Yin Ni¹, Wu Zeng^2,*, Peng Xia¹, Guang Stanley Yang³, Ruochen Tan⁴

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5295-5312, 2024, DOI:10.32604/cmc.2024.049911 - 20 June 2024

Abstract Deepfake-generated fake faces, commonly utilized in identity-related activities such as political propaganda, celebrity impersonations, evidence forgery, and familiar fraud, pose new societal threats. Although current deepfake generators strive for high realism in visual effects, they do not replicate biometric signals indicative of cardiac activity. Addressing this gap, many researchers have developed detection methods focusing on biometric characteristics. These methods utilize classification networks to analyze both temporal and spectral domain features of the remote photoplethysmography (rPPG) signal, resulting in high detection accuracy. However, in the spectral analysis, existing approaches often only consider the power spectral density… More >

Zuozheng Lian^1,2, Yongchao Yin¹, Haizhen Wang^1,2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3939-3954, 2024, DOI:10.32604/cmc.2024.049780 - 20 June 2024

Abstract The relationship between users and items, which cannot be recovered by traditional techniques, can be extracted by the recommendation algorithm based on the graph convolution network. The current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction data. This paper presents a new approach to address such issues, utilizing the graph convolution network to extract association relations. The proposed approach mainly includes three modules: Embedding layer, forward propagation layer, and score prediction layer. The embedding layer models users and items according to their interaction information and… More >

Feng Zhao, Guangdi Liu^*, Xiaoqiang Chen, Ying Wang

Energy Engineering, Vol.121, No.7, pp. 1865-1882, 2024, DOI:10.32604/ee.2024.048209 - 11 June 2024

Abstract In light of the prevailing issue that the existing convolutional neural network (CNN) power quality disturbance identification method can only extract single-scale features, which leads to a lack of feature information and weak anti-noise performance, a new approach for identifying power quality disturbances based on an adaptive Kalman filter (KF) and multi-scale channel attention (MS-CAM) fused convolutional neural network is suggested. Single and composite-disruption signals are generated through simulation. The adaptive maximum likelihood Kalman filter is employed for noise reduction in the initial disturbance signal, and subsequent integration of multi-scale features into the conventional CNN… More >

Haoan Gu¹, Kai Zhu¹, Alfred Strauss², Yehui Shi^3,4, Dragoslav Sumarac⁵, Maosen Cao^1,*

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 363-380, 2024, DOI:10.32604/sdhm.2024.042388 - 05 June 2024

Abstract Concrete materials and structures are extensively used in transformation infrastructure and they usually bear cracks during their long-term operation. Detecting cracks using deep-learning algorithms like YOLOv3 (You Only Look Once version 3) is a new trend to pursue intelligent detection of concrete surface cracks. YOLOv3 is a typical deep-learning algorithm used for object detection. Owing to its generality, YOLOv3 lacks specific efficiency and accuracy in identifying concrete surface cracks. An improved algorithm based on YOLOv3, specialized in the rapid and accurate identification of concrete surface cracks is worthy of investigation. This study proposes a tailored… More >

Lin Ma¹, Liyong Wang¹, Shuang Zeng¹, Yutong Zhao¹, Chang Liu¹, Heng Zhang¹, Qiong Wu^2,*, Hongbo Ren²

Energy Engineering, Vol.121, No.6, pp. 1473-1493, 2024, DOI:10.32604/ee.2024.047332 - 21 May 2024

Abstract Accurate load forecasting forms a crucial foundation for implementing household demand response plans and optimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations, a single prediction model is hard to capture temporal features effectively, resulting in diminished prediction accuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neural network (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), is proposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features from the original data, enhancing the quality of data… More >

Xiaonan Li^1,2, Qiang Wang¹, Conglai Fan^2,3, Wei Zhan¹, Mingliang Zhang^4,*

Computer Systems Science and Engineering, Vol.48, No.3, pp. 773-792, 2024, DOI:10.32604/csse.2024.039849 - 20 May 2024

Abstract The field of finance heavily relies on cybersecurity to safeguard its systems and clients from harmful software. The identification of malevolent code within financial software is vital for protecting both the financial system and individual clients. Nevertheless, present detection models encounter limitations in their ability to identify malevolent code and its variations, all while encompassing a multitude of parameters. To overcome these obstacles, we introduce a lean model for classifying families of malevolent code, formulated on Ghost-DenseNet-SE. This model integrates the Ghost module, DenseNet, and the squeeze-and-excitation (SE) channel domain attention mechanism. It substitutes the… More >

Zhenxiang He^*, Zhenyu Zhao, Ke Chen, Yanlin Liu

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3023-3045, 2024, DOI:10.32604/cmc.2024.050281 - 15 May 2024

Abstract The fast-paced development of blockchain technology is evident. Yet, the security concerns of smart contracts represent a significant challenge to the stability and dependability of the entire blockchain ecosystem. Conventional smart contract vulnerability detection primarily relies on static analysis tools, which are less efficient and accurate. Although deep learning methods have improved detection efficiency, they are unable to fully utilize the static relationships within contracts. Therefore, we have adopted the advantages of the above two methods, combining feature extraction mode of tools with deep learning techniques. Firstly, we have constructed corresponding feature extraction mode for… More >

Zhongyuan Zhang, Wenqiu Zhu^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2547-2563, 2024, DOI:10.32604/cmc.2024.048755 - 15 May 2024

Abstract Remote sensing imagery, due to its high altitude, presents inherent challenges characterized by multiple scales, limited target areas, and intricate backgrounds. These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery. Additionally, these complexities contribute to inaccuracies in target localization and hinder precise target categorization. This paper addresses these challenges by proposing a solution: The YOLO-MFD model (YOLO-MFD: Remote Sensing Image Object Detection with Multi-scale Fusion Dynamic Head). Before presenting our method, we delve into the prevalent issues faced in remote sensing imagery… More >

Chunhua Wang^1,2, Wenqian Shang^1,2,*, Tong Yi^3,*, Haibin Zhu⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1939-1956, 2024, DOI:10.32604/cmc.2024.048135 - 15 May 2024

Abstract The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms, yielding outstanding achievements across diverse domains. Nonetheless, self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures. In response, this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network (DSLD), which adopts a diffusion sampling method to capture more comprehensive semantic information of the data. Additionally, the model leverages the joint correlation information of labels and data to introduce the computation of text representation, correcting semantic representation biases in the data, and More >

Zhijun Guo¹, Yun Sun^2,*, Ying Wang¹, Chaoqi Fu³, Jilong Zhong^4,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2375-2398, 2024, DOI:10.32604/cmc.2024.048112 - 15 May 2024

Abstract Due to the time-varying topology and possible disturbances in a conflict environment, it is still challenging to maintain the mission performance of flying Ad hoc networks (FANET), which limits the application of Unmanned Aerial Vehicle (UAV) swarms in harsh environments. This paper proposes an intelligent framework to quickly recover the cooperative coverage mission by aggregating the historical spatio-temporal network with the attention mechanism. The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model. A spatio-temporal node pooling method is proposed to ensure all node location features… More >