Measures of dispersion.
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TLDR
The Range: This is simply the difference between the largest and smallest item of data, easy to find and where there are no extremes, reasonably useful.Abstract:
The Range: This is simply the difference between the largest and smallest item of data. It is easy to find and where there are no extremes, reasonably useful. However it cannot be used for further analysis. The Inter-Quartile Range: This eliminates the problem of extreme values as it is the difference between the upper quartile, the value above which 25% of the data lies, and the lower quartile, below which 25% of the data lies. However it also cannot be used for further analysis. It is very often used with the median.read more
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Journal ArticleDOI
Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions
Rajeev Sharma,Ram Bilas Pachori +1 more
TL;DR: New features based on the 2D and 3D PSRs of IMFs have been proposed for classification of epileptic seizure and seizure-free EEG signals.
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Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis
TL;DR: Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25%, indicating a powerful method in the detection and classification of epileptic seizures.
Journal ArticleDOI
Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers
Md. Maniruzzaman,Md. Maniruzzaman,Md. Jahanur Rahman,Md. Al-MehediHasan,Harman S. Suri,Md. Menhazul Abedin,Ayman El-Baz,Jasjit S. Suri +7 more
TL;DR: The results demonstrate that on replacing the missing values and outliers by group median and median values, respectively, an accuracy, sensitivity, specificity, positive predictive value, negative predictive value and area under the curve is an improvement of 10% over previously developed techniques published in literature.
Journal ArticleDOI
Multi-Sensor Fusion for Activity Recognition-A Survey.
TL;DR: This survey reviews the many fusion methods for information acquired from sensors that have been proposed in the literature for activity recognition, and examines their relative merits; a comparison of these methods is made, as well as an assessment of the trends in the area.
Journal ArticleDOI
Stacking-based multi-objective evolutionary ensemble framework for prediction of diabetes mellitus
TL;DR: A stacking-based evolutionary ensemble learning system “NSGA-II-Stacking” for predicting the onset of Type-2 diabetes mellitus (T2DM) within five years is developed and significantly outperforms several individual ML approaches and conventional ensemble approaches.