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Modeling Incomplete Knowledge of Semantic Web Using Bayesian Networks
Applied Artificial Intelligence, 2019 Interoperable ontologies already exist in the biomedical field, enabling scientists to communicate with minimum ambiguity. Unfortunately, ontology languages, in the semantic web, such as OWL and RDF(S), are based on crisp logic and thus they cannot ...
Messaouda Fareh
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Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification [PDF]
Journal of Computational Physics, 2018 We are interested in the development of surrogate models for uncertainty quantification and propagation in problems governed by stochastic PDEs using a deep convolutional encoder–decoder network in a similar fashion to approaches considered in deep ...
Yinhao Zhu, N. Zabaras
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Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers
Complexity, 2018 Bayesian networks are useful machine learning techniques that are able to combine quantitative modeling, through probability theory, with qualitative modeling, through graph theory for visualization.
Gonzalo A. Ruz, Pamela Araya-Díaz
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Journal of Industrial Engineering and Management, 2016 Purpose: Propose a modeling and analysis methodology based on the combination of Bayesian networks and Petri networks of the reverse logistics integrated the direct supply chain.
Faycal Mimouni, Abdellah Abouabdellah
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Machine Learning based on Probabilistic Models Applied to Medical Data: The Case of Prostate Cancer
Journal of Innovation Information Technology and Application, 2023 The growth in the amount of data in companies puts analysts in difficulties when extracting hidden knowledge from data. Several models have emerged that focus on the notion of distances while ignoring the notion of conditional probability density.
Anaclet Tshikutu Bikengela +4 more
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CATENA, 2020 This study developed a deep learning based technique for the assessment of landslide susceptibility through a one-dimensional convolutional network (1D-CNN) and Bayesian optimisation in Southern Yangyang Province, South Korea.
M. I. Sameen, B. Pradhan, Saro Lee
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Learning Bayesian Networks with the bnlearn R Package [PDF]
, 2009 bnlearn is an R package (R Development Core Team 2010) which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables.
M. Scutari
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Solving inference problems of Bayesian networks by probabilistic computing
AIP Advances, 2023 Recently, probabilistic computing approach has shown its broad application in problems ranging from combinatorial optimizations and machine learning to quantum simulation where a randomly fluctuating bit called p-bit constitutes a basic building block ...
Seokmin Hong
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Bayesian networks approach on intelligent system design for the diagnosis of heat exchanger
Jurnal Ilmiah SINERGI, 2022 The heat exchanger highly influences the series of cooling processes. Therefore, it is required to have maximum performance. Some of the factors causing a decrease in its performance are increased pressure drop in the Plate Heat Exchanger (PHE ...
Dedik Romahadi +4 more
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Monotonicity in Bayesian Networks
CoRR, 2004 Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004)
van der Gaag, L.C. +2 more
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