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  • Open Access

    ARTICLE

    Multi-Branch High-Dimensional Guided Transformer-Based 3D Human Posture Estimation

    Xianhua Li1,2,*, Haohao Yu1, Shuoyu Tian1, Fengtao Lin3, Usama Masood1

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3551-3564, 2024, DOI:10.32604/cmc.2024.047336

    Abstract The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional (3D) method that takes into account self-occlusion, badly posedness, and a lack of depth data in the per-frame 3D posture estimation from two-dimensional (2D) mapping to 3D mapping. Firstly, by examining the relationship between the movements of different bones in the human body, four virtual skeletons are proposed to enhance the cyclic constraints of limb joints. Then, multiple parameters describing the skeleton are fused and projected into a high-dimensional space. Utilizing a multi-branch network, motion features between bones and overall motion features are extracted to mitigate… More >

  • Open Access

    ARTICLE

    Path Planning for AUVs Based on Improved APF-AC Algorithm

    Guojun Chen*, Danguo Cheng, Wei Chen, Xue Yang, Tiezheng Guo

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3721-3741, 2024, DOI:10.32604/cmc.2024.047325

    Abstract With the increase in ocean exploration activities and underwater development, the autonomous underwater vehicle (AUV) has been widely used as a type of underwater automation equipment in the detection of underwater environments. However, nowadays AUVs generally have drawbacks such as weak endurance, low intelligence, and poor detection ability. The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks. To improve the underwater operation ability of the AUV, this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm. In response to the limitations of a single… More >

  • Open Access

    ARTICLE

    RoBGP: A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

    Xiaohui Cui1,2,#, Chao Song1,2,#, Dongmei Li1,2,*, Xiaolong Qu1,2, Jiao Long1,2, Yu Yang1,2, Hanchao Zhang3

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3603-3618, 2024, DOI:10.32604/cmc.2024.047321

    Abstract Named Entity Recognition (NER) stands as a fundamental task within the field of biomedical text mining, aiming to extract specific types of entities such as genes, proteins, and diseases from complex biomedical texts and categorize them into predefined entity types. This process can provide basic support for the automatic construction of knowledge bases. In contrast to general texts, biomedical texts frequently contain numerous nested entities and local dependencies among these entities, presenting significant challenges to prevailing NER models. To address these issues, we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer (RoBGP). Our model… More >

  • Open Access

    ARTICLE

    Multimodality Medical Image Fusion Based on Pixel Significance with Edge-Preserving Processing for Clinical Applications

    Bhawna Goyal1, Ayush Dogra2, Dawa Chyophel Lepcha1, Rajesh Singh3, Hemant Sharma4, Ahmed Alkhayyat5, Manob Jyoti Saikia6,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4317-4342, 2024, DOI:10.32604/cmc.2024.047256

    Abstract Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis. It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases. However, recent image fusion techniques have encountered several challenges, including fusion artifacts, algorithm complexity, and high computing costs. To solve these problems, this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance. First, the method employs a cross-bilateral… More >

  • Open Access

    REVIEW

    A Review of Computing with Spiking Neural Networks

    Jiadong Wu, Yinan Wang*, Zhiwei Li*, Lun Lu, Qingjiang Li

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2909-2939, 2024, DOI:10.32604/cmc.2024.047240

    Abstract Artificial neural networks (ANNs) have led to landmark changes in many fields, but they still differ significantly from the mechanisms of real biological neural networks and face problems such as high computing costs, excessive computing power, and so on. Spiking neural networks (SNNs) provide a new approach combined with brain-like science to improve the computational energy efficiency, computational architecture, and biological credibility of current deep learning applications. In the early stage of development, its poor performance hindered the application of SNNs in real-world scenarios. In recent years, SNNs have made great progress in computational performance and practicability compared with the… More >

  • Open Access

    ARTICLE

    Predicting Traffic Flow Using Dynamic Spatial-Temporal Graph Convolution Networks

    Yunchang Liu1,*, Fei Wan1, Chengwu Liang2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4343-4361, 2024, DOI:10.32604/cmc.2024.047211

    Abstract Traffic flow prediction plays a key role in the construction of intelligent transportation system. However, due to its complex spatio-temporal dependence and its uncertainty, the research becomes very challenging. Most of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between nodes. However, due to the time-varying spatial correlation of the traffic network, there is no fixed node relationship, and these methods cannot effectively integrate the temporal and spatial features. This paper proposes a novel temporal-spatial dynamic graph convolutional network (TSADGCN). The dynamic… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Secure Transmission Strategy with Sensor-Transmission-Computing Linkage for Power Internet of Things

    Bin Li*, Linghui Kong, Xiangyi Zhang, Bochuo Kou, Hui Yu, Bowen Liu

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3267-3282, 2024, DOI:10.32604/cmc.2024.047193

    Abstract The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power grid data. While wireless communication offers a convenient channel for grid terminal access and data transmission, it is important to note that the bandwidth of wireless communication is limited. Additionally, the broadcast nature of wireless transmission raises concerns about the potential for unauthorized eavesdropping during data transmission. To address these challenges and achieve reliable, secure, and real-time transmission of power grid data, an intelligent security transmission strategy with sensor-transmission-computing… More >

  • Open Access

    ARTICLE

    Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

    Ying Su1, Morgan C. Wang1, Shuai Liu2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3529-3549, 2024, DOI:10.32604/cmc.2024.047189

    Abstract Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning (AutoML). At present, forecasting, whether rooted in machine learning or statistical learning, typically relies on expert input and necessitates substantial manual involvement. This manual effort spans model development, feature engineering, hyper-parameter tuning, and the intricate construction of time series models. The complexity of these tasks renders complete automation unfeasible, as they inherently demand human intervention at multiple junctures. To surmount these challenges, this article proposes leveraging Long Short-Term Memory, which is the variant of Recurrent Neural Networks, harnessing memory cells and gating mechanisms… More >

  • Open Access

    ARTICLE

    Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

    Qazi Mazhar ul Haq1, Fahim Arif2,3, Khursheed Aurangzeb4, Noor ul Ain3, Javed Ali Khan5, Saddaf Rubab6, Muhammad Shahid Anwar7,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4379-4397, 2024, DOI:10.32604/cmc.2024.047172

    Abstract Software project outcomes heavily depend on natural language requirements, often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements. Researchers are exploring machine learning to predict software bugs, but a more precise and general approach is needed. Accurate bug prediction is crucial for software evolution and user training, prompting an investigation into deep and ensemble learning methods. However, these studies are not generalized and efficient when extended to other datasets. Therefore, this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems. The methods involved feature selection, which is used to… More >

  • Open Access

    ARTICLE

    Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems

    Xia Li1, Zhanyou Ma1,*, Zhibao Mian2, Ziyuan Liu1, Ruiqi Huang1, Nana He1

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4129-4152, 2024, DOI:10.32604/cmc.2024.047168

    Abstract Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property specifications. Although there is an extensive literature on qualitative properties such as safety and liveness, there is still a lack of quantitative and uncertain property verifications for these systems. In uncertain environments, agents must make judicious decisions based on subjective epistemic. To verify epistemic and measurable properties in multi-agent systems, this paper extends fuzzy computation tree logic by introducing epistemic modalities and proposing a new Fuzzy Computation Tree Logic of Knowledge (FCTLK). We represent fuzzy multi-agent systems as distributed knowledge bases with fuzzy… More >

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