Path extension similarity link prediction method based on matrix algebra in directed networks

Guo, Feipeng; Zhou, Wei; Lü, Qing; Zhang, Chen

doi:10.1016/j.comcom.2022.02.002

Cited by 79 publications

(30 citation statements)

References 26 publications

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“…As depicted in Figure 5, this approach ensures that cells are constructed using adjacent servers. However, significant benefits, such as reduced energy consumption and increased bandwidth, can be achieved if we confine adjacent servers in the same cluster to form cells, thereby minimizing communication with remote servers (Cao, Zhao et al 2020, Guo, Zhou et al 2022, Sofla, Kashani et al 2022. Additionally, Li, Zhuang et al (2018) suggest that servers within a close geographical area tend to exhibit similar behaviour, such as server joins or crashes (Jiang, Xiao et al 2020).…”

Section: Hybrid Frameworkmentioning

confidence: 99%

HybOff: A Hybrid Offloading Approach to Improve Load Balancing in Fog Networks

Sulimani,

Ramezani

et al. 2023

Preprint

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Load balancing is crucial in distributed systems like fog computing, where efficiency is paramount. Offloading with different approaches is the key to balancing the load in distributed environments. Static offloading (SOS) falls short in heterogeneous networks, necessitating dynamic offloading to reduce latency in time-sensitive tasks. However, prevalent dynamic offloading (DOS) solutions often come with hidden costs that impact sensitive applications, including decision time, networks congested and distance offloading. This paper introduces the Hybrid Offloading (HybOff) algorithm, which substantially enhances load balancing and resource utilization in fog networks, addressing issues in both static and dynamic approaches while leveraging clustering theory. Its goal is to create a uncomplicated low-cost offloading approach that enhances IoT application performance by eliminating the consequences of hidden costs regardless of network size. Experimental results using the iFogSim simulation tool show that HybOff significantly reduces offloading messages, distance, and decision-offloading consequences. It improves load balancing by 97%, surpassing SOS (64%) and DOS (88%). Additionally, it increases system utilization by an average of 50% and enhances system performance 1.6 times and 1.4 times more than SOS and DOS, respectively. In summary, HybOff substantially contributes to load balancing and offloading research in fog computing.

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Section: Hybrid Frameworkmentioning

confidence: 99%

HybOff: A Hybrid Offloading Approach to Improve Load Balancing in Fog Networks

Sulimani,

Ramezani

et al. 2023

Preprint

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“…Numerical solutions may also be challenging to comprehend in the absence of a closed-form solution, while convergence and stability problems necessitate careful method selection and testing. Therefore, analytical techniques such as the Fractional Differential Transform Method (FDTM) [15], Variational Iteration Method (VIM) [16], (G /G)-expansion method [17], tan-expansion method [18], exp-function method [19], mEDAM [20] and many others [21][22][23][24][25] are created to tackle the complexity of FPDEs and provide distinct benefits over numerical techniques.…”

Section: Introductionmentioning

confidence: 99%

Investigating Families of Soliton Solutions for the Complex Structured Coupled Fractional Biswas–Arshed Model in Birefringent Fibers Using a Novel Analytical Technique

et al. 2023

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This research uses a novel analytical method known as the modified Extended Direct Algebraic Method (mEDAM) to explore families of soliton solutions for the complex structured Coupled Fractional Biswas–Arshed Model (CFBAM) in Birefringent Fibers. The Direct Algebraic Method (DAM) is extended by the mEDAM’s methodology to compute more analytical solutions that would otherwise be difficult to acquire. We use this method to derive several families of soliton solutions and examine their characteristics. We also look at how different model parameters, such as amplitude, width, and propagation speed, affect the dynamics of soliton. Our use of 2D and 3D graphics to illustrate the soliton solutions also makes it possible to see the soliton dynamics more clearly. The outcomes also demonstrate that the method suggested has proven successful in producing soliton solutions for intricate structures such as the CFBAM.

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“…A software flaw is any error or defectiveness in a software product or software development (SD), often known as a fault or bug [9][10][11]. Software defect prediction is one of the most practical tasks in the testing process, which recognizes the segments that are problem-predisposed to and need rigorous testing [12][13][14]. This allows experiment resources to be utilized more effectively while adhering to restrictions.…”

Section: Introductionmentioning

confidence: 99%

Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers

Babatunde,

Ogundokun,

Adeoye

et al. 2023

Mathematics

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To guarantee that software does not fail, software quality assurance (SQA) teams play a critical part in the software development procedure. As a result, prioritizing SQA activities is a crucial stage in SQA. Software defect prediction (SDP) is a procedure for recognizing high-risk software components and determining the influence of software measurements on the likelihood of software modules failure. There is a continuous need for sophisticated and better SDP models. Therefore, this study proposed the use of dagging-based and baseline classifiers to predict software defects. The efficacy of the dagging-based SDP model for forecasting software defects was examined in this study. The models employed were naïve Bayes (NB), decision tree (DT), and k-nearest neighbor (kNN), and these models were used on nine NASA datasets. Findings from the experimental results indicated the superiority of SDP models based on dagging meta-learner. Dagging-based models significantly outperformed experimented baseline classifiers built on accuracy, the area under the curve (AUC), F-measure, and precision-recall curve (PRC) values. Specifically, dagging-based NB, DT, and kNN models had +6.62%, +3.26%, and +4.14% increments in average accuracy value over baseline NB, DT, and kNN models. Therefore, it can be concluded that the dagging meta-learner can advance the recognition performances of SDP methods and should be considered for SDP processes.

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Path extension similarity link prediction method based on matrix algebra in directed networks

Cited by 79 publications

References 26 publications

HybOff: A Hybrid Offloading Approach to Improve Load Balancing in Fog Networks

HybOff: A Hybrid Offloading Approach to Improve Load Balancing in Fog Networks

Investigating Families of Soliton Solutions for the Complex Structured Coupled Fractional Biswas–Arshed Model in Birefringent Fibers Using a Novel Analytical Technique

Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers

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