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  • Open Access

    ARTICLE

    Emotion Detection Using ECG Signals and a Lightweight CNN Model

    Amita U. Dessai*, Hassanali G. Virani

    Computer Systems Science and Engineering, Vol.48, No.5, pp. 1193-1211, 2024, DOI:10.32604/csse.2024.052710

    Abstract Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction (HCI). However, physical methods of emotion recognition such as facial expressions, voice, and text data, do not always indicate true emotions, as users can falsify them. Among the physiological methods of emotion detection, Electrocardiogram (ECG) is a reliable and efficient way of detecting emotions. ECG-enabled smart bands have proven effective in collecting emotional data in uncontrolled environments. Researchers use deep machine learning techniques for emotion recognition using ECG signals, but there is a need to develop efficient models… More >

  • Open Access

    ARTICLE

    Phenotypic Image Recognition of Asparagus Stem Blight Based on Improved YOLOv8

    Shunshun Ji, Jiajun Sun, Chao Zhang*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4017-4029, 2024, DOI:10.32604/cmc.2024.055038

    Abstract Asparagus stem blight, also known as “asparagus cancer”, is a serious plant disease with a regional distribution. The widespread occurrence of the disease has had a negative impact on the yield and quality of asparagus and has become one of the main problems threatening asparagus production. To improve the ability to accurately identify and localize phenotypic lesions of stem blight in asparagus and to enhance the accuracy of the test, a YOLOv8-CBAM detection algorithm for asparagus stem blight based on YOLOv8 was proposed. The algorithm aims to achieve rapid detection of phenotypic images of asparagus… More >

  • Open Access

    ARTICLE

    Rail-PillarNet: A 3D Detection Network for Railway Foreign Object Based on LiDAR

    Fan Li1,2, Shuyao Zhang3, Jie Yang1,2,*, Zhicheng Feng1,2, Zhichao Chen1,2

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3819-3833, 2024, DOI:10.32604/cmc.2024.054525

    Abstract Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional (2D) images, such as short detection distance, strong influence of environment and lack of distance information, we propose Rail-PillarNet, a three-dimensional (3D) LIDAR (Light Detection and Ranging) railway foreign object detection method based on the improvement of PointPillars. Firstly, the parallel attention pillar encoder (PAPE) is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder. Secondly, a fine backbone network is designed to improve the feature extraction… More >

  • Open Access

    ARTICLE

    A Lightweight Intrusion Detection System Using Convolutional Neural Network and Long Short-Term Memory in Fog Computing

    Hawazen Alzahrani1, Tarek Sheltami1, Abdulaziz Barnawi2, Muhammad Imam2,*, Ansar Yaser3

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4703-4728, 2024, DOI:10.32604/cmc.2024.054203

    Abstract The Internet of Things (IoT) links various devices to digital services and significantly improves the quality of our lives. However, as IoT connectivity is growing rapidly, so do the risks of network vulnerabilities and threats. Many interesting Intrusion Detection Systems (IDSs) are presented based on machine learning (ML) techniques to overcome this problem. Given the resource limitations of fog computing environments, a lightweight IDS is essential. This paper introduces a hybrid deep learning (DL) method that combines convolutional neural networks (CNN) and long short-term memory (LSTM) to build an energy-aware, anomaly-based IDS. We test this… More >

  • Open Access

    ARTICLE

    Vehicle Head and Tail Recognition Algorithm for Lightweight DCDSNet

    Chao Wang1,3, Kaijie Zhang1,2,*, Xiaoyong Yu1, Dejun Li2, Wei Xie2, Xinqiao Wang2

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4451-4473, 2024, DOI:10.32604/cmc.2024.051764

    Abstract In the model of the vehicle recognition algorithm implemented by the convolutional neural network, the model needs to compute and store a lot of parameters. Too many parameters occupy a lot of computational resources making it difficult to run on computers with poor performance. Therefore, obtaining more efficient feature information of target image or video with better accuracy on computers with limited arithmetic power becomes the main goal of this research. In this paper, a lightweight densely connected, and deeply separable convolutional network (DCDSNet) algorithm is proposed to achieve this goal. Visual Geometry Group (VGG) More >

  • Open Access

    ARTICLE

    Identification of Mulberry Bacterial Blight Caused by Klebsiella oxytoca in Bazhong, Sichuan, China

    Yuan Huang1, Jia Wei2, Peigang Liu2, Yan Zhu2, Tianbao Lin2, Zhiqiang Lv2, Yijun Li3, Mei Zong3, Yun Zhou3, Junshan Gao1,*, Zilong Xu2,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.8, pp. 1995-2008, 2024, DOI:10.32604/phyton.2024.052353

    Abstract To provide a scientific basis for controlling mulberry bacterial blight in Bazhong, Sichuan, China (BSC), this study aimed to isolate and purify pathogenic bacteria from diseased branches of mulberry trees in the region and to clarify their taxonomic status using morphological observation, physiological and biochemical detection, molecular-level identification, and the construction of a phylogenetic tree. A total of 218 bacterial strains were isolated from samples of diseased mulberry branches. Of these, 7 strains were identified as pathogenic bacteria based on pathogenicity tests conducted in accordance with Koch’s postulates. Preliminary findings from the analysis of the More >

  • Open Access

    ARTICLE

    DPAL-BERT: A Faster and Lighter Question Answering Model

    Lirong Yin1, Lei Wang1, Zhuohang Cai2, Siyu Lu2,*, Ruiyang Wang2, Ahmed AlSanad3, Salman A. AlQahtani3, Xiaobing Chen4, Zhengtong Yin5, Xiaolu Li6, Wenfeng Zheng2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 771-786, 2024, DOI:10.32604/cmes.2024.052622

    Abstract Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems. However, with the constant evolution of algorithms, data, and computing power, the increasing size and complexity of these models have led to increased training costs and reduced efficiency. This study aims to minimize the inference time of such models while maintaining computational performance. It also proposes a novel Distillation model for PAL-BERT (DPAL-BERT), specifically, employs knowledge distillation, using the PAL-BERT model as the teacher model to train two student models: DPAL-BERT-Bi and DPAL-BERT-C. This research enhances the dataset More >

  • Open Access

    ARTICLE

    CAEFusion: A New Convolutional Autoencoder-Based Infrared and Visible Light Image Fusion Algorithm

    Chun-Ming Wu1, Mei-Ling Ren2,*, Jin Lei2, Zi-Mu Jiang3

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2857-2872, 2024, DOI:10.32604/cmc.2024.053708

    Abstract To address the issues of incomplete information, blurred details, loss of details, and insufficient contrast in infrared and visible image fusion, an image fusion algorithm based on a convolutional autoencoder is proposed. The region attention module is meant to extract the background feature map based on the distinct properties of the background feature map and the detail feature map. A multi-scale convolution attention module is suggested to enhance the communication of feature information. At the same time, the feature transformation module is introduced to learn more robust feature representations, aiming to preserve the integrity of… More >

  • Open Access

    ARTICLE

    An Attention-Based Approach to Enhance the Detection and Classification of Android Malware

    Abdallah Ghourabi*

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2743-2760, 2024, DOI:10.32604/cmc.2024.053163

    Abstract The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in malware. These malicious applications have become a serious concern to the security of Android systems. To address this problem, researchers have proposed several machine-learning models to detect and classify Android malware based on analyzing features extracted from Android samples. However, most existing studies have focused on the classification task and overlooked the feature selection process, which is crucial to reduce the training time and maintain or improve the classification results. The… More >

  • Open Access

    ARTICLE

    CMMCAN: Lightweight Feature Extraction and Matching Network for Endoscopic Images Based on Adaptive Attention

    Nannan Chong1,2,*, Fan Yang1

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2761-2783, 2024, DOI:10.32604/cmc.2024.052217

    Abstract In minimally invasive surgery, endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural cavities. However, in clinical operating environments, endoscopic images often suffer from challenges such as low texture, uneven illumination, and non-rigid structures, which affect feature observation and extraction. This can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images, leading to treatment and postoperative recovery issues for patients. To address these challenges, this paper introduces, for the first time, a Cross-Channel Multi-Modal… More >

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