A brief introduction to expert systems
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Cited by (9)
Explainable machine learning using real, synthetic and augmented fire tests to predict fire resistance and spalling of RC columns
2022, Engineering StructuresCitation Excerpt :This technology capitalizes on the notion that machines can be trained to learn how humans (i.e. structural fire engineers) think and practice to identify patterns and solutions to complex phenomena (e.g. spalling). In a way, these envisioned tools are “expert systems” that complement human engineers' and researchers’ expertise [10,11]. The use of ML has been rapidly evolving over the past few years but has been slowly implemented into structural and fire engineering applications.
Deriving conclusions in expert systems when knowledge is incomplete
1993, Computers and Operations ResearchA survey on multi-agent based collaborative intrusion detection systems
2021, Journal of Artificial Intelligence and Soft Computing ResearchRating firms and sensitivity analysis
2020, Journal of the Operational Research SocietyDZC DIAG: mobile application based on expert system to aid in the diagnosis of dengue, Zika, and chikungunya
2020, Medical and Biological Engineering and Computing
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James P. Ignizio is Professor and Chairman of Industrial Engineering at the University of Houston. Prior to joining the University of Houston in 1986, Dr Ignizio taught for 12 years at Pennsylvania State University. He received a Ph.D. in Industrial Engineering and Operations Research from Virginia Polytechnic Institute. His teaching and research interests center about the applications of operations research, including the methods of heuristic programming, expert systems, neural networks and multiple objective optimization. Dr Ignizio is the author of five textbooks, several monographs and numerous journal articles.