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2d Respiratory Sound Analysis To Detect Lung Abnormalities, Rafia Sharmin Alice, Kc Santosh
2d Respiratory Sound Analysis To Detect Lung Abnormalities, Rafia Sharmin Alice, Kc Santosh
SDSU Data Science Symposium
Abstract. In this paper, we analyze deep visual features from 2D data representation(s) of the respiratory sound to detect evidence of lung abnormalities. The primary motivation behind this is that visual cues are more important in decision-making than raw data (lung sound). Early detection and prompt treatments are essential for any future possible respiratory disorders, and respiratory sound is proven to be one of the biomarkers. In contrast to state-of-the-art approaches, we aim at understanding/analyzing visual features using our Convolutional Neural Networks (CNN) tailored Deep Learning Models, where we consider all possible 2D data such as Spectrogram, Mel-frequency Cepstral Coefficients …
Minque: An R Package For Analyzing Various Linear Mixed Models, Jixiang Wu
Minque: An R Package For Analyzing Various Linear Mixed Models, Jixiang Wu
SDSU Data Science Symposium
Linear mixed model (LMM) approaches offer much more flexibility comparing ANOVA (analysis of variance) based methods. There are three commonly used LMM approaches: maximum likelihood, restricted maximum likelihood, and minimum norm quadratic unbiased estimation. These three approaches, however, sometimes could also lead low testing power compared to ANOVA methods. Integration of resampling techniques like jackknife could help improve testing power based on both our simulation studies. In this presentation, I will introduce a R package, minque, which integrates LMM approaches and resampling techniques and demonstrate the use of this packages in various linear mixed model analyses.