Data-driven Modelling of Engineering Systems with Small Data, a Comparative Study of Artificial Intelligence Techniques

2019 ◽  
Author(s):  
Morteza Mohammadzaheri ◽  
Hamidreza Ziaeifar ◽  
Issam Bahadur ◽  
Mussab Zarog ◽  
Mohammadreza Emadi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Comparative Study ◽  
Data Driven ◽  
Small Data ◽  
Engineering Systems ◽  
Artificial Intelligence Techniques

scholarly journals Retrospect and prospect of a section-based stratigraphic and palaeontological database – Geobiodiversity Database

2020 ◽  
Author(s):  
Hong-He Xu ◽  
Zhi-Bin Niu ◽  
Yan-Sen Chen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Quantitative Analysis ◽  
Quantitative Research ◽  
Scientific Research ◽  
Data Driven ◽  
Artificial Intelligence Techniques ◽  
Existing Problems ◽  
Push Forward ◽  
Early Palaeozoic

Abstract. Big data are significant to the quantitative analysis and contribute to the data-driven scientific research and discoveries. Here the thorough introduction is given on the Geobiodiversity database (GBDB), a comprehensive stratigraphic and palaeontological database. The GBDB includes abundant geological records from China and contributes a serial of scientific studies on early Palaeozoic palaeogeography, tectonic and biodiversity evolution of China. Nevertheless, the existing problems of the GBDB limited the using of its data. The turnover and improvement of the GBDB were started in 2019. Besides the data collecting, processing and visualization as the GBDB did previously, the database and the website are optimized and re-designed, the new GBDB working team pays more attention to data analyzing with the professional artificial intelligence techniques. GBDB is complementary to other related databases, and further collaborations are proposed to mutually benefit and push forward the quantitative research of palaeontology and stratigraphy in the era of big data. The datasets (Xu, 2020) are freely downloadable from http://doi.org/10.5281/zenodo.3667645.

scholarly journals Retrospect and prospect of a section-based stratigraphic and palaeontological database – Geobiodiversity Database

2020 ◽  
Author(s):  
Hong-He Xu ◽  
Zhi-Bin Niu ◽  
Yan-Sen Chen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Quantitative Analysis ◽  
Quantitative Research ◽  
Scientific Research ◽  
Data Driven ◽  
Artificial Intelligence Techniques ◽  
Existing Problems ◽  
Push Forward ◽  
Early Palaeozoic

Abstract. Big data are significant to the quantitative analysis and contribute to the data-driven scientific research and discoveries. Here the thorough introduction is given on the Geobiodiversity database (GBDB), a comprehensive stratigraphic and palaeontological database. The GBDB includes abundant geological records from China and contributes a serial of scientific studies on early Palaeozoic palaeogeography, tectonic and biodiversity evolution of China. Nevertheless, the existing problems of the GBDB limited the using of its data. The turnover and improvement of the GBDB were started in 2019. Besides the data collecting, processing and visualization as the GBDB did previously, the database and the website are optimized and re-designed, the new GBDB working team pays more attention to data analyzing with the professional artificial intelligence techniques. GBDB is complementary to other related databases and further collaborations are proposed to mutually benefit and push forward the quantitative research of palaeontology and stratigraphy in the era of big data. The persistent snapshot of the GBDB data can be found at: http://doi.org/10.5281/zenodo.3667645 (Xu, 2020).

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