AI Medical Compendium Topic:
Learning

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DACFL: Dynamic Average Consensus-Based Federated Learning in Decentralized Sensors Network.

Sensors (Basel, Switzerland)
Federated Learning (FL) is a privacy-preserving way to utilize the sensitive data generated by smart sensors of user devices, where a central parameter server (PS) coordinates multiple user devices to train a global model. However, relying on central...

Chalcogenide optomemristors for multi-factor neuromorphic computation.

Nature communications
Neuromorphic hardware that emulates biological computations is a key driver of progress in AI. For example, memristive technologies, including chalcogenide-based in-memory computing concepts, have been employed to dramatically accelerate and increase...

Learning aerodynamics with neural network.

Scientific reports
We propose a neural network (NN) architecture, the Element Spatial Convolution Neural Network (ESCNN), towards the airfoil lift coefficient prediction task. The ESCNN outperforms existing state-of-the-art NNs in terms of prediction accuracy, with two...

Design and Implementation of Tourism Teaching System Based on Artificial Intelligence Technology.

Computational intelligence and neuroscience
The tourism teaching system provides all kinds of teaching resources for students and shares good teachers, which greatly improves the quality of teaching and learning, and it enables students to teach and learn randomly in the system. The mode of ed...

Training of artificial neural networks with the multi-population based artifical bee colony algorithm.

Network (Bristol, England)
Nowadays, artificial intelligence has gained recognition in every aspect of life. Artificial neural networks, one of the most efficient artificial intelligence techniques, is remarkably successful in computers' acquisition of the learning and interpr...

Attributed graph clustering with multi-task embedding learning.

Neural networks : the official journal of the International Neural Network Society
Attributed graph clustering is challenging as it needs to effectively combine both graph structure and node feature information to accomplish node clustering. Recent studies mostly adopt graph neural networks to learn node embeddings, then apply trad...

Seabed Modelling by Means of Airborne Laser Bathymetry Data and Imbalanced Learning for Offshore Mapping.

Sensors (Basel, Switzerland)
An important problem associated with the aerial mapping of the seabed is the precise classification of point clouds characterizing the water surface, bottom, and bottom objects. This study aimed to improve the accuracy of classification by addressing...

The neural coding framework for learning generative models.

Nature communications
Neural generative models can be used to learn complex probability distributions from data, to sample from them, and to produce probability density estimates. We propose a computational framework for developing neural generative models inspired by the...

Memristor-based analogue computing for brain-inspired sound localization with in situ training.

Nature communications
The human nervous system senses the physical world in an analogue but efficient way. As a crucial ability of the human brain, sound localization is a representative analogue computing task and often employed in virtual auditory systems. Different fro...

Online Course Model of Social and Political Education Using Deep Learning.

Computational intelligence and neuroscience
This study aims to improve the social and political literacy of college students. Social and Political Education (SPE) is studied for undergraduates. Firstly, the background of the subject research is introduced. The face recognition module is built ...