EVALUATION OF BLOOD PARAMETERS BY LINEAR DISCRIMINANT MODELS FOR THE DETECTION OF TESTOSTERONE ADMINISTRATION

2021 ◽  
Author(s):  
Vinod S. Nair ◽  
Ken Sharpe ◽  
Jacob Husk ◽  
Geoffrey D. Miller ◽  
Peter Eenoo ◽  
...  
Keyword(s):  
Blood Parameters ◽  
Linear Discriminant ◽  
Testosterone Administration ◽  
Discriminant Models

scholarly journals Simultaneous classification of bilateral hand gestures using bilateral microelectrode recordings in a tetraplegic patient

2020 ◽  
Author(s):  
Tessy M. Thomas ◽  
Robert W. Nickl ◽  
Margaret C. Thompson ◽  
Daniel N. Candrea ◽  
Matthew S. Fifer ◽  
...  
Keyword(s):  
Unit Activity ◽  
Brain Machine Interface ◽  
Linear Discriminant ◽  
Robotic Arms ◽  
Microelectrode Recordings ◽  
Simultaneous Control ◽  
Somatosensory Cortices ◽  
Machine Interface ◽  
Discriminant Models

ABSTRACTMost daily tasks require simultaneous control of both hands. Here we demonstrate simultaneous classification of gestures in both hands using multi-unit activity recorded from bilateral motor and somatosensory cortices of a tetraplegic participant. Attempted gestures were classified using hierarchical linear discriminant models trained separately for each hand. In an online experiment, gestures were continuously classified and used to control two robotic arms in a center-out movement task. Bimanual trials that required keeping one hand still resulted in the best performance (70.6%), followed by symmetric movement trials (50%) and asymmetric movement trials (22.7%). Our results indicate that gestures can be simultaneously decoded in both hands using two independently trained hand models concurrently, but online control using this approach becomes more difficult with increased complexity of bimanual gesture combinations. This study demonstrates the potential for restoring simultaneous control of both hands using a bilateral intracortical brain-machine interface.

scholarly journals Multielemental Analysis Associated with Chemometric Techniques for Geographical Origin Discrimination of Tea Leaves (Camelia sinensis) in Guizhou Province, SW China

Molecules ◽  
2018 ◽  
Vol 23 (11) ◽  
pp. 3013 ◽  
Author(s):  
Jian Zhang ◽  
Ruidong Yang ◽  
Rong Chen ◽  
Yuncong Li ◽  
Yishu Peng ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Geographical Origin ◽  
Principal Component ◽  
Mineral Elements ◽  
Guizhou Province ◽  
Correct Classification ◽  
Tea Leaves ◽  
Linear Discriminant ◽  
Discriminant Models ◽  
Origin Identification

This study aimed to construct objective and accurate geographical discriminant models for tea leaves based on multielement concentrations in combination with chemometrics tools. Forty mineral elements in 87 tea samples from three growing regions in Guizhou Province (China), namely Meitan and Fenggang (MTFG), Anshun (AS) and Leishan (LS) were analyzed. Chemometrics evaluations were conducted using a one-way analysis of variance (ANOVA), principal component analysis (PCA), linear discriminant analysis (LDA), and orthogonal partial least squares discriminant analysis (OPLS-DA). The results showed that the concentrations of the 28 elements were significantly different among the three regions (p < 0.05). The correct classification rates for the 87 tea samples were 98.9% for LDA and 100% for OPLS-DA. The variable importance in the projection (VIP) values ranged between 1.01–1.73 for 11 elements (Sb, Pb, K, As, S, Bi, U, P, Ca, Na, and Cr), which can be used as important indicators for geographical origin identification of tea samples. In conclusion, multielement analysis coupled with chemometrics can be useful for geographical origin identification of tea leaves.

Comparison and selection of vegetation indices for detection of Sclerotinia Stem Rot on oilseed rape leaves using ground-based hyperspectral imaging

2017 ◽  
Vol 8 (2) ◽  
pp. 264-266 ◽  
Author(s):  
C. Zhang ◽  
F. Liu ◽  
X. P. Feng ◽  
Y. He ◽  
Y. D. Bao ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Vegetation Indices ◽  
Stem Rot ◽  
Sclerotinia Stem Rot ◽  
Linear Discriminant ◽  
Discriminant Models ◽  
Selection Of

A ground-based hyperspectral imaging system covering the spectral range of 384–1034 nm was used for Sclerotinia Stem Rot (SSR) detection. Two sample sets of oilseed leaves were collected. Four vegetation indices were extracted and evaluated by analysis of variance (ANOVA) combined with linear discriminant analysis (LDA) for the two sample sets. Discriminant models were built using the 4 vegetation indices. The discriminant results of the two sample sets were good with classification accuracies of the calibration set and the prediction set over 85%. The overall results indicated that vegetation indices calculated from ground-based hyperspectral imaging could be used as reliable and accurate indices for SSR detection.

scholarly journals Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-5
Author(s):  
Liwen Huang
Keyword(s):  
Discriminant Analysis ◽  
Real Data ◽  
Classification Algorithms ◽  
Data Sets ◽  
Analysis Method ◽  
Linear Discriminant ◽  
Discriminant Model ◽  
Discriminant Models ◽  
The Common ◽  
Better Than

This paper presents a new hybrid discriminant analysis method, and this method combines the ideas of linearity and nonlinearity to establish a two-layer discriminant model. The first layer is a linear discriminant model, which is mainly used to determine the distinguishable samples and subsample; the second layer is a nonlinear discriminant model, which is used to determine the subsample type. Numerical experiments on real data sets show that this method performs well compared to other classification algorithms, and its stability is better than the common discriminant models.

Sign in / Sign up

Export Citation Format

Share Document