AIMC Topic: Humans

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4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection.

IEEE transactions on neural networks and learning systems
Due to the high availability of large-scale annotated image datasets, knowledge transfer from pretrained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with da...

Computer-assisted surgery in medical and dental applications.

Expert review of medical devices
INTRODUCTION: Computer-assisted surgery (CAS) is a broad surgical methodology that utilizes computer technology to both plan and execute surgical intervention. CAS is widespread in both medicine and dentistry as it allows for minimally invasive and p...

Human-in-the-Loop Low-Shot Learning.

IEEE transactions on neural networks and learning systems
We consider a human-in-the-loop scenario in the context of low-shot learning. Our approach was inspired by the fact that the viability of samples in novel categories cannot be sufficiently reflected by those limited observations. Some heterogeneous s...

Incremental Concept Learning via Online Generative Memory Recall.

IEEE transactions on neural networks and learning systems
The ability to learn more concepts from incrementally arriving data over time is essential for the development of a lifelong learning system. However, deep neural networks often suffer from forgetting previously learned concepts when continually lear...

Intrinsic Plasticity-Based Neuroadptive Control With Both Weights and Excitability Tuning.

IEEE transactions on neural networks and learning systems
This brief presents an intrinsic plasticity (IP)-driven neural-network-based tracking control approach for a class of nonlinear uncertain systems. Inspired by the neural plasticity mechanism of individual neuron in nervous systems, a learning rule re...

A Survey of Stochastic Computing Neural Networks for Machine Learning Applications.

IEEE transactions on neural networks and learning systems
Neural networks (NNs) are effective machine learning models that require significant hardware and energy consumption in their computing process. To implement NNs, stochastic computing (SC) has been proposed to achieve a tradeoff between hardware effi...

Time-Frequency Representation and Convolutional Neural Network-Based Emotion Recognition.

IEEE transactions on neural networks and learning systems
Emotions composed of cognizant logical reactions toward various situations. Such mental responses stem from physiological, cognitive, and behavioral changes. Electroencephalogram (EEG) signals provide a noninvasive and nonradioactive solution for emo...

Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm.

Computational intelligence and neuroscience
With the comprehensive development of national fitness, men, women, young, and old in China have joined the ranks of fitness. In order to increase the understanding of human movement, many researches have designed a lot of software or hardware to rea...

AGTH-Net: Attention-Based Graph Convolution-Guided Third-Order Hourglass Network for Sports Video Classification.

Journal of healthcare engineering
As a hot research topic, sports video classification research has a wide range of applications in switched TV, video on demand, smart TV, and other fields and is closely related to people's lives. Under this background, sports video classification re...