Yitao Yang^1,2, Peng Wu^1,2,*, Xiaoming Zhang^3,*, Renjie Xu³, Yong Zhang³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085505 - 13 August 2026

Abstract Existing graph-enhanced sequential recommendation methods typically adopt a unidirectional information flow, in which graph embeddings are injected into the sequential encoder only at the input stage, after which the graph signal is progressively diluted through multiple layers of deep processing. In this paper, the graph signal dilution phenomenon is analyzed systematically across three levels—the input, representation, and prediction layers—and the GSPRec model is proposed to address this issue. The core of GSPRec is the Graph-Sequence Collaborative Injection (GSCI) module, comprising three lightweight components: the Graph Confidence Gate (GCG) controls GCN smoothing via dimension-wise bounded interpolation;… More >

Yuzhi Tao¹, Anhui Tan^2,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085488 - 13 August 2026

Abstract Partial multi-label learning addresses scenarios where each instance is associated with a set of candidate labels that include both relevant and irrelevant ones. In practical scenarios, such label sets are often simultaneously incomplete and noisy, which severely hampers the ability of models to extract compact and discriminative features. To address these issues, we propose an integrated learning paradigm that simultaneously enhances feature compactness and improves robustness against label noise. Our method learns an adaptive fuzzy neighborhood graph to capture the intrinsic relationships among instances. The resulting graph enables reliable label propagation, which effectively rectifies incorrect More >

Huayu Li¹, Xiang Wang¹, Jia Luo^2,3,4,*, Xiaotong He¹, Peiying Zhang¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085456 - 13 August 2026

Abstract Multimodal Entity Linking (MEL) aims to map ambiguous mentions in multimodal contexts to their corresponding entities in a multimodal knowledge base. However, existing methods still face limitations in terms of feature extraction granularity, the depth of cross-modal interaction, and architectural coupling. To address these issues, we propose a Two-stage Decoupled Matching Network (TDMN) for multimodal entity linking. The matching process is divided into two stages: intra-modal matching and cross-modal interaction. In the intra-modal stage, textual and visual inputs are processed independently. The framework then proceeds to the cross-modal interaction stage, following the principle of “enhancement… More >

Mian Muhammad Kamal^1,*, Tianjun Ma^1,*, Mohammed K. Alzaylaee², Husam S. Samkari^3,4, Mohammed F. Allehyani³, Omar Almomani⁵, Heba G. Mohamed^6,7

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085412 - 13 August 2026

Abstract The cyber-physical network (CPS) combines sensing, communication, and control in physical processes, making them very susceptible to sophisticated cyber-attacks that may cause safety-critical effects. There are two core shortcomings to existing intrusion detection systems (IDS): generative-only models have little transparency of decision-making, while explainable-only models have low robustness in the presence of imbalanced and zero-day attacks. This paper presents a sequentially integrated trustworthy intrusion detection (ID) framework that combines generative learning and explainable AI (XAI) to boost robustness and transparency. The generative module enhances training data diversity, while the explainability module provides post-hoc interpretations during… More >

Weijun Gao, Ziyang Zhang^*, Maotang Su

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085382 - 13 August 2026

Abstract Multimodal sentiment analysis (MSA) has made significant progress in integrating heterogeneous information from text, speech, and vision. However, real-world multimodal data often suffer from modality noise, semantic inconsistency, and incomplete modality information, which can weaken cross-modal fusion and reduce the reliability of sentiment prediction. To address these challenges, this paper proposes RUAL, a robust uncertainty-aware learning framework for multimodal sentiment analysis. Specifically, RUAL first employs a Gathered Multi-Head Attention Pooling (GMHA) module to aggregate intra-modal features and estimate modality uncertainty based on attention entropy. Then, an Uncertainty-Aware Cross-Modal Coupled Layer (UACCL) is introduced to dynamically More >

Xue-Wei Liu¹, Shao-Qiang Ye², Feng Qin³, Kai-Qing Zhou^1,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085316 - 13 August 2026

Abstract Weighted fuzzy production rules (WFPRs) provide superior expressiveness and interpretability in knowledge engineering area. However, manual construction of WFPRs is labor-intensive, time-consuming, and inherently subjective, which greatly restricts their practical application. The back propagation neural network (BPNN) has been widely adopted for automatic WFPR extraction. Nevertheless, its high sensitivity to initial weight configurations frequently results in premature convergence to local optima, generating redundant, poorly interpretable rule sets that compromise the inherent interpretability advantage of WFPRs. This paper proposes an elite dynamic scout-guided grey wolf optimizer (EDSG-GWO) and integrates it into a BPNN-based WFPR extraction framework… More >

Dong-Fang Wu^1,2, Jiaojiao Deng^1,2, Zhiwei Ye^1,2,*, Rong Gao^1,2, Fan Ma^1,2, Dingfeng Song^1,2

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085308 - 13 August 2026

Abstract Anomaly detection is used to identify data points deviating from normal patterns and plays a significant role in fields such as fault diagnosis, biomedicine, and cybersecurity. However, anomaly detection tasks in practical applications often involve high-dimensional data which presents challenges such as severe feature redundancy, hidden anomaly patterns, and difficulty in capturing uncertainty. These issues make it difficult to effectively identify anomalies, thereby limiting the model’s discriminative power and stability. To address these challenges, we propose a high-dimensional anomaly detection model, MSFO-EIF, integrating multi-strategy feature optimization with rough set modeling. First, in the feature space… More >

Chanchan Zhao^1,#, Xiaoyu Gao^1,#, Bao Shi^2,*, Ziyang Zhang¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085282 - 13 August 2026

Abstract Detecting objects in unmanned aerial vehicle (UAV) imagery is challenging because most targets occupy only a small number of pixels and are often distributed in crowded regions with cluttered backgrounds. For edge-side UAV applications, the detector must also remain compact enough for real-time inference on low-power computing platforms. To meet these requirements, this study develops a YOLOv11n-based small-object detector by redesigning feature extraction, cross-scale fusion, and prediction modules. In the backbone, the proposed Dual-Context Large-Small Convolution (DCLSConv) is embedded into the C3k2 structure to form C3k2-DC, allowing the network to capture broader contextual cues while… More >

Amine Gonca Toprak^1,*, Aytuğ Onan²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085268 - 13 August 2026

Abstract Biomedical texts present significant challenges for natural language processing (NLP) due to their complex terminology, intricate contextual dependencies, and highly domain-specific semantics. This study investigates the effectiveness of knowledge distillation (KD) for biomedical text classification, aiming to develop lightweight, resource-efficient models that remain competitive with larger architectures. A balanced dataset of 25,000 PubMed records was constructed, equally distributed across five biomedical domains. Two teacher models (BERT and PubMedBERT) and five student models (DistilBERT, BioClinicalBERT, BioBERT, DistilBioBERT, and DistilRoBERTa) were evaluated across ten distinct KD configurations. Each student model was also directly fine-tuned to serve as… More >

Aisha Tanveer¹, Noraini Ibrahim¹, Muhammad Zubair Rehman^2,*, Abdullah Khan³, Nazri Mohd. Nawi¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085167 - 13 August 2026

Abstract Metaheuristic optimization algorithms have gained wide adoption in engineering and scientific domains. However, many swarm-based methods struggle to balance exploration and exploitation, often converging prematurely on suboptimal solutions. The Sand Cat Swarm Optimization (SCSO) algorithm is one such method, with limited exploration ability constraining its performance on complex problem landscapes. This paper introduced the Enhanced Sand Cat with Selective Opposition (ESCSO) algorithm which combines opposition-based learning with a velocity mechanism to overcome this limitation. In ESCSO, under-performing candidates referred to as Sigma-variant cats are identified using Spearman correlation and replaced with their opposite solutions to More >