Yufei Wang¹, Jiayi Shang¹, Fang Liu^1,*, Jun Liu²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085708 - 13 August 2026

Abstract Ship detection is an effective way of sea area supervision, which has important research value in both military and civil fields. For small ship targets in the sea scene, the deep feature map is difficult to effectively capture their subtle features, resulting in the decline of small target detection accuracy and the increase of the missing detection rate. To solve this problem, this paper proposes a detection algorithm called YOLO-MARALight, which adds a small target detection layer in the head network, uses a larger scale feature map to retain the details, and improves the discrimination… More > Graphic Abstract

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 >

Tian Liu¹, Xichao Wang^1,*, Jun Wang², Song Gao², Hang Gao¹, Yuxi Liu¹, Yitao Zhuang¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084300 - 13 August 2026

Abstract In unmanned aerial vehicle (UAV) flight behaviour understanding, the lack of a unified semantic representation for continuous multi-source temporal data makes it difficult to model flight events, behavioural relationships, and composite behaviours in an interpretable manner. To address this issue, this paper proposes a knowledge graph construction method for UAV flight behaviour understanding. The proposed method integrates atomic event detection, temporal knowledge modelling, and composite behaviour reasoning, thereby enabling the automatic transformation of continuous flight logs into structured behavioural knowledge. First, local statistical features, including smoothed velocity and robust climb rate, are extracted from multi-source… More >

Nourah Fahad Janbi^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084270 - 13 August 2026

Abstract Automatic pain assessment from speech is an emerging approach for non-invasive and objective healthcare monitoring. However, many existing methods are evaluated on single datasets or under controlled conditions, which limits their robustness under diverse real-world conditions. This paper presents a lightweight, end-to-end deep learning framework for speech-based pain level classification that explicitly targets multi-dataset robustness assessment. The proposed approach uses log-Mel spectrograms with an EfficientNetV2B0 backbone to learn discriminative acoustic features without relying on handcrafted feature engineering or multi-stage pipelines. The model is evaluated on heterogeneous datasets, including clinical recordings, controlled experimental data, and a More >

Peixuan Wang¹, Lingyun Yuan^1,2,*, Yi Xiang¹, Tianyu Xie^1,2, Haochen Bao¹, Kexin Wang^1,2

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083887 - 13 August 2026

Abstract With the development of the Internet of Things (IoT), there is a rising demand for ciphertext retrieval. However, existing searchable encryption schemes mainly support single-modal retrieval, while current cross-modal searchable encryption methods often suffer from high computational overhead and lack reliable result verification. To address these problems, we propose a cross-modal searchable encryption scheme with result verification (VCMSE). First, we design a cross-modal hash extraction method that combines contrastive learning with a residual similarity matrix to generate encryption-friendly binary features with enhanced semantic consistency. Second, we designed a lightweight garbled circuit-based matching mechanism that enables More >

Wang Zhang¹, Lanlan Li², Jiayi Xing¹, Qiangqiang Yao^1,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083877 - 13 August 2026

Abstract Lightweight semantic segmentation remains challenging because compact backbones often weaken feature discriminability and lose fine-grained boundary details. In DeepLabV3+-style encoder-decoder architectures, the direct fusion of high-level semantic features and low-level spatial features may introduce semantic-spatial misalignment, resulting in blurred object contours and fragmented predictions. To address these issues, this paper proposes BFANet, a boundary-aware lightweight semantic segmentation framework based on DeepLabV3+ with a MobileNetV2 backbone. BFANet integrates parameter-free SimAM feature refinement, low-level-guided Dynamic Feature Alignment, and progressive decoder fusion to enhance discriminative feature responses, reduce cross-level feature inconsistency, and recover fine boundary structures. Experiments on… More >

Kezhen Xie, Syed Mohd Zahid Syed Zainal Ariffin^*, Muhammad Izzad Ramli

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083216 - 13 August 2026

Abstract Low-light infrared–visible image fusion remains challenging due to severe modality imbalance caused by visible-image degradation under insufficient illumination. In low-light conditions, visible images often suffer from luminance attenuation, blurred details, and amplified noise, whereas infrared images preserve stable structural information but lack texture representation. Existing fusion frameworks commonly perform feature interaction within a shared representation space, which may cause degraded visible responses to be progressively suppressed by dominant infrared structures during fusion. To address this issue, this paper proposes DSCAttFuseNet for low-light infrared–visible image fusion. The proposed framework adopts a structure–detail–luminance decoupled modeling strategy to… More >

Jordi Vallverdú^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.082945 - 13 August 2026

Abstract Large rule-based systems—from automated theorem provers to diagnostic engines and expert systems—face a common bottleneck: when many rules are simultaneously applicable, choosing which rule to fire can dominate search effort. We present HL-W, a formally constrained adaptive proof-search control layer over a fixed base calculus. Each inference rule R is assigned a scalar weight w(R,t)∈[0,1] at search stage t; rule applications are scheduled by combining a threshold condition w(R,t)≥θ with an explicit fairness mechanism. Because the underlying inference rules are left unchanged, every derivation produced by the framework… More >

Aigerim Altayeva^1,*, Nurzhan Omarov²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.080878 - 13 August 2026

Abstract Hazardous impulsive sound detection plays a critical role in intelligent surveillance, public safety monitoring, and automated emergency response systems. This study proposes a lightweight channel-attention-enhanced deep learning architecture for real-time detection and classification of hazardous acoustic events. The proposed framework utilizes mel-spectrogram representations to capture time-frequency characteristics of audio signals and employs a compact convolutional neural backbone to efficiently extract hierarchical features. To enhance feature discrimination, a squeeze-and-excitation channel-attention mechanism is integrated into the architecture, enabling adaptive recalibration of feature channels and improved robustness under noisy and complex acoustic environments. A custom dataset consisting of More >