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Variational Bayesian Inference Clustering Based Joint User Activity an by Zhaoji Zhang, Qinghua Guo et al

Tailor-made for massive connectivity and sporadic access, grant-free random access has become a promising candidate access protocol for massive machine-type communications (mMTC). Compared with conventional grant-based protocols, grant-free random access skips the exchange of scheduling information to reduce the signaling overhead, and facilitates sharing of access resources to enhance access efficiency. However, some challenges remain to be addressed in the receiver design, such as unknown identity of active users and multi-user interference (MUI) on shared access resources. In this work, we deal with the problem of joint user activity and data detection for grant-free random access. Specifically, the approximate message passing (AMP) algorithm is first employed to mitigate MUI and decouple the signals of different users. Then, we extend the data symbol alphabet to incorporate the null symbols from inactive users. In this way, the joint user activity and data detection problem is form

Approximate message passingBayes methodsClustering algorithmsGrant freeNference algorithmsNternet of thingsOint user activity and data detectionMassive machine type communicationsAtching pursuit algorithmsUltiuser detectionAriational bayesian inference

Computational Intelligence Inspired Adaptive Opportunistic Clustering by Premkumar Chithaluru, Fadi AL-Turjman et al

The major issues and challenges of the Industrial Internet of Things (IIoT) include network resource management, self-organization; routing, mobility, scalability, security, and data aggregation. Resource management in IIoT is a challenging issue, starting from the deployment and design of sensor nodes, networking at cross-layer, networking software development, application types, environmental conditions, monitoring user decisions, querying process, etc. In this paper, computational intelligence (CI) and its computing, such as neural networks and fuzzy logic, are used to tackle the challenges of resource management in the IIoT. The incorporation of the neuro-fuzzy technique into the IIoT contributes to the self-managing intelligence systems’ self-organizing and self-sustaining capabilities, offering real-time computations and services in a pervasive networking environment. Most of the problems in IIoT are realtime based; they require fast computation, real-time optimal solutions, an

Industrial internetClustering algorithmsComputational intelligenceIndustrial internet of thingsIndustrial iotObile nodeEuro fuzzy techniqueEer to peer computingQuality of serviceReal time systemsResource managementElf managingSelf organizingSelf sustaining

Region-Aware Hierarchical Latent Feature Representation Learning-Guide by Jun Wang, Chang Tang et al

Hyperspectral band selection aims to identify an optimal subset of bands for hyperspectral images (HSIs). For most existing clustering-based band selection methods, they directly stretch each band into a single feature vector and employ the pixelwise features to address band redundancy. In this way, they do not take full consideration of the spatial information and deal with the importance of different regions in HSIs, which leads to a nonoptimal selection. To address these issues, a region-aware hierarchical latent feature representation learning-guided clustering (HLFC) method is proposed. Specifically, in order to fully preserve the spatial information of HSIs, the superpixel segmentation algorithm is adopted to segment HSIs into multiple regions first. For each segmented region, the similarity graph is constructed to reflect the bands-wise similarity, and its corresponding Laplacian matrix is generated for learning low-dimensional latent features in a hierarchical way. All latent f

Clustering algorithmsClustering methodsFeature extractionFeature fusionIerarchical latent feature learningYperspectral band selectionHyperspectral imagingInformation entropyAplace equationsRepresentation learning

10 Clustering Algorithms With Python

10 Clustering Algorithms With Python
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Lars plougmannLibrary installationLast updatedMachine learning mastery with pythonAlgorithms with pythonClustering algorithmsData miningPractical machine learning toolsMachine learningProbabilistic perspectiveStatistical learningSynthetic clustering dataset with points coloredPassing messages between data pointsDataset with clusters identified using affinityBalanced iterative reducingDataset with clusters identified using

Space-time risk cluster of visceral leishmaniasis in Brazilian endemic region with high social vulnerability: An ecological time series study

Author summary Visceral leishmaniasis (VL) remains a worldwide health issue, with increasing rates of mortality being observed. Brazil has an epidemiological scenario of expanding VL transmission, especially in the Northeast region. In the present study, we analysed spatiotemporal dynamics of VL cases and its association with social vulnerability in Brazilian Northeast. Briefly, data was analysed of all VL confirmed cases during the years of 2000 to 2017 and the Social Vulnerability Index (SVI) from 1,794 municipalities of Brazilian Northeast. Results revealed that VL continues to spread heterogeneously, with space-time high-risk clusters in the most socially vulnerable areas. We observed increasing trends of new cases among male subjects ≥ 40 years of age and urban residents. Our study represents the first investigation that demonstrates associations between VL and social vulnerability in the Northeast region of Brazil. These findings could contribute to VL prevention, surveillance

United statesEteläuomen läiRio grande do norteMonte carloApcs aapcsHelsinki conventionInformation management service incBrazilian guide to health surveillanceHarvard medical schoolUs national cancer instituteResearch ethics committeeFederal universityRio grandeSistema de informaDepartamento de informBrazilian northeastern