Classification of Patents according to Industry 4.0 Pillars using Machine Learning Algorithms

Jafery, Wan Ain Zubaidah Wan Chek; Omar, Mustaffa; Ahmad, Noor Azurati; Ithnin, Hafizah

doi:10.1109/icriis48246.2019.9073669

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…O melhor resultado dos experimentos foi obtido pelo algoritmo de classificação Light GBM com o vetorizador Word2Vec, cuja acurácia foi de 82,74% no conjunto de treino e 83,36% no conjunto de teste. É um resultado similar e até superior aos encontrados na literatura (Jafery, 2019;Lyu, 2019).…”

Section: Resultsunclassified

Classificação e visualização de dados de patentes

Martins

Francisco

Farias

2021

Anais Da XI Escola Regional De Informática De Mato Grosso (ERI-MT 2021)

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Section: Resultsunclassified

Classificação e visualização de dados de patentes

Martins

Francisco

Farias

2021

Anais Da XI Escola Regional De Informática De Mato Grosso (ERI-MT 2021)

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“…I4.0 encompasses several key technologies and associated paradigms from interdisciplinary areas (Mabkhot et al , 2021; Lu, 2017). In total, nine dimensions or key enabling technologies are described in the literature: Big Data Analytics; Autonomous Robots; Simulation; Horizontal and Vertical Integration System; IoT; Cloud computing; Additive Manufacturing; Virtual/Augmented Reality and Cyber Security (Mabkhot et al , 2021; Hasnan and Yusoff, 2018; Jafery et al , 2019; Erboz, 2017).…”

Section: Discussionmentioning

confidence: 99%

“….Big Data(Mabkhot et al, 2021;Nosalska et al, 2019;Frank et al, 2019;Jafery et al, 2019;Fatorachian and Kazemi, 2018;Erboz, 2017) Data Analytic (Mabkhot et al, 2021; Nosalska et al, 2019; Jafery et al, 2019) Data Process, Data Access (Mabkhot et al, 2021) Data Mining (Frank et al, 2019)Internet of Things, RFID, Sensor/Sensory/Sensorial(Mabkhot et al, 2021;Nosalska et al, 2019;Frank et al, 2019;Mueller et al, 2017;Lu, 2017;Roblek et al, 2016;Hozdi c, 2015;Atzori et al, 2010) Cloud Computing(Mabkhot et al, 2021;Nosalska et al, 2019;Jafery et al, 2019;Fatorachian and Kazemi, 2018;Roblek et al, 2016) …”

mentioning

confidence: 99%

A study on the relation between industry 4.0 technologies and gamification in e-learning

Oliveira

Santos

Souza

et al. 2022

ITSE

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Purpose COVID-19 played a crucial role in the development and enlargement of learning via electronic media. Still, the recent fourth industrial revolution [Industry 4.0 (I4.0)] paved the road toward Education 4.0. In this regard, several research challenges arise, involving the preparation of gamification strategies in online learning environments. In this sense, this paper aims to investigate the relationship between technologies of I4.0 and gamification practices in online learning around the world. Specifically, to categorize the studies of the scientific literature in the area into knowledge domains and the mention of I4.0 technologies and to verify the relationship of these technologies with the different educational levels. Design/methodology/approach The preferred reporting items for systematic reviews and meta-analysis protocol was used as a research method with 130 papers included for full content analysis and obtained from the Web of Science. Findings The leading I4.0 technologies mentioned in the analyzed papers were simulation, Internet of Things and augmented/virtual reality, in this order. Although there are more mentioned technologies, the domain of knowledge to be applied and the educational level interfere in choosing these pillars. With this, the main findings of this relationship were exposed in a singular, modern, active, realist, technological framework to demonstrate how I4.0 relates to the practice of gamification in online educational environments. Originality/value To the best of the authors’ knowledge, this is the first study that brings together the relationship of gamification applied in e-learning with I4.0 technologies.

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“…Patent information consists of unstructured and structured data [15]. Patent data are organized in structure, which makes them easy to find and access specific information [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Patent Data Analytics for Technology Forecasting of the Railway Main Transformer

et al. 2022

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The railway main transformer is considered one of the most important electrical equipment for trains. Companies and research institutes around the world are striving to develop high-performance railway main transformers. In order to be the first mover for railway main transformer technology, companies and research institutes should predict vacant technology based on the analysis of promising detailed technology areas. Therefore, in this study, a patent analysis to predict vacant technologies based on identified promising IPC technology areas is provided. In order to identify promising detailed IPC technology areas, the technology mapping analysis, the time series analysis, and the social network analysis are conducted based on the patent-IPC matrix, extracted from the data information of 707 patents from the patent database of Korea, China, Japan, United States, Canada, and Europe. Then, through the GTM analysis based on promising detailed IPC technology areas, one vacant technology node and three analysis target nodes surrounding the vacant technology node are obtained to predict vacant technologies. From the analysis, we predict the following three groups of vacant technologies: (1) blowerless technology, (2) oil-free technology, and (3) solid-state technology. This study provides insights on the technology trend in railway main transformers, as well as the analysis framework for the development of R&D strategies based on the patent data.

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Classification of Patents according to Industry 4.0 Pillars using Machine Learning Algorithms

Cited by 4 publications

References 20 publications

Classificação e visualização de dados de patentes

Classificação e visualização de dados de patentes

A study on the relation between industry 4.0 technologies and gamification in e-learning

Patent Data Analytics for Technology Forecasting of the Railway Main Transformer

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