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  • Open Access


    DeBERTa-GRU: Sentiment Analysis for Large Language Model

    Adel Assiri1, Abdu Gumaei2,*, Faisal Mehmood3,*, Touqeer Abbas4, Sami Ullah5

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4219-4236, 2024, DOI:10.32604/cmc.2024.050781

    Abstract Modern technological advancements have made social media an essential component of daily life. Social media allow individuals to share thoughts, emotions, and ideas. Sentiment analysis plays the function of evaluating whether the sentiment of the text is positive, negative, neutral, or any other personal emotion to understand the sentiment context of the text. Sentiment analysis is essential in business and society because it impacts strategic decision-making. Sentiment analysis involves challenges due to lexical variation, an unlabeled dataset, and text distance correlations. The execution time increases due to the sequential processing of the sequence models. However,… More >

  • Open Access


    Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

    Arnab Dey1,*, Samit Biswas1, Dac-Nhuong Le2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3067-3087, 2024, DOI:10.32604/cmc.2024.049512

    Abstract Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers the likelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in video streams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enable instant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing action datasets often lack diversity and specificity for workout actions, hindering the development of accurate recognition models. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significant… More >

  • Open Access


    RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

    Farah Mohammad1,*, Saad Al-Ahmadi2, Jalal Al-Muhtadi1,2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3175-3192, 2024, DOI:10.32604/cmc.2023.042873

    Abstract Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users. It hinders the economic growth of utility companies, poses electrical risks, and impacts the high energy costs borne by consumers. The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data, including information on client consumption, which may be used to identify electricity theft using machine learning and deep learning techniques. Moreover, there also exist different solutions such as hardware-based solutions to detect electricity theft that… More >

  • Open Access


    ResNeSt-biGRU: An Intrusion Detection Model Based on Internet of Things

    Yan Xiang1,2, Daofeng Li1,2,*, Xinyi Meng1,2, Chengfeng Dong1,2, Guanglin Qin1,2

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1005-1023, 2024, DOI:10.32604/cmc.2024.047143

    Abstract The rapid expansion of Internet of Things (IoT) devices across various sectors is driven by steadily increasing demands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device has caught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. This has resulted in a myriad of security challenges, including information leakage, malware propagation, and financial loss, among others. Consequently, developing an intrusion detection system to identify both active and potential intrusion traffic in IoT networks is of paramount importance. In this paper, we propose… More >

  • Open Access


    An Approach for Human Posture Recognition Based on the Fusion PSE-CNN-BiGRU Model

    Xianghong Cao, Xinyu Wang, Xin Geng*, Donghui Wu, Houru An

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 385-408, 2024, DOI:10.32604/cmes.2024.046752

    Abstract This study proposes a pose estimation-convolutional neural network-bidirectional gated recurrent unit (PSE-CNN-BiGRU) fusion model for human posture recognition to address low accuracy issues in abnormal posture recognition due to the loss of some feature information and the deterioration of comprehensive performance in model detection in complex home environments. Firstly, the deep convolutional network is integrated with the Mediapipe framework to extract high-precision, multi-dimensional information from the key points of the human skeleton, thereby obtaining a human posture feature set. Thereafter, a double-layer BiGRU algorithm is utilized to extract multi-layer, bidirectional temporal features from the human… More >

  • Open Access


    Strengthening Network Security: Deep Learning Models for Intrusion Detection with Optimized Feature Subset and Effective Imbalance Handling

    Bayi Xu1, Lei Sun2,*, Xiuqing Mao2, Chengwei Liu3, Zhiyi Ding2

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1995-2022, 2024, DOI:10.32604/cmc.2023.046478

    Abstract In recent years, frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security. This paper presents a novel intrusion detection system consisting of a data preprocessing stage and a deep learning model for accurately identifying network attacks. We have proposed four deep neural network models, which are constructed using architectures such as Convolutional Neural Networks (CNN), Bi-directional Long Short-Term Memory (BiLSTM), Bidirectional Gate Recurrent Unit (BiGRU), and Attention mechanism. These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the More >

  • Open Access


    An Intelligent Algorithm for Solving Weapon-Target Assignment Problem: DDPG-DNPE Algorithm

    Tengda Li, Gang Wang, Qiang Fu*, Xiangke Guo, Minrui Zhao, Xiangyu Liu

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3499-3522, 2023, DOI:10.32604/cmc.2023.041253

    Abstract Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decision-making, such as large computational amount, slow solution speed, and low calculation accuracy, combined with deep reinforcement learning theory, an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed, which uses a double noise mechanism to expand the search range of the action, and introduces a priority experience playback mechanism to effectively achieve data utilization. Finally, the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield. The results of the experiment show that, under the… More >

  • Open Access


    Anomaly Detection and Access Control for Cloud-Edge Collaboration Networks

    Bingcheng Jiang, Qian He*, Zhongyi Zhai, Hang Su

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2335-2353, 2023, DOI:10.32604/iasc.2023.039989

    Abstract Software-defined networking (SDN) enables the separation of control and data planes, allowing for centralized control and management of the network. Without adequate access control methods, the risk of unauthorized access to the network and its resources increases significantly. This can result in various security breaches. In addition, if authorized devices are attacked or controlled by hackers, they may turn into malicious devices, which can cause severe damage to the network if their abnormal behaviour goes undetected and their access privileges are not promptly restricted. To solve those problems, an anomaly detection and access control mechanism… More >

  • Open Access


    Multimodal Sentiment Analysis Using BiGRU and Attention-Based Hybrid Fusion Strategy

    Zhizhong Liu*, Bin Zhou, Lingqiang Meng, Guangyu Huang

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1963-1981, 2023, DOI:10.32604/iasc.2023.038835

    Abstract Recently, multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams, which has great potential to surpass unimodal sentiment analysis. One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy. Unfortunately, existing work always considers feature-level fusion or decision-level fusion, and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion. To improve the performance of multimodal sentiment analysis, we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy (BAHFS). Firstly, we apply BiGRU to More >

  • Open Access


    Intrusion Detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model

    Mohammad S. Al-kahtani1, Zahid Mehmood2,3,*, Tariq Sadad4, Islam Zada5, Gauhar Ali6, Mohammed ElAffendi6

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2279-2290, 2023, DOI:10.32604/iasc.2023.037673

    Abstract Cybersecurity threats are increasing rapidly as hackers use advanced techniques. As a result, cybersecurity has now a significant factor in protecting organizational limits. Intrusion detection systems (IDSs) are used in networks to flag serious issues during network management, including identifying malicious traffic, which is a challenge. It remains an open contest over how to learn features in IDS since current approaches use deep learning methods. Hybrid learning, which combines swarm intelligence and evolution, is gaining attention for further improvement against cyber threats. In this study, we employed a PSO-GA (fusion of particle swarm optimization (PSO) More >

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