Crop water productivity assessment and planting structure optimization in typical arid irrigation district using dynamic Bayesian network. [PDF]
Sci RepMa Y +7 more
europepmc +1 more source
Dynamic Bayesian network-based acoustic models incorporating speaking rate effects
, 2004 隆宏 篠崎 +3 more
openalex +1 more source
Dynamic Bayesian network model on two opposite types of sensory adaptation
, 2010 Kazuyuki Aihara
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Prognostic Modelling with Dynamic Bayesian Networks
, 2009 In this paper, we review the application of dynamic Bayesian networks to prognostic modelling. An example is provided for illustration. With this example, we show how the equipment’s reliability decays over time in the situation where repair is not possible and then how a simple change to the model allows us to represent different maintenance policies ...
McNaught, K., Zagorecki, A.
openaire +1 more source
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
Advanced Intelligent Discovery, EarlyView.The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source
Statistical machine learning models for prediction of China's maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model. [PDF]
Front Public HealthYang P +5 more
europepmc +1 more source
Predicting ecosystem components in the Gulf of Mexico and their responses to climate variability with a dynamic Bayesian network model. [PDF]
PLoS One, 2019 Trifonova N, Karnauskas M, Kelble C.
europepmc +1 more source
Computational Intelligent Systems: Evolving Dynamic Bayesian Networks
, 2009 Isaac O. Osunmakinde
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Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy
Advanced Intelligent Discovery, EarlyView.Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang +9 more
wiley +1 more source