Results 81 to 90 of about 235,639 (329)

Exploiting sparsity and sharing in probabilistic sensor data models [PDF]

, 2008
Probabilistic sensor models defined as dynamic Bayesian networks can possess an inherent sparsity that is not reflected in the structure of the network. Classical inference algorithms like variable elimination and junction tree propagation cannot exploit
Evers, S.
core   +2 more sources

Bidirectional Process Prediction in the Laser‐Induced‐Graphene Production Using Blackbox Deep Learning

Advanced Materials Technologies, EarlyView.
This study shows that a lightweight blackbox neural network provides a practical, cost‐effective solution for bidirectional process prediction in laser‐induced graphene (LIG) fabrication. Achieving high predictive performance with minimal overhead, the approach democratizes machine learning (ML) for resource‐limited environments.
Maxim Polomoshnov, Nitheesh M. Nair, Gerardo Hernández‐Sosa, Markus Reischl   +3 more
wiley   +1 more source

Review on Predictive Modelling Techniques for Identifying Students at Risk in University Environment

MATEC Web of Conferences, 2019
Predictive analytics including statistical techniques, predictive modelling, machine learning, and data mining that analyse current and historical facts to make predictions about future or otherwise unknown events.
Nik Nurul Hafzan Mat Yaacob, Safaai Deris, Asiah Mat, Mohd Saberi Mohamad, Siti Syuhaida Safaai   +4 more
doaj   +1 more source

Bayesian Learning of Dynamic Multilayer Networks

, 2016
A plethora of networks is being collected in a growing number of fields, including disease transmission, international relations, social interactions, and others. As data streams continue to grow, the complexity associated with these highly multidimensional connectivity data presents novel challenges.
DURANTE, DANIELE, Mukherjee, Nabanita, Steorts, Rebecca C.   +2 more
openaire   +3 more sources

Multimodal Actuation and Environment Adaptive Strategies of Bio‐Inspired Micro/Nanorobots in Precision Medicine

Advanced Robotics Research, EarlyView.
An introduction for multidrive and environment‐adaptive micro/nanorobotics: design and fabrication strategies, intelligent actuation, and their applications. Various intelligent actuation approaches—magnetic, acoustic, optical, chemical, and biological—can be synergistically designed to enhance flexibility and adaptive behavior for precision medicine ...
Aiqing Ma, Han Gong, Jiacheng Ouyang, Liqi Chen, Tianfang Kang, Yiran He, Guojun Huang, Sidi Zheng, Ting Yin, Hong Pan, Lintao Cai   +10 more
wiley   +1 more source

Research of trust evaluation model based on dynamic Bayesian network

Tongxin xuebao, 2013
Trust evaluation model needs to be developed for trusted network.Based on interpersonal trust model in sociology,the trusted relationship between network nodes was researched,and a trust evaluation model based on dynamic bayesian network associating with
Hong-quan LIANG, Wei WU
doaj   +2 more sources

A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution

IEEE Access, 2020
Sentiment analysis is one of the key tasks of natural language understanding. Sentiment Evolution models the dynamics of sentiment orientation over time.
Huizhi Liang, Umarani Ganeshbabu, Thomas Thorne   +2 more
doaj   +1 more source

Modeling dynamic reliability using dynamic Bayesian networks

Journal Européen des Systèmes Automatisés, 2006
This paper considers the problem of modeling and analyzing the reliability of a system or a component (system) where the state of the system and the state of process variables influences each other in addition to an exogenous perturbation influence: this is the dynamic reliability. We consider discrete time case, that is the state of the system as well
Tchangani, Ayeley, Noyes, Daniel
openaire   +3 more sources

Hard‐Magnetic Soft Millirobots in Underactuated Systems

Advanced Robotics Research, EarlyView.
This review provides a comprehensive overview of hard‐magnetic soft millirobots in underactuated systems. It examines key advances in structural design, physics‐informed modeling, and control strategies, while highlighting the interplay among these domains.
Qiong Wang, Zixuan Zhang, Bingyan Li, Junkai Wang, Hongcheng Gu   +4 more
wiley   +1 more source

dbnR: Gaussian Dynamic Bayesian Network Learning and Inference in R

Journal of Statistical Software
Dynamic Bayesian networks are a type of multivariate time series forecasting model capable of a level of interpretability thanks to their graphical representation.
David Quesada, Pedro Larrañaga, Concha Bielza   +2 more
doaj   +1 more source