Dynamic Bayesian network
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Dynamic Key-Value Memory Networks for Knowledge Tracing Open
Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities. One important purpose of KT is to personalize the practice sequence …
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LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes Open
Human Activity Recognition (HAR) employing inertial motion data has gained considerable momentum in recent years, both in research and industrial applications. From the abstract perspective, this has been driven by an acceleration in the b…
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Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach Open
The ripple effect can occur when a supplier base disruption cannot be localised and consequently propagates downstream the supply chain (SC), adversely affecting performance. While stress-testing of SC designs and assessment of their vulne…
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Inference of Gene Regulatory Network Based on Local Bayesian Networks Open
The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches h…
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Risk evolution analysis of ship pilotage operation by an integrated model of FRAM and DBN Open
The risks involved in ship pilotage operations are characterized by random, uncertain and complex features. To reveal the spatiotemporal evolution of ship collision risks in the pilotage operations process, a risk evolution analysis model …
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A Dynamic Bayesian Network for Vehicle Maneuver Prediction in Highway Driving Scenarios: Framework and Verification Open
Accurate maneuver prediction for surrounding vehicles enables intelligent vehicles to make safe and socially compliant decisions in advance, thus improving the safety and comfort of the driving. The main contribution of this paper is propo…
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Bayesian Dynamic Mode Decomposition Open
Dynamic mode decomposition (DMD) is a data-driven method for calculating a modal representation of a nonlinear dynamical system, and it has been utilized in various fields of science and engineering. In this paper, we propose Bayesian DMD,…
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Dynamic system safety analysis in HiP-HOPS with Petri Nets and Bayesian Networks Open
Dynamic systems exhibit time-dependent behaviours and complex functional dependencies amongst their components. Therefore, to capture the full system failure behaviour, it is not enough to simply determine the consequences of different com…
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An Adaptive Bayesian System for Context-Aware Data Fusion in Smart Environments Open
The adoption of multi-sensor data fusion techniques is essential to effectively merge and analyze heterogeneous data collected by multiple sensors, pervasively deployed in a smart environment. Existing literature leverages contextual infor…
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Bayesian Network Inference Modeling Identifies TRIB1 as a Novel Regulator of Cell-Cycle Progression and Survival in Cancer Cells Open
Molecular networks governing responses to targeted therapies in cancer cells are complex dynamic systems that demonstrate nonintuitive behaviors. We applied a novel computational strategy to infer probabilistic causal relationships between…
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A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data Open
Dynamic functional connectivity, i.e., the study of how interactions among brain regions change dynamically over the course of an fMRI experiment, has recently received wide interest in the neuroimaging literature. Current approaches for s…
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A dynamic Bayesian network approach to forecast short-term urban rail passenger flows with incomplete data Open
International audience
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Interaction-Aware Probabilistic Behavior Prediction in Urban Environments Open
Planning for autonomous driving in complex, urban scenarios requires accurate prediction of the trajectories of surrounding traffic participants. Their future behavior depends on their route intentions, the road-geometry, traffic rules and…
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Dynamic Role-Based Access Control Policy for Smart Grid Applications: An Offline Deep Reinforcement Learning Approach Open
Role-based access control (RBAC) is adopted in the information and communication technology domain for authentication purposes. However, due to a very large number of entities within organizational access control (AC) systems, static RBAC …
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Identifying Dynamic Functional Connectivity Changes in Dementia with Lewy Bodies Based on Product Hidden Markov Models Open
Exploring time-varying connectivity networks in neurodegenerative disorders is a recent field of research in functional MRI. Dementia with Lewy bodies (DLB) represents 20% of the neurodegenerative forms of dementia. Fluctuations of cogniti…
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Network analysis: An overview for mental health research Open
Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying ne…
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ODE2VAE: Deep generative second order ODEs with Bayesian neural networks Open
We present Ordinary Differential Equation Variational Auto-Encoder (ODE2VAE), a latent second order ODE model for high-dimensional sequential data. Leveraging the advances in deep generative models, ODE2VAE can simultaneously learn the emb…
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Bayesian dynamic forecasting of structural strain response using structural health monitoring data Open
Research on structural health monitoring (SHM) is nowadays evolving from SHM-based diagnosis towards SHM-based prognosis. The structural strain response, as a localized response, has gained growing attention for application to structural c…
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A Review of Inference Algorithms for Hybrid Bayesian Networks Open
Hybrid Bayesian networks have received an increasing attention during the last years. The difference with respect to standard Bayesian networks is that they can host discrete and continuous variables simultaneously, which extends the appli…
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Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum Open
The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass s…
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Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data Open
While a number of large consortia collect and profile several different types of microbiome and genomic time series data, very few methods exist for joint modeling of multi-omics data sets. We developed a new computational pipeline, PALM, …
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Modeling Context Aware Dynamic Trust Using Hidden Markov Model Open
Modeling trust in complex dynamic environments is an important yet challenging issue since an intelligent agent may strategically change its behavior to maximize its profits. In thispaper, we propose a context aware trust model to predict …
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Probabilistic Prediction of Significant Wave Height Using Dynamic Bayesian Network and Information Flow Open
Short-term prediction of wave height is paramount in oceanic operation-related activities. Statistical models have advantages in short-term wave prediction as complex physical process is substantially simplified. However, previous statisti…
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Fault Diagnosis of Train Network Control Management System Based on Dynamic Fault Tree and Bayesian Network Open
Train network control management system (TCMS) is an important part of the High-speed rail train. Because of the TCMS's complex and redundant structure, long-term operation environment, etc., breakdowns inevitably in the long-time running.…
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BMAE-Net: A Data-Driven Weather Prediction Network for Smart Agriculture Open
Weather is an essential component of natural resources that affects agricultural production and plays a decisive role in deciding the type of agricultural production, planting structure, crop quality, etc. In field agriculture, medium- and…
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Risk Analysis of Earth-Rock Dam Breach Based on Dynamic Bayesian Network Open
Despite the fact that the Bayesian network has great advantages in logical reasoning and calculation compared with the other traditional risk analysis methods, there are still obvious shortcomings in the study of dynamic risk. The risk fac…
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Dynamic evolution of maritime accidents: Comparative analysis through data-driven Bayesian Networks Open
Maritime accident research has primarily focused on characteristics and risk analysis, which often overlooks the evolution of the associated risk patterns over time. This study aims to investigate the dynamic changes in maritime accidents …
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Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer’s disease Open
In the study of Alzheimer’s disease, researchers often collect repeated measurements of clinical variables, event history, and functional data. If the health measurements deteriorate rapidly, patients may reach a level of cognitive impairm…
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A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution Open
Sentiment analysis is one of the key tasks of natural language understanding. Sentiment \nEvolution models the dynamics of sentiment orientation over time. It can help people have a more profound \nand deep understanding of opinion and sen…
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Semantic Modelling of Ship Behavior in Harbor Based on Ontology and Dynamic Bayesian Network Open
Recognizing ship behavior is important for maritime situation awareness and intelligent transportation management. Some scholars extracted ship behaviors from massive trajectory data by statistical analysis. However, the meaning of the beh…