Mingjing Li^1,*, Leiting Dong¹, Satya N. Atluri²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.4, pp. 1-2, 2023, DOI:10.32604/icces.2023.09689

Abstract The Fragile Points Method (FPM) is a discontinuous meshless method based on the Galerkin weak form [1]. In the FPM, the problem domain is discretized by spatial points and subdomains, and the displacement trial function of each subdomain is derived based on the points within the support domain. For this reason, the FPM doesn’t suffer from the mesh distortion and is suitable to model complex structural deformations. Furthermore, similar to the discontinuous Galerkin finite element method, the displacement trial functions used in the FPM is piece-wise continuous, and the numerical flux is introduced across each interior interface to guarantee the… More >

Karim Ragui¹, Mengshuai Chen^1,2, Lin Chen^1,2,3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-2, 2023, DOI:10.32604/icces.2023.010378

Abstract The ambiguous dynamics associated with heat and mass transfer of invading carbon dioxide in sub-critical and supercritical states, as well as the response of pore-scale resident fluids, play a key role in understanding CO₂ capture and storage (CCUS) and the corresponding phase equilibrium mechanisms. To this end, this paper reveals the transport mechanisms of invading supercritical carbon dioxide (sCO₂) in polluted micromodels using a variant of Lattice-Boltzmann Color Fluid model and descriptive experimental data. The breakthrough time is evaluated by characterizing the displacement velocity, the capillary to pressuredifference ratio, and the transient heat and mass diffusion at a series of… More >

Adil Hussain¹, Amna Khatoon^2,*, Ayesha Aslam², Tariq¹, Muhammad Asif Khosa¹

Journal of Cyber Security, Vol.6, pp. 1-23, 2024, DOI:10.32604/jcs.2023.046915

Abstract The importance of cybersecurity in contemporary society cannot be inflated, given the substantial impact of networks on various aspects of daily life. Traditional cybersecurity measures, such as anti-virus software and firewalls, safeguard networks against potential threats. In network security, using Intrusion Detection Systems (IDSs) is vital for effectively monitoring the various software and hardware components inside a given network. However, they may encounter difficulties when it comes to detecting solitary attacks. Machine Learning (ML) models are implemented in intrusion detection widely because of the high accuracy. The present work aims to assess the performance of machine learning algorithms in the… More >

Sicheng Zhu, Dingli Liu^*, Weijun Liu, Ying Li, Tian Zhou

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 483-513, 2024, DOI:10.32604/cmes.2023.044809

Abstract In recent years, frequent fire disasters have led to enormous damage in China. Effective firefighting rescues can minimize the losses caused by fires. During the rescue processes, the travel time of fire trucks can be severely affected by traffic conditions, changing the effective coverage of fire stations. However, it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions. This paper addresses this issue by combining the traveling time calculation model with the effective coverage simulation model. In addition, it proposes a new index of total effective coverage area (TECA) based on the time-weighted average… More >

Weisi Chen^1,*, Walayat Hussain^2,*, Francesco Cauteruccio³, Xu Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 187-224, 2024, DOI:10.32604/cmes.2023.031388

Abstract Financial time series prediction, whether for classification or regression, has been a heated research topic over the last decade. While traditional machine learning algorithms have experienced mediocre results, deep learning has largely contributed to the elevation of the prediction performance. Currently, the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking, making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better, what techniques and components are involved, and how the model can be designed and implemented. This review article provides an overview of techniques, components and… More > Graphic Abstract

Ming Gao^1,#, Weiwei Cai^1,#, Yizhang Jiang¹, Wenjun Hu³, Jian Yao², Pengjiang Qian^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 259-277, 2024, DOI:10.32604/cmes.2023.029015

Abstract Currently, applications accessing remote computing resources through cloud data centers is the main mode of operation, but this mode of operation greatly increases communication latency and reduces overall quality of service (QoS) and quality of experience (QoE). Edge computing technology extends cloud service functionality to the edge of the mobile network, closer to the task execution end, and can effectively mitigate the communication latency problem. However, the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management, and the booming development of artificial neural networks provides us with more powerful methods… More >

Xiulai Li^1,2,3,4, Jieren Cheng^1,3,*, Chengchun Ruan^1,3, Bin Zhang^1,3, Xiangyan Tang^1,3, Mengzhe Sun⁵

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3265-3288, 2023, DOI:10.32604/cmc.2023.045588

Abstract With the rising adoption of blockchain technology due to its decentralized, secure, and transparent features, ensuring its resilience against network threats, especially Distributed Denial of Service (DDoS) attacks, is crucial. This research addresses the vulnerability of blockchain systems to DDoS assaults, which undermine their core decentralized characteristics, posing threats to their security and reliability. We have devised a novel adaptive integration technique for the detection and identification of varied DDoS attacks. To ensure the robustness and validity of our approach, a dataset amalgamating multiple DDoS attacks was derived from the CIC-DDoS2019 dataset. Using this, our methodology was applied to detect… More >

Mohammed S. Alsaqer¹, Majid H. Alsulami^2,*, Rami N. Alkhawaji³, Abdulellah A. Alaboudi²

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3943-3968, 2023, DOI:10.32604/cmc.2023.042379

Abstract Blockchain technology has revolutionized conventional trade. The success of blockchain can be attributed to its distributed ledger characteristic, which secures every record inside the ledger using cryptography rules, making it more reliable, secure, and tamper-proof. This is evident by the significant impact that the use of this technology has had on people connected to digital spaces in the present-day context. Furthermore, it has been proven that blockchain technology is evolving from new perspectives and that it provides an effective mechanism for the intelligent transportation system infrastructure. To realize the full potential of the accurate and efficacious use of blockchain in… More >

Shoaib Khan, Bilal Khan, Saifullah Jan^*, Subhan Ullah, Aiman

Journal of Cyber Security, Vol.5, pp. 47-66, 2023, DOI:10.32604/jcs.2023.045579

Abstract Phishing attacks pose a significant security threat by masquerading as trustworthy entities to steal sensitive information, a problem that persists despite user awareness. This study addresses the pressing issue of phishing attacks on websites and assesses the performance of three prominent Machine Learning (ML) models—Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM)—utilizing authentic datasets sourced from Kaggle and Mendeley repositories. Extensive experimentation and analysis reveal that the CNN model achieves a better accuracy of 98%. On the other hand, LSTM shows the lowest accuracy of 96%. These findings underscore the potential of ML techniques in… More >

Deepak Kumar¹, Vinay Kukreja¹, Ayush Dogra^1,*, Bhawna Goyal², Talal Taha Ali³

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2097-2121, 2023, DOI:10.32604/cmc.2023.044287

Abstract Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20% every year. The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques. The experienced evaluators take time to identify the disease which is highly laborious and too costly. If wheat rust diseases are predicted at the development stages, then fungicides are sprayed earlier which helps to increase wheat yield quality. To solve the experienced evaluator issues, a combined region extraction and cross-entropy support vector machine (CE-SVM) model is proposed for wheat rust disease identification. In the proposed… More >