Lei Yang^1,*, Jianfeng Wang¹, Minjing Liu¹, Chunyu Chen², Zhanjun Wu^3,4

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 265-279, 2025, DOI:10.32604/cmc.2025.065676 - 09 June 2025

Abstract Embedding optical fiber sensors into composite materials offers the advantage of real-time structural monitoring. However, there is an order-of-magnitude difference in diameter between optical fibers and reinforcing fibers, and the detailed mechanism of how embedded optical fibers affect the micromechanical behavior and damage failure processes within composite materials remains unclear. This paper presents a micromechanical simulation analysis of composite materials embedded with optical fibers. By constructing representative volume elements (RVEs) with randomly distributed reinforcing fibers, the optical fiber, the matrix, and the interface phase, the micromechanical behavior and damage evolution under transverse tensile and compressive… More >

Menwa Alshammeri¹, Mamoona Humayun^2,*, Khalid Haseeb³, Ghadah Naif Alwakid¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 433-446, 2025, DOI:10.32604/cmc.2025.065660 - 09 June 2025

Abstract Wireless technologies and the Internet of Things (IoT) are being extensively utilized for advanced development in traditional communication systems. This evolution lowers the cost of the extensive use of sensors, changing the way devices interact and communicate in dynamic and uncertain situations. Such a constantly evolving environment presents enormous challenges to preserving a secure and lightweight IoT system. Therefore, it leads to the design of effective and trusted routing to support sustainable smart cities. This research study proposed a Genetic Algorithm sentiment-enhanced secured optimization model, which combines big data analytics and analysis rules to evaluate… More >

Ammar Odeh^*, Anas Abu Taleb

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 447-461, 2025, DOI:10.32604/cmc.2025.065426 - 09 June 2025

Abstract The increasing deployment of Internet of Things (IoT) devices has introduced significant security challenges, including identity spoofing, unauthorized access, and data integrity breaches. Traditional security mechanisms rely on centralized frameworks that suffer from single points of failure, scalability issues, and inefficiencies in real-time security enforcement. To address these limitations, this study proposes the Blockchain-Enhanced Trust and Access Control for IoT Security (BETAC-IoT) model, which integrates blockchain technology, smart contracts, federated learning, and Merkle tree-based integrity verification to enhance IoT security. The proposed model eliminates reliance on centralized authentication by employing decentralized identity management, ensuring tamper-proof… More >

Saadia Tabassum^1,2, Fazal Muhammad², Muhammad Ayaz Khan³, Muhammad Uzair Khan^2,4, Dawar Awan⁴, Neelam Gohar⁵, Shahid Khan⁶, Amal Al-Rasheed^7,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1253-1278, 2025, DOI:10.32604/cmc.2025.065423 - 09 June 2025

Abstract Early and accurate detection of Heart Disease (HD) is critical for improving patient outcomes, as HD remains a leading cause of mortality worldwide. Timely and precise prediction can aid in preventive interventions, reducing fatal risks associated with misdiagnosis. Machine learning (ML) models have gained significant attention in healthcare for their ability to assist professionals in diagnosing diseases with high accuracy. This study utilizes 918 instances from publicly available UCI and Kaggle datasets to develop and compare the performance of various ML models, including Adaptive Boosting (AB), Naïve Bayes (NB), Extreme Gradient Boosting (XGB), Bagging, and… More >

Yuhan Xia¹, Xu Li¹, Ye Liu¹, Wenbo Zhou², Yiming Tang^1,3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 625-651, 2025, DOI:10.32604/cmc.2025.065358 - 09 June 2025

Abstract Applying domain knowledge in fuzzy clustering algorithms continuously promotes the development of clustering technology. The combination of domain knowledge and fuzzy clustering algorithms has some problems, such as initialization sensitivity and information granule weight optimization. Therefore, we propose a weighted kernel fuzzy clustering algorithm based on a relative density view (RDVWKFC). Compared with the traditional density-based methods, RDVWKFC can capture the intrinsic structure of the data more accurately, thus improving the initial quality of the clustering. By introducing a Relative Density based Knowledge Extraction Method (RDKM) and adaptive weight optimization mechanism, we effectively solve the… More >

Wenbing Wang^*, Yongwen Liu

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 763-790, 2025, DOI:10.32604/cmc.2025.065179 - 09 June 2025

Abstract Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk. However, the problem of matching functions between multiple firmwares has not been studied before. This paper proposes a multi-firmware comparison method based on evolutionary algorithms and trusted base points. We first model the multi-firmware comparison as a multi-sequence matching problem. Then, we propose an adaptation function and a population generation method based on trusted base points. Finally, we apply an evolutionary algorithm to find the optimal result. At the same time, we design the similarity of matching results as an evaluation metric to measure More >

Kunbolat T. Algazy¹, Kairat S. Sakan^1,2,*, Saule E. Nyssanbayeva^1,2, Ardabek Khompysh^1,3

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1581-1595, 2025, DOI:10.32604/cmc.2025.065085 - 09 June 2025

Abstract This paper examines the application of the Verkle tree—an efficient data structure that leverages commitments and a novel proof technique in cryptographic solutions. Unlike traditional Merkle trees, the Verkle tree significantly reduces signature size by utilizing polynomial and vector commitments. Compact proofs also accelerate the verification process, reducing computational overhead, which makes Verkle trees particularly useful. The study proposes a new approach based on a non-positional polynomial notation (NPN) employing the Chinese Remainder Theorem (CRT). CRT enables efficient data representation and verification by decomposing data into smaller, independent components, simplifying computations, reducing overhead, and enhancing… More >

Yu-Xiang Zhao^*, Yi Lee

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 791-810, 2025, DOI:10.32604/cmc.2025.065048 - 09 June 2025

Abstract Bird species classification is not only a challenging topic in artificial intelligence but also a domain closely related to environmental protection and ecological research. Additionally, performing edge computing on low-level devices using small neural networks can be an important research direction. In this paper, we use the EfficientNetV2B0 model for bird species classification, applying transfer learning on a dataset of 525 bird species. We also employ the BiRefNet model to remove backgrounds from images in the training set. The generated background-removed images are mixed with the original training set as a form of data augmentation.… More >

Maazen Alsabaan¹, Zaid Bin Faheem², Yuanyuan Zhu², Jehad Ali^3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 491-512, 2025, DOI:10.32604/cmc.2025.064971 - 09 June 2025

Abstract Image watermarking is a powerful tool for media protection and can provide promising results when combined with other defense mechanisms. Image watermarking can be used to protect the copyright of digital media by embedding a unique identifier that identifies the owner of the content. Image watermarking can also be used to verify the authenticity of digital media, such as images or videos, by ascertaining the watermark information. In this paper, a mathematical chaos-based image watermarking technique is proposed using discrete wavelet transform (DWT), chaotic map, and Laplacian operator. The DWT can be used to decompose… More >

Pu Yang^1,*, Wanning Yan¹, Rong Li¹, Lei Chen², Lijie Guo²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 699-725, 2025, DOI:10.32604/cmc.2025.064947 - 09 June 2025

Abstract Lithium-ion batteries (LIBs) have been widely used in mobile energy storage systems because of their high energy density, long life, and strong environmental adaptability. Accurately estimating the state of health (SOH) for LIBs is promising and has been extensively studied for many years. However, the current prediction methods are susceptible to noise interference, and the estimation accuracy has room for improvement. Motivated by this, this paper proposes a novel battery SOH estimation method, the Beluga Whale Optimization (BWO) and Noise-Input Gaussian Process (NIGP) Stacked Model (BGNSM). This method integrates the BWO-optimized Gaussian Process Regression (GPR)… More >