View Vol. 4 ,  Iss. 1 (April 2022)

Artificial Intelligence Advances

ISSN: 2661-3220 (Online)

Vol. 4 , Iss. 1 (April 2022)


  • Metric-based Few-shot Classification in Remote Sensing Image

    Mengyue Zhang, Jinyong Chen, Gang Wang, Min Wang, Kang Sun

    Article ID: 4124
    360  (Abstract) 106  (Download)

    Target recognition based on deep learning relies on a large quantity of samples, but in some specific remote sensing scenes, the samples are very rare. Currently, few-shot learning can obtain high-performance target classification models using only a few samples, but most researches are based on the natural scene. Therefore, this paper proposes a metric-based...

  • Elderly Fall Detection by Sensitive Features Based on Image Processing and Machine Learning

    Mohammad Hasan Olyaei, Ali Olyaei, Sumaya Hamidi

    Article ID: 4419
    291  (Abstract) 64  (Download)

    The world’s elderly population is growing every year. It is easy to say that the fall is one of the major dangers that threaten them. This paper offers a Trained Model for fall detection to help the older people live comfortably and alone at home. The purpose of this paper is to investigate appropriate methods...

  • Safety-critical Policy Iteration Algorithm for Control under Model Uncertainty

    Navid Moshtaghi Yazdani, Reihaneh Kardehi Moghaddam, Mohammad Hasan Olyaei

    Article ID: 4361
    316  (Abstract) 71  (Download)
    Safety is an important aim in designing safe-critical systems. To design such systems, many policy iterative algorithms are introduced to find safe optimal controllers. Due to the fact that in most practical systems, finding accurate information from the system is rather impossible, a new online training method is presented in this paper to perform an...
  • Efficient Parallel Processing of k-Nearest Neighbor Queries by Using a Centroid-based and Hierarchical Clustering Algorithm

    Elaheh Gavagsaz

    Article ID: 4668
    241  (Abstract) 47  (Download)

    The k-Nearest Neighbor method is one of the most popular techniques for both classification and regression purposes. Because of its operation, the application of this classification may be limited to problems with a certain number of instances, particularly, when run time is a consideration. However, the classification of large amounts of data has become a...