Zhaojun Gu¹, Wenlong Yue², Chunbo Liu^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077388 - 09 April 2026

Abstract Anomaly detection in system logs is a critical technical means for identifying potential faults and security risks. In distributed environments, traditional deep learning-based log anomaly detection methods often suffer from shortcomings in transparency, computational overhead, and data privacy protection. To address these issues, this paper proposes a federated learning-driven lightweight and explainable log anomaly detection framework named FedXLog. The framework adapts to heterogeneous logs through hierarchical feature extraction, introduces the Federated Gradient Trajectory Aggregation algorithm (FedGradTrace) to enhance the explainability of the parameter aggregation process, constructs lightweight models using knowledge distillation, and achieves globally consistent… More >

Hamed Alqahtani¹, Gulshan Kumar^2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077367 - 09 April 2026

Abstract Large Language Models (LLMs) are becoming integral components of modern cybersecurity ecosystems, simultaneously strengthening defensive capabilities while giving rise to a new class of Artificial Intelligence–Generated Content (AIGC)-driven threats. This PRISMA-guided systematic review synthesises 167 peer-reviewed studies published between 2022 and 2025 and proposes a unified threat–defence–evaluation taxonomy as a central analytical framework to consolidate a previously fragmented body of research. Guided by this taxonomy, the review first examines AIGC-enabled threats, including automated and highly personalised phishing, polymorphic malware and exploit generation, jailbreak and adversarial prompting, prompt-injection attack vectors, multimodal deception, persona-steering attacks, and large-scale… More >

Malik Al-Essa^1,*, Mohammad Qatawneh^2,1, Ahmad Sami Al-Shamayleh³, Orieb Abualghanam¹, Wesam Almobaideen^4,1

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076608 - 09 April 2026

Abstract Machine Learning (ML) intrusion detection systems (IDS) are vulnerable to manipulations: small, protocol-valid manipulations can push samples across brittle decision boundaries. We study two complementary remedies that reshape the learner in distinct ways. Adversarial Training (AT) exposes the model to worst-case, in-threat perturbations during learning to thicken local margins; Counterfactual Augmentation (CF-Aug) adds near-boundary exemplars that are explicitly constrained to be feasible, causally consistent, and operationally meaningful for defenders. The main goal of this work is to investigate and compare how AT and CF-Aug can reshape the decision surface of the IDS. eXplainable Artificial Intelligence More >

Zhi Zhang^1,2, Bingyu Sun^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076236 - 09 April 2026

Abstract In facial landmark detection, shape deviations induced by large poses and exaggerated expressions often prevent existing algorithms from simultaneously achieving fine-grained local accuracy and holistic global shape constraints. To address this, we propose a Transformer-Conv Attention-based Method (TCAM). Built upon a hybrid coordinate-heatmap regression backbone, TCAM integrates the long-range dependency modeling of Transformers with the local feature extraction advantages of Depthwise Convolution (DWConv). Specifically, by partitioning feature maps into sub-regions and applying Transformer modeling, the module enforces sparse linear constraints on global information, effectively mitigating the issues caused by discontinuous landmark distributions. Experimental results on More >

Mohammed A. Ahmed¹, Jian Dong^2,*, Ronghua Shi², Ammar Nassr³, Hani Almaqtari³, Ala A. Alsanabani³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075616 - 09 April 2026

Abstract Facial Expression Recognition (FER) is an essential endeavor in computer vision, applicable in human-computer interaction, emotion assessment, and mental health surveillance. Although Convolutional Neural Networks (CNNs) have proven effective in Facial Emotion Recognition, they encounter difficulties in capturing long-range connections and global context. To address these constraints, we propose Spatial Quad-Similarity Network (SQSNet), an innovative hybrid framework that integrates the local feature extraction capabilities of CNNs with the global contextual modeling efficacy of Swin Transformers via a cohesive fusion technique. SQSNet introduces the Spatial Quad-Similarity (SQS) module, a feature refinement approach that amplifies discriminative characteristics… More >

Xiaofeng Wang^1,#, Da Huo^2,3,#, Shuo Li^4,#, Wenlong Wang¹, Qian Zhang¹, Ya Gao⁵, Xu Wang^1,*

Structural and Congenital Heart Disease, Vol.21, No.1, 2026, DOI:10.32604/schd.2026.077612 - 31 March 2026

Abstract Background: This study evaluated the impact of a comprehensive prevention program, which integrated eight evidence-based measures consistent with current clinical guidelines and practice standards, on ventilator-associated pneumonia (VAP) rates in a pediatric cardiac surgical intensive care unit (ICU). Methods: A quasi-experimental study was conducted from 2023 to 2024. We compared VAP rates across a 5-month pre-intervention period, a 12-month intervention period, and a 7-month post-intervention period in patients receiving mechanical ventilation for over 48 h. Additional outcomes, including postoperative length of stay were also assessed before and after the intervention. Results: Among 829 at-risk patients and 5677… More >

Jia Yuan^1,2,#, Yanxing Lv^2,#, Xinyuan Ding³, Yunyi Zeng¹, Li Ma⁴, Hang Yang¹, Lin Jiang¹, Kamil Bildebayev⁵, Boiko Yuliya Nikolaevna⁵, Na Zhou^1,*

Structural and Congenital Heart Disease, Vol.21, No.1, 2026, DOI:10.32604/schd.2026.076648 - 31 March 2026

Abstract Backgorund: Anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA) is a rare congenital anomaly of coronary artery anatomy, usually diagnosed in infancy, but adults may also be affected by this deformity. Objectives: The aim of this study is to examine long-term outcomes in patients with ALCAPA and analyze the relevant factors influencing postoperative outcomes. Methods: The records of patients with ALCAPA admitted from January 2015 to December 2024 were retrospectively reviewed. Clinical data of the patients were retrieved from the records. The follow-up data included mortality rates and complications. Kaplan-Meier survival curves were… More >

Wei-Chiang Hong^1,*, Yi Liang²

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.080415 - 30 March 2026

Abstract This article has no abstract. More >

Ilsun You^1,*, Gaurav Choudhary², Gökhan Kul³, Francesco Falmieri⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.080244 - 30 March 2026

Abstract This article has no abstract. More >

Jaeho Hwang¹, Moohong Min^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078940 - 30 March 2026

Abstract Illicit web ecosystems, encompassing phishing, illegal online gambling, scam platforms, and malicious advertising, have rapidly expanded in scale and complexity, creating severe social, financial, and cybersecurity risks. Traditional rule-based and blacklist-driven detection approaches struggle to cope with polymorphic, multilingual, and adversarially manipulated threats, resulting in increasing demand for Artificial Intelligence (AI)-based solutions. This review provides a comprehensive synthesis of research on AI-driven threat detection for illicit web environments. It surveys detection models across multiple modalities, including text-based analysis of Uniform Resource Locator (URL) and HyperText Markup Language (HTML), vision-based recognition of webpage layouts and logos,… More >