Infoecology of the deep learning and smart manufacturing: thematic and concept interactions

Abstract

PurposeThis infecological study mainly aimed to know the thematic and conceptual relationship in published papers in deep learning (DL) and smart manufacturing (SM).Design/methodology/approachThe research methodology has specific research objectives based on the type and method of research, data analysis tools. In general, description methods are applied by Web of Science (WoS) analysis tools and Voyant tools as a web-based reading and analysis environment for digital texts. The Yewno tool is applied to draw a knowledge map to show the concept's interaction between DL and SM.FindingsThe knowledge map of DL and SM concepts shows that there are currently few concepts interacting with each other, while the rapid growth of technology and the needs of today's society have revealed the need to use more and more DL in SM. The results of this study can provide a coherent and well-mapped road map to the main policymakers of the field of research in DL and SM, through the study of coexistence and interactions of the thematic categories with other thematic areas. In this way, they can design more effective guidelines and strategies to solve the problems of researchers in conducting their studies and direct. The analysis results demonstrated that the information ecosystem of DL and SM studies dynamically developed over time. The continuous conduction flow of scientific studies in this field brought continuous changes into the infoecology of subjects and concepts in this area.Originality/valueThe paper investigated the thematic interaction of the scientific productions in DL and SM and discussed possible implications. We used of the variety tools and techniques to draw our own perspective. Also, we drew arguments from other research work to back up our findings.

Guest editorial: Social robots, services and applications

“…Asemi et al (2022a) presented a fully automated usability evaluation method for interactive social robots by a fuzzy inference system based on ISO 9241-210:2019. Asemi et al (2022b) studied the thematic and conceptual relationship in published papers on deep learning and smart manufacturing and its possible implications. Next, Agarwal et al (2022) reviewed the literature on chatbots and virtual assistants, which showed that this area has been increasing in the last few years.…”

Evolutionary trend analysis of the pharmaceutical management research field from the perspective of mapping the knowledge domain

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