AIMC Topic: Animals
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Hierarchical image classification using transfer learning to improve deep learning model performance for amazon parrots.
Scientific reports
Jan 30, 2025
Numerous studies have proven the potential of deep learning models for classifying wildlife. Such models can reduce the workload of experts by automating species classification to monitor wild populations and global trade. Although deep learning mode...
Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning model.
Scientific reports
Jan 30, 2025
Various modelling techniques are available to understand the temporal and spatial variations of the phenology of species. Scientists often rely on correlative models, which establish a statistical relationship between a response variable (such as spe...
Prediction of dry matter intake in growing Black Bengal goats using artificial neural networks.
Tropical animal health and production
Jan 30, 2025
Dry matter intake (DMI) determination is essential for effective management of meat goats, especially in optimizing feed utilization and production efficiency. Unfortunately, farmers often face challenges in accurately predicting DMI which leads to w...
Preliminary exploration of ChatGPT-4 shows the potential of generative artificial intelligence for culturally tailored, multilingual antimicrobial resistance awareness messaging.
American journal of veterinary research
Jan 29, 2025
OBJECTIVE: Antimicrobial resistance (AMR), a global threat driven by factors such as improper antimicrobial use in humans and animals, is projected to cause 10 million annual deaths by 2050. For behavior change, public health messages must be tailore...
Fluo-Cast-Bright: a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live oocytes.
Communications biology
Jan 29, 2025
In mammalian oocytes, large-scale chromatin organization regulates transcription, nuclear architecture, and maintenance of chromosome stability in preparation for meiosis onset. Pre-ovulatory oocytes with distinct chromatin configurations exhibit pro...
Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.
Journal of neural engineering
Jan 29, 2025
Creating an intracortical brain computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI de...
Tailless control of a four-winged flapping-wing micro air vehicle with wing twist modulation.
Bioinspiration & biomimetics
Jan 29, 2025
This paper describes the tailless control system design of a flapping-wing micro air vehicle in a four-winged configuration, which can provide high control authority to be stable and agile in flight conditions from hovering to maneuvering flights. Th...
Optimizing gelation time for cell shape control through active learning.
Soft matter
Jan 29, 2025
Hydrogels are popular platforms for cell encapsulation in biomedicine and tissue engineering due to their soft, porous structures, high water content, and excellent tunability. Recent studies highlight that the timing of network formation can be just...
Leveraging machine learning to uncover multi-pathogen infection dynamics across co-distributed frog families.
PeerJ
Jan 29, 2025
BACKGROUND: Amphibians are experiencing substantial declines attributed to emerging pathogens. Efforts to understand what drives patterns of pathogen prevalence and differential responses among species are challenging because numerous factors related...
The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis.
PLoS computational biology
Jan 29, 2025
Theoretical neuroscientists and machine learning researchers have proposed a variety of learning rules to enable artificial neural networks to effectively perform both supervised and unsupervised learning tasks. It is not always clear, however, how t...
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