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 >

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 >

Li-Woei Chen¹, Kun-Lin Tsai^2,*, Fang-Yie Leu³, Chao-Tung Yang^3,4,5, Wei-Zong Liang²

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083880 - 27 July 2026

Abstract Unmanned aerial vehicle (UAV) swarm networks are increasingly deployed in surveillance, disaster response, and intelligent transportation systems, where secure and efficient communication is critical under resource-constrained environments. However, conventional public-key-based security mechanisms introduce excessive computational overhead, while standalone intrusion detection systems are insufficient to defend against dynamic and multi-vector attacks in swarm networks. To address these challenges, in this paper, a lightweight time-indexed secure communication framework with intrusion detection modeling (TSCID) is proposed for resource-constrained UAV swarm networks. The proposed TSCID integrates a time-indexed session key derivation mechanism with lightweight authenticated encryption to ensure confidentiality,… More >

Khanh Nguyen-Trong^1,*, Tan Nguyen-Thi-Thanh²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083365 - 23 July 2026

Abstract Automated wood surface defect detection is difficult to evaluate reliably because defects are often small, low-contrast, and visually confounded by natural wood texture, while reported performance can vary substantially with benchmark design and domain shift. To address this issue, we conduct a comparative study across three practically relevant settings: a curated seven-class benchmark, a broader in-domain seven-class protocol derived from the same source dataset, and supervised adaptation to a low-resource Vietnamese target domain. We compare lightweight two-stage detectors based on Faster Region-based Convolutional Neural Network (Faster R-CNN) with MobileNetV3-FPN against a compact You Only Look… More >

Hongzhen Liu¹, Liang Xie¹, Zhiqiang Ru^2,*, Yuan Wan¹, Zhe Zhang¹, Xi Fang^1,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082755 - 23 July 2026

Abstract Federated learning is a privacy-preserving machine learning framework that facilitates model training directly on decentralized data that, due to privacy concerns or transmission costs, cannot be centralized on a server for traditional model training. To prevent adversaries from reconstructing the original data via parameters transmitted during the process, homomorphic encryption is a commonly adopted method. However, it introduces significant communication and computation costs and risks total security failure if any secret key is compromised. This paper proposes a privacy-preserving aggregation mechanism that enables each client to independently generate partial keys for encryption while allowing decryption… More >

Akash Shah¹, Mudasir Ahmad Wani^2,*, Ravi Prakash Chaturvedi³, Shri Kant³, Nidhi Sindhwani¹, Kashish Ara Shakil⁴, Sulieman Alshuhri²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082713 - 23 July 2026

Abstract Membership Inference Attacks (MIAs) pose a significant privacy risk in machine learning by enabling adversaries to infer whether specific data samples were used during training, particularly in sensitive domains such as social media and mental health analytics. To address this challenge, this paper proposes HEbdMIA, a lightweight homomorphic encryption-based defense that operates at the post-inference stage by encrypting model output logits without requiring retraining or architectural modifications. The proposed approach preserves the relative ordering of predictions while obscuring confidence patterns exploited by MIAs. Experimental evaluation on DepInferAttack and BotInferAttack demonstrates that HEbdMIA achieves a reduction More >

Suchang Yang, Hongtao Yu^*, Ruiyang Huang, Huansha Wang, Ran Li, Junzheng Li

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082651 - 23 July 2026

Abstract Modeling dynamic graphs in continuous time is critical for applications such as user behavior prediction and recommendation systems. These models can effectively capture fine-grained and long-term temporal dependencies. However, existing approaches often suffer from high computational costs and optimization difficulties, especially when handling time-sorted neighborhood sequences over long horizons. In this work, we propose DyG-Hyena, a novel continuous-time dynamic graph learning framework that combines conditional variational autoencoder (CVAE)-assisted temporal modeling with efficient feature fusion. Our approach has two main innovations: (i) Efficient temporal fusion—we replace the Transformer with an improved, lightweight Hyena module to model More >

Hongliang Tian, Xiaoke Liu^*, Bolin Song, Chenying Pei

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082430 - 23 July 2026

Abstract In the intelligent inspection of power systems, the detection of equipment defects is confronted with problems such as low background discrimination, multi-scale morphological differences, and the difficulty in identifying small targets and fine-grained defects, which makes it hard for existing models to balance detection accuracy and computational efficiency. To address this, this study proposes an improved lightweight detection framework, GRID-YOLO. This framework enhances the semantic discrimination ability of the backbone network for complex defects by introducing a cross-stage hierarchical multi-cognitive spatial attention module (C2MSA), designs an enhanced multi-scale bidirectional feature pyramid network (EMFPN) to achieve… More >