Jingyu Zhang^1,2, Pian Zhou¹, Jin Wang¹, Osama Alfarraj³, Saurabh Singh⁴, Min Zhu^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1613-1633, 2024, DOI:10.32604/cmes.2023.044418

Abstract Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the system. This technology has been widely used and has developed rapidly in big data systems across various fields. An increasing number of users are participating in application systems that use blockchain as their underlying architecture. As the number of transactions and the capital involved in blockchain grow, ensuring information security becomes imperative. Addressing the verification of transactional information security and privacy has emerged as a critical challenge. Blockchain-based verification methods can effectively eliminate the need for centralized third-party organizations. However,… More >

Faidance Mashauri^1,2,*, Mokili Mbuluyo^1,3, Nsalambi Nkongolo^2,4

Revue Internationale de Géomatique, Vol.32, pp. 99-122, 2023, DOI:10.32604/rig.2023.044899

Abstract L’analyse et la quantification des caractéristiques hydro-morphométriques sont essentielles pour une meilleure gestion des ressources en eau et une planification plus efficace des projets hydroélectriques dans le bassin de la Tshopo. Malheureusement, peu d’études ont été réalisées pour évaluer ces caractéristiques à l’échelle de ce bassin. Notre approche méthodologique consiste à utiliser les outils d’analyse des logiciels Système d’Information Géographique (SIG) appliqués au Modèle Numérique de Terrain (MNT) dérivé de l’image Advanced Land Observing Satellite (ALOS) World 3D-30m. Cela nous a permis d’extraire automatiquement le réseau hydrographique et de générer les sous-bassins versants de la Tshopo. Les résultats de cette… More >

Abdul Haseeb¹, Muhammad Attique Khan^2,*, Majed Alhaisoni³, Ghadah Aldehim⁴, Leila Jamel⁴, Usman Tariq⁵, Taerang Kim⁶, Jae-Hyuk Cha⁶

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3895-3920, 2023, DOI:10.32604/cmc.2023.045244

Abstract Diagnosing gastrointestinal cancer by classical means is a hazardous procedure. Years have witnessed several computerized solutions for stomach disease detection and classification. However, the existing techniques faced challenges, such as irrelevant feature extraction, high similarity among different disease symptoms, and the least-important features from a single source. This paper designed a new deep learning-based architecture based on the fusion of two models, Residual blocks and Auto Encoder. First, the Hyper-Kvasir dataset was employed to evaluate the proposed work. The research selected a pre-trained convolutional neural network (CNN) model and improved it with several residual blocks. This process aims to improve… More >

Yuesheng Zhao¹, Xiaoling Wang^1,*, Yutong Luo^2,*, Muhammad Shamrooz Aslam³

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3325-3347, 2023, DOI:10.32604/cmc.2023.040528

Abstract With the rapid advancement of wearable devices, Human Activities Recognition (HAR) based on these devices has emerged as a prominent research field. The objective of this study is to enhance the recognition performance of HAR by proposing an LSTM-1DCNN recognition algorithm that utilizes a single triaxial accelerometer. This algorithm comprises two branches: one branch consists of a Long and Short-Term Memory Network (LSTM), while the other parallel branch incorporates a one-dimensional Convolutional Neural Network (1DCNN). The parallel architecture of LSTM-1DCNN initially extracts spatial and temporal features from the accelerometer data separately, which are then concatenated and fed into a fully… More >

Jieren Cheng^1,2, Xiaolong Chen^1,*, Wenghang Xu³, Shuai Hua³, Zhu Tang¹, Victor S. Sheng⁴

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1779-1793, 2023, DOI:10.32604/cmc.2023.042980

Abstract In the realm of Multi-Label Text Classification (MLTC), the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches. Many studies in semantic feature extraction have turned to external knowledge to augment the model’s grasp of textual content, often overlooking intrinsic textual cues such as label statistical features. In contrast, these endogenous insights naturally align with the classification task. In our paper, to complement this focus on intrinsic knowledge, we introduce a novel Gate-Attention mechanism. This mechanism adeptly integrates statistical features from the text itself into the semantic fabric, enhancing the model’s capacity… More >

Yujun Zhang^*, Dezhi Han, Peng Chen

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2657-2675, 2023, DOI:10.32604/cmc.2023.042311

Abstract Synthetic Aperture Radar (SAR) image target detection has widespread applications in both military and civil domains. However, SAR images pose challenges due to strong scattering, indistinct edge contours, multi-scale representation, sparsity, and severe background interference, which make the existing target detection methods in low accuracy. To address this issue, this paper proposes a multi-scale fusion framework (Swin-PAFF) for SAR target detection that utilizes the global context perception capability of the Transformer and the multi-layer feature fusion learning ability of the feature pyramid structure (FPN). Firstly, to tackle the issue of inadequate perceptual image context information in SAR target detection, we… More >

Lizhe Zhang^1,2,*, Yongqiang Huang², Jia Nie², Kenian Wang^1,2

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1467-1488, 2023, DOI:10.32604/cmc.2023.042305

Abstract System-wide information management (SWIM) is a complex distributed information transfer and sharing system for the next generation of Air Transportation System (ATS). In response to the growing volume of civil aviation air operations, users accessing different authentication domains in the SWIM system have problems with the validity, security, and privacy of SWIM-shared data. In order to solve these problems, this paper proposes a SWIM cross-domain authentication scheme based on a consistent hashing algorithm on consortium blockchain and designs a blockchain certificate format for SWIM cross-domain authentication. The scheme uses a consistent hash algorithm with virtual nodes in combination with a… More >

Muhammad Usman Ghani Khan¹, Abid Mehmood², Mourad Elhadef², Shehzad Ashraf Chaudhry^2,3,*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2225-2249, 2023, DOI:10.32604/cmc.2023.038303

Abstract The proliferation of deluding data such as fake news and phony audits on news web journals, online publications, and internet business apps has been aided by the availability of the web, cell phones, and social media. Individuals can quickly fabricate comments and news on social media. The most difficult challenge is determining which news is real or fake. Accordingly, tracking down programmed techniques to recognize fake news online is imperative. With an emphasis on false news, this study presents the evolution of artificial intelligence techniques for detecting spurious social media content. This study shows past, current, and possible methods that… More >

Zhicheng Liu¹, Long Zhao^1,*, Guanru Wen¹, Peng Yuan², Qiu Jin¹

Structural Durability & Health Monitoring, Vol.17, No.6, pp. 541-555, 2023, DOI:10.32604/sdhm.2023.029760

Abstract The displacement of transmission tower feet can seriously affect the safe operation of the tower, and the accuracy of structural health monitoring methods is limited at the present stage. The application of deep learning method provides new ideas for structural health monitoring of towers, but the current amount of tower vibration fault data is restricted to provide adequate training data for Deep Learning (DL). In this paper, we propose a DT-DL based tower foot displacement monitoring method, which firstly simulates the wind-induced vibration response data of the tower under each fault condition by finite element method. Then the vibration signal… More > Graphic Abstract

Cheng Wang^1,2,*, Zhuowei Zheng¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1563-1592, 2024, DOI:10.32604/cmes.2023.031103

Abstract The advent of Industry 4.0 has compelled businesses to adopt digital approaches that combine software to enhance production efficiency. In this rapidly evolving market, software development is an ongoing process that must be tailored to meet the dynamic needs of enterprises. However, internal research and development can be prohibitively expensive, driving many enterprises to outsource software development and upgrades to external service providers. This paper presents a software upgrade outsourcing model for enterprises and service providers that accounts for the impact of market fluctuations on software adaptability. To mitigate the risk of adverse selection due to asymmetric information about the… More >