Raj Sonani¹, Reham Alhejaili^2,*, Pushpalika Chatterjee³, Khalid Hamad Alnafisah⁴, Jehad Ali^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3169-3189, 2025, DOI:10.32604/cmes.2025.070225 - 30 September 2025

Abstract Healthcare networks are transitioning from manual records to electronic health records, but this shift introduces vulnerabilities such as secure communication issues, privacy concerns, and the presence of malicious nodes. Existing machine and deep learning-based anomalies detection methods often rely on centralized training, leading to reduced accuracy and potential privacy breaches. Therefore, this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection (BFL-MND) model. It trains models locally within healthcare clusters, sharing only model updates instead of patient data, preserving privacy and improving accuracy. Cloud and edge computing enhance the model’s scalability, while blockchain ensures More >

Guoqiang Long^*, Wenling Chen, Yanghui Ou, Shuting Yang

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.010487

Abstract China's marine carbonate rocks are widely distributed, the reservoir is deeply buried, high temperature, high closure pressure, and the reservoir has strong non-homogeneity, porosity and permeability are generally low, while the natural cracks and dissolution pore (hole) is more developed. Currently, carbonate reservoir reforming technology is developing rapidly, and more and more marine carbonates can be developed. The Permian Maokou-Qixia Formation in the Sichuan Basin has good hydrocarbon source rocks of marine carbonates. The Longniusi Hechuan block of Permian Maokou-Qixia Formation develops a set of carbonate reservoirs interacting with leopard dolomite and mud crystal clastic… 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 >

Fang-Yie Leu¹, Kun-Lin Tsai^2,*, Li-Woei Chen³, Deng-Yao Yao², Jian-Fu Tsai², Ju-Wei Zhu², Guo-Wei Wang²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1937-1957, 2025, DOI:10.32604/cmc.2025.068486 - 29 August 2025

Abstract With the rapid development of Cloud-Edge-End (CEE) computing, the demand for secure and lightweight communication protocols is increasingly critical, particularly for latency-sensitive applications such as smart manufacturing, healthcare, and real-time monitoring. While traditional cryptographic schemes offer robust protection, they often impose excessive computational and energy overhead, rendering them unsuitable for use in resource-constrained edge and end devices. To address these challenges, in this paper, we propose a novel lightweight encryption framework, namely Dynamic Session Key Allocation with Time-Indexed Ascon (DSKA-TIA). Built upon the NIST-endorsed Ascon algorithm, the DSKA-TIA introduces a time-indexed session key generation mechanism… More >

Yu-Sheng Su^1,2,3,*, Yi-Wen Wang¹, Yun-Chin Wu³, Zheng-Yun Xiao¹, Ting-Jou Ding⁴

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1095-1111, 2025, DOI:10.32604/cmc.2025.067129 - 29 August 2025

Abstract In the context of global change, understanding changes in water resources requires close monitoring of groundwater levels. A mismatch between water supply and demand could lead to severe consequences such as land subsidence. To ensure a sustainable water supply and to minimize the environmental effects of land subsidence, groundwater must be effectively monitored and managed. Despite significant global progress in groundwater management, the swift advancements in technology and artificial intelligence (AI) have spurred extensive studies aimed at enhancing the accuracy of groundwater predictions. This study proposes an AI-based method that combines deep learning with a… More >

Puneet Sharma¹, Dhirendra Prasad Yadav¹, Bhisham Sharma^2,*, Surbhi B. Khan^3,4,*, Ahlam Almusharraf ⁵

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2179-2193, 2025, DOI:10.32604/cmc.2025.063190 - 29 August 2025

Abstract The swift expansion of cloud computing has heightened the demand for energy-efficient and high-performance resource allocation solutions across extensive systems. This research presents an innovative hybrid framework that combines a Quantum Tensor-based Deep Neural Network (QT-DNN) with Binary Bird Swarm Optimization (BBSO) to enhance resource allocation while preserving Quality of Service (QoS). In contrast to conventional approaches, the QT-DNN accurately predicts task-resource mappings using tensor-based task representation, significantly minimizing computing overhead. The BBSO allocates resources dynamically, optimizing energy efficiency and task distribution. Experimental results from extensive simulations indicate the efficacy of the suggested strategy; the… More >

P. William^1,2, Ved Prakash Mishra¹, Osamah Ibrahim Khalaf^3,*, Arvind Mukundan⁴, Yogeesh N⁵, Riya Karmakar⁶

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5133-5156, 2025, DOI:10.32604/cmc.2025.065840 - 30 July 2025

Abstract Cloud computing has become an essential technology for the management and processing of large datasets, offering scalability, high availability, and fault tolerance. However, optimizing data replication across multiple data centers poses a significant challenge, especially when balancing opposing goals such as latency, storage costs, energy consumption, and network efficiency. This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization (DMGO), designed to enhance data replication efficiency in cloud environments. Unlike traditional static replication systems, DMGO adapts dynamically to variations in network conditions, system demand, and resource availability. The approach utilizes multi-objective optimization More >

Shui Liu^1,2, Ke Xiong^1,2,*, Yeshen Li^1,2, Zhifei Zhang^1,2,*, Yu Zhang³, Pingyi Fan⁴

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5077-5093, 2025, DOI:10.32604/cmc.2025.064880 - 30 July 2025

Abstract Accurate prediction of cloud resource utilization is critical. It helps improve service quality while avoiding resource waste and shortages. However, the time series of resource usage in cloud computing systems often exhibit multidimensionality, nonlinearity, and high volatility, making the high-precision prediction of resource utilization a complex and challenging task. At present, cloud computing resource prediction methods include traditional statistical models, hybrid approaches combining machine learning and classical models, and deep learning techniques. Traditional statistical methods struggle with nonlinear predictions, hybrid methods face challenges in feature extraction and long-term dependencies, and deep learning methods incur high… More >

Wuyi Zhu¹, Meng Zou¹, Wenji Liu¹, Linlin Cui^2,*

Revue Internationale de Géomatique, Vol.34, pp. 363-377, 2025, DOI:10.32604/rig.2025.065708 - 14 July 2025

Abstract Exploring the spatiotemporal changes in Fractional Vegetation Coverage (FVC) helps to more accurately understand the quality of the ecological environment, which is of great significance for regional ecological protection and sustainable economic development. The study takes the Chengdu-Chongqing urban agglomeration as the research area, analyzes the characteristics and trends of vegetation cover changes from 2000 to 2020 using the Google Earth Engine cloud platform, and explores its driving factors based on the enhanced regression tree model. The results show that: (1) From 2000 to 2020, the annual FVC of the Chengdu-Chongqing urban agglomeration showed a… More >

Jitendra Kumar Samriya¹, Virendra Singh², Gourav Bathla³, Meena Malik⁴, Varsha Arya^5,6, Wadee Alhalabi⁷, Brij B. Gupta^8,9,10,11,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3893-3910, 2025, DOI:10.32604/cmc.2025.063242 - 03 July 2025

Abstract The integration of the Internet of Things (IoT) into healthcare systems improves patient care, boosts operational efficiency, and contributes to cost-effective healthcare delivery. However, overcoming several associated challenges, such as data security, interoperability, and ethical concerns, is crucial to realizing the full potential of IoT in healthcare. Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems. In this context, this paper presents a novel method for healthcare data privacy analysis. The technique is based on the identification of anomalies in… More >