AIMC Topic: Poisson Distribution

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Modeling the number of new cases of childhood type 1 diabetes using Poisson regression and machine learning methods; a case study in Saudi Arabia.

PloS one
Diabetes mellitus stands out as one of the most prevalent chronic conditions affecting pediatric populations. The escalating incidence of childhood type 1 diabetes (T1D) globally is a matter of increasing concern. Developing an effective model that l...

Prediction of Poisson's ratio for a petroleum engineering application: Machine learning methods.

PloS one
Static Poisson's ratio (νs) is an essential property used in petroleum calculations, namely fracture pressure (FP). The νs is often determined in the laboratory; however, due to time and cost constraints, quicker and cheaper alternatives are sought, ...

Predictive performance of count regression models versus machine learning techniques: A comparative analysis using an automobile insurance claims frequency dataset.

PloS one
Accurate forecasting of claim frequency in automobile insurance is essential for insurers to assess risks effectively and establish appropriate pricing policies. Traditional methods typically rely on a Poisson distribution for modeling claim counts; ...

Investigating stronger tolerant network against cascading failures in focusing on changing degree distributions.

PloS one
Many real-world networks with Scale-Free structure are significantly vulnerable against both intentional attacks and catastrophic cascading failures. On the other hand, it has been shown that networks with narrower degree distributions have strong ro...

Radiation dose estimation with multiple artificial neural networks in dicentric chromosome assay.

International journal of radiation biology
PURPOSE: The dicentric chromosome assay (DCA), often referred to as the 'gold standard' in radiation dose estimation, exhibits significant challenges as a consequence of its labor-intensive nature and dependency on expert knowledge. Existing automate...

Poisson-Boltzmann-based machine learning model for electrostatic analysis.

Biophysical journal
Electrostatics is of paramount importance to chemistry, physics, biology, and medicine. The Poisson-Boltzmann (PB) theory is a primary model for electrostatic analysis. However, it is highly challenging to compute accurate PB electrostatic solvation ...

Unsupervised learning of stationary and switching dynamical system models from Poisson observations.

Journal of neural engineering
. Investigating neural population dynamics underlying behavior requires learning accurate models of the recorded spiking activity, which can be modeled with a Poisson observation distribution. Switching dynamical system models can offer both explanat...

A deep learning based method for automatic analysis of high-throughput droplet digital PCR images.

The Analyst
Droplet digital PCR (ddPCR) is a technique for absolute quantification of nucleic acid molecules and is widely used in biomedical research and clinical diagnosis. ddPCR partitions the reaction solution containing target molecules into a large number ...

Artificial intelligence for quality assurance in radiotherapy.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
In radiotherapy, patient-specific quality assurance is very time-consuming and causes machine downtime. It consists of testing (using measurement with a phantom and detector) if a modulated plan is correctly delivered by a treatment unit. Artificial ...

A Multivariate Poisson Deep Learning Model for Genomic Prediction of Count Data.

G3 (Bethesda, Md.)
The paradigm called genomic selection (GS) is a revolutionary way of developing new plants and animals. This is a predictive methodology, since it uses learning methods to perform its task. Unfortunately, there is no universal model that can be used ...