Duc Cuong Pham¹, Tien Duc Ngo², Hoai Nam Vu^1,3,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2959-2974, 2025, DOI:10.32604/cmc.2025.064672 - 03 July 2025

Abstract Air quality estimation assesses the pollution level in the air, supports public health warnings, and is a valuable tool in environmental management. Although air sensors have proven helpful in this task, sensors are often expensive and difficult to install, while cameras are becoming more popular and accessible, from which images can be collected as data for deep learning models to solve the above task. This leads to another problem: several labeled images are needed to achieve high accuracy when deep-learning models predict air quality. In this research, we have three main contributions: (1) Collect and… More >

Jiho Bae, Bogyu Choi, Sumin Yeon, Suwon Lee^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2991-3003, 2025, DOI:10.32604/cmes.2025.064789 - 30 June 2025

Abstract Scene graph prediction has emerged as a critical task in computer vision, focusing on transforming complex visual scenes into structured representations by identifying objects, their attributes, and the relationships among them. Extending this to 3D semantic scene graph (3DSSG) prediction introduces an additional layer of complexity because it requires the processing of point-cloud data to accurately capture the spatial and volumetric characteristics of a scene. A significant challenge in 3DSSG is the long-tailed distribution of object and relationship labels, causing certain classes to be severely underrepresented and suboptimal performance in these rare categories. To address… More > Graphic Abstract

Xiong Xu¹, Chun Zhou^2,*, Chenggang Wang¹, Xiaoyan Zhang², Hua Meng²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 815-831, 2023, DOI:10.32604/iasc.2023.034656 - 29 April 2023

Abstract Clustering analysis is one of the main concerns in data mining. A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other. Therefore, measuring the distance between sample points is crucial to the effectiveness of clustering. Filtering features by label information and measuring the distance between samples by these features is a common supervised learning method to reconstruct distance metric. However, in many application scenarios, it is very expensive to obtain a large number of labeled samples. In this… More >

Zengchen Yu¹, Ke Wang^2,*, Shuxuan Xie¹, Yuanfeng Zhong¹, Zhihan Lv³

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 655-675, 2022, DOI:10.32604/cmes.2022.019612 - 14 March 2022

Abstract Few-shot Learning algorithms can be effectively applied to fields where certain categories have only a small amount of data or a small amount of labeled data, such as medical images, terrorist surveillance, and so on. The Metric Learning in the Few-shot Learning algorithm is classified by measuring the similarity between the classified samples and the unclassified samples. This paper improves the Prototypical Network in the Metric Learning, and changes its core metric function to Manhattan distance. The Convolutional Neural Network of the embedded module is changed, and mechanisms such as average pooling and Dropout are More >