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  • Open Access

    ARTICLE

    Prediction on Failure Pressure of Pipeline Containing Corrosion Defects Based on ISSA-BPNN Model

    Qi Zhuang1,*, Dong Liu2, Zhuo Chen3

    Energy Engineering, Vol.121, No.3, pp. 821-834, 2024, DOI:10.32604/ee.2023.044054

    Abstract Oil and gas pipelines are affected by many factors, such as pipe wall thinning and pipeline rupture. Accurate prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety management. Aiming at the shortcomings of the BP Neural Network (BPNN) model, such as low learning efficiency, sensitivity to initial weights, and easy falling into a local optimal state, an Improved Sparrow Search Algorithm (ISSA) is adopted to optimize the initial weights and thresholds of BPNN, and an ISSA-BPNN failure pressure prediction model for corroded pipelines is established. Taking 61 sets More >

  • Open Access

    ARTICLE

    A Time Series Intrusion Detection Method Based on SSAE, TCN and Bi-LSTM

    Zhenxiang He*, Xunxi Wang, Chunwei Li

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 845-871, 2024, DOI:10.32604/cmc.2023.046607

    Abstract In the fast-evolving landscape of digital networks, the incidence of network intrusions has escalated alarmingly. Simultaneously, the crucial role of time series data in intrusion detection remains largely underappreciated, with most systems failing to capture the time-bound nuances of network traffic. This leads to compromised detection accuracy and overlooked temporal patterns. Addressing this gap, we introduce a novel SSAE-TCN-BiLSTM (STL) model that integrates time series analysis, significantly enhancing detection capabilities. Our approach reduces feature dimensionality with a Stacked Sparse Autoencoder (SSAE) and extracts temporally relevant features through a Temporal Convolutional Network (TCN) and Bidirectional Long… More >

  • Open Access

    ARTICLE

    An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

    Syeda Wajiha Zahra1, Muhammad Nadeem2, Ali Arshad3,*, Saman Riaz3, Muhammad Abu Bakr4, Ashit Kumar Dutta5, Zaid Alzaid6, Badr Almutairi7, Sultan Almotairi8

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 589-616, 2024, DOI:10.32604/cmc.2023.042386

    Abstract Various organizations store data online rather than on physical servers. As the number of user’s data stored in cloud servers increases, the attack rate to access data from cloud servers also increases. Different researchers worked on different algorithms to protect cloud data from replay attacks. None of the papers used a technique that simultaneously detects a full-message and partial-message replay attack. This study presents the development of a TKN (Text, Key and Name) cryptographic algorithm aimed at protecting data from replay attacks. The program employs distinct ways to encrypt plain text [P], a user-defined Key… More >

  • Open Access

    ARTICLE

    An End-To-End Hyperbolic Deep Graph Convolutional Neural Network Framework

    Yuchen Zhou1, Hongtao Huo1, Zhiwen Hou1, Lingbin Bu1, Yifan Wang1, Jingyi Mao1, Xiaojun Lv2, Fanliang Bu1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 537-563, 2024, DOI:10.32604/cmes.2023.044895

    Abstract Graph Convolutional Neural Networks (GCNs) have been widely used in various fields due to their powerful capabilities in processing graph-structured data. However, GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions, resulting in substantial distortions. Moreover, most of the existing GCN models are shallow structures, which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures. To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information… More >

  • Open Access

    ARTICLE

    Leveraging diverse cell-death patterns to predict the clinical outcome of immune checkpoint therapy in lung adenocarcinoma: Based on muti-omics analysis and vitro assay

    HONGYUAN LIANG1,#, YANQIU LI2,#, YONGGANG QU3, LINGYUN ZHANG4,*

    Oncology Research, Vol.32, No.2, pp. 393-407, 2024, DOI:10.32604/or.2023.031134

    Abstract Advanced LUAD shows limited response to treatment including immune therapy. With the development of sequencing omics, it is urgent to combine high-throughput multi-omics data to identify new immune checkpoint therapeutic response markers. Using GSE72094 (n = 386) and GSE31210 (n = 226) gene expression profile data in the GEO database, we identified genes associated with lung adenocarcinoma (LUAD) death using tools such as “edgeR” and “maftools” and visualized the characteristics of these genes using the “circlize” R package. We constructed a prognostic model based on death-related genes and optimized the model using LASSO-Cox regression methods.… More >

  • Open Access

    ARTICLE

    Analytical and Numerical Methods to Study the MFPT and SR of a Stochastic Tumor-Immune Model

    Ying Zhang1, Wei Li1,*, Guidong Yang1, Snezana Kirin2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2177-2199, 2024, DOI:10.32604/cmes.2023.030728

    Abstract The Mean First-Passage Time (MFPT) and Stochastic Resonance (SR) of a stochastic tumor-immune model with noise perturbation are discussed in this paper. Firstly, considering environmental perturbation, Gaussian white noise and Gaussian colored noise are introduced into a tumor growth model under immune surveillance. As follows, the long-time evolution of the tumor characterized by the Stationary Probability Density (SPD) and MFPT is obtained in theory on the basis of the Approximated Fokker-Planck Equation (AFPE). Herein the recurrence of the tumor from the extinction state to the tumor-present state is more concerned in this paper. A more… More >

  • Open Access

    ARTICLE

    Convolutional Neural Network Model for Fire Detection in Real-Time Environment

    Abdul Rehman, Dongsun Kim*, Anand Paul

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2289-2307, 2023, DOI:10.32604/cmc.2023.036435

    Abstract Disasters such as conflagration, toxic smoke, harmful gas or chemical leakage, and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent. The calamities are causing massive fiscal and human life casualties. However, Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco. The authors have proposed an early fire detection system uses machine and/or deep learning algorithms. The article presents an Intelligent Industrial Monitoring System (IIMS) and introduces an Industrial Smart Social Agent (ISSA) in the Industrial SIoT (ISIoT) paradigm.… More >

  • Open Access

    ARTICLE

    Hsa_circ_0036740 in familial adenomatous polyposis: Immune regulation and neutrophil effects in CRC based on high-throughput assay

    ZHIWANG LI, HAN YU, YUPING LIU, WEISHENG WU, HAIJING ZENG, EN LI*

    BIOCELL, Vol.47, No.11, pp. 2409-2422, 2023, DOI:10.32604/biocell.2023.031186

    Abstract Familial adenomatous polyposis (FAP) is an autosomal dominant disease with a high probability of becoming cancerous. Many RNAs potentially associated with FAP have not been identified. In this study, a circRNA (circular RNA) expression profile of FAP was established using a circRNA microarray, and differentially expressed circRNAs were verified by RT-qPCR. The effects of hsa_circ_0036740 on the malignant behavior of tumor cells (proliferation, apoptosis, and epithelial mesenchymal transition) and the levels of C3A complement protein expression were evaluated. Moreover, neutrophils were isolated and co-cultured with colorectal cancer cells (CRCs), followed by measurements of MPO-DNA, citrullinated… More > Graphic Abstract

    Hsa_circ_0036740 in familial adenomatous polyposis: Immune regulation and neutrophil effects in CRC based on high-throughput assay

  • Open Access

    ARTICLE

    NPBMT: A Novel and Proficient Buffer Management Technique for Internet of Vehicle-Based DTNs

    Sikandar Khan1, Khalid Saeed1, Muhammad Faran Majeed2,*, Salman A. AlQahtani3, Khursheed Aurangzeb3, Muhammad Shahid Anwar4,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1303-1323, 2023, DOI:10.32604/cmc.2023.039697

    Abstract Delay Tolerant Networks (DTNs) have the major problem of message delay in the network due to a lack of end-to-end connectivity between the nodes, especially when the nodes are mobile. The nodes in DTNs have limited buffer storage for storing delayed messages. This instantaneous sharing of data creates a low buffer/shortage problem. Consequently, buffer congestion would occur and there would be no more space available in the buffer for the upcoming messages. To address this problem a buffer management policy is proposed named “A Novel and Proficient Buffer Management Technique (NPBMT) for the Internet of… More >

  • Open Access

    ARTICLE

    An Enhanced Automatic Arabic Essay Scoring System Based on Machine Learning Algorithms

    Nourmeen Lotfy1, Abdulaziz Shehab1,2,*, Mohammed Elhoseny1,3, Ahmed Abu-Elfetouh1

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1227-1249, 2023, DOI:10.32604/cmc.2023.039185

    Abstract Despite the extensive effort to improve intelligent educational tools for smart learning environments, automatic Arabic essay scoring remains a big research challenge. The nature of the writing style of the Arabic language makes the problem even more complicated. This study designs, implements, and evaluates an automatic Arabic essay scoring system. The proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing tasks. Then, it comprises two main components: the grading engine and the adaptive fusion engine. The grading engine employs string-based and corpus-based similarity algorithms separately.… More >

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