Results 81 to 90 of about 155,766 (268)
Moving Objects Segmentation in Video Sequence Based on Bayesian Network
This paper proposes an improvement over moving objects segmentation method for video sequence based on Bayesian network. The method integrates temporal and spatial features by Bayesian network through three fields, which are motion vector field ...
Duong, Anh-Duc +3 more
core +1 more source
Muscle Control of an Extra Robotic Digit
This study compares muscle‐ and movement‐based control for operating a supernumerary robotic thumb. While movement control performs better in the proposed tasks, muscle‐based (EMG) control promotes broader motor learning. The results highlight the promise and challenges of using biosignals for human augmentation, offering new insights into intuitive ...
Julien Russ +7 more
wiley +1 more source
Inference in Bayesian Networks.
A Bayesian network is a compact, expressive representation of uncertain relationships among parameters in a domain. In this article, I introduce basic methods for computing with Bayesian networks, starting with the simple idea of summing the probabilities of events of interest. The article introduces major current methods for exact computation, briefly
openaire +2 more sources
Bayesian neural network learning for repeat purchase modelling in direct marketing. [PDF]
We focus on purchase incidence modelling for a European direct mail company. Response models based on statistical and neural network techniques are contrasted.
Van den Poel, D +4 more
core
Consensus Formation and Change are Enhanced by Neutrality
Neutral agents are shown to enhance both the formation and overturning of consensus in collective decision‐making. A general mathematical model and experiments with locusts and humans reveal that neutrality enables robust consensus via simple interactions and accelerates consensus change by reducing effective population size.
Andrei Sontag +3 more
wiley +1 more source
Bayesian network, a model for NLP? [PDF]
The NLP systems often have low performances because they rely on unreliable and heterogeneous knowledge. We show on the task of non-anaphoric it identification how to overcome these handicaps with the Bayesian Network (BN) formalism. The first results are very encouraging compared with the state-of-the-art systems.
openaire +3 more sources
Prognostic Modelling with Dynamic Bayesian Networks [PDF]
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 ...
McNaught, Ken R., Zagorecki, A.
core
Dissecting the Ecological Structure of Health and Disease in the Global Gut Microbiome
We introduce Wiredancer, a framework that identifies three continuous ecological factors of the gut microbiota. These factors exhibit distinct patterns across health and disease, jointly capturing disrupted ecological stability and offering a new perspective for precision diagnostics and therapeutic strategies.
Baoyuan Zhu +19 more
wiley +1 more source
Summary: The paper presents a causal-probabilistic approach to the technical diagnosis in which the solution of the technical diagnostic problem is considered as a probabilistic inference on a special kind of Bayesian networks called diagnostic Bayesian networks.
openaire +2 more sources
Natural Variation of NAR5 Determines Nitrogenase Activity and the Yield in Soybean
This study identified NAR5, a gene encoding a subtilisin‐like protease, that regulates nitrogenase activity in soybean nodules. Overexpressing NAR5 delayed nodule senescence, enhancing nitrogenase activity, yield, and low‐nitrogen tolerance. The elite haplotype NAR5HapI‐1 linked to superior nitrogenase activity and greater seed weight has been ...
Chao Ma +11 more
wiley +1 more source

