Song Deng^1,*, Yiming Yuan²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3679-3697, 2024, DOI:10.32604/cmc.2024.052008

Abstract Malicious attacks against data are unavoidable in the interconnected, open and shared Energy Internet (EI), Intrusion tolerant techniques are critical to the data security of EI. Existing intrusion tolerant techniques suffered from problems such as low adaptability, policy lag, and difficulty in determining the degree of tolerance. To address these issues, we propose a novel adaptive intrusion tolerance model based on game theory that enjoys two-fold ideas: 1) it constructs an improved replica of the intrusion tolerance model of the dynamic equation evolution game to induce incentive weights; and 2) it combines a tournament competition More >

Djeldjli Halima^1,*, Benatiallah Djelloul¹, Ghasri Mehdi², Tanougast Camel³, Benatiallah Ali⁴, Benabdelkrim Bouchra¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4725-4740, 2024, DOI:10.32604/cmc.2024.051002

Abstract When designing solar systems and assessing the effectiveness of their many uses, estimating sun irradiance is a crucial first step. This study examined three approaches (ANN, GA-ANN, and ANFIS) for estimating daily global solar radiation (GSR) in the south of Algeria: Adrar, Ouargla, and Bechar. The proposed hybrid GA-ANN model, based on genetic algorithm-based optimization, was developed to improve the ANN model. The GA-ANN and ANFIS models performed better than the standalone ANN-based model, with GA-ANN being better suited for forecasting in all sites, and it performed the best with the best values in the… More > Graphic Abstract

Junqing Bai¹, Qiuchao Dai^1,*, Yingying Li²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5083-5103, 2024, DOI:10.32604/cmc.2024.050921

Abstract To support the explosive growth of Information and Communications Technology (ICT), Mobile Edge Computing (MEC) provides users with low latency and high bandwidth service by offloading computational tasks to the network’s edge. However, resource-constrained mobile devices still suffer from a capacity mismatch when faced with latency-sensitive and compute-intensive emerging applications. To address the difficulty of running computationally intensive applications on resource-constrained clients, a model of the computation offloading problem in a network consisting of multiple mobile users and edge cloud servers is studied in this paper. Then a user benefit function EoU (Experience of Users) is… More >

Imtiaz Ahmad¹, Tariq Hussain², Babar Shah³, Altaf Hussain⁴, Iqtidar Ali¹, Farman Ali^5,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3585-3629, 2024, DOI:10.32604/cmc.2024.050596

Abstract Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost. One of them is a sensor network with embedded sensors working as the primary nodes, termed Wireless Sensor Networks (WSNs), in which numerous sensors are connected to at least one Base Station (BS). These sensors gather information from the environment and transmit it to a BS or gathering location. WSNs have several challenges, including throughput, energy usage, and network lifetime concerns. Different strategies have been applied to get over these… More >

Yi-Chun Lai¹, Shu-Yin Chiang², Yao-Chiang Kan³, Hsueh-Chun Lin^4,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3783-3803, 2024, DOI:10.32604/cmc.2024.050376

Abstract Artificial intelligence (AI) technology has become integral in the realm of medicine and healthcare, particularly in human activity recognition (HAR) applications such as fitness and rehabilitation tracking. This study introduces a robust coupling analysis framework that integrates four AI-enabled models, combining both machine learning (ML) and deep learning (DL) approaches to evaluate their effectiveness in HAR. The analytical dataset comprises 561 features sourced from the UCI-HAR database, forming the foundation for training the models. Additionally, the MHEALTH database is employed to replicate the modeling process for comparative purposes, while inclusion of the WISDM database, renowned… More > Graphic Abstract

Amerah Alabrah^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3897-3912, 2024, DOI:10.32604/cmc.2024.048528

Abstract The emergence of digital networks and the wide adoption of information on internet platforms have given rise to threats against users’ private information. Many intruders actively seek such private data either for sale or other inappropriate purposes. Similarly, national and international organizations have country-level and company-level private information that could be accessed by different network attacks. Therefore, the need for a Network Intruder Detection System (NIDS) becomes essential for protecting these networks and organizations. In the evolution of NIDS, Artificial Intelligence (AI) assisted tools and methods have been widely adopted to provide effective solutions. However,… More >

Xiu Liu, Liang Gu^*, Xin Gong, Long An, Xurui Gao, Juying Wu

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4045-4061, 2024, DOI:10.32604/cmc.2024.048442

Abstract With the popularisation of intelligent power, power devices have different shapes, numbers and specifications. This means that the power data has distributional variability, the model learning process cannot achieve sufficient extraction of data features, which seriously affects the accuracy and performance of anomaly detection. Therefore, this paper proposes a deep learning-based anomaly detection model for power data, which integrates a data alignment enhancement technique based on random sampling and an adaptive feature fusion method leveraging dimension reduction. Aiming at the distribution variability of power data, this paper developed a sliding window-based data adjustment method for… More >

Widad Elbakri¹, Maheyzah Md. Siraj^1,*, Bander Ali Saleh Al-rimy¹, Sultan Noman Qasem², Tawfik Al-Hadhrami³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3725-3756, 2024, DOI:10.32604/cmc.2024.048105

Abstract Cloud computing environments, characterized by dynamic scaling, distributed architectures, and complex workloads, are increasingly targeted by malicious actors. These threats encompass unauthorized access, data breaches, denial-of-service attacks, and evolving malware variants. Traditional security solutions often struggle with the dynamic nature of cloud environments, highlighting the need for robust Adaptive Cloud Intrusion Detection Systems (CIDS). Existing adaptive CIDS solutions, while offering improved detection capabilities, often face limitations such as reliance on approximations for change point detection, hindering their precision in identifying anomalies. This can lead to missed attacks or an abundance of false alarms, impacting overall… More >

Feng Zhao, Guangdi Liu^*, Xiaoqiang Chen, Ying Wang

Energy Engineering, Vol.121, No.7, pp. 1865-1882, 2024, DOI:10.32604/ee.2024.048209

Abstract In light of the prevailing issue that the existing convolutional neural network (CNN) power quality disturbance identification method can only extract single-scale features, which leads to a lack of feature information and weak anti-noise performance, a new approach for identifying power quality disturbances based on an adaptive Kalman filter (KF) and multi-scale channel attention (MS-CAM) fused convolutional neural network is suggested. Single and composite-disruption signals are generated through simulation. The adaptive maximum likelihood Kalman filter is employed for noise reduction in the initial disturbance signal, and subsequent integration of multi-scale features into the conventional CNN… More >

Guanfu Wang¹, Yudie Sun¹, Jinling Li^2,3,*, Yu Jiang¹, Chunhui Li¹, Huanan Yu^2,3, He Wang^2,3, Shiqiang Li^2,3

Energy Engineering, Vol.121, No.6, pp. 1671-1695, 2024, DOI:10.32604/ee.2024.047794

Abstract Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which are difficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamic decisions continuously. This paper proposed a dynamic economic scheduling method for distribution networks based on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distribution network is established considering the action characteristics of micro-gas turbines, and the dynamic scheduling model based on deep reinforcement learning is constructed for the new energy distribution network system with a More >