Maximum Gaussian Mixture Model for Classification

Zhang, Jiehao; Hong, Xianbin; Guan, Sheng-Uei; Zhao, Xuan; Xin, Huang; Xue, Nian

doi:10.1109/itme.2016.0139

Cited by 17 publications

(5 citation statements)

References 3 publications

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“…In fact, there are more than 100 clustering algorithms known. But few of the algorithms are used popularly, such as Agglomerative Clustering, DBSCAN, K-Means, OPTICS, Spectral Clustering, and Mixture of Gaussians [53]. The psuedo-code for the community detection included in Algorithm 1 illustrates the clustering process with K-means.…”

Section: Graph Neural Network For Community Detectionmentioning

confidence: 99%

Context-Aware Service Discovery: Graph Techniques for IoT Network Learning and Socially Connected Objects

et al. 2022

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Adopting Internet-of-things (IoT) in large-scale environments such as smart cities raises compatibility and trustworthiness challenges, hindering conventional service discovery and network navigability processes. The IoT network is known for its highly dynamic topology and frequently changing characteristics (e.g., the devices' status, such as battery capacity and computational power); traditional methods fail to learn and understand the evolving behavior of the network to enable real-time and contextaware service discovery in such diverse and large-scale topologies of IoT networks. The Social IoT (SIoT) concept, which defines the relationships among the connected objects, can be exploited to extract established relationships between devices and enable trustworthy and context-aware services. In fact, SIoT expresses the possible connections that devices can establish in the network and reflect compatibility, trustworthiness, and so on. In this paper, we investigate the service discovery process in SIoT networks by proposing a lowcomplexity context-aware Graph Neural Network (GNN) approach to enable rapid and dynamic service discovery. Unlike the conventional graph-based techniques, the proposed approach simultaneously embeds the devices' features and their SIoT relations. Our simulations on a real-world IoT dataset show that the proposed GNN-based approach can provide more concise clusters compared to traditional techniques, namely the Louvain and Leiden algorithms. This allows a better IoT network learning and understanding and also, speeds up the service lookup search space. Finally, we discuss implementing the GNN-assisted context-service discovery processes in novel smart city IoT-enabled applications.

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Section: Graph Neural Network For Community Detectionmentioning

confidence: 99%

Context-Aware Service Discovery: Graph Techniques for IoT Network Learning and Socially Connected Objects

et al. 2022

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“…There are several possible machine learning approaches for clustering. The most known ones are Affinity Propagation, Agglomerative Clustering, DBSCAN, K-Means, OPTICS, Spectral Clustering, and Mixture of Gaussians [56]. As shown in Fig.…”

Section: Phase Iii: Clusteringmentioning

confidence: 99%

Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Hamrouni¹,

Ghazzai²,

Alelyani³

et al. 2021

Preprint

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Collaborative Mobile crowdsourcing (CMCS) allows entities, e.g., local authorities or individuals, to hire a team of workers from the crowd of connected people, to execute complex tasks. In this paper, we investigate two different CMCS recruitment strategies allowing task requesters to form teams of socially connected and skilled workers: i) a platform-based strategy where the platform exploits its own knowledge about the workers to form a team and ii) a leader-based strategy where the platform designates a group leader that recruits its own suitable team given its own knowledge about its Social Network (SN) neighbors. We first formulate the recruitment as an Integer Linear Program (ILP) that optimally forms teams according to four fuzzy-logic-based criteria: level of expertise, social relationship strength, recruitment cost, and recruiter's confidence level. To cope with NP-hardness, we design a novel low-complexity CMCS recruitment approach relying on Graph Neural Networks (GNNs), specifically graph embedding and clustering techniques, to shrink the workers' search space and afterwards, exploiting a meta-heuristic genetic algorithm to select appropriate workers. Simulation results applied on a real-world dataset illustrate the performance of both proposed CMCS recruitment approaches. It is shown that our proposed low-complexity GNN-based recruitment algorithm achieves close performances to those of the baseline ILP with significant computational time saving and ability to operate on large-scale mobile crowdsourcing platforms. It is also shown that compared to the leader-based strategy, the platform-based strategy recruits a more skilled team but with lower SN relationships and higher cost.

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“…It is known as soft clustering technique which is used to compute the probability of different types of clustered data. The implementation of this algorithm is based on expectation maximization [51].…”

Section: K Gaussian Mixture Algorithmmentioning

confidence: 99%

Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis

et al. 2020

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Breast cancer is type of tumor that occurs in the tissues of the breast. It is most common type of cancer found in women around the world and it is among the leading causes of deaths in women. This paper presents the comparative analysis of machine learning, deep learning and data mining techniques being used for the prediction of breast cancer. Many researchers have put their efforts on breast cancer diagnoses and prognoses, every technique has different accuracy rate and it varies for different situations, tools and datasets being used. Our main focus is to comparatively analyze different existing Machine Learning and Data Mining techniques in order to find out the most appropriate method that will support the large dataset with good accuracy of prediction. The main purpose of this review is to highlight all the previous studies of machine learning algorithms that are being used for breast cancer prediction and this paper provides the all necessary information to the beginners who want to analyze the machine learning algorithms to gain the base of deep learning.

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Maximum Gaussian Mixture Model for Classification

Cited by 17 publications

References 3 publications

Context-Aware Service Discovery: Graph Techniques for IoT Network Learning and Socially Connected Objects

Context-Aware Service Discovery: Graph Techniques for IoT Network Learning and Socially Connected Objects

Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis

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