Talal H. Noor^*

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1273-1299, 2024, DOI:10.32604/csse.2024.052510 - 13 September 2024

Abstract In recent years, container-based cloud virtualization solutions have emerged to mitigate the performance gap between non-virtualized and virtualized physical resources. However, there is a noticeable absence of techniques for predicting microservice performance in current research, which impacts cloud service users’ ability to determine when to provision or de-provision microservices. Predicting microservice performance poses challenges due to overheads associated with actions such as variations in processing time caused by resource contention, which potentially leads to user confusion. In this paper, we propose, develop, and validate a probabilistic architecture named Microservice Performance Diagnosis and Prediction (MPDP). MPDP… More >

Kun Liu^1,2, Peiran Li³, Yu Zhang^1,*, Jia Ren¹, Ming Li⁴, Xianyu Wang², Cong Li²

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2535-2555, 2024, DOI:10.32604/cmc.2024.052166 - 15 August 2024

Abstract When learning the structure of a Bayesian network, the search space expands significantly as the network size and the number of nodes increase, leading to a noticeable decrease in algorithm efficiency. Traditional constraint-based methods typically rely on the results of conditional independence tests. However, excessive reliance on these test results can lead to a series of problems, including increased computational complexity and inaccurate results, especially when dealing with large-scale networks where performance bottlenecks are particularly evident. To overcome these challenges, we propose a Markov blanket discovery algorithm based on constrained local neighborhoods for constructing undirected… More >

Shahad Alzahrani¹, Hatim Alsuwat², Emad Alsuwat^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1635-1654, 2024, DOI:10.32604/cmes.2023.044718 - 29 January 2024

Abstract Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables. However, the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams. One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks, wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance. In this research paper, we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms. Our framework… More >

Qing Xia¹, Yonghua Li^2,*, Dongxu Zhang², Yufeng Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1769-1794, 2024, DOI:10.32604/cmes.2023.030724 - 17 November 2023

Abstract For high-reliability systems in military, aerospace, and railway fields, the challenges of reliability analysis lie in dealing with unclear failure mechanisms, complex fault relationships, lack of fault data, and uncertainty of fault states. To overcome these problems, this paper proposes a reliability analysis method based on T-S fault tree analysis (T-S FTA) and Hyper-ellipsoidal Bayesian network (HE-BN). The method describes the connection between the various system fault events by T-S fuzzy gates and translates them into a Bayesian network (BN) model. Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network… More >

Kun Liu^1,2, Peiran Li³, Yu Zhang^1,*, Jia Ren¹, Xianyu Wang², Uzair Aslam Bhatti¹

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3257-3274, 2023, DOI:10.32604/cmc.2023.039430 - 08 October 2023

Abstract To obtain the optimal Bayesian network (BN) structure, researchers often use the hybrid learning algorithm that combines the constraint-based (CB) method and the score-and-search (SS) method. This hybrid method has the problem that the search efficiency could be improved due to the ample search space. The search process quickly falls into the local optimal solution, unable to obtain the global optimal. Based on this, the Particle Swarm Optimization (PSO) algorithm based on the search space constraint process is proposed. In the first stage, the method uses dynamic adjustment factors to constrain the structure search space… More >

Abdel Nasser H. Zaied¹, Ehab Rushdy², Mona Gamal^3,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1349-1364, 2023, DOI:10.32604/csse.2023.033596 - 28 July 2023

Abstract Acute leukemia is an aggressive disease that has high mortality rates worldwide. The error rate can be as high as 40% when classifying acute leukemia into its subtypes. So, there is an urgent need to support hematologists during the classification process. More than two decades ago, researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case. The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data. Ensemble machine learning is an effective method that combines individual classifiers to… More >

Bo Li¹, Zhaolong Ling¹, Yiwen Zhang^1,*, Yong Zhou¹, Yimin Hu², Haifeng Ling³

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 33-48, 2023, DOI:10.32604/iasc.2023.037267 - 29 April 2023

Abstract Learning the Markov blanket (MB) of a given variable has received increasing attention in recent years because the MB of a variable predicts its local causal relationship with other variables. Online MB Learning can learn MB for a given variable on the fly. However, in some application scenarios, such as image analysis and spam filtering, features may arrive by groups. Existing online MB learning algorithms evaluate features individually, ignoring group structure. Motivated by this, we formulate the group MB learning with streaming features problem, and propose an Online MB learning with Group Structure algorithm, OMBGS, More >

Muhammad Saad Salman¹, Ibrahim M. Almanjahie^2,3, AmanUllah Yasin¹, Ammara Nawaz Cheema^1,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4217-4229, 2023, DOI:10.32604/cmc.2023.034960 - 31 March 2023

Abstract At present Bayesian Networks (BN) are being used widely for demonstrating uncertain knowledge in many disciplines, including biology, computer science, risk analysis, service quality analysis, and business. But they suffer from the problem that when the nodes and edges increase, the structure learning difficulty increases and algorithms become inefficient. To solve this problem, heuristic optimization algorithms are used, which tend to find a near-optimal answer rather than an exact one, with particle swarm optimization (PSO) being one of them. PSO is a swarm intelligence-based algorithm having basic inspiration from flocks of birds (how they search… More >

Ayman A. El-Saleh¹, Abdulraqeb Alhammadi^2,*, Ibraheem Shayea³, Azizul Azizan⁴, Wan Haslina Hassan²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4571-4587, 2022, DOI:10.32604/cmc.2022.024642 - 21 April 2022

Abstract Mobile broadband (MBB) networks are expanding rapidly to deliver higher data speeds. The fifth-generation cellular network promises enhanced-MBB with high-speed data rates, low power connectivity, and ultra-low latency video streaming. However, existing cellular networks are unable to perform well due to high latency and low bandwidth, which degrades the performance of various applications. As a result, monitoring and evaluation of the performance of these network-supported services is critical. Mobile network providers optimize and monitor their network performance to ensure the highest quality of service to their end-users. This paper proposes a Bayesian model to estimate More >

Mohammad Hafiz Mohd Yusof^1,*, Abdullah Mohd Zin², Nurhizam Safie Mohd Satar²

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2445-2466, 2022, DOI:10.32604/cmc.2022.023571 - 29 March 2022

Abstract Due to polymorphic nature of malware attack, a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature of malware attacks. On the other hand, state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model. This is unlikely to be the case in production network as the dataset is unstructured and has no label. Hence an unsupervised learning is recommended. Behavioral study is one of the techniques to elicit traffic pattern. However, studies have shown that existing behavioral intrusion detection model had a few issues which had… More >