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Mohammad Mehdi Sharifi Nevisi¹, Elnaz Bashir², Diego Martín^3,*, Seyedkian Rezvanjou⁴, Farzaneh Shoushtari⁵, Ehsan Ghafourian²
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3971-3991, 2024, DOI:10.32604/cmc.2024.047875
（This article belongs to the Special Issue: Artificial Intelligence for Addressing Security and Communications Challenges of Internet-connected Critical Infrastructures)
Abstract This paper focuses on wireless-powered communication systems, which are increasingly relevant in the Internet of Things (IoT) due to their ability to extend the operational lifetime of devices with limited energy. The main contribution of the paper is a novel approach to minimize the secrecy outage probability (SOP) in these systems. Minimizing SOP is crucial for maintaining the confidentiality and integrity of data, especially in situations where the transmission of sensitive data is critical. Our proposed method harnesses the power of an improved biogeography-based optimization (IBBO) to effectively train a recurrent neural network (RNN). The proposed IBBO introduces an innovative… More >

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Changfeng Feng¹, Chunping Wang², Dongdong Zhang¹, Renke Kou¹, Qiang Fu^1,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3993-4013, 2024, DOI:10.32604/cmc.2024.048351
Abstract Transformer-based models have facilitated significant advances in object detection. However, their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle (UAV) imagery. Addressing these limitations, we propose a hybrid transformer-based detector, H-DETR, and enhance it for dense small objects, leading to an accurate and efficient model. Firstly, we introduce a hybrid transformer encoder, which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently. Furthermore, we propose two novel strategies to enhance detection performance without incurring additional inference computation. Query filter is designed… More >

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Ramiz Gorkem Birdal^*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4015-4028, 2024, DOI:10.32604/cmc.2024.048324
Abstract Maintaining a steady power supply requires accurate forecasting of solar irradiance, since clean energy resources do not provide steady power. The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network (CNN), but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions. This paper proposes a hybrid approach based on deep learning, expanding the feature set by adding new air pollution concentrations, and ranking these features to select and reduce their size to improve efficiency. In order to improve the accuracy… More >

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Jin Wang^1,2, Guoshu Huang¹, R. Simon Sherratt³, Ding Huang⁴, Jia Ni^4,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4029-4048, 2024, DOI:10.32604/cmc.2024.047468
Abstract With the development of Industry 4.0 and big data technology, the Industrial Internet of Things (IIoT) is hampered by inherent issues such as privacy, security, and fault tolerance, which pose certain challenges to the rapid development of IIoT. Blockchain technology has immutability, decentralization, and autonomy, which can greatly improve the inherent defects of the IIoT. In the traditional blockchain, data is stored in a Merkle tree. As data continues to grow, the scale of proofs used to validate it grows, threatening the efficiency, security, and reliability of blockchain-based IIoT. Accordingly, this paper first analyzes the inefficiency of the traditional blockchain… More >

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Bing Liu¹, Zhe Zhang¹, Shengrong Hu², Song Sun^3,*, Dapeng Liu⁴, Zhenyu Qiu⁵
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4049-4069, 2024, DOI:10.32604/cmc.2024.048099
（This article belongs to the Special Issue: Enhancing IoT Cyber-Resilience: Convergence of AI, Digital Twins, and the Metaverse )
Abstract Internet of Things (IoT) is vulnerable to data-tampering (DT) attacks. Due to resource limitations, many anomaly detection systems (ADSs) for IoT have high false positive rates when detecting DT attacks. This leads to the misreporting of normal data, which will impact the normal operation of IoT. To mitigate the impact caused by the high false positive rate of ADS, this paper proposes an ADS management scheme for clustered IoT. First, we model the data transmission and anomaly detection in clustered IoT. Then, the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on… More >

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Lujuan Deng, Ruochong Fu^*, Zuhe Li, Boyi Liu, Mengze Xue, Yuhao Cui
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4071-4089, 2024, DOI:10.32604/cmc.2024.048200
Abstract Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the… More >

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Qinyue Wu, Hui Xu^*, Mengran Liu
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4091-4107, 2024, DOI:10.32604/cmc.2024.048461
（This article belongs to the Special Issue: Multimedia Encryption and Information Security)
Abstract Network traffic identification is critical for maintaining network security and further meeting various demands of network applications. However, network traffic data typically possesses high dimensionality and complexity, leading to practical problems in traffic identification data analytics. Since the original Dung Beetle Optimizer (DBO) algorithm, Grey Wolf Optimization (GWO) algorithm, Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO) algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution, an Improved Dung Beetle Optimizer (IDBO) algorithm is proposed for network traffic identification. Firstly, the Sobol sequence is utilized to initialize the dung beetle population, laying the… More >

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Hongjun Zhang^1,2, Hao Zhang², Yu Lei³, Hao Ye¹, Peng Li^1,*, Desheng Shi¹
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4109-4128, 2024, DOI:10.32604/cmc.2024.048355
Abstract The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed… More >

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Xia Li¹, Zhanyou Ma^1,*, 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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Yumin Jo¹, Jongho Paik^2,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4153-4176, 2024, DOI:10.32604/cmc.2024.047046
（This article belongs to the Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
Abstract Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream. However, when the transmission environment is unstable, problems such as reduction in the lifespan of equipment due to frequent switching and interruption, delay, and stoppage of services may occur. Therefore, applying a machine learning (ML) method, which is possible to automatically judge and classify network-related service anomaly, and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when there are problems such as… More >

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Mai Alzamel^1,*, Hamza Ali Rizvi², Najwa Altwaijry¹, Isra Al-Turaiki¹
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4177-4204, 2024, DOI:10.32604/cmc.2024.048370
Abstract Scalability and information personal privacy are vital for training and deploying large-scale deep learning models. Federated learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web browsers. Nevertheless, relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client numbers. Additionally, information relating to the training dataset can possibly be extracted from the distributed weights, potentially reducing the privacy of the local data used for training. In this research paper, we aim to investigate… More >

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Youlin Ji, Bo Yin^*, Ke Gu
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4205-4223, 2024, DOI:10.32604/cmc.2024.049086
Abstract With its untameable and traceable properties, blockchain technology has been widely used in the field of data sharing. How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing. In this paper, we study verifiable keyword frequency (KF) queries with local differential privacy in blockchain. Both the numerical and the keyword attributes are present in data objects; the latter are sensitive and require privacy protection. However, prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries. We propose an efficient framework that… More >

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105

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Muhammad Armghan Latif¹, Zohaib Mushtaq², Saad Arif³, Sara Rehman⁴, Muhammad Farrukh Qureshi⁵, Nagwan Abdel Samee⁶, Maali Alabdulhafith^6,*, Yeong Hyeon Gu⁷, Mohammed A. Al-masni⁷
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4225-4241, 2024, DOI:10.32604/cmc.2024.047621
（This article belongs to the Special Issue: Privacy-Aware AI-based Models for Cancer Diagnosis)
Abstract Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland. Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care. This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques. Sequential forward feature selection, sequential backward feature elimination, and bidirectional feature elimination are investigated in this study. In ensemble learning, random forest, adaptive boosting, and bagging classifiers are employed. The effectiveness of these techniques is evaluated using… More >

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Youseef Alotaibi¹, B. Rajasekar², R. Jayalakshmi³, Surendran Rajendran^4,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4243-4262, 2024, DOI:10.32604/cmc.2024.047608
（This article belongs to the Special Issue: Secure Blockchain Clustering for 6G Wireless Technology with Advanced Artificial Intelligence Techniques)
Abstract Rapid development in Information Technology (IT) has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle (V2V) transmission. Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data. The communication occurs directly between V2V and Base Station (BS) units such as the Road Side Unit (RSU), named as a Vehicle to Infrastructure (V2I). However, the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time. Therefore, the scheme of an effectual routing protocol for reliable and stable communications is significant. Current research demonstrates… More >

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Muhammad Saad¹, Muhammad Raheel Bhutta², Jongik Kim^3,*, Tae-Sun Chung¹
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4263-4282, 2024, DOI:10.32604/cmc.2024.047132
Abstract With the increase in IoT (Internet of Things) devices comes an inherent challenge of security. In the world today, privacy is the prime concern of every individual. Preserving one’s privacy and keeping anonymity throughout the system is a desired functionality that does not come without inevitable trade-offs like scalability and increased complexity and is always exceedingly difficult to manage. The challenge is keeping confidentiality and continuing to make the person innominate throughout the system. To address this, we present our proposed architecture where we manage IoT devices using blockchain technology. Our proposed architecture works on and off blockchain integrated with… More >

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109

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Rahul Gupta¹, Kapil Sharma^1,*, R. K. Garg²
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4283-4301, 2024, DOI:10.32604/cmc.2024.046890
（This article belongs to the Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
Abstract The prevalence of smartphones is deeply embedded in modern society, impacting various aspects of our lives. Their versatility and functionalities have fundamentally changed how we communicate, work, seek entertainment, and access information. Among the many smartphones available, those operating on the Android platform dominate, being the most widely used type. This widespread adoption of the Android OS has significantly contributed to increased malware attacks targeting the Android ecosystem in recent years. Therefore, there is an urgent need to develop new methods for detecting Android malware. The literature contains numerous works related to Android malware detection. As far as our understanding… More >

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113

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Jing Yang, Bin Ji, Shasha Li^*, Jun Ma, Jie Yu
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4303-4315, 2024, DOI:10.32604/cmc.2023.035594
Abstract Named entity recognition (NER) is a fundamental task of information extraction (IE), and it has attracted considerable research attention in recent years. The abundant annotated English NER datasets have significantly promoted the NER research in the English field. By contrast, much fewer efforts are made to the Chinese NER research, especially in the scientific domain, due to the scarcity of Chinese NER datasets. To alleviate this problem, we present a Chinese scientific NER dataset–SciCN, which contains entity annotations of titles and abstracts derived from 3,500 scientific papers. We manually annotate a total of 62,059 entities, and these entities are classified… More >

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90

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Bhawna Goyal¹, Ayush Dogra², Dawa Chyophel Lepcha¹, Rajesh Singh³, Hemant Sharma⁴, Ahmed Alkhayyat⁵, Manob Jyoti Saikia^6,*
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 >

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Yunchang Liu^1,*, 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 >

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91

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Xiaoyan Zhu¹, Ruchun Jia², Tingrui Zhang³, Song Yao^4,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4363-4377, 2024, DOI:10.32604/cmc.2023.045615
（This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)
Abstract The traditional air traffic control information sharing data has weak security characteristics of personal privacy data and poor effect, which is easy to leads to the problem that the data is usurped. Starting from the application of the ATC (automatic train control) network, this paper focuses on the zero trust and zero trust access strategy and the tamper-proof method of information-sharing network data. Through the improvement of ATC’s zero trust physical layer authentication and network data distributed feature differentiation calculation, this paper reconstructs the personal privacy scope authentication structure and designs a tamper-proof method of ATC’s information sharing on the… More >

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Qazi Mazhar ul Haq¹, Fahim Arif^2,3, Khursheed Aurangzeb⁴, Noor ul Ain³, Javed Ali Khan⁵, Saddaf Rubab⁶, Muhammad Shahid Anwar^7,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4379-4397, 2024, DOI:10.32604/cmc.2024.047172
（This article belongs to the Special Issue: Requirements Engineering: Bridging Theory, Research and Practice)
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 >

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115

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Lifang Fu¹, Huanxin Peng^2,*, Changjin Ma², Yuhan Liu²
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4399-4416, 2024, DOI:10.32604/cmc.2024.047053
（This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
Abstract In recent years, how to efficiently and accurately identify multi-model fake news has become more challenging. First, multi-model data provides more evidence but not all are equally important. Secondly, social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical. Unfortunately, existing approaches fail to handle these problems. This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues (TD-MMC), which utilizes three valuable multi-model clues: text-model importance, text-image complementary, and text-image inconsistency. TD-MMC is dominated by textural content and… More >

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Fauziyah¹, Zhaoshun Wang^1,*, Mujahid Tabassum²
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4417-4452, 2024, DOI:10.32604/cmc.2024.046797
（This article belongs to the Special Issue: Cybersecurity for Cyber-attacks in Critical Applications in Industry)
Abstract In an era characterized by digital pervasiveness and rapidly expanding datasets, ensuring the integrity and reliability of information is paramount. As cyber threats evolve in complexity, traditional cryptographic methods face increasingly sophisticated challenges. This article initiates an exploration into these challenges, focusing on key exchanges (encompassing their variety and subtleties), scalability, and the time metrics associated with various cryptographic processes. We propose a novel cryptographic approach underpinned by theoretical frameworks and practical engineering. Central to this approach is a thorough analysis of the interplay between Confidentiality and Integrity, foundational pillars of information security. Our method employs a phased strategy, beginning… More >

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Jiangang Lu, Ruifeng Zhao^*, Zhiwen Yu, Yue Dai, Kaiwen Zeng
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4453-4469, 2024, DOI:10.32604/cmc.2024.048020
Abstract Time synchronization (TS) is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things (IoT). Multi-clock source time synchronization (MTS) has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales, and the coupling of synchronization latency jitter and pulse phase difference. In this paper, the multi-timescale MTS model is conducted, and the reinforcement learning (RL) and analytic hierarchy process (AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation… More >

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Guojun Wang^1,2, Qi Liu^3,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4471-4488, 2024, DOI:10.32604/cmc.2024.047669
Abstract With the rapid advancement in exploring perceptual interactions and digital twins, metaverse technology has emerged to transcend the constraints of space-time and reality, facilitating remote AI-based collaboration. In this dynamic metasystem environment, frequent information exchanges necessitate robust security measures, with Authentication and Key Agreement (AKA) serving as the primary line of defense to ensure communication security. However, traditional AKA protocols fall short in meeting the low-latency requirements essential for synchronous interactions within the metaverse. To address this challenge and enable nearly latency-free interactions, a novel low-latency AKA protocol based on chaotic maps is proposed. This protocol not only ensures mutual… More >

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Yunhe Wang¹, Yuxin Xia², Shuai Liu^2,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4489-4507, 2024, DOI:10.32604/cmc.2024.048813
（This article belongs to the Special Issue: Multimodal Learning in Image Processing)
Abstract In recent years, skeleton-based action recognition has made great achievements in Computer Vision. A graph convolutional network (GCN) is effective for action recognition, modelling the human skeleton as a spatio-temporal graph. Most GCNs define the graph topology by physical relations of the human joints. However, this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs, resulting in a low recognition rate for specific actions with implicit correlation between joint pairs. In addition, existing methods ignore the trend correlation between adjacent frames within an action and context clues, leading to… More >

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