Analyzing Empirical Quality Metrics Of Deep Learning Models For Antimicrobial Resistance,
2021
Southern Methodist University
Analyzing Empirical Quality Metrics Of Deep Learning Models For Antimicrobial Resistance, Huy H. Nguyen, Sanjay Pillay, Allison Roderick, Hao Wang, John Santerre
SMU Data Science Review
Antimicrobial Resistance (AMR) is a growing concern in the medical field. Over-prescription of antibiotics as well as bacterial mutations have caused some once lifesaving drugs to become ineffective against bacteria. However, the problem of AMR might be addressed using Machine Learning (ML) thanks to increased availability of genomic data and large computing resources. The Pathosystems Resource Integration Center (PATRIC) has genomic data of various bacterial genera with sample isolates that are either resistant or susceptible to certain antibiotics. Past research has used this database to use ML algorithms to model AMR with successful results, including accuracies over 80%. To better …
Quantitative Intersectional Data (Quinta): A #Metoo Case Study,
2021
DePaul University
Quantitative Intersectional Data (Quinta): A #Metoo Case Study, Alicia E. Boyd
College of Computing and Digital Media Dissertations
This research began as an investigation of the #metoo movement, with the initial impetus to illuminate the voices located on the margins, those who often go unheard or are never recognized. This work aimed to understand the intersectional aspects of how these hashtag variations of the hashtag #metoo (i.e. #metoomosque, #churchtoo, #metoodisable, #metooqueer, #metoochina, etc) reveal the inequities of the #metoo movement on Twitter. The proliferation of these hashtag variations has often been ignored by scholars, and therefore absorbed into the larger #metoo movement conversation on Twitter. Therefore, the term `hashtag derivative' was created to describe the variation on the …
Analysis Of Individual Player Performances And Their Effect On Winning In College Soccer,
2021
Southern Methodist University
Analysis Of Individual Player Performances And Their Effect On Winning In College Soccer, Angelo Bravo, Thomas Karba, Sean Mcwhirter, Billy Nayden
SMU Data Science Review
This study describes the process of modernizing the approach of the Southern Methodist University (SMU) Men's Soccer coaching staff through the use of location and tracking data from their matches in the 2019 season. This study utilizes a variety of modeling and analysis techniques to explore and categorize the data and use it to evaluate the types of plays that are most often correlated with victories. This study's contribution to college soccer analytics includes the implementation of a model to determine individual players' performance, the production of team-level metrics, and visualizations to increase the efficiency of the coaching staff's efforts. …
Machine Learning In The Health Industry: Predicting Congestive Heart Failure And Impactors,
2021
Southern Methodist University
Machine Learning In The Health Industry: Predicting Congestive Heart Failure And Impactors, Alexandra Norman, James Harding, Daria Zhukova
SMU Data Science Review
Cardiovascular diseases, Congestive Heart Failure in particular, are a leading cause of deaths worldwide. Congestive Heart Failure has high mortality and morbidity rates. The key to decreasing the morbidity and mortality rates associated with Congestive Heart Failure is determining a method to detect high-risk individuals prior to the development of this often-fatal disease. Providing high-risk individuals with advanced knowledge of risk factors that could potentially lead to Congestive Heart Failure, enhances the likelihood of preventing the disease through implementation of lifestyle changes for healthy living. When dealing with healthcare and patient data, there are restrictions that led to difficulties accessing …
Generating And Smoothing Handwriting With Long Short-Term Memory Networks,
2021
Southern Methodist University
Generating And Smoothing Handwriting With Long Short-Term Memory Networks, Muchigi Kimari, Edward Fry, Ikenna Nwaogu, Yumei Bennett, John Santerre
SMU Data Science Review
This project explores the different neural network methods to generate synthetic handwriting text. The goal is to offer an AI tool that generates handwriting, while maintaining an individual’s style, to people suffering with Dysgraphia. As part of this project, an application development framework is setup on GitHub, in such a way that others can continue to explore and improve the AI tool.
A Machine Learning Method Of Determining Causal Inference Applied To Shifts In Voting Preferences Between 2012-2016,
2021
Southern Methodist University
A Machine Learning Method Of Determining Causal Inference Applied To Shifts In Voting Preferences Between 2012-2016, Jaclyn A. Coate, Reagan Meagher, Megan Riley, John Santerre
SMU Data Science Review
This research investigates the application of machine learning techniques to assist in the execution of a synthetic control model. This model was performed to analyze counties within the United States that showed a voter shift from a majority of Democratic voter share to Republican between the 2012 and 2016 election cycles. The following study applies two steps of machine learning analysis. The first, which is the treatment discovery process, leverages a Random Forest to evaluate feature importance. The second step was the execution of the synthetic control model with two predictor variable lists. The first was the parametric method: …
Automated Analysis Of Rfps Using Natural Language Processing (Nlp) For The Technology Domain,
2021
Southern Methodist University
Automated Analysis Of Rfps Using Natural Language Processing (Nlp) For The Technology Domain, Sterling Beason, William Hinton, Yousri A. Salamah, Jordan Salsman
SMU Data Science Review
Much progress has been made in text analysis, specifically within the statistical domain of Term Frequency (TF) and Inverse Document Frequency (IDF). However, there is much room for improvement especially within the area of discovering Emerging Trends. Emerging Trend Detection Systems (ETDS) depend on ingesting a collection of textual data and TF/IDF to identify new or up-trending topics within the Corpus. However, the tremendous rate of change and the amount of digital information presents a challenge that makes it almost impossible for a human expert to spot emerging trends without relying on an automated ETD system. Since the U.S. Government …
The Social Market Economy As A Formula For Peace, Prosperity, And Sustainability,
2021
Kennesaw State University
The Social Market Economy As A Formula For Peace, Prosperity, And Sustainability, Almuth D. Merkel
Doctor of International Conflict Management Dissertations
The social market economy was developed in Germany during the interwar period amidst political and economic turmoil. With clear demarcation lines differentiating it from socialism and laissez-faire capitalism, the social market economy became a formula for peace and prosperity for post WWII Germany. Since then, the success of the social market economy has inspired many other countries to adopt its principles. Drawing on evidence from economic history and the history of economic thought, this thesis first reviews the evolution of the fundamental principles that form the foundation of social-market economic thought. Blending the micro-economic utility maximization framework with traditional growth …
Federated Learning For Secure Sensor Cloud,
2021
Kennesaw State University
Federated Learning For Secure Sensor Cloud, Viraaji Mothukuri
Master of Science in Software Engineering Theses
Intelligent sensing solutions bridge the gap between the physical world and the cyber world by digitizing the sensor data collected from sensor devices. Sensor cloud networks provide resources to physical and virtual sensing devices and enable uninterrupted intelligent solutions to end-users. Thanks to advancements in machine learning algorithms and big data, the automation of mundane tasks with artificial intelligence is becoming a more reliable smart option. However, existing approaches based on centralized Machine Learning (ML) on sensor cloud networks fail to ensure data privacy. Moreover, centralized ML works with the pre-requisite to have the entire training dataset from end-devices transferred …
Fine-Grained Sentiment Analysis For Customer Review,
2021
Kennesaw State University
Fine-Grained Sentiment Analysis For Customer Review, Bing Han, Meng Han, Jing (Selena) He
Master of Science in Computer Science Theses
Natural Language Processing (NLP) is one of the most attractive technologies in many applications in real-life. Sentiment analysis, which has devoted to know others' think or feel about an experience or an item and hence take an action, is one of the most developed area in both academia and industry. Among sentiment analysis, fine-grained aspect sentiment analysis attempts to analyze emotional attitude categorized into different aspects or features of an(a) experience/service/product. Although aspect level sentiment analysis could provide more useful information, the proposed models' performance were relative poor compared with document-level or sentence-level sentiment analysis due to the lack of …
Estimation Of The Planetary Boundary Layer Height: Part 1: Global Radar Wind Profiler Network Data; Part 2: A Comparison To Ceilometer Data,
2021
CUNY Hunter College
Estimation Of The Planetary Boundary Layer Height: Part 1: Global Radar Wind Profiler Network Data; Part 2: A Comparison To Ceilometer Data, Holly Josephs
Theses and Dissertations
Two methods for estimating the planetary boundary layer, an algorithm to identify a maximum in the backscatter and a covariance wavelet transform method, are explored and applied to global radar wind profiler network data and ceilometer data respectively. The objective of the study is to establish that the data sources and algorithms can be used to estimate planetary boundary layer heights so that global studies can make use of these estimates. Data from the global network of wind profilers required significant restructuring and quality control in order to be used for the present study. The maximum backscatter identification algorithm was …
The Effects Of Individual Differences, Non‐Stationarity, And The Importance Of Data Partitioning Decisions For Training And Testing Of Eeg Cross‐Participant Models,
2021
Air Force Institute of Technology
The Effects Of Individual Differences, Non‐Stationarity, And The Importance Of Data Partitioning Decisions For Training And Testing Of Eeg Cross‐Participant Models, Alexander J. Kamrud, Brett J. Borghetti, Christine M. Schubert Kabban
Faculty Publications
EEG-based deep learning models have trended toward models that are designed to perform classification on any individual (cross-participant models). However, because EEG varies across participants due to non-stationarity and individual differences, certain guidelines must be followed for partitioning data into training, validation, and testing sets, in order for cross-participant models to avoid overestimation of model accuracy. Despite this necessity, the majority of EEG-based cross-participant models have not adopted such guidelines. Furthermore, some data repositories may unwittingly contribute to the problem by providing partitioned test and non-test datasets for reasons such as competition support. In this study, we demonstrate how improper …
Research Focus: Pattern Recognition,
2021
DePaul University
Research Focus: Pattern Recognition
In The Loop
A CDM health informatics team joins a global race to advance COVID-19 diagnostics through X-ray insights.
Spring 2021,
2021
DePaul University
Spring 2021
In The Loop
IRL Programs Debut; Short & Sweet Pandemic Film Fest; New MS in Artificial Intelligence; Virtual Experts Talks; DePaul Trustee Producing Documentary; DemonHacks Hackathon; Silicon Valley 2.0: The DePaul Innovation Development Lab connects students and companies to spark solutions to technological challenges; Code Warrior: Ovetta Sampson has risen to challenges in digital design, journalism and athletics while inspiring others; Pattern Recognition: A CDM health informatics team joins a global race to advance COVID-19 diagnostics through X-ray insights
Application Of Randomness In Finance,
2021
CUNY New York City College of Technology
Application Of Randomness In Finance, Jose Sanchez, Daanial Ahmad, Satyanand Singh
Publications and Research
Brownian Motion which is also considered to be a Wiener process and can be thought of as a random walk. In our project we had briefly discussed the fluctuations of financial indices and related it to Brownian Motion and the modeling of Stock prices.
Profile Modeling In Hierarchical Deep Architecture By Mutual Support,
2021
Kennesaw State University
Profile Modeling In Hierarchical Deep Architecture By Mutual Support, Honglai Peng, Meng Han, Jing (Selena) He
Master of Science in Computer Science Theses
Despite significant advances in the field of face analysis over last decade, the current studies are still limited to specific face computation tasks using deep learning approaches. In this paper, we propose an end-to-end hierarchical deep learning structure, called Multi-Features Convolutional Neural Networks (MFCNN), which can comprehensively implement face analysis including age, gender, race and emotion. Moreover, we take the advantages of the mutual support among different facial features from individual tasks to improve the performance of our model. We also contribute one all-labeling dataset called Multiple Facial Features Computation (MFFC) based on Apparent-age-V2 dataset. Firstly, we train four different …
Assessing The Re-Identification Risk In Ecg Datasets And An Application Of Privacy Preserving Techniques In Ecg Analysis,
2021
Chapman University
Assessing The Re-Identification Risk In Ecg Datasets And An Application Of Privacy Preserving Techniques In Ecg Analysis, Arin Ghazarian
Computational and Data Sciences (PhD) Dissertations
In this work, first we investigate the use of ECG signal as a biometric in human identification systems using deep learning models. We train convolutional neural network models on ECG samples from approximately 81k patients. Our models achieved an over-all accuracy of 95.69%. Further, we assess the accuracy of our ECG identification model for distinct groups of patients with particular heart conditions and combinations of such conditions. For example, we observed that the identification accuracy was the highest (99.7%) for patients with both ST changes and supraventricular tachycardia. On the other hand, we also found that the identification rate was …
Using Deep Learning For Children Brain Image Analysis,
2021
University of Arkansas, Fayetteville
Using Deep Learning For Children Brain Image Analysis, Rafael Toche Pizano
Computer Science and Computer Engineering Undergraduate Honors Theses
Analyzing the correlation between brain volumetric/morphometry features and cognition/behavior in children is important in the field of pediatrics as identifying such relationships can help identify children who may be at risk for illnesses. Understanding these relationships can not only help identify children who may be at risk of illnesses, but it can also help evaluate strategies that promote brain development in children. Currently, one way to do this is to use traditional statistical methods such as a correlation analysis, but such an approach does not make it easy to generalize and predict how brain volumetric/morphometry will impact cognition/behavior. One of …
Semantic Classification Of Multidialectal Arabic Social Media,
2021
University of Southern Mississippi
Semantic Classification Of Multidialectal Arabic Social Media, Tom Rishel
Dissertations
Arabic is one of the most widely used languages in the world, but due in part to its morphological and syntactic richness, resources for automated processing of Arabic are relatively rare. Arabic takes three primary forms: Classical Arabic as seen in the Qur’an and other classical texts; Modern Standard Arabic (MSA) as seen in newspapers, formal documents, and other written text intended for widespread distribution; and dialectal Arabic as used in common speech and informal communication. Social media posts are often written in informal language and may include non-standard spellings, abbreviations, emoticons, hashtags, and emojis. Dialectal Arabic is commonly used …
Improving Bayesian Graph Convolutional Networks Using Markov Chain Monte Carlo Graph Sampling,
2021
University of Arkansas, Fayetteville
Improving Bayesian Graph Convolutional Networks Using Markov Chain Monte Carlo Graph Sampling, Aneesh Komanduri
Computer Science and Computer Engineering Undergraduate Honors Theses
In the modern age of social media and networks, graph representations of real-world phenomena have become incredibly crucial. Often, we are interested in understanding how entities in a graph are interconnected. Graph Neural Networks (GNNs) have proven to be a very useful tool in a variety of graph learning tasks including node classification, link prediction, and edge classification. However, in most of these tasks, the graph data we are working with may be noisy and may contain spurious edges. That is, there is a lot of uncertainty associated with the underlying graph structure. Recent approaches to modeling uncertainty have been …
