Wenjun Zhang¹, Wenfeng Wang^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1597-1619, 2023, DOI:10.32604/cmes.2022.020517 - 20 September 2022

Abstract Face recognition has been rapidly developed and widely used. However, there is still considerable uncertainty in the computational intelligence based on human-centric visual understanding. Emerging challenges for face recognition are resulted from information loss. This study aims to tackle these challenges with a broad learning system (BLS). We integrated two models, IR³C with BLS and IR³C with a triplet loss, to control the learning process. In our experiments, we used different strategies to generate more challenging datasets and analyzed the competitiveness, sensitivity, and practicability of the proposed two models. In the model of IR3C with BLS, More >

Weipeng Cao¹, Zhongwu Xie¹, Xiaofei Zhou², Zhiwu Xu¹, Cong Zhou¹, Georgios Theodoropoulos³, Qiang Wang^3,*

Journal on Artificial Intelligence, Vol.2, No.4, pp. 177-187, 2020, DOI:10.32604/jai.2020.014829 - 31 December 2020

Abstract Software verification is a key technique to ensure the correctness of software. Although numerous verification algorithms and tools have been developed in the past decades, it is still a great challenge for engineers to accurately and quickly choose the appropriate verification techniques for the software at hand. In this work, we propose a general learning framework for the intelligent selection of software verification algorithms, and instantiate the framework with two state-of-the-art learning algorithms: Broad learning (BL) and deep learning (DL). The experimental evaluation shows that the training efficiency of the BL-based model is much higher More >