Ziyu Xu¹, Kuo Tian^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011245

Abstract Considering experimental testing data is costly, and sensor data is often sparse, while simulation analysis provides overall strength information with lower accuracy, a digital twin framework is proposed for full-field structural strength assessment and prediction. The framework is mainly divided into two stages. In the offline stage, the simulation model of the structure is established, and the sensor layouts are completed. Then, the DNN pre-training model is constructed based on the reduced simulation data. In the online stage, the experimentally measured data are predicted to obtain the time-series sensors data, and the traditional transfer learning… More >

Yue Cao^1,2, Longsheng Bao¹, Xiaowei Zhang^1,*, Zhanfei Wang¹, Bingqian Li¹

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 485-503, 2024, DOI:10.32604/sdhm.2024.049698 - 05 June 2024

Abstract This study addresses the issue of inaccurate single damage fingerprint recognition during the process of bridge damage identification. To improve accuracy, the proposed approach involves fusing displacement mode difference and curvature mode difference data for single damage identification, and curvature mode difference and displacement mode wavelet coefficient difference data for two damage identification. The methodology begins by establishing a finite element model of the cable-stayed bridge and obtaining the original damage fingerprints, displacement modes, curvature modes, and wavelet coefficient differences of displacement modes through modal analysis. A fusion program based on the D-S evidence theory… More > Graphic Abstract

Yongwang Yuan¹, Xiangwei Liu^2,3,*, Ke Lu^1,3

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1227-1252, 2024, DOI:10.32604/cmc.2023.046937 - 30 January 2024

Abstract Predictive Business Process Monitoring (PBPM) is a significant research area in Business Process Management (BPM) aimed at accurately forecasting future behavioral events. At present, deep learning methods are widely cited in PBPM research, but no method has been effective in fusing data information into the control flow for multi-perspective process prediction. Therefore, this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data fusion. Firstly, the first layer BERT network learns the correlations between different category attribute data. Then, the attribute data is integrated into a weighted event-level feature vector and More >

Vinay Kukreja¹, Asha Abraham², K. Kalaiselvi³, K. Deepa Thilak³, Shanmugasundaram Hariharan⁴, Shih-Yu Chen^5,6,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2899-2914, 2023, DOI:10.32604/cmc.2023.045136 - 26 December 2023

Abstract Data fusion generates fused data by combining multiple sources, resulting in information that is more consistent, accurate, and useful than any individual source and more reliable and consistent than the raw original data, which are often imperfect, inconsistent, complex, and uncertain. Traditional data fusion methods like probabilistic fusion, set-based fusion, and evidential belief reasoning fusion methods are computationally complex and require accurate classification and proper handling of raw data. Data fusion is the process of integrating multiple data sources. Data filtering means examining a dataset to exclude, rearrange, or apportion data according to the criteria.… More >

Shuqin Zhang, Xinyu Su^*, Yunfei Han, Tianhui Du, Peiyu Shi

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3993-4023, 2023, DOI:10.32604/cmc.2023.044526 - 26 December 2023

Abstract Advanced Persistent Threat (APT) is now the most common network assault. However, the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks. They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats. To address the above problems, firstly, this paper constructs the multi-source threat element analysis ontology (MTEAO) by integrating multi-source network security knowledge bases. Subsequently, based on MTEAO, we propose a two-layer threat prediction model (TL-TPM) that combines… More >

Sahar Arooj¹, Muhammad Farhan Khan², Tariq Shahzad³, Muhammad Adnan Khan^4,5,6, Muhammad Umar Nasir⁷, Muhammad Zubair¹, Atta-ur-Rahman⁸, Khmaies Ouahada^3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2813-2831, 2023, DOI:10.32604/cmc.2023.043013 - 26 December 2023

Abstract Breast cancer (BC) is the most widespread tumor in females worldwide and is a severe public health issue. BC is the leading reason of death affecting females between the ages of 20 to 59 around the world. Early detection and therapy can help women receive effective treatment and, as a result, decrease the rate of breast cancer disease. The cancer tumor develops when cells grow improperly and attack the healthy tissue in the human body. Tumors are classified as benign or malignant, and the absence of cancer in the breast is considered normal. Deep learning,… More >

Shuqin Zhang, Xinyu Su^*, Peiyu Shi, Tianhui Du, Yunfei Han

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 349-377, 2023, DOI:10.32604/cmc.2023.040964 - 31 October 2023

Abstract Cyber Threat Intelligence (CTI) is a valuable resource for cybersecurity defense, but it also poses challenges due to its multi-source and heterogeneous nature. Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly. To address these challenges, we propose a novel approach that consists of three steps. First, we construct the attack and defense analysis of the cybersecurity ontology (ADACO) model by integrating multiple cybersecurity databases. Second, we develop the threat evolution prediction algorithm (TEPA), which can automatically detect threats at device nodes, correlate More >

Slim Ben Chaabane^1,2,*, Anas Bushnag¹

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2839-2852, 2023, DOI:10.32604/cmc.2023.032760 - 31 October 2022

Abstract A new model is proposed in this paper on color edge detection that uses the second derivative operators and data fusion mechanism. The second-order neighborhood shows the connection between the current pixel and the surroundings of this pixel. This connection is for each RGB component color of the input image. Once the image edges are detected for the three primary colors: red, green, and blue, these colors are merged using the combination rule. Then, the final decision is applied to obtain the segmentation. This process allows different data sources to be combined, which is essential More >

A. Reyana^1,*, P. Vijayalakshmi²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1013-1025, 2023, DOI:10.32604/iasc.2023.032538 - 29 September 2022

Abstract Destructive wildfires are becoming an annual event, similar to climate change, resulting in catastrophes that wreak havoc on both humans and the environment. The result, however, is disastrous, causing irreversible damage to the ecosystem. The location of the incident and the hotspot can sometimes have an impact on early fire detection systems. With the advancement of intelligent sensor-based control technologies, the multi-sensor data fusion technique integrates data from multiple sensor nodes. The primary objective to avoid wildfire is to identify the exact location of wildfire occurrence, allowing fire units to respond as soon as possible. More >

Bin Lin¹, Zhengyu Li^2,*, Dong Wen², Jianchao Liu², Shan Yang³, Yong Zhou², Chao Lu⁴, Qian Qiu^4,5

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1509-1521, 2023, DOI:10.32604/cmc.2023.032557 - 22 September 2022

Abstract The further development of catalytic elements has been plagued by activation and binary problems. The automatic shift model that has emerged in recent years helps components achieve full range. However, the detection data still remains unstable in the shift area (7%~13%). This paper proposes a Catalytic Combustion and Thermal Conductivity (CCTC) model for the specified range, which can be explained from two aspects based on the existing methods. On the one hand, it uses iterative location search to process heterogeneous data, judges the prediction position of data points, and then gives weight evaluation. On the… More >