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  • Open Access


    A GAN-EfficientNet-Based Traceability Method for Malicious Code Variant Families

    Li Li*, Qing Zhang, Youran Kong

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 801-818, 2024, DOI:10.32604/cmc.2024.051916

    Abstract Due to the diversity and unpredictability of changes in malicious code, studying the traceability of variant families remains challenging. In this paper, we propose a GAN-EfficientNetV2-based method for tracing families of malicious code variants. This method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code images. The method includes a lightweight classifier and a simulator. The classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile, embedded, and other devices. The simulator utilizes… More >

  • Open Access


    Enhancing Tea Leaf Disease Identification with Lightweight MobileNetV2

    Zhilin Li1,2, Yuxin Li1, Chunyu Yan1, Peng Yan1, Xiutong Li1, Mei Yu1, Tingchi Wen4,5, Benliang Xie1,2,3,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 679-694, 2024, DOI:10.32604/cmc.2024.051526

    Abstract Diseases in tea trees can result in significant losses in both the quality and quantity of tea production. Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations. However, existing methods face challenges such as a high number of parameters and low recognition accuracy, which hinders their application in tea plantation monitoring equipment. This paper presents a lightweight I-MobileNetV2 model for identifying diseases in tea leaves, to address these challenges. The proposed method first embeds a Coordinate Attention (CA) module into the original MobileNetV2 network, enabling the model to locate disease More >

  • Open Access


    Refined Anam-Net: Lightweight Deep Learning Model for Improved Segmentation Performance of Optic Cup and Disc for Glaucoma Diagnosis

    Khursheed Aurangzeb*, Syed Irtaza Haider, Musaed Alhussein

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1381-1405, 2024, DOI:10.32604/cmc.2024.048987

    Abstract In this work, we aim to introduce some modifications to the Anam-Net deep neural network (DNN) model for segmenting optic cup (OC) and optic disc (OD) in retinal fundus images to estimate the cup-to-disc ratio (CDR). The CDR is a reliable measure for the early diagnosis of Glaucoma. In this study, we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images. Our DNN model is based on modifications to Anam-Net, incorporating an anamorphic depth embedding block. To reduce computational complexity, we employ a fixed filter size for all convolution layers… More >

  • Open Access


    MG-YOLOv5s: A Faster and Stronger Helmet Detection Algorithm

    Zerui Xiao, Wei Liu, Zhiwei Ye*, Jiatang Yuan, Shishi Liu

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 1009-1029, 2024, DOI:10.32604/csse.2023.040475

    Abstract Nowadays, construction site safety accidents are frequent, and wearing safety helmets is essential to prevent head injuries caused by object collisions and falls. However, existing helmet detection algorithms have several drawbacks, including a complex structure with many parameters, high calculation volume, and poor detection of small helmets, making deployment on embedded or mobile devices difficult. To address these challenges, this paper proposes a YOLOv5-based multi-head detection safety helmet detection algorithm that is faster and more robust for detecting helmets on construction sites. By replacing the traditional DarkNet backbone network of YOLOv5s with a new backbone… More >

  • Open Access


    AFBNet: A Lightweight Adaptive Feature Fusion Module for Super-Resolution Algorithms

    Lirong Yin1, Lei Wang1, Siyu Lu2,*, Ruiyang Wang2, Haitao Ren2, Ahmed AlSanad3, Salman A. AlQahtani3, Zhengtong Yin4, Xiaolu Li5, Wenfeng Zheng3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2315-2347, 2024, DOI:10.32604/cmes.2024.050853

    Abstract At present, super-resolution algorithms are employed to tackle the challenge of low image resolution, but it is difficult to extract differentiated feature details based on various inputs, resulting in poor generalization ability. Given this situation, this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block (AFB) for feature extraction. This module mainly comprises dynamic convolution, attention mechanism, and pixel-based gating mechanism. Combined with dynamic convolution with scale information, the network can extract more differentiated feature information. The introduction of a channel More >

  • Open Access


    GliomaCNN: An Effective Lightweight CNN Model in Assessment of Classifying Brain Tumor from Magnetic Resonance Images Using Explainable AI

    Md. Atiqur Rahman1, Mustavi Ibne Masum1, Khan Md Hasib2, M. F. Mridha3,*, Sultan Alfarhood4, Mejdl Safran4,*, Dunren Che5

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2425-2448, 2024, DOI:10.32604/cmes.2024.050760

    Abstract Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality. This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging (MRI). It focuses on distinguishing between Low-Grade Gliomas (LGG) and High-Grade Gliomas (HGG). LGGs are benign and typically manageable with surgical resection, while HGGs are malignant and more aggressive. The research introduces an innovative custom convolutional neural network (CNN) model, Glioma-CNN. GliomaCNN stands out as a lightweight CNN model compared to its predecessors. The research utilized the BraTS 2020 More >

  • Open Access


    YOLO-CRD: A Lightweight Model for the Detection of Rice Diseases in Natural Environments

    Rui Zhang1,2, Tonghai Liu1,2,*, Wenzheng Liu1,2, Chaungchuang Yuan1,2, Xiaoyue Seng1,2, Tiantian Guo1,2, Xue Wang1,2

    Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1275-1296, 2024, DOI:10.32604/phyton.2024.052397

    Abstract Rice diseases can adversely affect both the yield and quality of rice crops, leading to the increased use of pesticides and environmental pollution. Accurate detection of rice diseases in natural environments is crucial for both operational efficiency and quality assurance. Deep learning-based disease identification technologies have shown promise in automatically discerning disease types. However, effectively extracting early disease features in natural environments remains a challenging problem. To address this issue, this study proposes the YOLO-CRD method. This research selected images of common rice diseases, primarily bakanae disease, bacterial brown spot, leaf rice fever, and dry… More >

  • Open Access


    The Lightweight Edge-Side Fault Diagnosis Approach Based on Spiking Neural Network

    Jingting Mei, Yang Yang*, Zhipeng Gao, Lanlan Rui, Yijing Lin

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4883-4904, 2024, DOI:10.32604/cmc.2024.051860

    Abstract Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management. Considering the unique characteristics of edge networks, such as limited resources, complex network faults, and the need for high real-time performance, enhancing and optimizing existing network fault diagnosis methods is necessary. Therefore, this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network (LSNN). Firstly, we use the Izhikevich neurons model to replace the Leaky Integrate and Fire (LIF) neurons model in the LSNN model. Izhikevich… More >

  • Open Access


    Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene

    Yunfeng Cai1, Ran Qin1, Jin Tang1, Long Zhang1, Xiaotian Bi1, Qing Yang2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4979-4994, 2024, DOI:10.32604/cmc.2024.050435

    Abstract Electric power training is essential for ensuring the safety and reliability of the system. In this study, we introduce a novel Abnormal Action Recognition (AAR) system that utilizes a Lightweight Pose Estimation Network (LPEN) to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios. The LPEN network, comprising three stages—MobileNet, Initial Stage, and Refinement Stage—is employed to swiftly extract image features, detect human key points, and refine them for accurate analysis. Subsequently, a Pose-aware Action Analysis Module (PAAM) captures the positional coordinates of human skeletal points in each frame. Finally, More >

  • Open Access


    An Improved UNet Lightweight Network for Semantic Segmentation of Weed Images in Corn Fields

    Yu Zuo1, Wenwen Li2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4413-4431, 2024, DOI:10.32604/cmc.2024.049805

    Abstract In cornfields, factors such as the similarity between corn seedlings and weeds and the blurring of plant edge details pose challenges to corn and weed segmentation. In addition, remote areas such as farmland are usually constrained by limited computational resources and limited collected data. Therefore, it becomes necessary to lighten the model to better adapt to complex cornfield scene, and make full use of the limited data information. In this paper, we propose an improved image segmentation algorithm based on unet. Firstly, the inverted residual structure is introduced into the contraction path to reduce the… More >

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