What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Management
- Machine learning
Zaili Yang spends much of his time researching Risk analysis, Evidential reasoning approach, Bayesian network, Fuzzy logic and Risk assessment.
His study in Risk analysis is interdisciplinary in nature, drawing from both Computer security, Port and Maritime security.
The Port study combines topics in areas such as DPSIR and Global environmental analysis.
His studies deal with areas such as Environmental resource management, Estimation, Civil engineering and Risk matrix as well as Bayesian network.
Zaili Yang studied Risk assessment and Fuzzy set that intersect with Resilience.
His Risk analysis research integrates issues from Supply chain and Dangerous goods.
His most cited work include:
- Fuzzy Rule-Based Bayesian Reasoning Approach for Prioritization of Failures in FMEA (215 citations)
- Incorporation of formal safety assessment and Bayesian network in navigational risk estimation of the Yangtze River (140 citations)
- The use of Bayesian network modelling for maintenance planning in a manufacturing industry (120 citations)
What are the main themes of his work throughout his whole career to date?
Zaili Yang mainly focuses on Risk analysis, Port, Operations research, Evidential reasoning approach and Bayesian network.
His research in the fields of Risk analysis overlaps with other disciplines such as Context.
His Port research focuses on Adaptation and how it connects with Environmental planning.
His Operations research research incorporates themes from Pareto principle, Selection and Fuzzy logic.
The study incorporates disciplines such as Ranking, TOPSIS, Data mining and Process in addition to Fuzzy logic.
As part of the same scientific family, Zaili Yang usually focuses on Bayesian network, concentrating on Bayesian inference and intersecting with Fuzzy rule.
He most often published in these fields:
- Risk analysis (23.74%)
- Port (28.77%)
- Operations research (22.37%)
What were the highlights of his more recent work (between 2019-2021)?
- Bayesian network (16.89%)
- Operations research (22.37%)
- Risk analysis (23.74%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Bayesian network, Operations research, Risk analysis, Environmental planning and Risk analysis.
His Bayesian network study combines topics in areas such as TOPSIS, Port State Control, Risk assessment and Bayesian inference.
His work on Bayesian inference is being expanded to include thematically relevant topics such as Port.
His Operations research research includes elements of Pareto principle and Particle swarm optimization.
His work in Risk analysis addresses issues such as Bayesian probability, which are connected to fields such as Fuzzy logic and Machine learning.
His Environmental planning research is multidisciplinary, relying on both Climate resilience, Climate risk and Adaptation.
Between 2019 and 2021, his most popular works were:
- Adaptively constrained dynamic time warping for time series classification and clustering (11 citations)
- Maritime accident prevention strategy formulation from a human factor perspective using Bayesian Networks and TOPSIS (9 citations)
- Comparative analysis of the impact of new inspection regime on port state control inspection (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Management
- Machine learning
His primary areas of study are Bayesian network, Risk analysis, Maritime safety, Risk analysis and Port State Control.
In his work, Zaili Yang performs multidisciplinary research in Bayesian network and Evidential reasoning approach.
His research in Risk analysis intersects with topics in Safety culture and TOPSIS.
His research is interdisciplinary, bridging the disciplines of Environmental resource management and Risk analysis.
His Port State Control study integrates concerns from other disciplines, such as Control, Performance indicator, Dependency and Bayesian inference.
His Bayesian inference research is multidisciplinary, incorporating perspectives in Port and Big data.
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