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Articles 1 - 11 of 11
Full-Text Articles in Physical Sciences and Mathematics
A Decision Tree Model To Predict Marginalized Zero-Inflated Poisson Mean, Philip Amewudah
A Decision Tree Model To Predict Marginalized Zero-Inflated Poisson Mean, Philip Amewudah
University of New Orleans Theses and Dissertations
No abstract provided.
A Visual Analytics System For Investigating Multimorbidity Using Supervised Machine Learning, Maede Sadat Nouri
A Visual Analytics System For Investigating Multimorbidity Using Supervised Machine Learning, Maede Sadat Nouri
Electronic Thesis and Dissertation Repository
Patterns of multimorbidity are complex and difficult to summarise using static visualization techniques like tables and charts. We present a visual analytics system with the goal of facilitating the process of making sense of data collected from patients with multimorbidity. The system reveals underlying patterns in the data visually and interactively, which enables users to easily assess both prevalence and correlation estimates of different chronic diseases among multimorbid patients with varying characteristics. To do so, the system uses count-based conditional probability, binary logistic regression, softmax regression and decision tree models to dynamically compute and visualize prevalence and correlation estimates for …
Machine Learning For Effective Parkinson's Disease Diagnosis, Brennon Brimhall
Machine Learning For Effective Parkinson's Disease Diagnosis, Brennon Brimhall
Undergraduate Honors Theses
Parkinson’s Disease is a degenerative neurological condition that affects approximately 10 million people globally. Because there is currently no cure, there is a strong motivation for research into improved and automated diagnostic procedures. Using Random Forests, a computer can effectively learn to diagnose Parkinson’s disease in a patient with high accuracy (94%), precision (95%), and recall (91%) across the data of over 2800 patients. Using similar techniques, I further determine that the most predictive medical tests relate to tremors observed in patients.
Automatic Distinction Between Twitter Bots And Humans, Jeremiah Stubbs
Automatic Distinction Between Twitter Bots And Humans, Jeremiah Stubbs
All Undergraduate Theses and Capstone Projects
Weak artificial intelligence uses encoded functions of rules to process information. This kind of intelligence is competent, but lacks consciousness, and therefore cannot comprehend what it is doing. In another view, strong artificial intelligence has a mind of its own that resembles a human mind. Many of the bots on Twitter are only following a set of encoded rules. Previous studies have created machine learning algorithms to determine whether a Twitter account was being run by a human or a bot. Twitter bots are improving and some are even fooling humans. Creating a machine learning algorithm that differentiates a bot …
Classifying Textual Fast Food Restaurant Reviews Quantitatively Using Text Mining And Supervised Machine Learning Algorithms, Lindsey Wright
Classifying Textual Fast Food Restaurant Reviews Quantitatively Using Text Mining And Supervised Machine Learning Algorithms, Lindsey Wright
Undergraduate Honors Theses
Companies continually seek to improve their business model through feedback and customer satisfaction surveys. Social media provides additional opportunities for this advanced exploration into the mind of the customer. By extracting customer feedback from social media platforms, companies may increase the sample size of their feedback and remove bias often found in questionnaires, resulting in better informed decision making. However, simply using personnel to analyze the thousands of relative social media content is financially expensive and time consuming. Thus, our study aims to establish a method to extract business intelligence from social media content by structuralizing opinionated textual data using …
Application Of Remote Sensing And Machine Learning Modeling To Post-Wildfire Debris Flow Risks, Priscilla Addison
Application Of Remote Sensing And Machine Learning Modeling To Post-Wildfire Debris Flow Risks, Priscilla Addison
Dissertations, Master's Theses and Master's Reports
Historically, post-fire debris flows (DFs) have been mostly more deadly than the fires that preceded them. Fires can transform a location that had no history of DFs to one that is primed for it. Studies have found that the higher the severity of the fire, the higher the probability of DF occurrence. Due to high fatalities associated with these events, several statistical models have been developed for use as emergency decision support tools. These previous models used linear modeling approaches that produced subpar results. Our study therefore investigated the application of nonlinear machine learning modeling as an alternative. Existing models …
A Study On User Demographic Inference Via Ratings In Recommender Systems, Changbin Li
A Study On User Demographic Inference Via Ratings In Recommender Systems, Changbin Li
LSU Master's Theses
Everyday, millions of people interact with online services that adopt recommender systems, such as personalized movie, news and product recommendation services. Research has shown that the demographic attributes of users such as age and gender can further improve the performance of recommender systems and can be very useful for many other applications such as marketing and social studies. However, users do not always provide those details in their online profiles due to privacy concern. On the other hand, user interactions such as ratings in recommender systems may provide an alternative way to infer demographic information. Most existing approaches can infer …
Classification Trees And Rule-Based Modeling Using The C5.0 Algorithm For Self-Image Across Sex And Race In St. Louis, Rohan Shirali
Classification Trees And Rule-Based Modeling Using The C5.0 Algorithm For Self-Image Across Sex And Race In St. Louis, Rohan Shirali
Arts & Sciences Electronic Theses and Dissertations
The study population comprised children, adolescents, and adults who were residents of the city of St. Louis at the time of data collection in 2015. The data collected includes sex, age, race, measured height and weight, self-reported height and weight, zip code, educational background, exercise and diet habits, and descriptions and strategies of participants' weight (i.e. overweight and trying to lose weight, respectively). I use the C5.0 algorithm to create classification trees and rule-based models to analyze this population. Specifically, I model a binary self-image variable as a function of sex, age, race, zip code, and a ratio of reported …
Prediction Of Time-To-Graduation For Stem Hispanic Undergraduate Students, Gejun Zhu
Prediction Of Time-To-Graduation For Stem Hispanic Undergraduate Students, Gejun Zhu
Theses and Dissertations - UTB/UTPA
In this thesis, we study the time-to-graduation problem for STEM Hispanic undergraduate students. The response, time-to-graduation, was treated in two different ways: as a binary variable with graduated (by the 6th year) and not-graduated values, and as an ordinal variable with values year-4, year-5, year-6, and not-graduate. Mathematics education plays critical role in students’ timely graduation, especially for STEM students. We used students records data obtained from The University of Texas-Pan American to illustrate how mathematics background factors (including SAT math score, ACT math score, TASP math score) and mathematics performance variables (including mathematics GPA, number of dropped mathematics courses, …
Combining Natural Language Processing And Statistical Text Mining: A Study Of Specialized Versus Common Languages, Jay Jarman
USF Tampa Graduate Theses and Dissertations
This dissertation focuses on developing and evaluating hybrid approaches for analyzing free-form text in the medical domain. This research draws on natural language processing (NLP) techniques that are used to parse and extract concepts based on a controlled vocabulary. Once important concepts are extracted, additional machine learning algorithms, such as association rule mining and decision tree induction, are used to discover classification rules for specific targets. This multi-stage pipeline approach is contrasted with traditional statistical text mining (STM) methods based on term counts and term-by-document frequencies. The aim is to create effective text analytic processes by adapting and combining individual …
Integrating Information Theory Measures And A Novel Rule-Set-Reduction Tech-Nique To Improve Fuzzy Decision Tree Induction Algorithms, Nael Mohammed Abu-Halaweh
Integrating Information Theory Measures And A Novel Rule-Set-Reduction Tech-Nique To Improve Fuzzy Decision Tree Induction Algorithms, Nael Mohammed Abu-Halaweh
Computer Science Dissertations
Machine learning approaches have been successfully applied to many classification and prediction problems. One of the most popular machine learning approaches is decision trees. A main advantage of decision trees is the clarity of the decision model they produce. The ID3 algorithm proposed by Quinlan forms the basis for many of the decision trees’ application. Trees produced by ID3 are sensitive to small perturbations in training data. To overcome this problem and to handle data uncertainties and spurious precision in data, fuzzy ID3 integrated fuzzy set theory and ideas from fuzzy logic with ID3. Several fuzzy decision trees algorithms and …