scholarly journals When a Tree Falls: Using Diversity in Ensemble Classifiers to Identify Evasion in Malware Detectors

2016 ◽  
Author(s):  
Charles Smutz ◽  
Angelos Stavrou
Keyword(s):  
Ensemble Classifiers ◽  
Tree Falls

If a Tree Falls in the Forest: COVID-19, Media Choices, and Presidential Agenda Setting

2020 ◽  
Author(s):  
Masha Krupenkin ◽  
Kai Zhu ◽  
Dylan Walker ◽  
David M. Rothschild
Keyword(s):  
Agenda Setting ◽  
Tree Falls ◽  
Media Choices

scholarly journals Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

2021 ◽  
Author(s):  
Antonio Giovannetti ◽  
Gianluca Susi ◽  
Paola Casti ◽  
Arianna Mencattini ◽  
Sandra Pusil ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Regions ◽  
Ensemble Classification ◽  
The Novel ◽  
Ensemble Classifiers ◽  
Deep Convolutional Neural Networks ◽  
Early Signs ◽  
Data Representations ◽  
The Brain

AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer’s disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships.

Heterogeneous ensemble classifiers for Malay syllables classification

2020 ◽  
Author(s):  
Zaridah Mat Zain ◽  
Zulkhairi Mohd Yusuf ◽  
Hariharan Muthusamy ◽  
Kushsairy Abd Kader ◽  
Nurul Aida Mohd Mortar
Keyword(s):  
Ensemble Classifiers ◽  
Heterogeneous Ensemble
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