Results 91 to 100 of about 23,776,838 (246)
PhysioDimClassifier—imbalance data classifier model for IoMT-based remote patient monitoring systems
MethodsXRemote patient monitoring systems (RPMS) using the Internet of Medical Things (IoMT) continuously collect and exchange periodic sensor-observations through communication modules.
Sayyed Johar, G.R. Manjula
doaj +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To clarify the clinical relevance of dopamine transporter single‐photon emission computed tomography (DAT‐SPECT) abnormalities in amyotrophic lateral sclerosis (ALS), with a prespecified focus on sex‐stratified associations with disease progression and short‐term prognosis.
Tomoya Kawazoe +7 more
wiley +1 more source
Forecasting cyberattacks with incomplete, imbalanced, and insignificant data
Cybersecurity, 2018 Having the ability to forecast cyberattacks before they happen will unquestionably change the landscape of cyber warfare and cyber crime. This work predicts specific types of attacks on a potential victim network before the actual malicious actions take ...
Ahmet Okutan +3 more
doaj +1 more source
Bi‐ and Mono‐Allelic RFC1 Expansion in a North American Cohort With Idiopathic Axonal Neuropathy
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective RFC1 biallelic repeat expansion is increasingly recognized as a cause of chronic idiopathic axonal polyneuropathy (CIAP), but it remains challenging to know who to test. This study aims to determine the prevalence of biallelic and monoallelic RFC1 expansions and their corresponding neuropathy phenotypes in CIAP patients and identify ...
Amro M. Stino +25 more
wiley +1 more source
Integrating Data Selection and Extreme Learning Machine for Imbalanced Data [PDF]
, 2015 Extreme Learning Machine (ELM) is one of the artificial neural network method that introduced by Huang, this method has very fast learning capability. ELM is designed for balance data. Common problems in real-life is imbalanced data problem.
Irawan, M. Isa +2 more
core +1 more source
Borderline-MASI: A Novel Data Adjustment Method for Imbalanced Data Classification
Applied Computational Intelligence and Soft ComputingImbalanced data are a vital issue in classification tasks, especially in real applications like fraud detection. Although various solutions have been proposed, ranging from sampling methods to algorithm methods, the performance of classifiers often ...
Thi-Lich Nghiem +3 more
doaj +1 more source
Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif +17 more
wiley +1 more source
Data for: Tree-Based Space Partition and Merging Ensemble Learning Framework for Imbalanced Problems
, 2019 In the experiment of imbalanced problems, 50 imbalanced data sets from the Knowledge Extraction based on Evolutionary Learning (KEEL: http://www.keel.es/) are used in this paper. Every data set is a 5x3 cell with 5 rows and 3 columns.
Wang, Z (via Mendeley Data)
core +1 more source
Multi-class Pattern Classification in Imbalanced Data [PDF]
, 2010 The majority of multi-class pattern classification techniques are proposed for learning from balanced datasets. However, in several real-world domains, the datasets have imbalanced data distribution, where some classes of data may have few training ...
Amal S. Ghanem +5 more
core +1 more source
Dual generative adversarial networks based on regression and neighbor characteristics.
PLoS ONEImbalanced data is a problem in that the number of samples in different categories or target value ranges varies greatly. Data imbalance imposes excellent challenges to machine learning and pattern recognition.
Weinan Jia +4 more
doaj +1 more source