Jayaram, K and Prakash, G and Jayaram, V (2022) Convolutional neural network for classification of SiO2 scanning electron microscope images. In: International Journal of Business Intelligence and Data Mining, 21 (1). pp. 118-128.
Full text not available from this repository.Abstract
The recent development in deep learning has made image and speech classification and recognition tasks possible with better accuracy. An attempt was made to automatically extract required sections from literature published in journals to analyse and classify them according to their application. This paper presents high-temperature materials classification into four categories according to their wide applications such as electronic, high temperature, semiconductors, and ceramics. The challenging act is to extract SEM images’ unique features as they are microscopic with different resolutions. A total of 10,000 scanning electron microscope (SEM) images are classified into two labelled categories namely crystalline and amorphous structure. The image classification and recognition process of SiO2 was implemented using convolutional neural network (CNN) deep learning framework. Our algorithm successfully classified with a precision of 96% and accuracy of 95.5% of the test dataset of SEM images.
| Item Type: | Journal Article |
|---|---|
| Publication: | International Journal of Business Intelligence and Data Mining |
| Publisher: | Inderscience Publishers |
| Additional Information: | The copyright for this article belongs to the Inderscience Publishers. |
| Keywords: | Convolution; Deep neural networks; Image classification; Scanning electron microscopy; Silica; Silicon; Speech recognition; Statistical tests, Classification and recognition; Classifieds; Convolution neural network; Convolutional neural network; Deep learning; Electron microscope images; Images classification; Machine-learning; Scanning electrons; Speech classification, Convolutional neural networks |
| Department/Centre: | Division of Chemical Sciences > Solid State & Structural Chemistry Unit |
| Date Deposited: | 28 Jul 2022 05:40 |
| Last Modified: | 28 Jul 2022 05:40 |
| URI: | https://eprints.iisc.ac.in/id/eprint/75017 |
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