Title: Text analytics of materials and scanning electron microscope image classification of cadmium sulphide

Authors: Kavitha Jayaram; G. Prakash; V. Jayaram

Addresses: Department of Computer Science and Engineering, BNM Institute of Technology, Bangalore, India ' VIT, Vellore, Tamil Nadu, India ' Solid State and Structural Chemistry Unit, Indian Institute of Science, Bengaluru, India

Abstract: A framework is proposed using machine learning techniques to extract information (text analytics and SEM image classification) from material research documents. This paper provides complete information on material [cadmium sulphide (CdS)] research about the ongoing research problem, knowing the country and funding agencies, different new joint research collaborations, and characterising techniques used to understand material's phase transformation. The proposed system includes a new citation network connecting author, material, phase change, and funding organisation related to material and instruments. The abstract section is extracted to build a summary of material research. SEM images of CdS using a deep CNN method are classified into the (cubic, hexagon, crystalline and amorphous) crystal structure. Based on the observation with extensive literature studies, it is found that no such work has been done with materials, characterisation techniques, and acknowledgment section in the material research domain using deep learning and machine learning techniques.

Keywords: text mining; classification; analysis; summarisation; SEM image classification; deep CNN; citation analysis.

DOI: 10.1504/IJBIDM.2023.129946

International Journal of Business Intelligence and Data Mining, 2023 Vol.22 No.3, pp.333 - 358

Received: 14 Aug 2021
Accepted: 08 Oct 2021
Published online: 03 Apr 2023 *

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