Muhammad Tashfeen¹, Fazal Dayan¹, Muhammad Aziz ur Rehman¹, Thabet Abdeljawad^2,3,4,5,*, Aiman Mukheimer²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2527-2554, 2024, DOI:10.32604/cmes.2024.056269 - 31 October 2024

Abstract Foot-and-mouth disease (FMD) is a viral disease that affects cloven-hoofed animals including cattle, pigs, and sheep, hence causing export bans among others, causing high economic losses due to reduced productivity. The global effect of FMD is most felt where livestock rearing forms an important source of income. It is therefore important to understand the modes of transmission of FMD to control its spread and prevent its occurrence. This work intends to address these dynamics by including the efficacy of active migrant animals transporting the disease from one area to another in a fuzzy mathematical modeling… More >

Muhammad Tashfeen¹, Fazal Dayan¹, Muhammad Aziz Ur Rehman¹, Thabet Abdeljawad^2,3,4,5,*, Aiman Mukheimer²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1419-1446, 2024, DOI:10.32604/cmes.2024.053242 - 27 September 2024

Abstract This study analyzes the transmission of typhoid fever caused by Salmonella typhi using a mathematical model that highlights the significance of delay in its effectiveness. Time delays can affect the nature of patterns and slow down the emergence of patterns in infected population density. The analyzed model is expanded with the equilibrium analysis, reproduction number, and stability analysis. This study aims to establish and explore the non-standard finite difference (NSFD) scheme for the typhoid fever virus transmission model with a time delay. In addition, the forward Euler method and Runge-Kutta method of order 4 (RK-4)… More >

Weijian Fan^1,*, Ziwei Shi²

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2723-2741, 2024, DOI:10.32604/cmc.2024.050344 - 15 May 2024

Abstract With the explosive growth of false information on social media platforms, the automatic detection of multimodal false information has received increasing attention. Recent research has significantly contributed to multimodal information exchange and fusion, with many methods attempting to integrate unimodal features to generate multimodal news representations. However, they still need to fully explore the hierarchical and complex semantic correlations between different modal contents, severely limiting their performance detecting multimodal false information. This work proposes a two-stage detection framework for multimodal false information detection, called ASMFD, which is based on image aesthetic similarity to segment and… More >

Han Zhou^1,2, Hongtao Xu^1,2, Xinyue Chang^1,2, Wei Zhang^1,2, Heng Dong^1,2,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2295-2313, 2024, DOI:10.32604/cmc.2024.047754 - 15 May 2024

Abstract Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes. However, these methods often lack constraint information and overlook semantic consistency, limiting their performance. To address these issues, we present a novel approach for medical image registration called the Dual-VoxelMorph, featuring a dual-channel cross-constraint network. This innovative network utilizes both intensity and segmentation images, which share identical semantic information and feature representations. Two encoder-decoder structures calculate deformation fields for intensity and segmentation images, as generated by the dual-channel cross-constraint network. This design facilitates bidirectional communication between grayscale More >

Jiao Wang, Bin Wu^*, Hongying Zhang

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 143-160, 2024, DOI:10.32604/cmc.2023.046011 - 25 April 2024

Abstract Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention due to its outstanding performance and nonlinear application. However, most existing methods neglect that view-private meaningless information or noise may interfere with the learning of self-expression, which may lead to the degeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistency and Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple views and fuses them based on their discrimination, so that it can effectively explore consistent and complementary information for achieving precise clustering. Specifically, the More >

Hung-Hsiang Wang¹, Chih-Ping Chen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 685-709, 2024, DOI:10.32604/cmes.2024.047630 - 16 April 2024

Abstract This paper presents a new method of using a convolutional neural network (CNN) in machine learning to identify brand consistency by product appearance variation. In Experiment 1, we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions. Results show that it is a challenge to distinguish periods for the subtle evolution of the mouse devices with such traditional methods as time series analysis and principal component analysis (PCA). In Experiment 2, we applied deep… More >

Xingli Wu^1,#, Huchang Liao^1,#, Shuxian Sun¹, Zhengjun Wan^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3125-3146, 2024, DOI:10.32604/cmes.2023.047031 - 11 March 2024

Abstract Agricultural investment project selection is a complex multi-criteria decision-making problem, as agricultural projects are easily influenced by various risk factors, and the evaluation information provided by decision-makers usually involves uncertainty and inconsistency. Existing literature primarily employed direct preference elicitation methods to address such issues, necessitating a great cognitive effort on the part of decision-makers during evaluation, specifically, determining the weights of criteria. In this study, we propose an indirect preference elicitation method, known as a preference disaggregation method, to learn decision-maker preference models from decision examples. To enhance evaluation ease, decision-makers merely need to compare… More >

Ziqi Wang^*, Weihan Tian, Baojiang Cui

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1797-1820, 2024, DOI:10.32604/cmc.2023.047051 - 27 February 2024

Abstract The API used to access cloud services typically follows the Representational State Transfer (REST) architecture style. RESTful architecture, as a commonly used Application Programming Interface (API) architecture paradigm, not only brings convenience to platforms and tenants, but also brings logical security challenges. Security issues such as quota bypass and privilege escalation are closely related to the design and implementation of API logic. Traditional code level testing methods are difficult to construct a testing model for API logic and test samples for in-depth testing of API logic, making it difficult to detect such logical vulnerabilities. We… More >

Ahmed H. Msmali¹, Fazal Dayan^2,*, Muhammad Rafiq³, Nauman Ahmed⁴, Abdullah Ali H. Ahmadini¹, Hassan A. Hamali⁵

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2251-2269, 2023, DOI:10.32604/cmc.2023.040266 - 29 November 2023

Abstract In this article, a Susceptible-Exposed-Infectious-Recovered (SEIR) epidemic model is considered. The equilibrium analysis and reproduction number are studied. The conventional models have made assumptions of homogeneity in disease transmission that contradict the actual reality. However, it is crucial to consider the heterogeneity of the transmission rate when modeling disease dynamics. Describing the heterogeneity of disease transmission mathematically can be achieved by incorporating fuzzy theory. A numerical scheme nonstandard, finite difference (NSFD) approach is developed for the studied model and the results of numerical simulations are presented. Simulations of the constructed scheme are presented. The positivity,… More >

Jing Deng¹, Xiaofei Xing¹, Guoqiang Deng^2,*, Ning Hu³, Shen Su³, Le Wang³, Md Zakirul Alam Bhuiyan⁴

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3783-3795, 2023, DOI:10.32604/cmc.2023.034116 - 08 October 2023

Abstract As one of the major threats to the current DeFi (Decentralized Finance) ecosystem, reentrant attack induces data inconsistency of the victim smart contract, enabling attackers to steal on-chain assets from DeFi projects, which could terribly do harm to the confidence of the blockchain investors. However, protecting DeFi projects from the reentrant attack is very difficult, since generating a call loop within the highly automatic DeFi ecosystem could be very practicable. Existing researchers mainly focus on the detection of the reentrant vulnerabilities in the code testing, and no method could promise the non-existent of reentrant vulnerabilities. More >