Qiwu Wu¹, Tao Yang^2,*, Yunchen Su², Lingzhi Jiang³, Tao Tong²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082757 - 23 July 2026

Abstract Deep reinforcement learning (DRL) has become an important method in Unmanned Aerial Vehicle(UAV) path planning, but the field still lacks a dedicated bibliometric review that summarizes its publication patterns, intellectual structure, and thematic evolution. This study analyzes 1402 Web of Science publications from 2010 to 2025 using CiteSpace, VOSviewer, and the Bibliometrix R package. Three main findings are reported. First, the bibliometric evidence suggests a four-phase evolution of the field—foundational exploration (2015–2016), continuous-control breakthrough (2017–2019), multi-agent collaborative coordination (2020–2022), and complex-scenario integration (2023–2025)—as reflected in publication trends, keyword bursts, and co-citation clusters. Second, co-citation and keyword More >

Alaa M. Momani¹, Deema Mohammed Alsekait², Mahmoud Ahmad Al-Khasawneh^1,*, Siti Hajar Othman³, Ibraheem Al-Tarawneh⁴, Nikunj Sharma⁵, Wee How Khoh⁶

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082572 - 23 July 2026

Abstract Unmanned Aerial Vehicles (UAVs) are finding more and more applications in logistics, surveillance, and other operations at a large scale. However, autonomous navigation in dynamic traffic situations is not an easy task due to limited energy, moving obstacles, and inter-agent interactions. The proposed paper can be discussed as a Generative World Modeling (GWM) framework of risk-focused UAV navigation in the dynamic traffic network. This paper proposes a GWM framework for risk-aware UAV navigation in dynamic traffic networks. The proposed design incorporates three key elements; a generative world model for predicting future environmental conditions, a diffusion-based… More >

Yiqiao Xiang¹, Jingchun Zhou^1,2,*, Ruijie Liu¹, Dehuan Zhang¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082357 - 23 July 2026

Abstract Underwater imagery is degraded by depth-dependent absorption and scattering, which often introduce color casts and contrast attenuation. Although recent Vision Mamba models provide efficient long-range dependency modeling, their conventional 2D scanning patterns are not explicitly designed to exploit the depth-correlated structure of underwater degradation and may therefore weaken geometry-aware feature dependencies. To address this limitation, we propose Isoline-Guided Evolutionary Mamba (IG-Mamba), a physics-inspired framework that uses a depth-correlated potential prior to organize state-space token propagation. Specifically, we introduce a Topology-Preserving Isoline Scanning mechanism. By leveraging a geometric prior, this mechanism quantizes the scene into discrete… More >

Kexin Zhang¹, Lihua Liu^1,*, Yuting Xue¹, Tao Zhou², Fengshuai Yue¹, Ruifeng Du¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082353 - 23 July 2026

Abstract Traditional Mamba-UNet integrations employ four-stage architectures, replacing conventional five-stage UNets with VMamba blocks for global dependency modeling. Unlike Transformers, which suffer from quadratic complexity and high memory consumption in self-attention, Mamba-UNet achieves efficient global modeling through linear-complexity state space modeling. This paper proposes TriLVM-UNet, a lightweight three-stage architecture that integrates parameter-efficient VMamba blocks and enhances cross-stage feature interaction via an improved skip-attention bridge (SAB) module inspired by UltraLight VM-UNet. The model incorporates a Lightweight Vision Mamba (LVM) layer for high-resolution feature extraction, alongside multi-scale dilated convolution (MSDC) and convolutional block attention module (CBAM) for enhanced More >

Shing-Tai Pan^*, Yi-Zhen Huang, Zhi-Qing Chen

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081460 - 23 July 2026

Abstract This paper proposes a multimodal fusion framework that integrates speech and visual features to enhance the accuracy of emotion recognition. The principal contribution lies in extending the visual component from single-image to multi-image emotion recognition. Specifically, the proposed framework employs an InceptionV3 Convolutional Neural Network (CNN)-based architecture to extract features from multiple facial images representing the speaker’s expressions throughout an utterance. These features are concatenated into a single vector and subsequently processed by Long Short-Term Memory (LSTM) or Hidden Markov Model (HMM) for temporal modeling. For the speech modality, Mel-Frequency Cepstral Coefficients (MFCC) or filter… More >

Thossapon Kaewrakmuk, Jakkree Srinonchat^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081437 - 23 July 2026

Abstract Robotic systems require reliable tactile perception to evaluate object stiffness during physical interaction. This study proposes a lightweight dual-branch architecture, named Hybrid-CNN-ResVgg, designed to improve hardness recognition using data from a low-cost piezoresistive tactile sensor. The model combines a one-dimensional convolutional neural network (1D-CNN) based on a ResNet8-Lite architecture for learning temporal signal patterns and a two-dimensional convolutional neural network (2D-CNN) based on a VGG6-Lite architecture for learning spatial representations derived from Gramian Angular Difference Fields (GADF). A cross-architecture fusion mechanism is introduced to integrate temporal and spatial features while reducing redundant representation learning. Experiments… More >

Yuanzhen Wang¹, Hongxin Zhang^2,3,*, Shaofei Sun¹, Yaqi Zhang², Xing Fang⁴, Zhi Sun²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081308 - 23 July 2026

Abstract The effectiveness of profiling deep learning side-channel attacks relies on the assumption that training and attack data follow the same distribution. However, when the profiling device differs from the target device, process-voltage-temperature (PVT) variations and clock jitter countermeasures cause distribution shifts in power traces, rendering models trained on the source device ineffective on the target. Existing domain adaptation methods typically rely on a single distributional constraint without jointly constraining kernel mean embeddings and covariance structure, thus limiting their effectiveness against strong defenses such as clock jitter. We propose Robust Feature Alignment for Side-Channel Analysis (RFA-SCA),… More >

Ashish Phogat¹, Akash Ahlawat¹, Virendra Singh², Deepak Chhabra^1,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080735 - 23 July 2026

Abstract Polyether ether ketone (PEEK) is a radical filament with excellent strength equivalent to cortical bone and high thermal-mechanical properties. PEEK’s acquisition is acceptable in the fabrication of cranio-maxillofacial implants because of its exceptional strength-to-weight ratio and biocompatibility. However, its implementation in fused filament fabrication (FFF) is impeded by the lack of a cohesive optimisation framework that involves varying vital parameters: layer height, infill density and two post-process parameters: annealing temperature, annealing time, which affect its mechanical performance. This research work introduces a comprehensive methodology that integrates experimental design, hybrid Genetic Algorithm Artificial Neural Network (GA-ANN)… More >

Rashid Jahangir^1,*, Muhammad Asif Nauman², Oumaima Saidani³, Faisal Ramzan²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080025 - 23 July 2026

Abstract Speech Emotion Recognition (SER) is a critical component of affective computing with broad applications in human–computer interaction, mental health monitoring, and intelligent multimedia systems. However, SER remains challenging due to the emotional ambiguity, lack of labeled data, class imbalance, and speaker variability. This study presents an effective SER framework that integrates contrastive representation learning, optimized spectrogram-based data augmentation, and selective synthetic data generation by using TimeGAN to enhance emotion classification performance. Contrastive learning enables the model to better discriminate acoustically similar emotions while Optuna automatically tunes augmentation strategies such as noise injection, time shifting, and More >

Zhengshun Fei^1,*, Qiao Sun¹, Chuang Yang², Siranee Nuchitprasitchai³, Yongping Zheng¹, Xinjian Xiang^1,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.078696 - 23 July 2026

Abstract Path planning is a critical component for enabling autonomous navigation in mobile robots. Sampling-based planners are widely adopted due to their strong generality, yet they rely heavily on uniform sampling, which often leads to unstable performance and high computational cost in complex environments. To address this issue, recent studies feed free-space point clouds into neural networks to infer a set of guidance states near the optimal path, thereby enabling non-uniform sampling; however, the accuracy of the guidance set becomes a key bottleneck for further improvement. In this paper, we propose an improved point-cloud neural RRT*… More >