AIMC Topic: Feeding Behavior
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An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection.
Scientific reports
Jan 8, 2020
Bees play a key role in pollination of crops and in diverse ecosystems. There have been multiple reports in recent years illustrating bee population declines worldwide. The search for more accurate forecast models can aid both in the understanding of...
Machine Learning Based Prediction of Insufficient Herbage Allowance with Automated Feeding Behaviour and Activity Data.
Sensors (Basel, Switzerland)
Oct 16, 2019
Sensor technologies that measure grazing and ruminating behaviour as well as physical activities of individual cows are intended to be included in precision pasture management. One of the advantages of sensor data is they can be analysed to support f...
Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes.
Artificial intelligence in medicine
Jul 26, 2019
BACKGROUND: Diabetes mellitus (DM) is a metabolic disorder that causes abnormal blood glucose (BG) regulation that might result in short and long-term health complications and even death if not properly managed. Currently, there is no cure for diabet...
Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis.
Malaria journal
May 30, 2019
BACKGROUND: The propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences their capacity to transmit pathogens to humans. Unfortunately, determining proportions of mosquitoes that have fed on humans, i.e. Hum...
Multiple Human-Behaviour Indicators for Predicting Lung Cancer Mortality with Support Vector Machine.
Scientific reports
Nov 9, 2018
Lung cancer is still one of the most common causes of death around the world, while there is overwhelming evidence that the environment and lifestyle factors are predominant causes of most sporadic cancers. However, when applying human-behaviour indi...
Social and nutritional factors shape larval aggregation, foraging, and body mass in a polyphagous fly.
Scientific reports
Oct 3, 2018
The majority of insect species have a clearly defined larval stage during development. Larval nutrition is crucial for individuals' growth and development, and larval foraging success often depends on both resource availability and competition for th...
Using person-specific neural networks to characterize heterogeneity in eating disorders: Illustrative links between emotional eating and ovarian hormones.
The International journal of eating disorders
Aug 21, 2018
OBJECTIVE: Emotional eating has been linked to ovarian hormone functioning, but no studies to-date have considered the role of brain function. This knowledge gap may stem from methodological challenges: Data are heterogeneous, violating assumptions o...
Impact of subclinical mastitis on greenhouse gas emissions intensity and profitability of dairy cows in Norway.
Preventive veterinary medicine
Nov 27, 2017
Impaired animal health causes both productivity and profitability losses on dairy farms, resulting in inefficient use of inputs and increase in greenhouse gas (GHG) emissions produced per unit of product (i.e. emissions intensity). Here, we used subc...
Adult fly age estimations using cuticular hydrocarbons and Artificial Neural Networks in forensically important Calliphoridae species.
Forensic science international
Oct 8, 2017
Blowflies (Diptera: Calliphoridae) are forensically important as they are known to be one of the first to colonise human remains. The larval stage is typically used to assist a forensic entomologists with adult flies rarely used as they are difficult...
An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications.
PloS one
Apr 3, 2017
This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. I...
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