Tamara Zhukabayeva^1,2, Zulfiqar Ahmad^1,3,*, Nurbolat Tasbolatuly⁴, Makpal Zhartybayeva¹, Yerik Mardenov^1,4, Nurdaulet Karabayev^1,*, Dilaram Baumuratova^1,4

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2573-2599, 2025, DOI:10.32604/cmes.2025.070426 - 26 November 2025

Abstract The Industrial Internet of Things (IIoT), combined with the Cyber-Physical Systems (CPS), is transforming industrial automation but also poses great cybersecurity threats because of the complexity and connectivity of the systems. There is a lack of explainability, challenges with imbalanced attack classes, and limited consideration of practical edge–cloud deployment strategies in prior works. In the proposed study, we suggest an Impact-Aware Taxonomy-Driven Machine Learning Framework with Edge Deployment and SHapley Additive exPlanations (SHAP)-based Explainable AI (XAI) to attack detection and classification in IIoT-CPS settings. It includes not only unsupervised clustering (K-Means and DBSCAN) to extract… More >

Zhen Yang¹, Yifan Liu^1,2,*, Fan Feng³, Yi Liu³, Zhenpeng Liu^1,3

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 357-380, 2025, DOI:10.32604/cmc.2025.060709 - 26 March 2025

Abstract Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’ devices without sharing private data. It trains a global model through collaboration between clients and the server. However, the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability. Meanwhile, data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks, and standalone personalization tasks may cause severe overfitting issues. To address these limitations, we introduce a federated learning dual optimization model based on… More >

Chao-Hsien Hsieh¹, Qingqing Yang^2,*, Dehong Kong², Fengya Xu², Hongmei Wang²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2869-2891, 2025, DOI:10.32604/cmc.2024.058122 - 17 February 2025

Abstract The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained devices. Their energy consumption is typically correlated with the amount of data collection. The purpose of data aggregation is to reduce data transmission, lower energy consumption, and reduce network congestion. For large-scale WSN, data aggregation can greatly improve network efficiency. However, as many heterogeneous data is poured into a specific area at the same time, it sometimes causes data loss and then results in incompleteness and irregularity of… More >

Shijie Tang^1,2, Yong Ding^1,3,4,*, Huiyong Wang⁵

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1129-1150, 2025, DOI:10.32604/cmc.2024.059143 - 03 January 2025

Abstract As more and more devices in Cyber-Physical Systems (CPS) are connected to the Internet, physical components such as programmable logic controller (PLC), sensors, and actuators are facing greater risks of network attacks, and fast and accurate attack detection techniques are crucial. The key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time series. To address this issue, we propose an anomaly detection method based on distributed deep learning. Our method uses a bilateral filtering algorithm for sequential sequences to remove noise in the More >

Abdullah Alabdulatif¹, Navod Neranjan Thilakarathne^2,*, Zaharaddeen Karami Lawal^3,4,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3917-3943, 2024, DOI:10.32604/cmc.2024.054150 - 12 September 2024

Abstract The advent of Industry 5.0 marks a transformative era where Cyber-Physical Systems (CPSs) seamlessly integrate physical processes with advanced digital technologies. However, as industries become increasingly interconnected and reliant on smart digital technologies, the intersection of physical and cyber domains introduces novel security considerations, endangering the entire industrial ecosystem. The transition towards a more cooperative setting, including humans and machines in Industry 5.0, together with the growing intricacy and interconnection of CPSs, presents distinct and diverse security and privacy challenges. In this regard, this study provides a comprehensive review of security and privacy concerns pertaining… More >

Prasanna Kumar Kannughatta Ranganna¹, Siddesh Gaddadevara Matt², Chin-Ling Chen^3,4,*, Ananda Babu Jayachandra⁵, Yong-Yuan Deng^4,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2557-2578, 2024, DOI:10.32604/cmc.2024.051634 - 15 August 2024

Abstract In recent decades, fog computing has played a vital role in executing parallel computational tasks, specifically, scientific workflow tasks. In cloud data centers, fog computing takes more time to run workflow applications. Therefore, it is essential to develop effective models for Virtual Machine (VM) allocation and task scheduling in fog computing environments. Effective task scheduling, VM migration, and allocation, altogether optimize the use of computational resources across different fog nodes. This process ensures that the tasks are executed with minimal energy consumption, which reduces the chances of resource bottlenecks. In this manuscript, the proposed framework… More >

Lin Ma¹, Liyong Wang¹, Shuang Zeng¹, Yutong Zhao¹, Chang Liu¹, Heng Zhang¹, Qiong Wu^2,*, Hongbo Ren²

Energy Engineering, Vol.121, No.6, pp. 1473-1493, 2024, DOI:10.32604/ee.2024.047332 - 21 May 2024

Abstract Accurate load forecasting forms a crucial foundation for implementing household demand response plans and optimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations, a single prediction model is hard to capture temporal features effectively, resulting in diminished prediction accuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neural network (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), is proposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features from the original data, enhancing the quality of data… More >

YINGHUA LUO^1,#, YU ZHANG^2,#, TONG ZHANG^2,#, YANNAN LI², HUI XUE², JINGLONG CAO², WENSHUANG HOU², JIAN LIU², YUHE CUI², TING XU², CHENGHAO JIN^2,3,*

BIOCELL, Vol.47, No.5, pp. 965-975, 2023, DOI:10.32604/biocell.2023.027729 - 10 April 2023

Abstract Marine collagen peptides (MCPs) are natural products prepared by hydrolyzing marine collagen protein through a variety of chemical methods or enzymes. MCPs have a range of structures and biological activities and are widely present in marine species. MCPs also have a small molecular weight, are easily modified, and absorbed by the body. These properties have attracted great interest from researchers studying antioxidant, anti-tumor, and anti-aging activities. MCPs of specific molecular weights have significant anti-tumor activity and no toxic side effects. Thus, MCPs have the potential use as anti-cancer adjuvant drugs. Free radicals produced by oxidation More >

Ahmed S. Almasoud¹, Abdelzahir Abdelmaboud², Faisal S. Alsubaei³, Manar Ahmed Hamza^4,*, Ishfaq Yaseen⁴, Mohammed Abaker⁵, Abdelwahed Motwakel⁴, Mohammed Rizwanullah⁴

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2633-2648, 2022, DOI:10.32604/cmc.2022.024619 - 29 March 2022

Abstract Cyber-Physical System (CPS) involves the combination of physical processes with computation and communication systems. The recent advancements made in cloud computing, Wireless Sensor Network (WSN), healthcare sensors, etc. tend to develop CPS as a proficient model for healthcare applications especially, home patient care. Though several techniques have been proposed earlier related to CPS structures, only a handful of studies has focused on the design of CPS models for health care sector. So, the proposal for a dedicated CPS model for healthcare sector necessitates a significant interest to ensure data privacy. To overcome the challenges, the… More >

Kailash Kumar^*, Ahmad Abdullah Aljabr

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3781-3801, 2022, DOI:10.32604/cmc.2022.020510 - 27 September 2021

Abstract On average, every two years, the amount of data existing globally doubles. Software development will be affected and improved by Cyber-Physical Systems (CPS). The number of problems remained even though developments helped Information Technology experts extract better value from their storage investments. Because of poor interoperability between different vendors and devices, countless numbers of Storage Area Networks were created. Network setup used for data storage includes a complex and rigid arrangement of routers, switch, hosts/servers, storage arrays. We have evaluated the performance of routing protocol Transmission Control Protocol (TCP) and Fibre Channel Protocol (FCP) under More >