TY  - GEN
N2  - This volume includes selected technical papers presented at the Forum "Math-for-Industry" 2018. The papers written by eminent researchers and academics working in the area of industrial mathematics from the viewpoint of financial mathematics, machine learning, neural networks, inverse problems, stochastic modelling, etc., discuss how the ingenuity of science, technology, engineering and mathematics are and will be expected to be utilized. This volume focuses on the role that mathematics-for-industry can play in interdisciplinary research to develop new methods. The contents are useful for researchers both in academia and industry working in interdisciplinary sectors.
DO  - 10.1007/978-981-16-5576-0
DO  - doi
AB  - This volume includes selected technical papers presented at the Forum "Math-for-Industry" 2018. The papers written by eminent researchers and academics working in the area of industrial mathematics from the viewpoint of financial mathematics, machine learning, neural networks, inverse problems, stochastic modelling, etc., discuss how the ingenuity of science, technology, engineering and mathematics are and will be expected to be utilized. This volume focuses on the role that mathematics-for-industry can play in interdisciplinary research to develop new methods. The contents are useful for researchers both in academia and industry working in interdisciplinary sectors.
T1  - Proceedings of the Forum "Math-for-Industry" 2018 :big data analysis, AI, fintech, math in finances and economics /
DA  - 2021.
CY  - Singapore :
AU  - Cheng, Jin,
AU  - Dinghua, Xu,
AU  - Saeki, Osamu,
AU  - Shirai, Tomoyuki,
VL  - volume 35
CN  - T57
PB  - Springer,
PP  - Singapore :
PY  - 2021.
ID  - 1441521
KW  - Industrial engineering
SN  - 9789811655760
SN  - 9811655766
TI  - Proceedings of the Forum "Math-for-Industry" 2018 :big data analysis, AI, fintech, math in finances and economics /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-5576-0
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-5576-0
ER  -