TY - GEN
T1 - Artificial intelligence improving safety and risk analysis
T2 - 2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016
AU - Guzman, A.
AU - Ishida, S.
AU - Choi, E.
AU - Aoyama, A.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/27
Y1 - 2016/12/27
N2 - Recently, the sustainability of traditional technologies employed in critical infrastructure brings a serious challenge for our society. In order to make decisions related with safety of critical infrastructure, the values of accidental risk are becoming relevant points for discussion. However the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and deal with high amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI). Therefore, this paper aims to investigate and compare AI algorithms for risk assessment. These algorithms are classified mainly into Expert Systems, Artificial Neural Networks and Hybrid intelligent Systems. This paper explains the principles of each classification system, as well as its applications in safety. Lately, this paper performs a comparative analysis of three representative techniques, such as Fuzzy-Expert System, Neural Networks, and Adaptive Neuro Fuzzy Inference System.
AB - Recently, the sustainability of traditional technologies employed in critical infrastructure brings a serious challenge for our society. In order to make decisions related with safety of critical infrastructure, the values of accidental risk are becoming relevant points for discussion. However the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and deal with high amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI). Therefore, this paper aims to investigate and compare AI algorithms for risk assessment. These algorithms are classified mainly into Expert Systems, Artificial Neural Networks and Hybrid intelligent Systems. This paper explains the principles of each classification system, as well as its applications in safety. Lately, this paper performs a comparative analysis of three representative techniques, such as Fuzzy-Expert System, Neural Networks, and Adaptive Neuro Fuzzy Inference System.
KW - ANFIS
KW - Artificial Intelligence
KW - Risk Assessment
KW - Safety
UR - http://www.scopus.com/inward/record.url?scp=85009874301&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85009874301&partnerID=8YFLogxK
U2 - 10.1109/IEEM.2016.7797920
DO - 10.1109/IEEM.2016.7797920
M3 - Conference contribution
AN - SCOPUS:85009874301
T3 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 471
EP - 475
BT - 2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016
PB - IEEE Computer Society
Y2 - 4 December 2016 through 7 December 2016
ER -