Zilin Zhang¹, Yan Liu^1,*, Jia Liu², Senbao Hou³, Yuping Zhang¹, Chenyuan Wang¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-14, 2026, DOI:10.32604/cmc.2025.072286 - 09 December 2025

Abstract With the growing demand for more comprehensive and nuanced sentiment understanding, Multimodal Sentiment Analysis (MSA) has gained significant traction in recent years and continues to attract widespread attention in the academic community. Despite notable advances, existing approaches still face critical challenges in both information modeling and modality fusion. On one hand, many current methods rely heavily on encoders to extract global features from each modality, which limits their ability to capture latent fine-grained emotional cues within modalities. On the other hand, prevailing fusion strategies often lack mechanisms to model semantic discrepancies across modalities and to… More >

Guangyu Huo, Chang Su, Xiaoyu Zhang^*, Xiaohui Cui, Lizhong Zhang

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.072147 - 09 December 2025

Abstract Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks, requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize traffic management and enhance urban mobility and sustainability. However, traditional predictive models struggle to capture long-term temporal dependencies and are computationally intensive, limiting their practicality in real-time. Moreover, many approaches overlook the periodic characteristics inherent in traffic data, further impacting performance. To address these challenges, we introduce ST-MambaGCN, a State-Space-Based Spatio-Temporal Graph Convolution Network. Unlike conventional models, ST-MambaGCN replaces the temporal attention layer with Mamba, a state-space More >

Shaoping Xiao^1,*, Zhaoan Wang¹, Junchao Li², Caden Noeller¹, Jiefeng Jiang³, Jun Wang⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-62, 2026, DOI:10.32604/cmc.2025.072146 - 09 December 2025

Abstract The integration of human factors into artificial intelligence (AI) systems has emerged as a critical research frontier, particularly in reinforcement learning (RL), where human-AI interaction (HAII) presents both opportunities and challenges. As RL continues to demonstrate remarkable success in model-free and partially observable environments, its real-world deployment increasingly requires effective collaboration with human operators and stakeholders. This article systematically examines HAII techniques in RL through both theoretical analysis and practical case studies. We establish a conceptual framework built upon three fundamental pillars of effective human-AI collaboration: computational trust modeling, system usability, and decision understandability. Our… More >

Mohammed Debakla^1,*, Ali Mezaghrani¹, Khalifa Djemal², Imane Zouaneb¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-21, 2026, DOI:10.32604/cmc.2025.071927 - 09 December 2025

Abstract Magnetic Resonance Imaging (MRI) has a pivotal role in medical image analysis, for its ability in supporting disease detection and diagnosis. Fuzzy C-Means (FCM) clustering is widely used for MRI segmentation due to its ability to handle image uncertainty. However, the latter still has countless limitations, including sensitivity to initialization, susceptibility to local optima, and high computational cost. To address these limitations, this study integrates Grey Wolf Optimization (GWO) with FCM to enhance cluster center selection, improving segmentation accuracy and robustness. Moreover, to further refine optimization, Fuzzy Entropy Clustering was utilized for its distinctive features… More >

Shih-Lin Lin^*, Cheng-Wei Li

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071915 - 09 December 2025

Abstract This study focuses on developing a deep learning model capable of recognizing vehicle brands and models, integrated with a law enforcement intelligence platform to overcome the limitations of existing license plate recognition techniques—particularly in handling counterfeit, obscured, or absent plates. The research first entailed collecting, annotating, and classifying images of various vehicle models, leveraging image processing and feature extraction methodologies to train the model on Microsoft Custom Vision. Experimental results indicate that, for most brands and models, the system achieves stable and relatively high performance in Precision, Recall, and Average Precision (AP). Furthermore, simulated tests… More >

Zhonghao Wang^1,2, Xin Liu^1,2,*, Changhua Yue³, Haiwen Yuan⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.071813 - 09 December 2025

Abstract To address critical challenges in nighttime ship detection—high small-target missed detection (over 20%), insufficient lightweighting, and limited generalization due to scarce, low-quality datasets—this study proposes a systematic solution. First, a high-quality Night-Ships dataset is constructed via CycleGAN-based day-night transfer, combined with a dual-threshold cleaning strategy (Laplacian variance sharpness filtering and brightness-color deviation screening). Second, a Cross-stage Lightweight Fusion-You Only Look Once version 8 (CLF-YOLOv8) is proposed with key improvements: the Neck network is reconstructed by replacing Cross Stage Partial (CSP) structure with the Cross Stage Partial Multi-Scale Convolutional Block (CSP-MSCB) and integrating Bidirectional Feature Pyramid More >

Jay Barach^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.071705 - 09 December 2025

Abstract Human Resource (HR) operations increasingly rely on cloud-based platforms that provide hiring, payroll, employee management, and compliance services. These systems, typically built on multi-tenant microservice architectures, offer scalability and efficiency but also expand the attack surface for adversaries. Ransomware has emerged as a leading threat in this domain, capable of halting workflows and exposing sensitive employee records. Traditional defenses such as static hardening and signature-based detection often fail to address the dynamic requirements of HR Software as a Service (SaaS), where continuous availability and privacy compliance are critical. This paper presents a Moving Target Defense… More >

Yuntian Wang^1,2, Taohua Liang^1,2, Yuan Zhou^1,2, Weimei Shi^1,2, Lijuan Huang^1,2, Yuzhu Guo^3,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-26, 2026, DOI:10.32604/cmc.2025.071624 - 09 December 2025

Abstract This investigation utilizes non-equilibrium molecular dynamics (NEMD) simulations to explore shock-induced spallation in single-crystal tantalum across shock velocities of 0.75–4 km/s and initial temperatures from 300 to 2000 K. Two spallation modes emerge: classical spallation for shock velocity below 1.5 km/s, with solid-state reversible Body-Centered Cubic (BCC) to Face-Centered Cubic (FCC) or Hexagonal Close-Packed (HCP) phase transformations and discrete void nucleation-coalescence; micro-spallation for shock velocity above 3.0 km/s, featuring complete shock-induced melting and fragmentation, with a transitional regime (2.0–2.5 km/s) of partial melting. Spall strength decreases monotonically with temperature due to thermal softening. Elevated temperatures More >

Taehoon Kim, Sehun Lee, Junho Ahn^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-30, 2026, DOI:10.32604/cmc.2025.071597 - 09 December 2025

Abstract As large, room-scale environments become increasingly common, their spatial complexity increases due to variable, unstructured elements. Consequently, demand for room-scale service robots is surging, yet most technologies remain corridor-centric, and autonomous navigation in expansive rooms becomes unstable even around static obstacles. Existing approaches face several structural limitations. These include the labor-intensive requirement for large-scale object annotation and continual retraining, as well as the vulnerability of vanishing point or line-based methods when geometric cues are insufficient. In addition, the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter… More >

Rashid Jahangir^1,*, Nazik Alturki², Muhammad Asif Nauman³, Faiqa Hanif¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071574 - 09 December 2025

Abstract Human activity recognition (HAR) is a method to predict human activities from sensor signals using machine learning (ML) techniques. HAR systems have several applications in various domains, including medicine, surveillance, behavioral monitoring, and posture analysis. Extraction of suitable information from sensor data is an important part of the HAR process to recognize activities accurately. Several research studies on HAR have utilized Mel frequency cepstral coefficients (MFCCs) because of their effectiveness in capturing the periodic pattern of sensor signals. However, existing MFCC-based approaches often fail to capture sufficient temporal variability, which limits their ability to distinguish… More >