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Articles 1 - 4 of 4
Full-Text Articles in Life Sciences
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 …
Session 2: The Effect Of Boom Leveling On Spray Dispersion, Travis A. Burgers, Miguel Bustamante, Juan F. Vivanco
Session 2: The Effect Of Boom Leveling On Spray Dispersion, Travis A. Burgers, Miguel Bustamante, Juan F. Vivanco
SDSU Data Science Symposium
Self-propelled sprayers are commonly used in agriculture to disperse agrichemicals. These sprayers commonly have two boom wings with dozens of nozzles that disperse the chemicals. Automatic boom height systems reduce the variability of agricultural sprayer boom height, which is important to reduce uneven spray dispersion if the boom is not at the target height.
A computational model was created to simulate the spray dispersion under the following conditions: a) one stationary nozzle based on the measured spray pattern from one nozzle, b) one stationary model due to an angled boom, c) superposition of multiple stationary nozzles due an angled boom, …
Deploying Live Dashboard Data Using Usda Data Apis To Inform Farmers/Producers, Indira Fuyal, Paurakh Paudel, David Zeng
Deploying Live Dashboard Data Using Usda Data Apis To Inform Farmers/Producers, Indira Fuyal, Paurakh Paudel, David Zeng
SDSU Data Science Symposium
The main objective of this project is to provide the farmers/producers and small business owners with a data intensive information hub to better understand the market trends and patterns in an interactive way to help make informed decisions. This technology is part of a larger project that includes AI powered data services to provide users with decision making support for real time data solutions. Together with Natural Language Generation, there is a huge potential to deploy this technology. In this small demo of this hugely beneficial technology, we make use of the publicly available Open Data Services – ESR Data …
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.