Matching Items (39)
Filtering by
- All Subjects: Machine Learning
- Creators: School of Mathematical and Statistical Sciences
- Resource Type: Text
Description
Brown adipose tissue (BAT) is thought to be important in combating obesity as it can expend energy in the form of heat, e.g. thermogenesis. The goal of this study was to study the effect of injected norepinephrine (NE) on the activation of BAT in rats that were fed a high fat diet (HFD). A dose of 0.25 mg/kg NE was used to elicit a temperature response that was measured using transponders inserted subcutaneously over the BAT and lower back and intraperitoneally to measure the core temperature. The results found that the thermic effect of the BAT increased after the transition from low fat diet to a high fat diet (LFD) yet, after prolonged exposure to the HFD, the effects resembled levels found with the LFD. This suggests that while a HFD may stimulate the effect of BAT, long term exposure may have adverse effects on BAT activity. This may be due to internal factors that will need to be examined further.
ContributorsSion, Paul William (Author) / Herman, Richard (Thesis director) / Borges, Chad (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Bots tamper with social media networks by artificially inflating the popularity of certain topics. In this paper, we define what a bot is, we detail different motivations for bots, we describe previous work in bot detection and observation, and then we perform bot detection of our own. For our bot detection, we are interested in bots on Twitter that tweet Arabic extremist-like phrases. A testing dataset is collected using the honeypot method, and five different heuristics are measured for their effectiveness in detecting bots. The model underperformed, but we have laid the ground-work for a vastly untapped focus on bot detection: extremist ideal diffusion through bots.
ContributorsKarlsrud, Mark C. (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
The OMFIT (One Modeling Framework for Integrated Tasks) modeling environment and the BRAINFUSE module have been deployed on the PPPL (Princeton Plasma Physics Laboratory) computing cluster with modifications that have rendered the application of artificial neural networks (NNs) to the TRANSP databases for the JET (Joint European Torus), TFTR (Tokamak Fusion Test Reactor), and NSTX (National Spherical Torus Experiment) devices possible through their use. This development has facilitated the investigation of NNs for predicting heat transport profiles in JET, TFTR, and NSTX, and has promoted additional investigations to discover how else NNs may be of use to scientists at PPPL. In applying NNs to the aforementioned devices for predicting heat transport, the primary goal of this endeavor is to reproduce the success shown in Meneghini et al. in using NNs for heat transport prediction in DIII-D. Being able to reproduce the results from is important because this in turn would provide scientists at PPPL with a quick and efficient toolset for reliably predicting heat transport profiles much faster than any existing computational methods allow; the progress towards this goal is outlined in this report, and potential additional applications of the NN framework are presented.
ContributorsLuna, Christopher Joseph (Author) / Tang, Wenbo (Thesis director) / Treacy, Michael (Committee member) / Orso, Meneghini (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Physics (Contributor)
Created2015-05
Description
Twitter, the microblogging platform, has grown in prominence to the point that the topics that trend on the network are often the subject of the news and other traditional media. By predicting trends on Twitter, it could be possible to predict the next major topic of interest to the public. With this motivation, this paper develops a model for trends leveraging previous work with k-nearest-neighbors and dynamic time warping. The development of this model provides insight into the length and features of trends, and successfully generalizes to identify 74.3% of trends in the time period of interest. The model developed in this work provides understanding into why par- ticular words trend on Twitter.
ContributorsMarshall, Grant A (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
A model has been developed to modify Euler-Bernoulli beam theory for wooden beams, using visible properties of wood knot-defects. Treating knots in a beam as a system of two ellipses that change the local bending stiffness has been shown to improve the fit of a theoretical beam displacement function to edge-line deflection data extracted from digital imagery of experimentally loaded beams. In addition, an Ellipse Logistic Model (ELM) has been proposed, using L1-regularized logistic regression, to predict the impact of a knot on the displacement of a beam. By classifying a knot as severely positive or negative, vs. mildly positive or negative, ELM can classify knots that lead to large changes to beam deflection, while not over-emphasizing knots that may not be a problem. Using ELM with a regression-fit Young's Modulus on three-point bending of Douglass Fir, it is possible estimate the effects a knot will have on the shape of the resulting displacement curve.
ContributorsSexton, Thurston Bryant (Author) / Takahashi, Timothy (Thesis director) / Jones, Donald (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2015-05
Description
Cryptocurrencies have become one of the most fascinating forms of currency and economics due to their fluctuating values and lack of centralization. This project attempts to use machine learning methods to effectively model in-sample data for Bitcoin and Ethereum using rule induction methods. The dataset is cleaned by removing entries with missing data. The new column is created to measure price difference to create a more accurate analysis on the change in price. Eight relevant variables are selected using cross validation: the total number of bitcoins, the total size of the blockchains, the hash rate, mining difficulty, revenue from mining, transaction fees, the cost of transactions and the estimated transaction volume. The in-sample data is modeled using a simple tree fit, first with one variable and then with eight. Using all eight variables, the in-sample model and data have a correlation of 0.6822657. The in-sample model is improved by first applying bootstrap aggregation (also known as bagging) to fit 400 decision trees to the in-sample data using one variable. Then the random forests technique is applied to the data using all eight variables. This results in a correlation between the model and data of 9.9443413. The random forests technique is then applied to an Ethereum dataset, resulting in a correlation of 9.6904798. Finally, an out-of-sample model is created for Bitcoin and Ethereum using random forests, with a benchmark correlation of 0.03 for financial data. The correlation between the training model and the testing data for Bitcoin was 0.06957639, while for Ethereum the correlation was -0.171125. In conclusion, it is confirmed that cryptocurrencies can have accurate in-sample models by applying the random forests method to a dataset. However, out-of-sample modeling is more difficult, but in some cases better than typical forms of financial data. It should also be noted that cryptocurrency data has similar properties to other related financial datasets, realizing future potential for system modeling for cryptocurrency within the financial world.
ContributorsBrowning, Jacob Christian (Author) / Meuth, Ryan (Thesis director) / Jones, Donald (Committee member) / McCulloch, Robert (Committee member) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
For the past couple decades, there has been a continuous rise in obesity and Type II Diabetes which has been attributed to the rise in calorically dense diets, especially those heavy in fats. Because of its rising prevalence, accompanied health concerns, and high healthcare costs, detection and therapies for these metabolic diseases are in high demand. Insulin resistance is a typical hallmark of Type II Diabetes and the metabolic deficiencies in obesity and is the main focus of this project. The primary purpose of this study is (1) detect the presence of two types of insulin resistance (peripheral and hepatic) as a function of age, (2) distinguish if diet impacted the presence of insulin resistance, and (3) determine both the short-term and long-term effects of caloric restriction on metabolic health. The following study longitudinally observed the changes in insulin resistance in high-fat fed and low-fat fed rodents under ad libitum and caloric restriction conditions over the course of 23 weeks. Fasting blood glucose, fasting insulin, body weight, and sensitivity of insulin on tissue were monitored in order to determine peripheral and hepatic insulin resistance. A high fat diet resulted in higher body weights and higher hepatic insulin resistance with no notable effect on peripheral insulin resistance. Caloric restriction was found to alleviate insulin resistance both during caloric restriction and four weeks after caloric restriction ended. Due to sample size, the generalizability of the findings in this study are limited. However, the current study did provide considerable results and can be viewed as a pilot study for a larger-scale study.
ContributorsZuo, Dana (Author) / Trumble, Benjamin (Thesis director) / Herman, Richard (Committee member) / Department of Psychology (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Machine learning for analytics has exponentially increased in the past few years due to its ability to identify hidden insights in data. It also has a plethora of applications in healthcare ranging from improving image recognition in CT scans to extracting semantic meaning from thousands of medical form PDFs. Currently in the BioElectrical Systems and Technology Lab, there is a biosensor in development that retrieves and analyzes data manually. In a proof of concept, this project uses the neural network architecture to automatically parse and classify a cardiac disease data set as well as explore health related factors impacting cardiac disease in patients of all ages.
ContributorsMurella, Akhila Sainagaki (Author) / Blain-Christen, Jennifer (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of the Arts, Media and Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
With the influence of the Western Diet, obesity has become a rising problem in the country today. Western Diet is characterized by the overconsumption of processed food that is low in nutritional values and high in saturated fats. Study showed that every two out of three adults in the United States are either overweight or obese. Being obese increase the risk of many other disease such as diabetes, cardiovascular disease and insulin resistance. Besides being a great health concern, obesity is also cause a great financial burden. Many efforts have been made to understand the defense against obesity and weight loss. The goal of this study was to understand the characterization of food intake and weight gain responses when imposed on a high-fat diet (HFD) using rats. It was predicted that weight gain would be dependent on energy intake and it would have a significant effect on adiposity compared to energy intake. Data showed that energy intake had high significance with adiposity whereas weight gain showed no significance. Also for the rats that were on HFD, the obesity-prone (OP) rats exhibited a great amount of weight gain and energy intake while the obesity-resistance (OR) rats showed a similar weight gain to the controlled group on low-fat diet (LFD) despite being hyperphagic. This suggests that OR is characterized by equal weight gain despite hyperphagia but this alone cannot explain the boy defense against obesity. More research is needed with a larger sample size to understand weight gain responses in order to fight against the epidemic of obesity.
ContributorsMao, Samuel (Author) / Herman, Richard (Thesis director) / Baluch, Page (Committee member) / Lamb, Timothy (Committee member) / WPC Graduate Programs (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
In this paper, I will show that news headlines of global events can predict changes in stock price by using Machine Learning and eight years of data from r/WorldNews, a popular forum on Reddit.com. My data is confined to the top 25 daily posts on the forum, and due to the implicit filtering mechanism in the online community, these 25 posts are representative of the most popular news headlines and influential global events of the day. Hence, these posts shine a light on how large-scale social and political events affect the stock market. Using a Logistic Regression and a Naive Bayes classifier, I am able to predict with approximately 85% accuracy a binary change in stock price using term-feature vectors gathered from the news headlines. The accuracy, precision and recall results closely rival the best models in this field of research. In addition to the results, I will also describe the mathematical underpinnings of the two models; preceded by a general investigation of the intersection between the multiple academic disciplines related to this project. These range from social to computer science and from statistics to philosophy. The goal of this additional discussion is to further illustrate the interdisciplinary nature of the research and hopefully inspire a non-monolithic mindset when further investigations are pursued.
ContributorsPriniski, John Hunter (Author) / Haiyan, Wang (Thesis director) / Hazel, Kwon (Committee member) / School of Historical, Philosophical and Religious Studies (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12