Youshen Jiang¹, Tongqing Zhou¹, Zhilin Wang², Zhiping Cai^1,*, Qiang Ni³

Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 585-597, 2024, DOI:10.32604/iasc.2023.030221

Abstract Due to the increasingly severe challenges brought by various epidemic diseases, people urgently need intelligent outbreak trend prediction. Predicting disease onset is very important to assist decision-making. Most of the existing work fails to make full use of the temporal and spatial characteristics of epidemics, and also relies on multivariate data for prediction. In this paper, we propose a Multi-Scale Location Attention Graph Neural Networks (MSLAGNN) based on a large number of Centers for Disease Control and Prevention (CDC) patient electronic medical records research sequence source data sets. In order to understand the geography and… More >

Hongchi Liu¹, Xing Deng^1,*, Haijian Shao^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2397-2424, 2024, DOI:10.32604/cmes.2024.049737

Abstract The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle, profoundly impeding their effective utilization across various domains. Dehazing methodologies have emerged as pivotal components of image preprocessing, fostering an improvement in the quality of remote sensing imagery. This enhancement renders remote sensing data more indispensable, thereby enhancing the accuracy of target identification. Conventional defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed images. In response to this challenge, a novel UNet Residual Attention Network (URA-Net) is proposed. This paradigmatic approach… More > Graphic Abstract

Yuting Zhou¹, Xuemei Yang¹, Junping Yin^2,3,4,*, Shiqi Liu¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5313-5333, 2024, DOI:10.32604/cmc.2024.052060

Abstract Gliomas have the highest mortality rate of all brain tumors. Correctly classifying the glioma risk period can help doctors make reasonable treatment plans and improve patients’ survival rates. This paper proposes a hierarchical multi-scale attention feature fusion medical image classification network (HMAC-Net), which effectively combines global features and local features. The network framework consists of three parallel layers: The global feature extraction layer, the local feature extraction layer, and the multi-scale feature fusion layer. A linear sparse attention mechanism is designed in the global feature extraction layer to reduce information redundancy. In the local feature… More >

Tingting Su¹, Jia Wang^1,*, Wei Hu^2,*, Gaoqiang Dong¹, Jeon Gwanggil³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4433-4448, 2024, DOI:10.32604/cmc.2024.051535

Abstract Along with the progression of Internet of Things (IoT) technology, network terminals are becoming continuously more intelligent. IoT has been widely applied in various scenarios, including urban infrastructure, transportation, industry, personal life, and other socio-economic fields. The introduction of deep learning has brought new security challenges, like an increment in abnormal traffic, which threatens network security. Insufficient feature extraction leads to less accurate classification results. In abnormal traffic detection, the data of network traffic is high-dimensional and complex. This data not only increases the computational burden of model training but also makes information extraction more… More >

Tao Zhou^1,3, Yujie Guo^1,3,*, Caiyue Peng^1,3, Yuxia Niu^1,3, Yunfeng Pan^1,3, Huiling Lu²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4863-4882, 2024, DOI:10.32604/cmc.2024.050767

Abstract Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot. However, there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images. A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper. The main innovations are as follows: Firstly, the Multi-scale Residual Feature Extraction Module (MRFEM) is designed to effectively extract multi-scale features. The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively. Secondly, the… More >

Feng Zhao, Guangdi Liu^*, Xiaoqiang Chen, Ying Wang

Energy Engineering, Vol.121, No.7, pp. 1865-1882, 2024, DOI:10.32604/ee.2024.048209

Abstract In light of the prevailing issue that the existing convolutional neural network (CNN) power quality disturbance identification method can only extract single-scale features, which leads to a lack of feature information and weak anti-noise performance, a new approach for identifying power quality disturbances based on an adaptive Kalman filter (KF) and multi-scale channel attention (MS-CAM) fused convolutional neural network is suggested. Single and composite-disruption signals are generated through simulation. The adaptive maximum likelihood Kalman filter is employed for noise reduction in the initial disturbance signal, and subsequent integration of multi-scale features into the conventional CNN… More >

Zhongyuan Zhang, Wenqiu Zhu^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2547-2563, 2024, DOI:10.32604/cmc.2024.048755

Abstract Remote sensing imagery, due to its high altitude, presents inherent challenges characterized by multiple scales, limited target areas, and intricate backgrounds. These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery. Additionally, these complexities contribute to inaccuracies in target localization and hinder precise target categorization. This paper addresses these challenges by proposing a solution: The YOLO-MFD model (YOLO-MFD: Remote Sensing Image Object Detection with Multi-scale Fusion Dynamic Head). Before presenting our method, we delve into the prevalent issues faced in remote sensing imagery… More >

Xiangyan Tang^1,2, Chengchun Ruan^1,2,*, Xiulai Li^2,3, Binbin Li^1,2, Cebin Fu^1,2

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 983-1003, 2024, DOI:10.32604/cmc.2024.047541

Abstract Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in the field of small object detection on unmanned aerial vehicles (UAVs). This task is challenging due to variations in UAV flight altitude, differences in object scales, as well as factors like flight speed and motion blur. To enhance the detection efficacy of small targets in drone aerial imagery, we propose an enhanced You Only Look Once version 7 (YOLOv7) algorithm based on multi-scale spatial context. We build the MSC-YOLO model, which incorporates an additional prediction head, denoted as P2, to… More >

Yanjun Yu¹, Lei Yu^1,*, Huiqi Wang², Haodong Zheng¹, Yi Deng¹

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2225-2243, 2024, DOI:10.32604/cmc.2024.047641

Abstract Bone age assessment (BAA) helps doctors determine how a child’s bones grow and develop in clinical medicine. Traditional BAA methods rely on clinician expertise, leading to time-consuming predictions and inaccurate results. Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations. This operation is costly and subjective. To address these problems, we propose a multi-scale attentional densely connected network (MSADCN) in this paper. MSADCN constructs a multi-scale dense connectivity mechanism, which can avoid overfitting, obtain the local features effectively and prevent gradient vanishing even in limited… More >

Yuchun Li^1,4, Mengxing Huang^1,*, Yu Zhang², Zhiming Bai³

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1649-1668, 2024, DOI:10.32604/cmc.2023.046883

Abstract The precise and automatic segmentation of prostate magnetic resonance imaging (MRI) images is vital for assisting doctors in diagnosing prostate diseases. In recent years, many advanced methods have been applied to prostate segmentation, but due to the variability caused by prostate diseases, automatic segmentation of the prostate presents significant challenges. In this paper, we propose an attention-guided multi-scale feature fusion network (AGMSF-Net) to segment prostate MRI images. We propose an attention mechanism for extracting multi-scale features, and introduce a 3D transformer module to enhance global feature representation by adding it during the transition phase from More >