Jaeho Shin¹, Jaekwang Kim^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-26, 2026, DOI:10.32604/cmc.2025.068767 - 10 November 2025

Abstract Network attacks have become a critical issue in the internet security domain. Artificial intelligence technology-based detection methodologies have attracted attention; however, recent studies have struggled to adapt to changing attack patterns and complex network environments. In addition, it is difficult to explain the detection results logically using artificial intelligence. We propose a method for classifying network attacks using graph models to explain the detection results. First, we reconstruct the network packet data into a graphical structure. We then use a graph model to predict network attacks using edge classification. To explain the prediction results, we… More >

Haotong Wang^#,*, Liyan Wang^#, Yves Lepage

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 305-324, 2025, DOI:10.32604/cmc.2025.066351 - 09 June 2025

Abstract Data curation is vital for selecting effective demonstration examples in graph-to-text generation. However, evaluating the quality of Knowledge Graphs (KGs) remains challenging. Prior research exhibits a narrow focus on structural statistics, such as the shortest path length, while the correctness of graphs in representing the associated text is rarely explored. To address this gap, we introduce a dual-perspective evaluation framework for KG-text data, based on the computation of structural adequacy and semantic alignment. From a structural perspective, we propose the Weighted Incremental Edge Method (WIEM) to quantify graph completeness by leveraging agreement between relation models… More >

Qiming Li¹, Mengcheng Wu¹, Daozheng Chen^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4477-4494, 2025, DOI:10.32604/cmc.2025.062969 - 19 May 2025

Abstract Image style transfer is a research hotspot in the field of computer vision. For this job, many approaches have been put forth. These techniques do, however, still have some drawbacks, such as high computing complexity and content distortion caused by inadequate stylization. To address these problems, PhotoGAN, a new Generative Adversarial Network (GAN) model is proposed in this paper. A deeper feature extraction network has been designed to capture global information and local details better. Introducing multi-scale attention modules helps the generator focus on important feature areas at different scales, further enhancing the effectiveness of More >

Haotong Wang^*, Yves Lepage

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4141-4166, 2025, DOI:10.32604/cmc.2025.062806 - 19 May 2025

Abstract Knowledge graphs convey precise semantic information that can be effectively interpreted by neural networks, and generating descriptive text based on these graphs places significant emphasis on content consistency. However, knowledge graphs are inadequate for providing additional linguistic features such as paragraph structure and expressive modes, making it challenging to ensure content coherence in generating text that spans multiple sentences. This lack of coherence can further compromise the overall consistency of the content within a paragraph. In this work, we present the generation of scientific abstracts by leveraging knowledge graphs, with a focus on enhancing both… More >

Jizhao Liu, Minghao Guo^*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 817-842, 2025, DOI:10.32604/cmc.2024.057660 - 03 January 2025

Abstract The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats. Intrusion detection systems are crucial to network security, playing a pivotal role in safeguarding networks from potential threats. However, in the context of an evolving landscape of sophisticated and elusive attacks, existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts. To address these issues, this paper proposes a real-time network intrusion detection method based on… More >

Kun Liu^1,2, Peiran Li³, Yu Zhang^1,*, Jia Ren¹, Ming Li⁴, Xianyu Wang², Cong Li²

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2535-2555, 2024, DOI:10.32604/cmc.2024.052166 - 15 August 2024

Abstract When learning the structure of a Bayesian network, the search space expands significantly as the network size and the number of nodes increase, leading to a noticeable decrease in algorithm efficiency. Traditional constraint-based methods typically rely on the results of conditional independence tests. However, excessive reliance on these test results can lead to a series of problems, including increased computational complexity and inaccurate results, especially when dealing with large-scale networks where performance bottlenecks are particularly evident. To overcome these challenges, we propose a Markov blanket discovery algorithm based on constrained local neighborhoods for constructing undirected… More >

Hao Chen^#, Runfeng Xie^#, Xiangyang Cui, Zhou Yan, Xin Wang, Zhanwei Xuan^*, Kai Zhang^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4283-4296, 2024, DOI:10.32604/cmc.2024.049129 - 20 June 2024

Abstract Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems. Traditional methods are usually difficult to learn and acquire complex semantic information in news texts, resulting in unsatisfactory recommendation results. Besides, these traditional methods are more friendly to active users with rich historical behaviors. However, they can not effectively solve the long tail problem of inactive users. To address these issues, this research presents a novel general framework that combines Large Language Models (LLM) and Knowledge Graphs (KG) into traditional methods. To learn the contextual information of news text, we… More >

Mani Parimala¹, Karthikeyan Prakash¹, Ashraf Al-Quran^2,*, Muhammad Riaz³, Saeid Jafari⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1095-1118, 2024, DOI:10.32604/cmes.2023.031193 - 30 December 2023

Abstract The idea of linear Diophantine fuzzy set (LDFS) theory with its control parameters is a strong model for machine learning and optimization under uncertainty. The activity times in the critical path method (CPM) representation procedures approach are initially static, but in the Project Evaluation and Review Technique (PERT) approach, they are probabilistic. This study proposes a novel way of project review and assessment methodology for a project network in a linear Diophantine fuzzy (LDF) environment. The LDF expected task time, LDF variance, LDF critical path, and LDF total expected time for determining the project network… More > Graphic Abstract

Mohammad Hamidi^1,*, Florentin Smarandache², Mohadeseh Taghinezhad¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1907-1923, 2024, DOI:10.32604/cmes.2023.030284 - 17 November 2023

Abstract This paper explores the defects in fuzzy (hyper) graphs (as complex (hyper) networks) and extends the fuzzy (hyper) graphs to fuzzy (quasi) superhypergraphs as a new concept. We have modeled the fuzzy superhypergraphs as complex superhypernetworks in order to make a relation between labeled objects in the form of details and generalities. Indeed, the structure of fuzzy (quasi) superhypergraphs collects groups of labeled objects and analyzes them in the form of the part to part of objects, the part of objects to the whole group of objects, and the whole to the whole group of… More >

Shiyang Song^1,*, Tongxin Han²

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.1, pp. 127-145, 2024, DOI:10.32604/fdmp.2023.030137 - 08 November 2023

Abstract Aiming to mitigate the aerodynamic lift force imbalance between pantograph strips, which exacerbates wear and affects the current collection performance of the pantograph-catenary system, a study has been conducted to support the beam deflector optimization using a combination of experimental measurements and computational fluid dynamics (CFD) simulations. The results demonstrate that the size, position, and installation orientation of the wind deflectors significantly influence the amount of force compensation. They also indicate that the front strip deflectors should be installed downwards and the rear strip deflectors upwards, thereby forming a “π” shape. Moreover, the lift force More >