2025/

Publication series

Name	International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)
Publisher	IEEE

Conference

Conference	2025 International Conference on Computational Intelligence and Knowledge Economy
Abbreviated title	ICCIKE
Country/Territory	United Arab Emirates
City	Dubai
Period	27/11/25 → 28/11/25
Internet address	https://amityuniversity.ae/ICCIKE2025/

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10.1109/ICCIKE67021.2025.11318156

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This is the accepted author manuscript (AAM) of the article which has been made Open Access under the University of Bristol's Scholarly Works Policy. The final published version (Version of Record) can be found on the publisher's website. The copyright of any third-party content, such as images, remains with the copyright holder.
Accepted author manuscript, 343 KBLicence: CC BY

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John, R. E., Suresh Babu, M., Thomas, A. M., Prajina, O. K., Anusree P, G., & Varghese, S. S. (2026). Harnessing Machine Learning for EMI Shielding Materials: State-of-the-Art Advances and Prospects Ahead. In 2025 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) (International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICCIKE67021.2025.11318156

John, Reenu Elizabeth ; Suresh Babu, M ; Thomas, Aagii Mariam et al. / Harnessing Machine Learning for EMI Shielding Materials : State-of-the-Art Advances and Prospects Ahead. 2025 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). Institute of Electrical and Electronics Engineers (IEEE), 2026. (International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)).

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title = "Harnessing Machine Learning for EMI Shielding Materials: State-of-the-Art Advances and Prospects Ahead",

abstract = "The field of EMI shielding materials is undergoing rapid transformation, driven by escalating demands for high-performance shielding in modern electronics. However, the design and development of advanced shielding materials depend on empirical, trial and error methodologies. This dependance on iterative experiments not only brings up the cost but also results in longer developmental cycles presenting a significant bottleneck for innovations. The integration of machine learning (ML) in the field of EMI shielding materials drastically reduces the cost and delay in the conventional experimental method, and effectively establishes a rapid prediction model for shielding effectiveness and proper analysis and optimization of new and advanced shielding materials. This review critically examines the progress in applying ML to the field of EMI shielding materials. Furthermore, the article discusses the key challenges and emerging trends in ML to EMI shielding materials.",

author = "John, \{Reenu Elizabeth\} and \{Suresh Babu\}, M and Thomas, \{Aagii Mariam\} and Prajina, \{O. K\} and \{Anusree P\}, G and Varghese, \{Steffy Sara\}",

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publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

booktitle = "2025 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)",

address = "United States",

note = "2025 International Conference on Computational Intelligence and Knowledge Economy, ICCIKE ; Conference date: 27-11-2025 Through 28-11-2025",

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John, RE, Suresh Babu, M, Thomas, AM, Prajina, OK, Anusree P, G & Varghese, SS 2026, Harnessing Machine Learning for EMI Shielding Materials: State-of-the-Art Advances and Prospects Ahead. in 2025 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Institute of Electrical and Electronics Engineers (IEEE), 2025 International Conference on Computational Intelligence and Knowledge Economy, Dubai, United Arab Emirates, 27/11/25. https://doi.org/10.1109/ICCIKE67021.2025.11318156

Harnessing Machine Learning for EMI Shielding Materials: State-of-the-Art Advances and Prospects Ahead. / John, Reenu Elizabeth; Suresh Babu, M; Thomas, Aagii Mariam et al.
2025 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). Institute of Electrical and Electronics Engineers (IEEE), 2026. (International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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T1 - Harnessing Machine Learning for EMI Shielding Materials

T2 - 2025 International Conference on Computational Intelligence and Knowledge Economy

AU - John, Reenu Elizabeth

AU - Suresh Babu, M

AU - Thomas, Aagii Mariam

AU - Prajina, O. K

AU - Anusree P, G

AU - Varghese, Steffy Sara

PY - 2026/1/12

Y1 - 2026/1/12

N2 - The field of EMI shielding materials is undergoing rapid transformation, driven by escalating demands for high-performance shielding in modern electronics. However, the design and development of advanced shielding materials depend on empirical, trial and error methodologies. This dependance on iterative experiments not only brings up the cost but also results in longer developmental cycles presenting a significant bottleneck for innovations. The integration of machine learning (ML) in the field of EMI shielding materials drastically reduces the cost and delay in the conventional experimental method, and effectively establishes a rapid prediction model for shielding effectiveness and proper analysis and optimization of new and advanced shielding materials. This review critically examines the progress in applying ML to the field of EMI shielding materials. Furthermore, the article discusses the key challenges and emerging trends in ML to EMI shielding materials.

AB - The field of EMI shielding materials is undergoing rapid transformation, driven by escalating demands for high-performance shielding in modern electronics. However, the design and development of advanced shielding materials depend on empirical, trial and error methodologies. This dependance on iterative experiments not only brings up the cost but also results in longer developmental cycles presenting a significant bottleneck for innovations. The integration of machine learning (ML) in the field of EMI shielding materials drastically reduces the cost and delay in the conventional experimental method, and effectively establishes a rapid prediction model for shielding effectiveness and proper analysis and optimization of new and advanced shielding materials. This review critically examines the progress in applying ML to the field of EMI shielding materials. Furthermore, the article discusses the key challenges and emerging trends in ML to EMI shielding materials.

U2 - 10.1109/ICCIKE67021.2025.11318156

DO - 10.1109/ICCIKE67021.2025.11318156

M3 - Conference Contribution (Conference Proceeding)

SN - 9798331562489

T3 - International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)

BT - 2025 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)

PB - Institute of Electrical and Electronics Engineers (IEEE)

Y2 - 27 November 2025 through 28 November 2025

ER -

John RE, Suresh Babu M, Thomas AM, Prajina OK, Anusree P G, Varghese SS. Harnessing Machine Learning for EMI Shielding Materials: State-of-the-Art Advances and Prospects Ahead. In 2025 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). Institute of Electrical and Electronics Engineers (IEEE). 2026. (International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)). doi: 10.1109/ICCIKE67021.2025.11318156

Harnessing Machine Learning for EMI Shielding Materials: State-of-the-Art Advances and Prospects Ahead

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