Deep Learning Based Performance Prediction of Sustainable Microwave Absorbers
- Title
- Deep Learning Based Performance Prediction of Sustainable Microwave Absorbers
- Creator
- Govind, P Jai; Kumar, Naveen
- Description
- This paper proposed a convolutional neural network (CNN) based deep learning (DL) approach to predict performance of sustainable microwave absorbers. This study explores the transformative potential of DL in predicting and optimizing microwave absorber performance, offering a datadriven alternative to traditional approaches. The absorber is a composite of tea and carbon powder considered as waste mixed in various composition percentages. The measured S21 data is used for training the proposed DL model. The prediction of absorber's S21 performance shows an accuracy of above 98 %. 2025 IEEE.
- Source
- 2025 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2025;
- Date
- 01-01-2025
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- absorber; CNN; deep learning; microwave; sustainable
- Coverage
- Govind P.J., CHRIST University, RF and Microwave Research Laboratory, ECE Department, Bangalore, India; Kumar N., CHRIST University, RF and Microwave Research Laboratory, ECE Department, Bangalore, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 979-833153722-7;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Govind, P Jai; Kumar, Naveen, “Deep Learning Based Performance Prediction of Sustainable Microwave Absorbers,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26187.