Mengmeng Yan¹, Chuang Zhang^1,2,*, Jinqi Chu¹, Haichao Zhang¹, Tao Ge¹, Suting Chen¹

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1433-1449, 2023, DOI:10.32604/csse.2023.040132

Abstract An action recognition network that combines multi-level spatiotemporal feature fusion with an attention mechanism is proposed as a solution to the issues of single spatiotemporal feature scale extraction, information redundancy, and insufficient extraction of frequency domain information in channels in 3D convolutional neural networks. Firstly, based on 3D CNN, this paper designs a new multilevel spatiotemporal feature fusion (MSF) structure, which is embedded in the network model, mainly through multilevel spatiotemporal feature separation, splicing and fusion, to achieve the fusion of spatial perceptual fields and short-medium-long time series information at different scales with reduced network parameters; In the second step,… More >

C. S. S. Anupama¹, Rafina Zakieva², Afanasiy Sergin³, E. Laxmi Lydia⁴, Seifedine Kadry^5,6,7, Chomyong Kim⁸, Yunyoung Nam^8,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1453-1468, 2023, DOI:10.32604/iasc.2023.038321

Abstract Gait is a biological typical that defines the method by that people walk. Walking is the most significant performance which keeps our day-to-day life and physical condition. Surface electromyography (sEMG) is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any extent. Gait classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery treatment. Several approaches are established in the works for gait recognition utilizing conventional and deep learning (DL) approaches. This study designs an Enhanced Artificial Algae Algorithm with Hybrid Deep Learning… More >

Yong Cheng¹, Zexuan Yang^2,*, Wenjie Zhang^3,4, Ling Yang⁵, Jun Wang¹, Tingzhao Guan¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1469-1482, 2023, DOI:10.32604/iasc.2023.038251

Abstract The numerous photos captured by low-price Internet of Things (IoT) sensors are frequently affected by meteorological factors, especially rainfall. It causes varying sizes of white streaks on the image, destroying the image texture and ruining the performance of the outdoor computer vision system. Existing methods utilise training with pairs of images, which is difficult to cover all scenes and leads to domain gaps. In addition, the network structures adopt deep learning to map rain images to rain-free images, failing to use prior knowledge effectively. To solve these problems, we introduce a single image derain model in edge computing that combines… More >

Zhaomin Liang, Tingting Liang^*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 205-219, 2023, DOI:10.32604/csse.2023.037124

Abstract The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry. Most book recommendation systems also use this algorithm. However, the traditional recommendation algorithm represented by the collaborative filtering algorithm cannot deal with the data sparsity well. This algorithm only uses the shallow feature design of the interaction between readers and books, so it fails to achieve the high-level abstract learning of the relevant attribute features of readers and books, leading to a decline in recommendation performance. Given the above problems, this study uses deep learning technology to model… More >

Wenbo Li, Qi Wang^*, Shang Gao

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 921-938, 2023, DOI:10.32604/iasc.2023.038257

Abstract Infrared target detection models are more required than ever before to be deployed on embedded platforms, which requires models with less memory consumption and better real-time performance while considering accuracy. To address the above challenges, we propose a modified You Only Look Once (YOLO) algorithm PF-YOLOv4-Tiny. The algorithm incorporates spatial pyramidal pooling (SPP) and squeeze-and-excitation (SE) visual attention modules to enhance the target localization capability. The PANet-based-feature pyramid networks (P-FPN) are proposed to transfer semantic information and location information simultaneously to ameliorate detection accuracy. To lighten the network, the standard convolutions other than the backbone network are replaced with depthwise… More >

Zimeng Yang, Qiulan Wu, Feng Zhang^*, Xuefei Chen, Weiqiang Wang, Xueshen Zhang

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 491-506, 2023, DOI:10.32604/iasc.2023.037558

Abstract Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation. With the continuous development of artificial intelligence technology, the use of deep learning methods for interpreting remote-sensing images has matured. Existing neural networks disregard the spatial relationship between two targets in remote sensing images. Semantic segmentation models that combine convolutional neural networks (CNNs) and graph convolutional neural networks (GCNs) cause a lack of feature boundaries, which leads to the unsatisfactory segmentation of various target feature boundaries. In this paper, we propose a new semantic segmentation model for remote sensing images (called DGCN hereinafter),… More >

Kaiyue Wang¹, Jian Gao^1,2,*, Xinyan Lei¹

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 619-638, 2023, DOI:10.32604/iasc.2023.036701

Abstract Traffic characterization (e.g., chat, video) and application identification (e.g., FTP, Facebook) are two of the more crucial jobs in encrypted network traffic classification. These two activities are typically carried out separately by existing systems using separate models, significantly adding to the difficulty of network administration. Convolutional Neural Network (CNN) and Transformer are deep learning-based approaches for network traffic classification. CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence, and Transformer can capture long-distance feature dependencies while ignoring local details. Based on these characteristics, a multi-task learning model that combines Transformer and 1D-CNN for… More >

Peicheng Shi^1,*, Zhiqiang Liu¹, Heng Qi¹, Aixi Yang²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5615-5637, 2023, DOI:10.32604/cmc.2023.037794

Abstract In complex traffic environment scenarios, it is very important for autonomous vehicles to accurately perceive the dynamic information of other vehicles around the vehicle in advance. The accuracy of 3D object detection will be affected by problems such as illumination changes, object occlusion, and object detection distance. To this purpose, we face these challenges by proposing a multimodal feature fusion network for 3D object detection (MFF-Net). In this research, this paper first uses the spatial transformation projection algorithm to map the image features into the feature space, so that the image features are in the same spatial dimension when fused… More >

Hongyu Lin, Feng Jiang^*, Yu Jiang, Huiyin Luo, Jian Yao, Jiaxin Liu

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5321-5336, 2023, DOI:10.32604/cmc.2023.036893

Abstract Detecting non-motor drivers’ helmets has significant implications for traffic control. Currently, most helmet detection methods are susceptible to the complex background and need more accuracy and better robustness of small object detection, which are unsuitable for practical application scenarios. Therefore, this paper proposes a new helmet-wearing detection algorithm based on the You Only Look Once version 5 (YOLOv5). First, the Dilated convolution In Coordinate Attention (DICA) layer is added to the backbone network. DICA combines the coordinated attention mechanism with atrous convolution to replace the original convolution layer, which can increase the perceptual field of the network to get more… More >

Kun Yang^1,2, Lei Zhao¹, Xianghui Wang¹, Mingyang Zhang¹, Linyan Xue^1,2, Shuang Liu^1,2, Kun Liu^1,2,3,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5159-5176, 2023, DOI:10.32604/cmc.2023.036401

Abstract The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the resolution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images; and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion… More >