Multimodal remote sensing image recognition is a popular research topic in the field of
remote sensing. This recognition task is mostly solved by supervised learning methods that …

It is important to be able to measure the similarity between two uncertain concepts for many
real-life AI applications, such as image retrieval, collaborative filtering, risk assessment, and …

This study applies big data processing technology and parallel computing methods to
assess the teaching effect of Japanese in the Flipped Classroom (FC) and task-based …

Handling and quantifying two types of uncertainties, spatial and inherent, in land-cover
segmentation using multiple satellite images constitutes a primary concern within the …

Cloud model similarity algorithm is an important part of cloud modelling theory. Most of the
existing cloud model similarity algorithms suffer from poor discriminability, poor …

This study aims to predict diabetes by combining the Radial Basis Function Neural Network
(RBFNN) and Fuzzy C-Means (FCM) clustering methods. Diabetes prediction is an important …

The conventional fuzzy c-spherical shells (FCSS) clustering model is extended to cluster
shells involving non-crisp numbers, in this paper. This is achieved by a vectorized …

To improve the video quality, aiming at the problems of low peak signal‐to‐noise ratio, poor
visual effect, and low bit rate of traditional methods, this paper proposes a fast compensation …

This paper proposes a fuzzy K‐means clustering‐based optimization method for regional
economic industrial structure. This paper discusses the multisubject of regional industrial …

Cluster analysis is often used to solve the problems of assessing the technical condition of
construction objects. Among clustering methods, the cmeans method is the most popular …