Titlebook: Knowledge Discovery, Knowledge Engineering and Knowledge Management; 14th International J Frans Coenen,Ana Fred,Joaquim Filipe Conference p

上釉彩

絕食

Joerg Deigmoeller,Pavel Smirnov,Julian Eggert,Chao Wang,Johane Takeuchi

CIS

Davide Varagnolo,Dora Melo,Irene Pimenta Rodrigues,Rui Rodrigues,Paula Couto

原諒

Knowledge Discovery, Knowledge Engineering and Knowledge Management14th International J

Audiometry

fabricate

Degree Centrality Definition, and?Its Computation for?Homogeneous Multilayer Networks Using Heurististics-based algorithms for computing degree centrality on MLNs . using the decoupling-based approach which has been shown to be efficient as well as structure and semantics preserving. We compare the accuracy and precision of our algorithms with Boolean OR-aggregated graphs of Homogeneous MLNs as gr

RODE

A Dual-Stage Noise Training Scheme for?Breast Ultrasound Image Classification used backbone CNN architectures in the medical image classification field: AlexNet, ResNet-18, ResNet-50, and VGG16. They are fine-tuned on carefully constructed noisy datasets, and the test results suggest that they all acquire remarkable noise resistance, and this immunity is almost invariant to

不可磨滅

A General-Purpose Multi-stage Multi-group Machine Learning Framework for Knowledge Discovery and Decmedical problems that involve poorly separated data and imbalanced groups in which traditional classifiers yield low prediction accuracy: (a) multi-site treatment outcome prediction for best practice discovery in cardiovascular disease; and (b) diabetes; (c) early disease diagnosis in predicting sub

epicardium

Comparative Assessment of Deep End-To-End, Deep Hybrid and Deep Ensemble Learning Architectures for d voting over a hematoxylin and eosin (H&E)-stained pathological images. The experiments were conducted using the public BreakHis dataset, 5-fold cross validation method, four metrics of performances, the Scott Knott statistical test and the Borda Count voting method. The results showed that the dee

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