Zhijie Lin¹, Chao Yang^1,2,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.090428 - 15 September 2026

Abstract This article has no abstract. More >

Jihoon Moon^*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.089115 - 15 September 2026

Abstract Large language models and retrieval-augmented generation (RAG) systems are increasingly employed to transform evidence into decision-facing briefings, alerts, and recommendations. In these settings, explainability cannot be evaluated merely by fluency, readability, or factual correctness. A briefing may be factually correct while still being unsafe if it cites sources that do not substantiate the claim, suppresses uncertainty, converts correlational evidence into causal language, recommends an unauthorized action, or leaves no auditable path for human review. This review synthesizes 104 sources spanning explainable natural language processing (NLP), faithful explanation, hallucination and factuality evaluation, RAG, citation faithfulness, uncertainty… More >

Yongfeng Zhang^1,*, Jie Chen^2,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088561 - 15 September 2026

Abstract Speaker verification on phones, wearables, and voice-enabled Internet-of-Things gateways must balance local privacy with reliable decisions under adverse audio. Existing privacy-preserving verification schemes generally protect a fixed representation, whereas edge-offloading policies usually adapt computation without attaching an explicit feature-disclosure budget; neither line alone coordinates trial uncertainty, communication state, and privacy expenditure. This paper presents privacy-preserving uncertainty-aware adaptive feature offloading (P-UAFO), an edge-intelligence framework that keeps raw audio local and transmits only clipped, projected, quantized, and Gaussian-perturbed intermediate features when their expected benefit justifies resource cost. Its online pipeline first estimates decision uncertainty and resource state,… More >

Daniel Šolc^1,*, René Ivančák¹, Juraj Gazda¹, Eva Chovancová¹, Eugen Šlapák¹, Gabriel Bugár²

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088224 - 15 September 2026

Abstract Autonomous mobile robots increasingly rely on three tightly coupled capabilities: low-latency wireless connectivity, edge-side compute acceleration, and simulation-based pre-training of perception and control models. Each has been studied extensively in isolation, but their joint deployment on a single platform remains rare. This paper presents an integrated framework combining a private 5G Stand-Alone (5G SA) access network, a Multi-Access Edge Computing (MEC) layer with adaptive offloading, and a digital-twin training pipeline in NVIDIA Omniverse Isaac Sim. It is realised on the Yahboom ROSMASTER R2 with an NVIDIA Jetson Orin NX, a Quectel RM530N-GL 5G modem in… More >

Xiaolei Yin, Zihan Wang, Sanfeng Zhang^*, Shouwei Li^*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088083 - 15 September 2026

Abstract On-chain risky address identification is an important task in blockchain security analysis and digital asset risk management. In practical on-chain risk control, however, risky addresses are usually far fewer than benign ones. Fund flows also follow complex propagation paths and strict temporal orders, which makes it difficult for existing methods to handle class imbalance, structural semantic modeling, and information leakage under temporal split settings at the same time. To address these challenges, this paper proposes AMLHunter, an on-chain risky address identification method based on temporally consistent transaction semantic constraints and generative graph augmentation. AMLHunter first… More >

Jing Wang¹, Tian Xia², Hao Li^1,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087310 - 15 September 2026

Abstract Knowledge graphs organize real-world facts as structured triples and have become a fundamental resource for search engines, question answering, recommender systems, and knowledge-enhanced large language models. However, real-world knowledge graphs remain highly incomplete, which limits their downstream reasoning ability. Existing pre-trained language model-based knowledge graph completion methods provide strong textual semantic representations, but they usually model graph structure only as shallow auxiliary features and remain weak in distinguishing structurally similar entities and topology-near negative samples. To address this limitation, this paper proposes a Dual-Level Structural Context Collaborative Framework (DSC²F) for knowledge graph completion. At the instance… More >

Nashat Nawafleh^*, Faris M. Al-Oqla

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087255 - 15 September 2026

Abstract In order to mimic the thermal properties of various natural fibers, this research presents a novel prediction framework that combines Fuzzy Logic (FL) with Grey Wolf Optimization (GWO). While the GWO technique ensures mathematical correctness by fine-tuning membership function parameters, this research uses a hybrid fuzzy model to outline nonlinear relationships between fiber components and thermal performance, which significantly reduces the need for extensive, trial-and-error laboratory testing. In this study, moisture, cellulose, and hemicellulose levels are predicted to be used to identify the finest natural fibers for biomaterial uses. An optimization methodology is seen by More >

Oyku Berfin Mercan^1,2, Nezihe Turhan Turan³, Aytuğ Onan^4,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087209 - 15 September 2026

Abstract Biomedical texts naturally contain multiple biological and medical concepts within a document, resulting in a semantically rich and complex structure. Consequently, multi-label text classification (MLTC) has become a suitable framework for comprehensively modeling biomedical texts, including clinical reports, laboratory records, and scientific abstracts. However, relying solely on contextual language representations may be insufficient to explicitly reflect the broader scientific focus and conceptual orientation of a document. In this study, the MLTC problem in the biomedical domain is investigated using the Hallmarks of Cancer (HoC) dataset. Topic probability distributions obtained from CombinedTM are incorporated as an… More >

Rihem Farkh¹, Ghislain Oudinet², Alaeddine Moussa³, Yasser Fouad^4,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086607 - 15 September 2026

Abstract Autonomous driving systems must reason not only about the current scene but also about how the environment may evolve under alternative actions. Although predictive world models can generate future latent rollouts, these rollouts are often consumed directly by planners or explanation modules without an explicit and auditable interpretation stage. This paper presents a predictive and explainable driving framework centered on a Future Interpretation Module (FIM), which transforms action-conditioned future rollouts into structured descriptors, including risk trend, peak risk, time-to-critical, minimum clearance, predicted collision, dominant predicted event, and confidence. An aligned latent interface, trained with feature-alignment… More >

Shengxuan Dong, Xiongwei Li^*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086535 - 15 September 2026

Abstract To address the conflict between navigation performance and safety constraints in safe reinforcement learning, this paper proposes Dual Stream-Analytic Hierarchy Process-Safe Soft Actor (DS-AHP-SAC), a safe soft actor-critic algorithm based on a dual-stream Q-network and dynamic Analytic Hierarchy Process (AHP) stratified experience replay. The algorithm achieves a balance between reward maximization and constraint satisfaction through three synergistic designs: (1) decoupling the Q-network into independent navigation and safety value streams to eliminate gradient interference at the Critic level and mitigate gradient competition at the Actor level; (2) constructing a three-criterion dynamic sampling strategy based on AHP, More >