José Armando Santas Ciavatta, Juan Ramón Bermejo Higuera^*, Javier Bermejo Higuera, Juan Antonio Sicilia Montalvo, Tomás Sureda Riera, Jesús Pérez Melero

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.074566 - 12 January 2026

Abstract As artificial Intelligence (AI) continues to expand exponentially, particularly with the emergence of generative pre-trained transformers (GPT) based on a transformer’s architecture, which has revolutionized data processing and enabled significant improvements in various applications. This document seeks to investigate the security vulnerabilities detection in the source code using a range of large language models (LLM). Our primary objective is to evaluate the effectiveness of Static Application Security Testing (SAST) by applying various techniques such as prompt persona, structure outputs and zero-shot. To the selection of the LLMs (CodeLlama 7B, DeepSeek coder 7B, Gemini 1.5 Flash,… More >

Yaxin Zhao¹, Qi Han², Hui Shu², Yan Guang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-24, 2026, DOI:10.32604/cmc.2025.070511 - 09 December 2025

Abstract Large Language Models (LLMs) are increasingly applied in the field of code translation. However, existing evaluation methodologies suffer from two major limitations: (1) the high overlap between test data and pretraining corpora, which introduces significant bias in performance evaluation; and (2) mainstream metrics focus primarily on surface-level accuracy, failing to uncover the underlying factors that constrain model capabilities. To address these issues, this paper presents TCode (Translation-Oriented Code Evaluation benchmark)—a complexity-controllable, contamination-free benchmark dataset for code translation—alongside a dedicated static feature sensitivity evaluation framework. The dataset is carefully designed to control complexity along multiple dimensions—including syntactic… More >

Himadri Nath Saha¹, Dipanwita Chakraborty Bhattacharya^2,*, Sancharita Dutta³, Arnab Bera³, Srutorshi Basuray⁴, Satyasaran Changdar⁵, Saptarshi Banerjee⁶, Jon Turdiev⁷

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-56, 2026, DOI:10.32604/cmc.2025.070507 - 09 December 2025

Abstract The emergence of Medical Large Language Models has significantly transformed healthcare. Medical Large Language Models (Med-LLMs) serve as transformative tools that enhance clinical practice through applications in decision support, documentation, and diagnostics. This evaluation examines the performance of leading Med-LLMs, including GPT-4Med, Med-PaLM, MEDITRON, PubMedGPT, and MedAlpaca, across diverse medical datasets. It provides graphical comparisons of their effectiveness in distinct healthcare domains. The study introduces a domain-specific categorization system that aligns these models with optimal applications in clinical decision-making, documentation, drug discovery, research, patient interaction, and public health. The paper addresses deployment challenges of Medical-LLMs, More >

Abeer Alhuzali^1,*, Qamar Al-Qahtani¹, Asmaa Niyazi¹, Lama Alshehri¹, Fatemah Alharbi²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.069491 - 10 November 2025

Abstract The surge in smishing attacks underscores the urgent need for robust, real-time detection systems powered by advanced deep learning models. This paper introduces PhishNet, a novel ensemble learning framework that integrates transformer-based models (RoBERTa) and large language models (LLMs) (GPT-OSS 120B, LLaMA3.3 70B, and Qwen3 32B) to enhance smishing detection performance significantly. To mitigate class imbalance, we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques. Our system employs a dual-layer voting mechanism: weighted majority voting among LLMs and a final ensemble vote to classify messages as ham, spam, or smishing. Experimental More >

Yu Tao¹, Ruopeng Yang^1,2, Yongqi Wen^1,*, Yihao Zhong¹, Kaige Jiao¹, Xiaolei Gu^1,2

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-17, 2026, DOI:10.32604/cmc.2025.068670 - 10 November 2025

Abstract Since Google introduced the concept of Knowledge Graphs (KGs) in 2012, their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition, extraction, representation, modeling, fusion, computation, and storage. Within this framework, knowledge extraction, as the core component, directly determines KG quality. In military domains, traditional manual curation models face efficiency constraints due to data fragmentation, complex knowledge architectures, and confidentiality protocols. Meanwhile, crowdsourced ontology construction approaches from general domains prove non-transferable, while human-crafted ontologies struggle with generalization deficiencies. To address these challenges, this study proposes an Ontology-Aware LLM Methodology for Military Domain More >

Qianqian Hu, Chuhan Li, Mohan Zhang, Fang Liu^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-17, 2026, DOI:10.32604/cmc.2025.068225 - 10 November 2025

Abstract Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform, the demands of visual communication keep increasing for promoting traditional cultural artifacts online. As an effective medium, posters serve to attract public attention and facilitate broader engagement with cultural artifacts. However, existing poster generation methods mainly rely on fixed templates and manual design, which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts. Therefore, we propose CAPGen, an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language More >

Mostafa Mohamed Ahmed Mohamed Alsaedy^1,*, Atef Zaki Ghalwash¹, Aliaa Abd Elhalim Yousif², Safaa Magdy Azzam¹

Journal of Cyber Security, Vol.7, pp. 653-674, 2025, DOI:10.32604/jcs.2025.073547 - 24 December 2025

Abstract Mobile financial applications and payment systems face significant security challenges from reverse engineering attacks. Attackers can decompile Android Package Kit (APK) files, modify permissions, and repackage applications with malicious capabilities. This work introduces E-AAPIV (Enhanced Android Apps Permissions Integrity Verifier), an advanced framework that uses Merkle Tree technology for real-time manifest integrity verification. The proposed system constructs cryptographic Merkle Tree from AndroidManifest.xml permission structures. It establishes secure client-server connections using Elliptic Curve Diffie-Hellman Protocol (ECDH-P384) key exchange. Root hashes are encrypted with Advanced Encryption Standard-256-Galois/Counter Mode (AES-256-GCM), integrated with hardware-backed Android Keystore for enhanced security. More >

Talha Farooq Khan¹, Majid Hussain¹, Muhammad Arslan², Muhammad Saeed¹, Lal Khan^3,*, Hsien-Tsung Chang^4,5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3883-3911, 2025, DOI:10.32604/cmes.2025.073021 - 23 December 2025

Abstract Text clustering is an important task because of its vital role in NLP-related tasks. However, existing research on clustering is mainly based on the English language, with limited work on low-resource languages, such as Urdu. Low-resource language text clustering has many drawbacks in the form of limited annotated collections and strong linguistic diversity. The primary aim of this paper is twofold: (1) By introducing a clustering dataset named UNC-2025 comprises 100k Urdu news documents, and (2) a detailed empirical standard of Large Language Model (LLM) improved clustering methods for Urdu text. We explicitly evaluate the… More >

Qin Qin¹, Xuan Jiang^1,*, Jinhua Jiang¹, Dongfang Zhao¹, Zimei Tu¹, Zhiwei Shen²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4849-4867, 2025, DOI:10.32604/cmc.2025.067919 - 23 October 2025

Abstract The effectiveness of industrial character recognition on cast steel is often compromised by factors such as corrosion, surface defects, and low contrast, which hinder the extraction of reliable visual information. The problem is further compounded by the scarcity of large-scale annotated datasets and complex noise patterns in real-world factory environments. This makes conventional OCR techniques and standard deep learning models unreliable. To address these limitations, this study proposes a unified framework that integrates adaptive image preprocessing with collaborative reasoning among LLMs. A Biorthogonal 4.4 (bior4.4) wavelet transform is adaptively tuned using DE to enhance character… More >

Xiaoyue Zhao¹, Ting Wang², Baoyan Yang^3,*, Hongyu Zang¹, Qifeng Gao¹, Zhongyuan Zhang¹, Birong Xing¹

Journal of Psychology in Africa, Vol.35, No.5, pp. 701-711, 2025, DOI:10.32604/jpa.2025.067155 - 24 October 2025

Abstract This study explored the relationship between need fulfillment given by opposite-sex friends and romantic relationship quality, as well as the potential underlying mechanisms, including perceived quality of opposite-sex friend relationships, need fulfillment given by romantic partners and romantic desire towards opposite-sex friends. A total of 98 unmarried Chinese young adults in romantic relationships were participants (females = 45.9%; M = 22.71, SD = 2.81). The findings from regression, mediation, and moderation analyses indicated need fulfillment given by opposite-sex friends was associated with lower romantic relationship quality. Perceived quality of opposite-sex friend relationships mediated the relationship between… More >