scholarly journals Exploring the Research Trend of Smart Factory with Topic Modeling

2018 ◽  
Vol 10 (8) ◽  
pp. 2779 ◽  
Author(s):  
Hyun-Lim Yang ◽  
Tai-Woo Chang ◽  
Yerim Choi
Keyword(s):  
Topic Modeling ◽  
Industrial Revolution ◽  
Semantic Analysis ◽  
Research Trends ◽  
Information And Communications Technology ◽  
Smart Factory ◽  
Research Trend ◽  
Sustainable Business ◽  
Interdisciplinary Field ◽  
Smart Factories

Growing competition among manufacturing businesses and the advent of the Fourth Industrial Revolution has meant that many countries are conducting various research projects to understand how to introduce and populate smart factories. Smart factories are expected to provide a way of solving the manufacturing industries’ complex problems, to take a role in breakthroughs in factories and to carry on a sustainable business. Smart factories are currently in the introduction stage, so we should follow up on the majorities and check their tendencies. However, smart-factory research is an interdisciplinary field that should be studied by researchers with diverse backgrounds in various domains. Thus, studying the past and present overall research trends of smart factory studies is required for their successful introduction and sustainable research. In this study, we explored the research trends of smart factories in both international and specifically Korean research, as an example of a nation case, to determine the major research directions. We determined trends using latent semantic analysis, which is a known topic-modeling technique, and analyzed the trends with regression-based methods. As a result, we could read the clear trends by analyzing existing studies related to smart factories. In addition, it is possible to compare research trends in Korea and international research trends for the commonly appeared topics, such as ‘ICT’ (Information and Communications Technology) and ‘R&D (Research and Development)/Technology Innovation’. We expect that the quantitative analysis results and suggestions presented in this study can be used to formulate strategies for the future diffusion of smart factories.

Exploring research trends in big data across disciplines: A text mining analysis

2020 ◽  
pp. 016555152093285
Author(s):  
Ehsan Mohammadi ◽  
Amir Karami
Keyword(s):  
Big Data ◽  
Topic Modeling ◽  
Research Trends ◽  
Academic Disciplines ◽  
Research Trend ◽  
Medical Sciences ◽  
Academic Fields ◽  
Key Topics ◽  
New Research ◽  
Data Analytic

Using big data has been a prevailing research trend in various academic fields. However, no studies have explored the scope and structure of big data across disciplines. In this article, we applied topic modeling and word co-occurrence analysis methods to identify key topics from more than 36,000 big data publications across all academic disciplines between 2012 and 2017. The results revealed several topics associated with the storage, collection and analysis of large datasets; the publications were predominantly published in computational fields. Other identified research topics show the influence of big data methods and techniques in areas beyond computer science, such as education, urban informatics, business, health and medical sciences. In fact, the prevalence of these topics has increased over time. In contrast, some themes like parallel computing, network modeling and big data analytic techniques have lost their popularity in recent years. These results probably reflect the maturity of big data core topics and highlight flourishing new research trends pertinent to big data in new domains, especially in social sciences, health and medicine. Findings of this article can be beneficial for researchers and science policymakers to understand the scope and structure of big data in different academic disciplines.

Research Trends of Consumer Education Using Topic Modeling

2020 ◽  
Vol 16 (2) ◽  
pp. 83-115
Author(s):  
Mira Kim ◽  
◽  
Hye Sun Hwang ◽  
Xu Li
Keyword(s):  
Topic Modeling ◽  
Research Trends ◽  
Consumer Education

An Integrated Legacy of Modbus Devices to Industrial Internet of Things: Approach for Smart Industries

2020 ◽  
Author(s):  
Karthik Muthineni
Keyword(s):  
Internet Of Things ◽  
Industrial Revolution ◽  
Digital Technologies ◽  
Industrial Internet Of Things ◽  
Intelligent Decision Making ◽  
Internet Of Everything ◽  
Industrial Internet ◽  
Intelligent Decision ◽  
Network Platform ◽  
Smart Factories

The new industrial revolution Industry 4.0, connecting manufacturing process with digital technologies that can communicate, analyze, and use information for intelligent decision making includes Industrial Internet of Things (IIoT) to help manufactures and consumers for efficient controlling and monitoring. This work presents the design and implementation of an IIoT ecosystem for smart factories. The design is based on Siemens Simatic IoT2040, an intelligent industrial gateway that is connected to modbus sensors publishing data onto Network Platform for Internet of Everything (NETPIE). The design demonstrates the capabilities of Simatic IoT2040 by taking Python, Node-Red, and Mosca into account that works simultaneously on the device.

Social Inferences in Agenesis of the Corpus Callosum and Autism: Semantic Analysis and Topic Modeling

2021 ◽  
Author(s):  
Tiffany Renteria-Vazquez ◽  
Warren S. Brown ◽  
Christine Kang ◽  
Mark Graves ◽  
Fulvia Castelli ◽  
...  
Keyword(s):  
Corpus Callosum ◽  
Topic Modeling ◽  
Semantic Analysis ◽  
Social Inferences

scholarly journals Cancer Research Trend Analysis Based on Fusion Feature Representation

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 338
Author(s):  
Jingqiao Wu ◽  
Xiaoyue Feng ◽  
Renchu Guan ◽  
Yanchun Liang
Keyword(s):  
Cancer Research ◽  
Trend Analysis ◽  
Research Trends ◽  
Feature Representation ◽  
Research Trend ◽  
Good Representation ◽  
Text Representation ◽  
Classical Text ◽  
Representation Model ◽  
Text Feature

Machine learning models can automatically discover biomedical research trends and promote the dissemination of information and knowledge. Text feature representation is a critical and challenging task in natural language processing. Most methods of text feature representation are based on word representation. A good representation can capture semantic and structural information. In this paper, two fusion algorithms are proposed, namely, the Tr-W2v and Ti-W2v algorithms. They are based on the classical text feature representation model and consider the importance of words. The results show that the effectiveness of the two fusion text representation models is better than the classical text representation model, and the results based on the Tr-W2v algorithm are the best. Furthermore, based on the Tr-W2v algorithm, trend analyses of cancer research are conducted, including correlation analysis, keyword trend analysis, and improved keyword trend analysis. The discovery of the research trends and the evolution of hotspots for cancers can help doctors and biological researchers collect information and provide guidance for further research.

scholarly journals Exploring Latent Topics and International Research Trends in Competency-Based Education Using Topic Modeling

2021 ◽  
Vol 11 (6) ◽  
pp. 303
Author(s):  
Seungsu Paek ◽  
Taehun Um ◽  
Namhyoung Kim
Keyword(s):  
Education Reform ◽  
International Research ◽  
Topic Modeling ◽  
Modern Society ◽  
Research Trends ◽  
Competency Development ◽  
Competency Based Education ◽  
Competency Based ◽  
Latent Topics ◽  
International Research Trends

Recently, there has been growing educational interest in competency. Global organizations, such as the United Nations (UN) and Organization for Economic Co-operation and Development (OECD), which are leading the discourse on education reform, are undertaking the lead in spreading awareness regarding competency education. Since 2015, the number of published articles on competency education has been rapidly increasing. This paper aims to provide significant implications for creating a sustainable future of competency education. A topic modeling method was used to empirically analyze latent topics and international research trends in 26,532 articles published on competency-based education (CBE). As a result of the analysis, 15 topics were derived, including “approach to competency development.” In addition, five topics including “learning skills” and “teacher training” were found to be hot topics with the increasing article publication. The rapidly changing modern society is calling for a transformation in education. We hope that the results of this study paves the way for further research exploring new directions for education, such as competency education.

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