Sara Tehsin^1,*, Inzamam Mashood Nasir¹, Wiem Abdelbaki², Fadwa Alrowais³, Reham Abualhamayel⁴, Abdulsamad Ebrahim Yahya⁵, Radwa Marzouk⁶

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076514 - 12 March 2026

Abstract The rapid proliferation of multimodal misinformation on social media demands detection frameworks that are not only accurate but also robust to noise, adversarial manipulation, and semantic inconsistency between modalities. Existing multimodal fake news detection approaches often rely on deterministic fusion strategies, which limits their ability to model uncertainty and complex cross-modal dependencies. To address these challenges, we propose Q-ALIGNer, a quantum-inspired multimodal framework that integrates classical feature extraction with quantum state encoding, learnable cross-modal entanglement, and robustness-aware training objectives. The proposed framework adopts quantum formalism as a representational abstraction, enabling probabilistic modeling of multimodal alignment… More >

Adam Dudáš^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076507 - 12 March 2026

Abstract It is known that correlation does not imply causality. Some relationships identified in the analysis of data are coincidental or unknown, and some are produced by real-world causality of the situation, which is problematic, since there is a need to differentiate between these two scenarios. Until recently, the proper−semantic−causality of the relationship could have been determined only by human experts from the area of expertise of the studied data. This has changed with the advance of large language models, which are often utilized as surrogates for such human experts, making the process automated and readily… More >

A-Seong Moon, Haesung Kim, Ye-Chan Park, Jaesung Lee^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076411 - 12 March 2026

Abstract Multimodal emotion recognition has emerged as a key research area for enabling human-centered artificial intelligence, supported by the rapid progress in vision, audio, language, and physiological modeling. Existing approaches integrate heterogeneous affective cues through diverse embedding strategies and fusion mechanisms, yet the field remains fragmented due to differences in feature alignment, temporal synchronization, modality reliability, and robustness to noise or missing inputs. This survey provides a comprehensive analysis of MER research from 2021 to 2025, consolidating advances in modality-specific representation learning, cross-modal feature construction, and early, late, and hybrid fusion paradigms. We systematically review visual,… More >

Pablo Guillén¹, Enrique Frias-Martinez^2,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076400 - 12 March 2026

Abstract Alzheimer’s disease (AD) diagnosis and prognosis increasingly rely on machine learning (ML) models. Although these models provide good results, clinical adoption is limited by the need for technical expertise and the lack of trustworthy and consistent model explanations. SHAP (SHapley Additive exPlanations) is commonly used to interpret AD models, but existing studies tend to focus on explanations for isolated tasks, providing little evidence about their robustness across disease stages, model architectures, or prediction objectives. This paper proposes a multi-level explainability framework that measures the coherence, stability and consistency of explanations by integrating: (1) within-model coherence… More >

Salma Akther¹, Wencheng Yang^1,*, Song Wang², Shicheng Wei¹, Ji Zhang¹, Xu Yang³, Yanrong Lu⁴, Yan Li¹

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076358 - 12 March 2026

Abstract As deep learning (DL) models are increasingly deployed in sensitive domains (e.g., healthcare), concerns over privacy and security have intensified. Conventional penetration testing frameworks, such as OWASP and NIST, are effective for traditional networks and applications but lack the capabilities to address DL-specific threats, such as model inversion, membership inference, and adversarial attacks. This review provides a comprehensive analysis of penetration testing for the privacy of DL models, examining the shortfalls of existing frameworks, tools, and testing methodologies. Through systematic evaluation of existing literature and empirical analysis, we identify three major contributions: (i) a critical… More >

Rama Gautam^1,*, Nikhil Marriwala¹, Reeta Devi¹, Dhariya Singh Arya²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076136 - 12 March 2026

Abstract In this study, the multi-scale (meso and macro) modelling was used to predict the electric response of the material. Porosity was introduced through a sugar-templating process to enhance compressibility and sensitivity. Mean-field homogenization was employed to predict the electrical conductivity of the nanocomposites, which was validated experimentally through I–V characterisation, confirming stable Ohmic behavior. The homogenised material parameters were incorporated into COMSOL Multiphysics to simulate diaphragm deflection and capacitance variation under applied pressure. Experimental results showed a linear and stable capacitance response at the force magnitude of 0–7 N. The Graphene nanoplatelets (GnP)–Polydimethylsiloxane (PDMS) sensor demonstrated More >

Shasha Li, Bin Ji^*, Xiaodong Liu, Jun Ma, Jie Yu^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076083 - 12 March 2026

Abstract Aligning natural language with operating system (OS) commands allows users to perform complex computer tasks through simple natural language descriptions. However, due to the complex nature of natural language, it still remains challenging to achieve precise alignment. In this paper, we present ComAlign, a Chinese benchmark dataset that pairs Chinese natural language descriptions with corresponding OS commands. ComAlign covers a broad range of 82 distinct OS command types with a total of 1811 natural language descriptions. We elaborate on the construction of ComAlign and construct three baselines to evaluate the alignment accuracy on ComAlign. Experimental More >

Run Jiao, Qingyuan Hou, Ziyu Jiang, Hongxia Wang^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075967 - 12 March 2026

Abstract Mesh models are among the primary representations for storing 3-D objects, encapsulating detailed geometric information. 3-D mesh watermarking, in particular, plays a central role in the protection of 3-D content. However, frequency-domain methods rely on complex parameterization and spectral decomposition, which are sensitive to mesh topology and resolution and often introduce perceptible artifacts. Spatial-domain techniques, on the other hand, typically embed watermarks in global or randomly selected regions, leading to visible distortions and reduced robustness. To address the above limitations and protect model copyright without compromising the original aesthetic quality, we propose a deterministice PCA-synchronized… More >

Mohammed M. Alenazi^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075966 - 12 March 2026

Abstract Multimodal spatiotemporal data from smart city consumer electronics present critical challenges including cross-modal temporal misalignment, unreliable data quality, limited joint modeling of spatial and temporal dependencies, and weak resilience to adversarial updates. To address these limitations, EdgeST-Fusion is introduced as a cross-modal federated graph transformer framework for context-aware smart city analytics. The architecture integrates cross-modal embedding networks for modality alignment, graph transformer encoders for spatial dependency modeling, temporal self-attention for dynamic pattern learning, and adaptive anomaly detection to ensure data quality and security during aggregation. A privacy-preserving federated learning protocol with differential privacy guarantees enables… More >

Lixing Tan^1,2, Liusiyu Chen¹, Yang Wang¹, Zhenyu Song^1,*, Zenan Lu^1,3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075959 - 12 March 2026

Abstract Anomaly detection is a vibrant research direction in controller area networks, which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles. However, existing unsupervised learning methods suffer from insufficient temporal and spatial constraints on shallow features, resulting in fragmented feature representations that compromise model stability and accuracy. To improve the extraction of valuable features, this paper investigates the influence of clustering constraints on shallow feature convergence paths at the model level and further proposes an end-to-end intrusion detection system based on efficient deep embedded subspace clustering (EDESC-IDS). Following the… More >