supported by the Yonsei Fellow Program funded by Lee Youn Jae,Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government,Ministry of Science and ICT(MSIT)(No.2020-0-01361,Artificial Intelligence Graduate School Program(Yonsei University);No.2022-0-00113,Developing a Sustainable Collaborative Multi-modal Lifelong Learning Framework);the support of Teachers Associateship for Research Excellence(TARE)Fellowship(No.TAR/2021/00006)of the Science and Engineering Research Board(SERB),Government of India.
This study attempts to accelerate the learning ability of an artificial electric field algorithm(AEFA)by attributing it with two mechanisms:elitism and opposition-based learning.Elitism advances the convergence of the...
Due to the recent developments in communications technology,cognitive computations have been used in smart healthcare techniques that can combine massive medical data,artificial intelligence,federated learning,bio-ins...
Cognitive-inspired computational systems play a crucial role in designing intelligent health monitoring systems which help both patients and hospitals.It also helps in early and consistent decision-making for various ...
对无人机视觉导航图像配准SURF(speeded up robust features)算法进行改进,将SURF特征提取与BRISK特征描述相结合,提出SURF-BRISK算法。首先,采用相对运算速度更快的FLANN(fast library for approximate nearest neighbors)算法粗匹配...