Peizheng Lai¹, Minqing Zhang^1,2,*, Yixin Tang¹, Ya Yue¹, Fuqiang Di^1,2

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2935-2957, 2025, DOI:10.32604/cmc.2025.065887 - 03 July 2025

Abstract Federated Learning (FL) has emerged as a promising distributed machine learning paradigm that enables multi-party collaborative training while eliminating the need for raw data sharing. However, its reliance on a server introduces critical security vulnerabilities: malicious servers can infer private information from received local model updates or deliberately manipulate aggregation results. Consequently, achieving verifiable aggregation without compromising client privacy remains a critical challenge. To address these problem, we propose a reversible data hiding in encrypted domains (RDHED) scheme, which designs joint secret message embedding and extraction mechanism. This approach enables clients to embed secret messages… More >

Komal Rani Narejo¹, Hongying Zan^1,*, Kheem Parkash Dharmani², Orken Mamyrbayev^3,*, Ainur Akhmediyarova⁴, Zhibek Alibiyeva⁴, Janna Alimkulova⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3407-3429, 2025, DOI:10.32604/cmc.2025.065872 - 03 July 2025

Abstract While automatic image captioning systems have made notable progress in the past few years, generating captions that fully convey sentiment remains a considerable challenge. Although existing models achieve strong performance in visual recognition and factual description, they often fail to account for the emotional context that is naturally present in human-generated captions. To address this gap, we propose the Sentiment-Driven Caption Generator (SDCG), which combines transformer-based visual and textual processing with multi-level fusion. RoBERTa is used for extracting sentiment from textual input, while visual features are handled by the Vision Transformer (ViT). These features are More >

Weili Liu¹, Fayi Hao¹, Jiangping Yuan^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 1951-1979, 2025, DOI:10.32604/cmc.2025.065836 - 03 July 2025

Abstract Additive manufacturing (AM) and Three-dimensional (3D) printing build complex structures layer by layer, greatly expanding design possibilities. Traditional thermoplastics like Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), and Polyethylene Terephthalate Glycol (PETG) are widely used in 3D printing, but their non-renewable nature and limited biodegradability have driven research into plant fiber-based materials. These materials, mainly cellulose and lignin, come from sources like wood and agricultural waste, offering renewability, biodegradability, and biocompatibility. This paper reviews recent advances in plant fiber-based materials for 3D printing, covering their development from raw materials to applications. It highlights the sources,… More >

Lingyun Xiang^*, Xu He, Xi Zhang, Chengfu Ou

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2375-2389, 2025, DOI:10.32604/cmc.2025.065804 - 03 July 2025

Abstract Linguistic steganography (LS) aims to embed secret information into normal natural text for covert communication. It includes modification-based (MLS) and generation-based (GLS) methods. MLS often relies on limited manual rules, resulting in low embedding capacity, while GLS achieves higher embedding capacity through automatic text generation but typically ignores extraction efficiency. To address this, we propose a sentence attribute encoding-based MLS method that enhances extraction efficiency while maintaining strong performance. The proposed method designs a lightweight semantic attribute analyzer to encode sentence attributes for embedding secret information. When the attribute values of the cover sentence differ… More >

Tsu-Yang Wu^1,2,3, Haozhi Wu³, Maoxin Tang^1,2, Saru Kumari⁴, Chien-Ming Chen^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3175-3192, 2025, DOI:10.32604/cmc.2025.065717 - 03 July 2025

Abstract As a model for the next generation of the Internet, the metaverse—a fully immersive, hyper-temporal virtual shared space—is transitioning from imagination to reality. At present, the metaverse has been widely applied in a variety of fields, including education, social entertainment, Internet of vehicles (IoV), healthcare, and virtual tours. In IoVs, researchers primarily focus on using the metaverse to improve the traffic safety of vehicles, while paying limited attention to passengers’ social needs. At the same time, Social Internet of Vehicles (SIoV) introduces the concept of social networks in IoV to provide better resources and services… More >

Wenlong Ke^1,2,*, Zilong Li¹, Peiyu Chen¹, Benfeng Chen¹, Jinglin Lv¹, Qiang Wang², Ziyi Jia², Shigen Shen¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3305-3319, 2025, DOI:10.32604/cmc.2025.065665 - 03 July 2025

Abstract Zero Trust Network (ZTN) enhances network security through strict authentication and access control. However, in the ZTN, optimizing flow control to improve the quality of service is still facing challenges. Software Defined Network (SDN) provides solutions through centralized control and dynamic resource allocation, but the existing scheduling methods based on Deep Reinforcement Learning (DRL) are insufficient in terms of convergence speed and dynamic optimization capability. To solve these problems, this paper proposes DRL-AMIR, which is an efficient flow scheduling method for software defined ZTN. This method constructs a flow scheduling optimization model that comprehensively considers… More >

Ziyang Wang¹, Lian Duan^1,2,*, Lei Kuang¹, Haibo Zhou¹, Ji’an Duan¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2587-2604, 2025, DOI:10.32604/cmc.2025.065648 - 03 July 2025

Abstract The packaging quality of coaxial laser diodes (CLDs) plays a pivotal role in determining their optical performance and long-term reliability. As the core packaging process, high-precision laser welding requires precise control of process parameters to suppress optical power loss. However, the complex nonlinear relationship between welding parameters and optical power loss renders traditional trial-and-error methods inefficient and imprecise. To address this challenge, a physics-informed (PI) and data-driven collaboration approach for welding parameter optimization is proposed. First, thermal-fluid-solid coupling finite element method (FEM) was employed to quantify the sensitivity of welding parameters to physical characteristics, including… More >

Anees Ara¹, Muhammad Mujahid¹, Amal Al-Rasheed^2,*, Shaha Al-Otaibi², Tanzila Saba¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2717-2731, 2025, DOI:10.32604/cmc.2025.065566 - 03 July 2025

Abstract DeepSeek Chinese artificial intelligence (AI) open-source model, has gained a lot of attention due to its economical training and efficient inference. DeepSeek, a model trained on large-scale reinforcement learning without supervised fine-tuning as a preliminary step, demonstrates remarkable reasoning capabilities of performing a wide range of tasks. DeepSeek is a prominent AI-driven chatbot that assists individuals in learning and enhances responses by generating insightful solutions to inquiries. Users possess divergent viewpoints regarding advanced models like DeepSeek, posting both their merits and shortcomings across several social media platforms. This research presents a new framework for predicting… More >

Qian Wang¹, Ya Zhou^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3835-3856, 2025, DOI:10.32604/cmc.2025.065465 - 03 July 2025

Abstract Edge computing has transformed smart grids by lowering latency, reducing network congestion, and enabling real-time decision-making. Nevertheless, devising an optimal task-offloading strategy remains challenging, as it must jointly minimise energy consumption and response time under fluctuating workloads and volatile network conditions. We cast the offloading problem as a Markov Decision Process (MDP) and solve it with Deep Reinforcement Learning (DRL). Specifically, we present a three-tier architecture—end devices, edge nodes, and a cloud server—and enhance Proximal Policy Optimization (PPO) to learn adaptive, energy-aware policies. A Convolutional Neural Network (CNN) extracts high-level features from system states, enabling More >

Yiwei Zhang, Xin Cui^*, Qinghui Zhao

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2355-2373, 2025, DOI:10.32604/cmc.2025.065430 - 03 July 2025

Abstract The rapid advance of Connected-Automated Vehicles (CAVs) has led to the emergence of diverse delay-sensitive and energy-constrained vehicular applications. Given the high dynamics of vehicular networks, unmanned aerial vehicles-assisted mobile edge computing (UAV-MEC) has gained attention in providing computing resources to vehicles and optimizing system costs. We model the computing offloading problem as a multi-objective optimization challenge aimed at minimizing both task processing delay and energy consumption. We propose a three-stage hybrid offloading scheme called Dynamic Vehicle Clustering Game-based Multi-objective Whale Optimization Algorithm (DVCG-MWOA) to address this problem. A novel dynamic clustering algorithm is designed… More >