Ning Tang^1,2, Wang Luo^1,2,*, Yiwei Wang^1,2, Bao Feng^1,2, Shuang Yang^1,2, Jiangtao Xu³, Daohua Zhu³, Zhechen Huang³, Wei Liang³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4091-4113, 2025, DOI:10.32604/cmc.2025.066241 - 23 September 2025

Abstract With the deep integration of edge computing, 5G and Artificial Intelligence of Things (AIoT) technologies, the large-scale deployment of intelligent terminal devices has given rise to data silos and privacy security challenges in sensing-computing fusion scenarios. Traditional federated learning (FL) algorithms face significant limitations in practical applications due to client drift, model bias, and resource constraints under non-independent and identically distributed (Non-IID) data, as well as the computational overhead and utility loss caused by privacy-preserving techniques. To address these issues, this paper proposes an Efficient and Privacy-enhancing Clustering Federated Learning method (FedEPC). This method introduces… More >

Sami Alrabie^#,*, Ahmed Barnawi^#

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2481-2519, 2025, DOI:10.32604/cmes.2025.067977 - 31 August 2025

Abstract In the effort to enhance cardiovascular diagnostics, deep learning-based heart sound classification presents a promising solution. This research introduces a novel preprocessing method: iterative k-means clustering combined with silhouette score analysis, aimed at downsampling. This approach ensures optimal cluster formation and improves data quality for deep learning models. The process involves applying k-means clustering to the dataset, calculating the average silhouette score for each cluster, and selecting the cluster with the highest score. We evaluated this method using 10-fold cross-validation across various transfer learning models from different families and architectures. The evaluation was conducted on… More >

Kamepalli S. L. Prasanna¹, Vijaya J², Parvathaneni Naga Srinivasu¹, Babar Shah³, Farman Ali^4,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1603-1630, 2025, DOI:10.32604/cmc.2025.068707 - 29 August 2025

Abstract Cardiovascular disease prediction is a significant area of research in healthcare management systems (HMS). We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance. The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data. We present Clustered Butterfly Optimization Techniques (RoughK-means+BOA) as a new hybrid method for predicting heart disease. This method comprises two phases: clustering data using Roughk-means (RKM) and data analysis using the butterfly optimization algorithm (BOA). The benchmark dataset from the UCI More >

Ray-I Chang¹, Chia-Hui Wang^2,*, Yen-Ting Chang¹, Lien-Chen Wei²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 503-518, 2025, DOI:10.32604/cmc.2025.068516 - 29 August 2025

Abstract As data analysis often incurs significant communication and computational costs, these tasks are increasingly outsourced to cloud computing platforms. However, this introduces privacy concerns, as sensitive data must be transmitted to and processed by untrusted parties. To address this, fully homomorphic encryption (FHE) has emerged as a promising solution for privacy-preserving Machine-Learning-as-a-Service (MLaaS), enabling computation on encrypted data without revealing the plaintext. Nevertheless, FHE remains computationally expensive. As a result, approximate homomorphic encryption (AHE) schemes, such as CKKS, have attracted attention due to their efficiency. In our previous work, we proposed RP-OKC, a CKKS-based clustering… More >

Deyu Lin^1,2, Yujie Zhang ², Zhiwei Hua², Jianfeng Xu^2,3,*, Yufei Zhao¹, Yong Liang Guan¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1859-1882, 2025, DOI:10.32604/cmc.2025.066283 - 29 August 2025

Abstract Energy efficiency is critical in Wireless Sensor Networks (WSNs) due to the limited power supply. While clustering algorithms are commonly used to extend network lifetime, most of them focus on single-layer optimization. To this end, an Energy-efficient Cross-layer Clustering approach based on the Gini (ECCG) index theory was proposed in this paper. Specifically, a novel mechanism of Gini Index theory-based energy-efficient Cluster Head Election (GICHE) is presented based on the Gini Index and the expected energy distribution to achieve balanced energy consumption among different clusters. In addition, to improve inter-cluster energy efficiency, a Queue synchronous More >

Junyi Wang^*

Journal of Psychology in Africa, Vol.35, No.4, pp. 549-555, 2025, DOI:10.32604/jpa.2025.070065 - 17 August 2025

Abstract The transition to university life presents unique challenges, increasing the risk of mental health issues among first-year students. This study evaluated the efficacy of an eight-week structured mental health education course in enhancing resilience among first-year college students and reducing their stress levels. Utilizing a cluster-randomized controlled trial, a total of 509 first-year students (age range 18–20 years) were allocated to either an intervention group receiving the mental health education course (n = 252), or a control group with no intervention (n = 257) over an 8 week period. They completed self-reported measures of resilience… More >

Baishun Zhou^1,2, Haijiao Zhao³, Yuxin Wen², Gangyi Ding¹, Ying Xing^3,*, Xinyang Lin⁴, Lei Xiao⁵

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5201-5221, 2025, DOI:10.32604/cmc.2025.065726 - 30 July 2025

Abstract In recent years, with the rapid development of software systems, the continuous expansion of software scale and the increasing complexity of systems have led to the emergence of a growing number of software metrics. Defect prediction methods based on software metric elements highly rely on software metric data. However, redundant software metric data is not conducive to efficient defect prediction, posing severe challenges to current software defect prediction tasks. To address these issues, this paper focuses on the rational clustering of software metric data. Firstly, multiple software projects are evaluated to determine the preset number… More >

Peng Zhou^1,2, Wei Chen¹, Bingyu Cao^1,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5337-5360, 2025, DOI:10.32604/cmc.2025.065561 - 30 July 2025

Abstract Wireless Sensor Networks (WSNs), as a crucial component of the Internet of Things (IoT), are widely used in environmental monitoring, industrial control, and security surveillance. However, WSNs still face challenges such as inaccurate node clustering, low energy efficiency, and shortened network lifespan in practical deployments, which significantly limit their large-scale application. To address these issues, this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm (AC-ACO), aiming to optimize the energy utilization and system lifespan of WSNs. AC-ACO combines the path-planning capability of Ant Colony Optimization (ACO) with the dynamic characteristics of chaotic mapping and… More >

Muhammad Salman Qamar^1,*, Muhammad Fahad Munir²

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5463-5485, 2025, DOI:10.32604/cmc.2025.064442 - 30 July 2025

Abstract Wireless Sensor Networks (WSNs) have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes (SNs). However, the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs. Current energy efficiency strategies, such as clustering, multi-hop routing, and data aggregation, face challenges, including uneven energy depletion, high computational demands, and suboptimal cluster head (CH) selection. To address these limitations, this paper proposes a hybrid methodology that optimizes energy consumption (EC) while maintaining network performance. The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic (LEACH-D) protocol using More >

Yunong Yang¹, Long Ma¹, Liang Fan², Tao Xie^3,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3459-3476, 2025, DOI:10.32604/cmc.2025.064103 - 03 July 2025

Abstract Federated learning has emerged as an essential technique of protecting privacy since it allows clients to train models locally without explicitly exchanging sensitive data. Extensive research has been conducted on the issue of data heterogeneity in federated learning, but effective model training with severely imbalanced label distributions remains an unexplored area. This paper presents a novel Cluster Federated Learning Algorithm with Intra-cluster Correction (CFIC). First, CFIC selects samples from each cluster during each round of sampling, ensuring that no single category of data dominates the model training. Second, in addition to updating local models, CFIC… More >