By transferring temporal knowledge from complex time-series models to a compact model through knowledge distillation and attention mechanisms, the ...
A research team has introduced a lightweight artificial intelligence method that accurately identifies wheat growth stages ...
This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...
The standard backpropagation learning algorithm for feedforward networks aims to minimise the mean square error defined over a set of training data. This form of error measure can lead to the problem ...
ABSTRACT: This paper proposes a unique approach to load forecasting using a fast convergent artificial neural network (ANN) and is driven by the critical need for power system planning. The Mazoon ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Breast cancer poses a significant global health challenge, requiring improved ...
Artificial neural networks (ANNs) are powerful nonparametric tools for estimating genomic breeding values (GEBVs) in genetic breeding. One significant advantage of ANNs is their ability to make ...
ABSTRACT: The stock market faces persistent challenges, including inefficiencies, volatility, and barriers to entry, which hinder its accessibility and reliability for investors. This paper explores ...
Forbes contributors publish independent expert analyses and insights. Gil Press writes about technology, entrepreneurs and innovation. The term “artificial intelligence” was coined seventy years ago, ...
Cardiac disease refers to diseases that affect the heart such as coronary artery diseases, arrhythmia and heart defects and is amongst the most difficult health conditions known to humanity. According ...
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