Xiaodan Wang^*, Peng Wang, Yafei Song, Qian Xiang, Jingtai Li

Computer Systems Science and Engineering, Vol.48, No.1, pp. 217-246, 2024, DOI:10.32604/csse.2023.039846

Abstract High-resolution range profile (HRRP) automatic recognition has been widely applied to military and civilian domains. Present HRRP recognition methods have difficulty extracting deep and global information about the HRRP sequence, which performs poorly in real scenes due to the ambient noise, variant targets, and limited data. Moreover, most existing methods improve the recognition performance by stacking a large number of modules, but ignore the lightweight of methods, resulting in over-parameterization and complex computational effort, which will be challenging to meet the deployment and application on edge devices. To tackle the above problems, this paper proposes an HRRP sequence recognition method… More >

Yu Li, Mingxiao Li, Dongyang Ou^*, Junjie Guo, Fangyuan Pan

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 893-909, 2024, DOI:10.32604/cmes.2023.031350

Abstract With the rapid development of mobile Internet, spatial crowdsourcing has become more and more popular. Spatial crowdsourcing consists of many different types of applications, such as spatial crowd-sensing services. In terms of spatial crowd-sensing, it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models. Besides collecting sensing data, spatial crowdsourcing also includes spatial delivery services like DiDi and Uber. Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications. Previous research conducted task assignments via traditional matching approaches or using simple network models. However, advanced mining… More > Graphic Abstract

Jian Wei, Qinzhao Wang^*, Zixu Zhao

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1699-1717, 2023, DOI:10.32604/cmc.2023.044284

Abstract The detection of large-scale objects has achieved high accuracy, but due to the low peak signal to noise ratio (PSNR), fewer distinguishing features, and ease of being occluded by the surroundings, the detection of small objects, however, does not enjoy similar success. Endeavor to solve the problem, this paper proposes an attention mechanism based on cross-Key values. Based on the traditional transformer, this paper first improves the feature processing with the convolution module, effectively maintaining the local semantic context in the middle layer, and significantly reducing the number of parameters of the model. Then, to enhance the effectiveness of the… More >

Yujun Zhang^*, Dezhi Han, Peng Chen

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2657-2675, 2023, DOI:10.32604/cmc.2023.042311

Abstract Synthetic Aperture Radar (SAR) image target detection has widespread applications in both military and civil domains. However, SAR images pose challenges due to strong scattering, indistinct edge contours, multi-scale representation, sparsity, and severe background interference, which make the existing target detection methods in low accuracy. To address this issue, this paper proposes a multi-scale fusion framework (Swin-PAFF) for SAR target detection that utilizes the global context perception capability of the Transformer and the multi-layer feature fusion learning ability of the feature pyramid structure (FPN). Firstly, to tackle the issue of inadequate perceptual image context information in SAR target detection, we… More >

Muhammad Usman Ghani Khan¹, Abid Mehmood², Mourad Elhadef², Shehzad Ashraf Chaudhry^2,3,*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2225-2249, 2023, DOI:10.32604/cmc.2023.038303

Abstract The proliferation of deluding data such as fake news and phony audits on news web journals, online publications, and internet business apps has been aided by the availability of the web, cell phones, and social media. Individuals can quickly fabricate comments and news on social media. The most difficult challenge is determining which news is real or fake. Accordingly, tracking down programmed techniques to recognize fake news online is imperative. With an emphasis on false news, this study presents the evolution of artificial intelligence techniques for detecting spurious social media content. This study shows past, current, and possible methods that… More >

Fufang Li, Manlin Luo^*, Ming Hu, Guobin Wang, Yan Chen

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2835-2850, 2023, DOI:10.32604/csse.2023.039765

Abstract Liver cancer has the second highest incidence rate among all types of malignant tumors, and currently, its diagnosis heavily depends on doctors’ manual labeling of CT scan images, a process that is time-consuming and susceptible to subjective errors. To address the aforementioned issues, we propose an automatic segmentation model for liver and tumors called Res2Swin Unet, which is based on the Unet architecture. The model combines Attention-Res2 and Swin Transformer modules for liver and tumor segmentation, respectively. Attention-Res2 merges multiple feature map parts with an Attention gate via skip connections, while Swin Transformer captures long-range dependencies and models the input… More >

Cheng Feng^*, Dezhi Han, Chongqing Chen

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 245-261, 2023, DOI:10.32604/cmc.2023.042765

Abstract Person search mainly consists of two submissions, namely Person Detection and Person Re-identification (re-ID). Existing approaches are primarily based on Faster R-CNN and Convolutional Neural Network (CNN) (e.g., ResNet). While these structures may detect high-quality bounding boxes, they seem to degrade the performance of re-ID. To address this issue, this paper proposes a Dual-Transformer Head Network (DTHN) for end-to-end person search, which contains two independent Transformer heads, a box head for detecting the bounding box and extracting efficient bounding box feature, and a re-ID head for capturing high-quality re-ID features for the re-ID task. Specifically, after the image goes through… More >

Litian Huang, Xinguo Yu, Lei Niu^*, Zihan Feng

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 517-539, 2023, DOI:10.32604/cmc.2023.041206

Abstract Solving Algebraic Problems with Geometry Diagrams (APGDs) poses a significant challenge in artificial intelligence due to the complex and diverse geometric relations among geometric objects. Problems typically involve both textual descriptions and geometry diagrams, requiring a joint understanding of these modalities. Although considerable progress has been made in solving math word problems, research on solving APGDs still cannot discover implicit geometry knowledge for solving APGDs, which limits their ability to effectively solve problems. In this study, a systematic and modular three-phase scheme is proposed to design an algorithm for solving APGDs that involve textual and diagrammatic information. The three-phase scheme… More >

Yongzhi Min^1,2,*, Jiafeng Li³, Yaxing Li¹

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 941-962, 2023, DOI:10.32604/cmc.2023.041182

Abstract To guarantee the safety of railway operations, the swift detection of rail surface defects becomes imperative. Traditional methods of manual inspection and conventional nondestructive testing prove inefficient, especially when scaling to extensive railway networks. Moreover, the unpredictable and intricate nature of defect edge shapes further complicates detection efforts. Addressing these challenges, this paper introduces an enhanced Unified Perceptual Parsing for Scene Understanding Network (UPerNet) tailored for rail surface defect detection. Notably, the Swin Transformer Tiny version (Swin-T) network, underpinned by the Transformer architecture, is employed for adept feature extraction. This approach capitalizes on the global information present in the image… More >

Ali Khan^1,4, Somaiya Khan², Bilal Hassan³, Rizwan Khan^1,4, Zhonglong Zheng^1,4,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 403-424, 2023, DOI:10.32604/cmc.2023.040251

Abstract Smoking has an economic and environmental impact on society due to the toxic substances it emits. Convolutional Neural Networks (CNNs) need help describing low-level features and can miss important information. Moreover, accurate smoker detection is vital with minimum false alarms. To answer the issue, the researchers of this paper have turned to a self-attention mechanism inspired by the ViT, which has displayed state-of-the-art performance in the classification task. To effectively enforce the smoking prohibition in non-smoking locations, this work presents a Vision Transformer-inspired model called SmokerViT for detecting smokers. Moreover, this research utilizes a locally curated dataset of 1120 images… More >