Jianfeng Lu^1,2, Xiangye Huang¹, Caijin Li¹, Renlin Xin¹, Shanqing Zhang^1,2, Mahmoud Emam^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 405-423, 2025, DOI:10.32604/cmes.2024.058121 - 17 December 2024

Abstract Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting. Stamped seal inspection is commonly audited manually to ensure document authenticity. However, manual assessment of seal images is tedious and labor-intensive due to human errors, inconsistent placement, and completeness of the seal. Traditional image recognition systems are inadequate enough to identify seal types accurately, necessitating a neural network-based method for seal image recognition. However, neural network-based classification algorithms, such as Residual Networks (ResNet) and Visual Geometry Group with 16 layers… More >

Anastasios Dounis^*, Ioannis Palaiothodoros, Anna Panagiotou

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 759-811, 2025, DOI:10.32604/cmes.2024.057888 - 17 December 2024

Abstract Accurate medical diagnosis, which involves identifying diseases based on patient symptoms, is often hindered by uncertainties in data interpretation and retrieval. Advanced fuzzy set theories have emerged as effective tools to address these challenges. In this paper, new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets (q-ROFS) and interval-valued q-rung orthopair fuzzy sets (IVq-ROFS). Three aggregation operators are proposed in our methodologies: the q-ROF weighted averaging (q-ROFWA), the q-ROF weighted geometric (q-ROFWG), and the q-ROF weighted neutrality averaging (q-ROFWNA), which enhance decision-making under uncertainty. These operators are paired More > Graphic Abstract

Khuram Ali Khan¹, Saba Mubeen Ishfaq¹, Atiqe Ur Rahman², Salwa El-Morsy^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 501-530, 2025, DOI:10.32604/cmes.2024.057865 - 17 December 2024

Abstract Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty, evaluating educational institutions can be difficult. The concept of a possibility Pythagorean fuzzy hypersoft set (pPyFHSS) is more flexible in this regard than other theoretical fuzzy set-like models, even though some attempts have been made in the literature to address such uncertainties. This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union, intersection, complement, OR- and AND-operations. Some results related to these operations are also modified for pPyFHSS. Additionally, the similarity measures between pPyFHSSs are More >

Shahlaa Mashhadani^1,*, Wisal Hashim Abdulsalam¹, Oday Ali Hassen², Saad M. Darwish³

Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 805-828, 2024, DOI:10.32604/iasc.2024.054611 - 31 October 2024

Abstract Signature verification involves vague situations in which a signature could resemble many reference samples or might differ because of handwriting variances. By presenting the features and similarity score of signatures from the matching algorithm as fuzzy sets and capturing the degrees of membership, non-membership, and indeterminacy, a neutrosophic engine can significantly contribute to signature verification by addressing the inherent uncertainties and ambiguities present in signatures. But type-1 neutrosophic logic gives these membership functions fixed values, which could not adequately capture the various degrees of uncertainty in the characteristics of signatures. Type-1 neutrosophic representation is also… More >

Jian Yu^1,2,*, Gaofeng Yu³, Xiangmei Xiao^1,2, Zhixing Lin^1,2

Journal of Cyber Security, Vol.6, pp. 41-68, 2024, DOI:10.32604/jcs.2024.049658 - 04 June 2024

Abstract With the rapid advancement of the Internet, network attack methods are constantly evolving and adapting. To better identify the network attack behavior, a universal gravitation clustering algorithm was proposed by analyzing the dissimilarities and similarities of the clustering algorithms. First, the algorithm designated the cluster set as vacant, with the introduction of a new object. Subsequently, a new cluster based on the given object was constructed. The dissimilarities between it and each existing cluster were calculated using a defined difference measure. The minimum dissimilarity was selected. Through comparing the proposed algorithm with the traditional Back More >

P. Pai Nityanand, B. Devaki, G. Bhat Pareekshith, V. S. Sampath Kumar^*

Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 655-673, 2024, DOI:10.32604/fhmt.2024.050237 - 20 May 2024

Abstract The study in this manuscript aims to analyse the impact of thermal radiation on the two-dimensional magnetohydrodynamic flow of upper convected Maxwell (UCM) fluid between parallel plates. The lower plate is porous and stationary, while the top plate is impermeable and moving. The equations that describe the flow are transformed into non-linear ordinary differential equations with boundary conditions by employing similarity transformations. The Homotopy Perturbation Method (HPM) is then employed to approach the obtained non-linear ordinary differential equations and get an approximate analytical solution. The analysis includes plotting the velocity profile for different Reynolds number… 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 >

Fangfang Shan^1,2,*, Huifang Sun^1,2, Mengyi Wang^1,2

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 581-605, 2024, DOI:10.32604/cmc.2024.046202 - 25 April 2024

Abstract As social networks become increasingly complex, contemporary fake news often includes textual descriptions of events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely to create a misleading perception among users. While early research primarily focused on text-based features for fake news detection mechanisms, there has been relatively limited exploration of learning shared representations in multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal model for detecting fake news, which relies on similarity reasoning and adversarial networks. The model employs Bidirectional Encoder Representation from Transformers… More >

Yumin Jo¹, Jongho Paik^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4153-4176, 2024, DOI:10.32604/cmc.2024.047046 - 26 March 2024

Abstract Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream. However, when the transmission environment is unstable, problems such as reduction in the lifespan of equipment due to frequent switching and interruption, delay, and stoppage of services may occur. Therefore, applying a machine learning (ML) method, which is possible to automatically judge and classify network-related service anomaly, and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when… More >

Xin Fan^1,2, Shuqing Zhang^1,2,*, Kaisheng Wu^1,2, Wei Zheng^1,2, Yu Ge^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1687-1711, 2024, DOI:10.32604/cmc.2023.046187 - 27 February 2024

Abstract Cross-Project Defect Prediction (CPDP) is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project. However, existing CPDP methods only consider linear correlations between features (indicators) of the source and target projects. These models are not capable of evaluating non-linear correlations between features when they exist, for example, when there are differences in data distributions between the source and target projects. As a result, the performance of such CPDP models is compromised. In this paper, this paper proposes a novel CPDP method based on… More >