Jinfeng Ji¹, Geunseok Yang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071733 - 12 January 2026

Abstract Automated Program Repair (APR) techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects. Despite considerable progress in APR methodologies, existing approaches frequently lack contextual awareness of runtime behaviors and structural intricacies inherent in buggy source code. In this paper, we propose a novel APR approach that integrates attention mechanisms within an autoencoder-based framework, explicitly utilizing structural code affinity and execution context correlation derived from stack trace analysis. Our approach begins with an innovative preprocessing pipeline, where code segments and stack traces are… More >

Karim Boudjebbour^1,2, Abdelkader Belkhir¹, Hamza Kheddar^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071532 - 12 January 2026

Abstract Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties. Although several community detection methods have been proposed, many are unsuitable for social networks due to significant limitations. Specifically, most approaches depend mainly on user–user structural links while overlooking service-centric, semantic, and multi-attribute drivers of community formation, and they also lack flexible filtering mechanisms for large-scale, service-oriented settings. Our proposed approach, called community discovery-based service (CDBS), leverages user profiles and their interactions with consulted web services. The method introduces a novel similarity measure, global similarity interaction profile (GSIP), which… More >

Ran Wei^*, Hui Shu

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069562 - 10 November 2025

Abstract Transformer-based models have significantly advanced binary code similarity detection (BCSD) by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings. Although adversarial examples can strategically undermine the accuracy of BCSD models and protect critical code, existing techniques predominantly depend on inserting artificial instructions, which incur high computational costs and offer limited diversity of perturbations. To address these limitations, we propose AIMA, a novel gradient-guided assembly instruction relocation method. Our method decouples the detection model into tokenization, embedding, and encoding layers to enable efficient gradient computation. Since token IDs of instructions are… More >

Damodharan Palaniappan¹, Tan Kuan Tak², K. Vijayan³, Balajee Maram⁴, Pravin R Kshirsagar⁵, Naim Ahmad^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4107-4127, 2025, DOI:10.32604/cmes.2025.073387 - 23 December 2025

Abstract A phase-aware cross-modal framework is presented that synthesizes UWF_FA from non-invasive UWF_RI for diabetic retinopathy (DR) stratification. A curated cohort of 1198 patients (2915 UWF_RI and 17,854 UWF_FA images) with strict registration quality supports training across three angiographic phases (initial, mid, final). The generator is based on a modified pix2pixHD with an added Gradient Variance Loss to better preserve microvasculature, and is evaluated using MAE, PSNR, SSIM, and MS-SSIM on held-out pairs. Quantitatively, the mid phase achieves the lowest MAE (98.76 ± 42.67), while SSIM remains high across phases. Expert review shows substantial agreement (Cohen’s More >

Faraj M. Zaid^1,2, Thaar M. Aljuwaya^3,4,*, Muthanna H. Al-Dahhan^1,3,5,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.10, pp. 2443-2471, 2025, DOI:10.32604/fdmp.2025.067557 - 30 October 2025

Abstract This study presents a detailed experimental evaluation of a newly developed mechanistic scale-up methodology for gas-solid fluidized beds. Traditional scale-up approaches typically rely on matching global dimensionless groups, which often fail to ensure local hydrodynamic similarity. In contrast, the new mechanistic method aims to achieve scale-up by matching the radial profiles of gas holdup between geometrically similar beds at corresponding dimensionless axial positions (z/D_c). This approach is based on the premise that when gas holdup profiles align, other key hydrodynamic parameters—such as solids holdup and particle velocity—also become similar. To validate this methodology, experiments were conducted More >

Shengjia Chang, Baojiang Cui^*, Shaocong Feng

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4345-4374, 2025, DOI:10.32604/cmc.2025.070195 - 23 October 2025

Abstract Binary Code Similarity Detection (BCSD) is vital for vulnerability discovery, malware detection, and software security, especially when source code is unavailable. Yet, it faces challenges from semantic loss, recompilation variations, and obfuscation. Recent advances in artificial intelligence—particularly natural language processing (NLP), graph representation learning (GRL), and large language models (LLMs)—have markedly improved accuracy, enabling better recognition of code variants and deeper semantic understanding. This paper presents a comprehensive review of 82 studies published between 1975 and 2025, systematically tracing the historical evolution of BCSD and analyzing the progressive incorporation of artificial intelligence (AI) techniques. Particular… More >

Fatima Abu Siryeh^*, Hussein Alrammahi, Abdullahi Abdu İbrahim

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5021-5046, 2025, DOI:10.32604/cmc.2025.065206 - 30 July 2025

Abstract Biometric template protection is essential for finger-based authentication systems, as template tampering and adversarial attacks threaten the security. This paper proposes a DCT-based fragile watermarking scheme incorporating AI-based tamper detection to improve the integrity and robustness of finger authentication. The system was tested against NIST SD4 and Anguli fingerprint datasets, wherein 10,000 watermarked fingerprints were employed for training. The designed approach recorded a tamper detection rate of 98.3%, performing 3–6% better than current DCT, SVD, and DWT-based watermarking approaches. The false positive rate (≤1.2%) and false negative rate (≤1.5%) were much lower compared to previous… More >

Yu-Shiuan Tsai^*, Zhen-Rong Wu, Jian-Zhi Liu

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3431-3457, 2025, DOI:10.32604/cmc.2025.066509 - 03 July 2025

Abstract Few-shot learning has emerged as a crucial technique for coral species classification, addressing the challenge of limited labeled data in underwater environments. This study introduces an optimized few-shot learning model that enhances classification accuracy while minimizing reliance on extensive data collection. The proposed model integrates a hybrid similarity measure combining Euclidean distance and cosine similarity, effectively capturing both feature magnitude and directional relationships. This approach achieves a notable accuracy of 71.8% under a 5-way 5-shot evaluation, outperforming state-of-the-art models such as Prototypical Networks, FEAT, and ESPT by up to 10%. Notably, the model demonstrates high… More >

Adéla Hamplová^*, Tomáš Novák, Miroslav Žáček, Jiří Brožek

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3329-3349, 2025, DOI:10.32604/cmes.2025.066025 - 30 June 2025

Abstract This study proposes a new component of the composite loss function minimised during training of the Super-Resolution (SR) algorithms—the normalised structural similarity index loss , which has the potential to improve the natural appearance of reconstructed images. Deep learning-based super-resolution (SR) algorithms reconstruct high-resolution images from low-resolution inputs, offering a practical means to enhance image quality without requiring superior imaging hardware, which is particularly important in medical applications where diagnostic accuracy is critical. Although recent SR methods employing convolutional and generative adversarial networks achieve high pixel fidelity, visual artefacts may persist, making the design of… More >

Ali Hamid Farea^1,*, Iman Askerzade^1,2, Omar H. Alhazmi³, Savaş Takan⁴

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1457-1484, 2025, DOI:10.32604/cmc.2025.064872 - 09 June 2025

Abstract Feature selection (FS) is a pivotal pre-processing step in developing data-driven models, influencing reliability, performance and optimization. Although existing FS techniques can yield high-performance metrics for certain models, they do not invariably guarantee the extraction of the most critical or impactful features. Prior literature underscores the significance of equitable FS practices and has proposed diverse methodologies for the identification of appropriate features. However, the challenge of discerning the most relevant and influential features persists, particularly in the context of the exponential growth and heterogeneity of big data—a challenge that is increasingly salient in modern artificial… More >