Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis

Granato, Giuseppe; Martino, Alessio; Baiocchi, Andrea; Rizzi, Antonello

doi:10.3390/app122111303

Cited by 3 publications

(1 citation statement)

References 49 publications

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“…Suppose it is required to find the minimum vertex cover (MVC) of graph G(V,U) (Figure 1). Graph G can be specified by the matrix A = {a ik }, in which each row corresponds to a vertex of the graph, and each column-to an edge of the graph [38,39]. This matrix differs from the canonical incidence matrix by having rows representing all nodes, including the base node.…”

Section: Extremal Graph Problems Under Study: Spreadsheet Modelingmentioning

confidence: 99%

Solving Some Graph Problems in Composite 3D Printing Using Spreadsheet Modeling

Hlinenko,

Fast,

Yakovenko

et al. 2023

J. Compos. Sci.

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The use of composite materials in additive manufacturing has significant potential and prospects for development. However, the 3D printing of composite materials also has some challenges, such as tool path planning and optimization, material distribution and planning, optimization of printing parameters, and others. Graph theory may be suitable for solving some of them. Many practical problems can be modeled as problems of identifying subsets of graph vertices or edges with certain extremal properties. Such problems belong to the category of graph extremal problems. Some of these problems can be represented as integer linear programming problems, for which, in order to solve, modifications of simplex method can be used. These methods are supported by MS Excel Solver add-in, which suggests the possibility of solving these problems effectively with its help. The task of implementing procedures for solving such problems by means of standard engineering software seems to be possible. This paper aims to develop efficient spreadsheet models of some extremal problems for graphs of higher strength in order to prove the feasibility and to unify the procedures of solving such problems via the MS Excel Solver add-in. Several spreadsheet models based on the graph representation by its expanded incidence matrix, while specifying a vector of unknowns as the vector of binary variables associated with vertices or edges of the sought parts of the graph, have been developed and proven to be efficient for solving such problems by simplex method via the MS Excel Solver add-in.

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Section: Extremal Graph Problems Under Study: Spreadsheet Modelingmentioning

confidence: 99%

Solving Some Graph Problems in Composite 3D Printing Using Spreadsheet Modeling

Hlinenko,

Fast,

Yakovenko

et al. 2023

J. Compos. Sci.

View full text Add to dashboard Cite

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Hier-SFL: Client-edge-cloud collaborative traffic classification framework based on hierarchical federated split learning

Cheng

Wei

Yao

2023

Future Generation Computer Systems

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Distributed Online Multi-Label Learning with Privacy Protection in Internet of Things

et al. 2023

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With the widespread use of end devices, online multi-label learning has become popular as the data generated by users using the Internet of Things devices have become huge and rapidly updated. However, in many scenarios, the user data are often generated in a geographically distributed manner that is often inefficient and difficult to centralize for training machine learning models. At the same time, current mainstream distributed learning algorithms always require a centralized server to aggregate data from distributed nodes, which inevitably causes risks to the privacy of users. To overcome this issue, we propose a distributed approach for multi-label classification, which trains the models in distributed computing nodes without sharing the source data from each node. In our proposed method, each node trains its model with its local online data while it also learns from the neighbour nodes without transferring the training data. As a result, our proposed method achieved the online distributed approach for multi-label classification without losing performance when taking existing centralized algorithms as a reference. Experiments show that our algorithm outperforms the centralized online multi-label classification algorithm in F1 score, being 0.0776 higher in macro F1 score and 0.1471 higher for micro F1 score on average. However, for the Hamming loss, both algorithms beat each other on some datasets, and our proposed algorithm loses 0.005 compared to the centralized approach on average, which can be neglected. Furthermore, the size of the network and the degree of connectivity are not factors that affect the performance of this distributed online multi-label learning algorithm.

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Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis

Cited by 3 publications

References 49 publications

Solving Some Graph Problems in Composite 3D Printing Using Spreadsheet Modeling

Solving Some Graph Problems in Composite 3D Printing Using Spreadsheet Modeling

Hier-SFL: Client-edge-cloud collaborative traffic classification framework based on hierarchical federated split learning

Distributed Online Multi-Label Learning with Privacy Protection in Internet of Things

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