AIMC Topic: Swine

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Machine learning models provide modest accuracy in predicting clinical impact of porcine reproductive and respiratory syndrome type 2 in Canadian sow herds.

American journal of veterinary research
OBJECTIVE: To determine the predictive potential of the open reading frame 5 nucleotide sequence of porcine reproductive and respiratory syndrome (PRRS) virus and the basic demographic data on the severity of the impact on selected production paramet...

Estimating body weight in Sujiang pigs using artificial neural network, nearest neighbor, and CART algorithms: a comparative study using morphological measurements.

Tropical animal health and production
The objectives of this study were to evaluate different machine learning algorithms for predicting body weight (BW) in Sujiang pigs using the following morphological traits: age, body length (BL), backfat thickness (BFT), chest circumference (CC), bo...

A new prediction model based on deep learning for pig house environment.

Scientific reports
A prediction model of the pig house environment based on Bayesian optimization (BO), squeeze and excitation block (SE), convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed to improve the prediction accuracy and animal welfar...

Noninvasive estimation of PaCO from volumetric capnography in animals with injured lungs: an Artificial Intelligence approach.

Journal of clinical monitoring and computing
To investigate the feasibility of non-invasively estimating the arterial partial pressure of carbon dioxide (PaCO) using a computational Adaptive Neuro-Fuzzy Inference System (ANFIS) model fed by noninvasive volumetric capnography (VCap) parameters. ...

Preparation, characterization, and protective effects of carbon dots against oxidative damage induced by LPS in IPEC-J2 cells.

Frontiers in cellular and infection microbiology
This study aimed to prepare carbon dots (GF-CDs) and examine their efficacy in mitigating oxidative stress and apoptosis in intestinal porcine epithelial cells from the jejunum (IPEC-J2 cells) induced by lipopolysaccharide (LPS). The GF-CDs were syn...

Mind the Step: An Artificial Intelligence-Based Monitoring Platform for Animal Welfare.

Sensors (Basel, Switzerland)
We present an artificial intelligence (AI)-enhanced monitoring framework designed to assist personnel in evaluating and maintaining animal welfare using a modular architecture. This framework integrates multiple deep learning models to automatically ...

Porkolor: A deep learning framework for pork color classification.

Meat science
Pork color is crucial for assessing its safety and freshness, and traditional methods of observing through human eyes are inefficient and subjective. In recent years, several methods have been proposed based on computer vision and deep learning have ...

Development of deep learning-based mobile application for the identification of Coccidia species in pigs using microscopic images.

Veterinary parasitology
Coccidiosis is a gastrointestinal parasitic disease caused by different species of Eimeria and Isospora, poses a significant threat to pig farming, leading to substantial economic losses attributed to reduced growth rates, poor feed conversion, incre...

ATP2A3 in Primary Aldosteronism: Machine Learning-Based Discovery and Functional Validation.

Hypertension (Dallas, Tex. : 1979)
BACKGROUND: Aldosterone-producing adenomas (APAs) are a common cause of primary aldosteronism that can lead to cardiovascular complications if left untreated. Machine learning-based bioinformatics approaches have emerged as powerful tools for identif...

How peptide migration and fraction bioactivity are modulated by applied electrical current conditions during electromembrane process separation: A comprehensive machine learning-based peptidomic approach.

Food research international (Ottawa, Ont.)
Industrial wastewaters are significant global concerns due to their environmental impact. Yet, protein-rich wastewaters can be valorized by enzymatic hydrolysis to release bioactive peptides. However, achieving selective molecular differentiation and...