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Full-Text Articles in Physical Sciences and Mathematics
Predicting Post-Procedural Complications Using Neural Networks On Mimic-Iii Data, Namratha Mohan
Predicting Post-Procedural Complications Using Neural Networks On Mimic-Iii Data, Namratha Mohan
LSU Master's Theses
The primary focus of this paper is the creation of a Machine Learning based algorithm for the analysis of large health based data sets. Our input was extracted from MIMIC-III, a large Health Record database of more than 40,000 patients. The main question was to predict if a patient will have complications during certain specified procedures performed in the hospital. These events are denoted by the icd9 code 996 in the individuals' health record. The output of our predictive model is a binary variable which outputs the value 1 if the patient is diagnosed with the specific complication or 0 …
A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab
A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab
Electronic Theses and Dissertations
The idea of developing machine learning systems or Artificial Intelligence agents that would learn from different tasks and be able to accumulate that knowledge with time so that it functions successfully on a new task that it has not seen before is an idea and a research area that is still being explored. In this work, we will lay out an algorithm that allows a machine learning system or an AI agent to learn from k different domains then uses some or no data from the new task for the system to perform strongly on that new task. In order …
Cleaver: Classification Of Everyday Activities Via Ensemble Recognizers, Samantha Hsu
Cleaver: Classification Of Everyday Activities Via Ensemble Recognizers, Samantha Hsu
Master's Theses
Physical activity can have immediate and long-term benefits on health and reduce the risk for chronic diseases. Valid measures of physical activity are needed in order to improve our understanding of the exact relationship between physical activity and health. Activity monitors have become a standard for measuring physical activity; accelerometers in particular are widely used in research and consumer products because they are objective, inexpensive, and practical. Previous studies have experimented with different monitor placements and classification methods. However, the majority of these methods were developed using data collected in controlled, laboratory-based settings, which is not reliably representative of real …
Cryptovisor: A Cryptocurrency Advisor Tool, Matthew Baldree, Paul Widhalm, Brandon Hill, Matteo Ortisi
Cryptovisor: A Cryptocurrency Advisor Tool, Matthew Baldree, Paul Widhalm, Brandon Hill, Matteo Ortisi
SMU Data Science Review
In this paper, we present a tool that provides trading recommendations for cryptocurrency using a stochastic gradient boost classifier trained from a model labeled by technical indicators. The cryptocurrency market is volatile due to its infancy and limited size making it difficult for investors to know when to enter, exit, or stay in the market. Therefore, a tool is needed to provide investment recommendations for investors. We developed such a tool to support one cryptocurrency, Bitcoin, based on its historical price and volume data to recommend a trading decision for today or past days. This tool is 95.50% accurate with …
A Multiple Classifier System For Predicting Best-Selling Amazon Products, Michael Kranzlein
A Multiple Classifier System For Predicting Best-Selling Amazon Products, Michael Kranzlein
Master of Science in Computer Science Theses
In this work, I examine a dataset of Amazon product metadata and propose a heterogeneous multiple classifier system for the task of identifying best-selling products in multiple categories. This system of classifiers consumes the product description and the featured product image as input and feeds them through binary classifiers of the following types: Convolutional Neural Network, Na¨ıve Bayes, Random Forest, Ridge Regression, and Support Vector Machine. While each individual model is largely successful in identifying best-selling products from non best-selling products and from worst-selling products, the multiple classifier system is shown to be stronger than any individual model in the …
Ai-Human Collaboration Via Eeg, Adam Noack
Ai-Human Collaboration Via Eeg, Adam Noack
All College Thesis Program, 2016-2019
As AI becomes ever more competent and integrated into our lives, the issue of AI-human goal misalignment looms larger. This is partially because there is often a rift between what humans explicitly command and what they actually mean. Most contemporary AI systems cannot bridge this gap. In this study we attempted to reconcile the goals of human and machine by using EEG signals from a human to help a simulated agent complete a task.
Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels
Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels
SMU Data Science Review
In this paper, we present a comparative evaluation of deep learning approaches to network intrusion detection. A Network Intrusion Detection System (NIDS) is a critical component of every Internet connected system due to likely attacks from both external and internal sources. A NIDS is used to detect network born attacks such as Denial of Service (DoS) attacks, malware replication, and intruders that are operating within the system. Multiple deep learning approaches have been proposed for intrusion detection systems. We evaluate three models, a vanilla deep neural net (DNN), self-taught learning (STL) approach, and Recurrent Neural Network (RNN) based Long Short …
Cognitive Virtual Admissions Counselor, Kumar Raja Guvindan Raju, Cory Adams, Raghuram Srinivas
Cognitive Virtual Admissions Counselor, Kumar Raja Guvindan Raju, Cory Adams, Raghuram Srinivas
SMU Data Science Review
Abstract. In this paper, we present a cognitive virtual admissions counselor for the Master of Science in Data Science program at Southern Methodist University. The virtual admissions counselor is a system capable of providing potential students accurate information at the time that they want to know it. After the evaluation of multiple technologies, Amazon’s LEX was selected to serve as the core technology for the virtual counselor chatbot. Student surveys were leveraged to collect and generate training data to deploy the natural language capability. The cognitive virtual admissions counselor platform is currently capable of providing an end-to-end conversational dialog to …
Using Data Visualization To Inform Machine Learning Approaches, Eric N. Zielonka, Aleksandr Fritz
Using Data Visualization To Inform Machine Learning Approaches, Eric N. Zielonka, Aleksandr Fritz
STEM Student Research Symposium Posters
Machine learning with big data is a complicated task to tackle. Using data visualizations, one can find trends, anomalies, and patterns to help select the appropriate approach to the problem in machine learning. Using 2D visualizations, we’ve displayed flight data on interactive maps, visualizing density and property changes in an area. We’ve also used frequency histograms to view the quantitative properties of each point to look for trends. Using scatterplots, anomalies in data collection were found. Other plots confirmed previously found trends and initial thoughts about the data. These visualizations helped inform a machine learning approach to our problem and …
Old English Character Recognition Using Neural Networks, Sattajit Sutradhar
Old English Character Recognition Using Neural Networks, Sattajit Sutradhar
Electronic Theses and Dissertations
Character recognition has been capturing the interest of researchers since the beginning of the twentieth century. While the Optical Character Recognition for printed material is very robust and widespread nowadays, the recognition of handwritten materials lags behind. In our digital era more and more historical, handwritten documents are digitized and made available to the general public. However, these digital copies of handwritten materials lack the automatic content recognition feature of their printed materials counterparts. We are proposing a practical, accurate, and computationally efficient method for Old English character recognition from manuscript images. Our method relies on a modern machine learning …