Ying Chen

TL;DR: This paper develops a GWO variant enhanced with a covariance matrix adaptation evolution strategy (CMAES), levy flight mechanism, and orthogonal learning (OL) strategy named GWOCMALOL, which could reach higher classification accuracy and fewer feature selections than other optimization algorithms.

TL;DR: Results for every optimization task demonstrate that LSEOFOA can provide a high-performance and self-assured tradeoff between exploration and exploitation, and overall research findings show that the proposed model is superior in terms of classification accuracy, Matthews correlation coefficient, sensitivity, and specificity.

TL;DR: Zhang et al. as mentioned in this paper proposed an end-to-end unified framework based on deep learning that does not include normalization in order to achieve improved accuracy in iris segmentation and recognition.

TL;DR: An improved three-factor user authentication scheme is proposed to overcome those flaws utilizing password, smart card, and biometric feature and is suitable for practical application in WMSN.

TL;DR: An architecture based on CNNs combined with dense blocks for iris segmentation, referred to as a dense-fully convolutional network (DFCN), and adopt some popular optimizer methods, such as batch normalization (BN) and dropout.