Qun Song¹, Chao Gao¹, Han Wu¹, Zhiheng Rao¹, Huafeng Qin^1,*, Simon Fong^1,2,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070918

Abstract Metaheuristic algorithms, renowned for strong global search capabilities, are effective tools for solving complex optimization problems and show substantial potential in e-Health applications. This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health. We selected representative algorithms published between 2019 and 2024, and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts. CThe Harris Hawks Optimizer (HHO) demonstrated the highest early citation impact. The study also examined applications in disease prediction models, clinical decision support, and intelligent health monitoring. Notably, the More >

Yaxin Zhao¹, Qi Han², Hui Shu², Yan Guang^2,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070511

Abstract Large Language Models (LLMs) are increasingly applied in the field of code translation. However, existing evaluation methodologies suffer from two major limitations: (1) the high overlap between test data and pretraining corpora, which introduces significant bias in performance evaluation; and (2) mainstream metrics focus primarily on surface-level accuracy, failing to uncover the underlying factors that constrain model capabilities. To address these issues, this paper presents TCode (Translation-Oriented Code Evaluation benchmark)—a complexity-controllable, contamination-free benchmark dataset for code translation—alongside a dedicated static feature sensitivity evaluation framework. The dataset is carefully designed to control complexity along multiple dimensions—including syntactic… More >

Yuchao Hou^1,2, Biaobiao Bai³, Shuai Zhao³, Yue Wang³, Jie Wang³, Zijian Li^4,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069789

Abstract Recently, large-scale deep learning models have been increasingly adopted for point cloud classification. However, these methods typically require collecting extensive datasets from multiple clients, which may lead to privacy leaks. Federated learning provides an effective solution to data leakage by eliminating the need for data transmission, relying instead on the exchange of model parameters. However, the uneven distribution of client data can still affect the model’s ability to generalize effectively. To address these challenges, we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework (FDASS-MRFCF).… More >

Rekha Phadke¹, Abdul Lateef Haroon Phulara Shaik², Dayanidhi Mohapatra³, Doaa Sami Khafaga^4,*, Eman Abdullah Aldakheel⁴, N. Sathyanarayana⁵

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.067564

Abstract Recently, the Internet of Things (IoT) technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices. Furthermore, the IoT plays a key role in multiple domains, including industrial automation, smart homes, and intelligent transportation systems. However, an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness. To address these issue, this research proposes a Modified Walrus Optimization Algorithm (MWaOA) for effective resource management in smart IoT systems. In the proposed MWaOA, a crowding process… More >

Muhammad Ejaz¹, Muhammad Asim^2,*, Mudasir Ahmad Wani^2,3, Kashish Ara Shakil^4,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072464

Abstract The integration of High-Altitude Platform Stations (HAPS) with Reconfigurable Intelligent Surfaces (RIS) represents a critical advancement for next-generation wireless networks, offering unprecedented opportunities for ubiquitous connectivity. However, existing research reveals significant gaps in dynamic resource allocation, joint optimization, and equitable service provisioning under varying channel conditions, limiting practical deployment of these technologies. This paper addresses these challenges by proposing a novel Fairness-Aware Deep Q-Learning (FAIR-DQL) framework for joint resource management and phase configuration in HAPS-RIS systems. Our methodology employs a comprehensive three-tier algorithmic architecture integrating adaptive power control, priority-based user scheduling, and dynamic learning mechanisms. 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, DOI:10.32604/cmc.2025.071624

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 >

Tai Liu^1,2, Mao Ye^2,*, Feng Wu³, Chao Zhu², Bo Chen², Guoyan Zhang^1,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072692

Abstract With the continuous advancement of unmanned technology in various application domains, the development and deployment of blind-spot-free panoramic video systems have gained increasing importance. Such systems are particularly critical in battlefield environments, where advanced panoramic video processing and wireless communication technologies are essential to enable remote control and autonomous operation of unmanned ground vehicles (UGVs). However, conventional video surveillance systems suffer from several limitations, including limited field of view, high processing latency, low reliability, excessive resource consumption, and significant transmission delays. These shortcomings impede the widespread adoption of UGVs in battlefield settings. To overcome these… More >

Bin Zhang^*, Zhancheng Xu

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069979

Abstract This study aimed to enhance the performance of semantic segmentation for autonomous driving by improving the 2DPASS model. Two novel improvements were proposed and implemented in this paper: dynamically adjusting the loss function ratio and integrating an attention mechanism (CBAM). First, the loss function weights were adjusted dynamically. The grid search method is used for deciding the best ratio of 7:3. It gives greater emphasis to the cross-entropy loss, which resulted in better segmentation performance. Second, CBAM was applied at different layers of the 2D encoder. Heatmap analysis revealed that introducing it after the second… More >

Bing Wei, Ming Zhong, Qian Chen, Yi Wu^*, Yubin Li

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069910

Abstract In erasure-coded storage systems, updating data requires parity maintenance, which often leads to significant I/O amplification due to “write-after-read” operations. Furthermore, scattered parity placement increases disk seek overhead during repair, resulting in degraded system performance. To address these challenges, this paper proposes a Cognitive Update and Repair Method (CURM) that leverages machine learning to classify files into write-only, read-only, and read-write categories, enabling tailored update and repair strategies. For write-only and read-write files, CURM employs a data-difference mechanism combined with fine-grained I/O scheduling to minimize redundant read operations and mitigate I/O amplification. For read-write files,… More >

Xinwei Zhu, Jianxun Zhang^*, Huan Zeng

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.068578

Abstract Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering, color distortion, and detail blurring, limiting their application performance. Existing underwater image enhancement methods, although they can improve the image quality to some extent, often lead to problems such as detail loss and edge blurring. To address these problems, we propose FENet, an efficient underwater image enhancement method. FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra, respectively. Then, a distance… More >