Exploration of a Prediction Model of Aggression in Children Using Bayesian Networks

Patients with chronic diseases should be made aware of their planned treatments as well as being kept informed of the progress of those treatments. The Chronic Prediction model was designed not only to educate patients and assist them with some chronic non-communicable disease, but to control the risk factors that affect their diseases. The model utilizes Bayesian networks to map three things: to identify the cause and effect relationships among existing risk factors; to provide treatment recommendations about these risk factors and; to aid caregivers in the treatment of the patients.

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Multi-Step Prediction Algorithm for State Prediction Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.143-144.634 ◽

2010 ◽

Vol 143-144 ◽

pp. 634-638

Author(s):

Zi Li Zhang ◽

Hong Wei Song

Keyword(s):

Prediction Model ◽

Bayesian Networks ◽

Influencing Factors ◽

Dynamic Bayesian Networks ◽

Prediction Algorithm ◽

Time Varying ◽

System State ◽

Reasoning Process ◽

State Prediction ◽

The Relationship

Dynamic Bayesian networks can be well dealt with the time-varying multivariable problem. The state model based on Dynamic Bayesian networks can more accurately describe the relationship between the system state and the influencing factors. In this paper, the width of the reasoning is used to simplify the amount of data in the reasoning process. Multi-step state prediction is achieved by extending time-slice. Experiment has shown that the proposed algorithm can achieve better prediction results.

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CRAFFT: an activity prediction model based on Bayesian networks

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-014-0219-x ◽

2014 ◽

Vol 6 (2) ◽

pp. 193-205 ◽

Cited By ~ 30

Author(s):

Ehsan Nazerfard ◽

Diane J. Cook

Keyword(s):

Prediction Model ◽

Bayesian Networks ◽

Activity Prediction ◽

Model Based

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Probabilistic outage prediction model with Bayesian networks for electric power systems subjected to hurricane events

Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures ◽

10.1201/b16387-448 ◽

2014 ◽

pp. 3091-3098

Author(s):

A Mensah ◽

L Dueñas-Osorio

Keyword(s):

Power Systems ◽

Prediction Model ◽

Bayesian Networks ◽

Electric Power ◽

Electric Power Systems

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Task specialization across research careers

10.1101/2020.07.01.181669 ◽

2020 ◽

Author(s):

Nicolas Robinson-Garcia ◽

Rodrigo Costas ◽

Cassidy R. Sugimoto ◽

Vincent Larivière ◽

Gabriela F. Nane

Keyword(s):

Prediction Model ◽

Bayesian Networks ◽

Gender Bias ◽

Early Career ◽

Career Stages ◽

Career Stage ◽

Career Trajectory ◽

Task Specialization ◽

Archetypal Analysis ◽

Scientific Careers

AbstractScientific careers are conceived as one unique pathway which scientists must follow to succeed. We report the diversity of profiles scientists exhibit based on their contributorship and look into biases in their career trajectory. We use Bayesian networks to train a prediction model based on a dataset of 70,694 publications from PLoS journals representing 347,136 distinct authors and their associated contribution statements. This model is used to predict the contributions of 222,925 authors in 6,236,239 publications, and apply a robust archetypal analysis to profile scientists by career stage. We divide scientific careers into four stages: junior, early-career, mid-career and late-career. Three scientific archetypes are found throughout the four career stages: leader, specialized, and supporting. All three archetypes are encountered for the early- and mid-career stages, whereas for junior and late-career stages only two archetypes are found. Scientists assigned to the leader and specialized archetypes tend to have longer careers than researchers who belong to the supporting archetype. There is consistent gender bias at all stages: the majority of male scientists belong to the leader archetype, while the larger proportion of women belong to the specialized archetype, especially for early and mid-career researchers.

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