Jiuwen Cao

TL;DR: Zhang et al. as discussed by the authors analyzed the differences between different age and gender groups, and considered the influence of height, weight, shoe size, and body mass index (BMI).

TL;DR: In this paper , the authors presented a comprehensive analysis of EEG features at three different periods: pre-seizure, seizure and post seizure, and extracted coherent features to characterize EEG signals in EIEE syndrome, and Kruskal-Wallis H Test and Gradient-weighted Class Activation Mapping (Grad-CAM) are used to investigate and visualize the significance of features in different frequency band for distinguishing the three stages.

TL;DR: In this article, an electric quantity abnormity detection method based on an ensemble learning model was proposed, which consists of data integration and user classification on acquired partial power utilization data; carrying out feature extraction on the processed power utilisation data based on the abnormal auditing rule; constructing an ensembles learning model, dividing the data after feature extraction into n groups of training sets and one test set, and importing the training sets into the ensemble learning model with anELM as a base model for training to obtain the classification detection models; putting the test set into the trained model

TL;DR: Wang et al. as mentioned in this paper proposed a parallel enhanced SR generative adversarial network (PESRGAN) for low-resolution (LR) SLP images to improve the performance of SLP recognition.

TL;DR: Wang et al. as discussed by the authors proposed an incremental quaternion random neural network trained by extreme learning machine (IQ-ELM), where the output weight is optimized by minimizing the residual error based on the fundamental of the generalized HR calculus (GHR).