Artificial intelligence and machine learning have transformed how we process information, make decisions, and solve complex problems. Behind every ...
As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...
The agent acquires a vocabulary of neuro-symbolic concepts for objects, relations, and actions, represented through a ...
Integrating deep learning in optical microscopy enhances image analysis, overcoming traditional limitations and improving ...
In a study titled Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples, published in the ...
(2026) AI Assisted Material Selection Framework for Corrosion Resistant Steels in Onshore Oil and Gas Pipelines. Open Journal ...
The development of humans and other animals unfolds gradually over time, with cells taking on specific roles and functions ...
Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...
Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...
Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural ...
Medical imaging has become an essential tool for identifying and treating neurological conditions. Traditional deep learning (DL) models have made tremendous advances in neuroimaging analysis; however ...
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