Physical Sciences and Mathematics Commons^™

Soil Moisture And Geomorphologic Data For Use In Dynamic And Forecastable Landslide Hazard Analyses In Eastern Kentucky, Daniel M. Francis, L. Sebastian Bryson

Civil Engineering Research Data

These data are the geomorphologic and land information system-based soil moisture estimates from assimilation of NASA SMAP satellite-based observations and NOAH 3.6 Land Surface Model estimates over known landslides in Eastern Kentucky. Additionally Long Short-Term Memory Recurrent Neural Network and logistic regression machine learning codes, as well as an Application programming interface code are included. Finally, in-situ data from Eastern Kentucky is included.

Application Of A Gene Modular Approach For Clinical Phenotype Genotype Association And Sepsis Prediction Using Machine Learning In Meningococcal Sepsis, Asrar Rashid, Arif R. Anwary, Feras Al-Obeidat, Joe Brierley, Mohammed Uddin, Hoda Alkhzaimi, Amrita Sarpal, Mohammed Toufiq, Zainab A. Malik, Raziya Kadwa, Praveen Khilnani, M. Guftar Shaikh, Govind Benakatti, Javed Sharief, Syed Ahmed Zaki, Abdulrahman Zeyada, Ahmed Al-Dubai, Wael Hafez, Amir Hussain

All Works

Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The research used Weighted Gene Co-expression Network Analysis (WGCNA) to establish links between gene expression and clinical parameters in infants admitted to the Pediatric Critical Care Unit with MSS. Additionally, various machine learning (ML) algorithms, including Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Artificial Neural Network (ANN) were implemented to predict sepsis survival. The findings revealed a transition …

Spatiotemporal Retrievals Of Soil Moisture And Geomorphologic Data For Landslide Sites In Eastern Kentucky, Lindsey Sebastian Bryson, Daniel M. Francis

Civil Engineering Research Data

These data are the soil texture, land information system-based soil moisture estimates from assimilation of NASA SMAP satellite-based observations and NOAH 3.6 Land Surface Model estimates, artificial neural network machine learning code, and in-situ soil moisture measurements.

A Course In Data Science: R And Prediction Modeling, Adam Kapelner

Open Educational Resources

This is a self-contained course in data science and machine learning using R. It covers philosophy of modeling with data, prediction via linear models, machine learning including support vector machines and random forests, probability estimation and asymmetric costs using logistic regression and probit regression, underfitting vs. overfitting, model validation, handling missingness and much more. There is formal instruction of data manipulation using dplyr and data.table, visualization using ggplot2 and statistical computing.

Concept Drift Datasets, Patrick Lindstrom

Doctoral

This zip file contains the datasets used in the PhD thesis:

Lindstrom, P., 2013. Handling Concept Drift in the Context of Expensive Labels. Technological University Dublin. For more information about the datasets please see the README file and the aforementioned thesis.