scholarly journals CASAnova: a multiclass support vector machine model for the classification of human sperm motility patterns†

2017 ◽  
Vol 97 (5) ◽  
pp. 698-708 ◽  
Author(s):  
Summer G Goodson ◽  
Sarah White ◽  
Alicia M Stevans ◽  
Sanjana Bhat ◽  
Chia-Yu Kao ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Sperm Motility ◽  
Support Vector Machine Model ◽  
Human Sperm ◽  
Support Vector ◽  
Machine Model ◽  
Multiclass Support Vector Machine

A Multiclass Support Vector Machine Model for the Identification of Sperm Motility Patterns in Heterogeneous Populations.

2010 ◽  
Vol 83 (Suppl_1) ◽  
pp. 140-140
Author(s):  
Summer G. Goodson ◽  
Zhaojun Zhang ◽  
James K. Tsuruta ◽  
Wei Wang ◽  
Deborah A. O'Brien
Keyword(s):  
Support Vector Machine ◽  
Sperm Motility ◽  
Support Vector Machine Model ◽  
Support Vector ◽  
Machine Model ◽  
Heterogeneous Populations ◽  
Multiclass Support Vector Machine

scholarly journals COMPRESSIVE STRENGTH CLASSIFICATION OF LIGHTWEIGHT AGGREGATE CONCRETE USING A SUPPORT VECTOR MACHINE MODEL

2019 ◽  
Author(s):  
ANTONIO JOSÉ TENZA-ABRIL ◽  
ROSANA SATORRE-CUERDA ◽  
PATRICIA COMPAÑ-ROSIQUE ◽  
FRANCISCO JOSÉ NAVARRO-GONZÁLEZ ◽  
YOLANDA VILLACAMPA
Keyword(s):  
Support Vector Machine ◽  
Compressive Strength ◽  
Support Vector Machine Model ◽  
Lightweight Aggregate ◽  
Lightweight Aggregate Concrete ◽  
Support Vector ◽  
Machine Model ◽  
Aggregate Concrete

A mixed effects least squares support vector machine model for classification of longitudinal data

2012 ◽  
Vol 56 (3) ◽  
pp. 611-628 ◽  
Author(s):  
Jan Luts ◽  
Geert Molenberghs ◽  
Geert Verbeke ◽  
Sabine Van Huffel ◽  
Johan A.K. Suykens
Keyword(s):  
Support Vector Machine ◽  
Longitudinal Data ◽  
Least Squares ◽  
Support Vector Machine Model ◽  
Mixed Effects ◽  
Support Vector ◽  
Machine Model

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

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