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Articles 1 - 14 of 14
Full-Text Articles in Physical Sciences and Mathematics
Deep Learning Image Analysis To Isolate And Characterize Different Stages Of S-Phase In Human Cells, Kevin A. Boyd, Rudranil Mitra, John Santerre, Christopher L. Sansam
Deep Learning Image Analysis To Isolate And Characterize Different Stages Of S-Phase In Human Cells, Kevin A. Boyd, Rudranil Mitra, John Santerre, Christopher L. Sansam
SMU Data Science Review
Abstract. This research used deep learning for image analysis by isolating and characterizing distinct DNA replication patterns in human cells. By leveraging high-resolution microscopy images of multiple cells stained with 5-Ethynyl-2′-deoxyuridine (EdU), a replication marker, this analysis utilized Convolutional Neural Networks (CNNs) to perform image segmentation and to provide robust and reliable classification results. First multiple cells in a field of focus were identified using a pretrained CNN called Cellpose. After identifying the location of each cell in the image a python script was created to crop out each cell into individual .tif files. After careful annotation, a CNN was …
Differentiation Of Human, Dog, And Cat Hair Fibers Using Dart Tofms And Machine Learning, Laura Ahumada, Erin R. Mcclure-Price, Chad Kwong, Edgard O. Espinoza, John Santerre
Differentiation Of Human, Dog, And Cat Hair Fibers Using Dart Tofms And Machine Learning, Laura Ahumada, Erin R. Mcclure-Price, Chad Kwong, Edgard O. Espinoza, John Santerre
SMU Data Science Review
Hair is found in over 90% of crime scenes and has long been analyzed as trace evidence. However, recent reviews of traditional hair fiber analysis techniques, primarily morphological examination, have cast doubt on its reliability. To address these concerns, this study employed machine learning algorithms, specifically Linear Discriminant Analysis (LDA) and Random Forest, on Direct Analysis in Real Time time-of-flight mass spectra collected from human, cat, and dog hair samples. The objective was to develop a chemistry- and statistics-based classification method for unbiased taxonomic identification of hair. The results of the study showed that LDA and Random Forest were highly …
Comparison Of Sampling Methods For Predicting Wine Quality Based On Physicochemical Properties, Robert Burigo, Scott Frazier, Eli Kravez, Nibhrat Lohia
Comparison Of Sampling Methods For Predicting Wine Quality Based On Physicochemical Properties, Robert Burigo, Scott Frazier, Eli Kravez, Nibhrat Lohia
SMU Data Science Review
Using the physicochemical properties of wine to predict quality has been done in numerous studies. Given the nature of these properties, the data is inherently skewed. Previous works have focused on handful of sampling techniques to balance the data. This research compares multiple sampling techniques in predicting the target with limited data. For this purpose, an ensemble model is used to evaluate the different techniques. There was no evidence found in this research to conclude that there are specific oversampling methods that improve random forest classifier for a multi-class problem.
Professor Text: University Fundraising Optimization, Braden Anderson, Connor Dobbs, Hien Lam, John Santerre
Professor Text: University Fundraising Optimization, Braden Anderson, Connor Dobbs, Hien Lam, John Santerre
SMU Data Science Review
University fundraising campaigns are a unique type of cause-related marketing with its own challenges and opportunities. Campaigns like this typically last an extended period, such as five or more years, and goals exist beyond the dollar amount raised. These supplemental goals, such as awareness among potential future donators or brand reputation within the local community, are important to consider and strategize. There can also be unique limitations, such as requiring advertising specifically on recent large gifts or endowment programs. This research explores how machine learning techniques such as natural language processing can be used to optimize a fundraising campaign strategy, …
Aspect-Based Sentiment Analysis Of Movie Reviews, Samuel Onalaja, Eric Romero, Bosang Yun
Aspect-Based Sentiment Analysis Of Movie Reviews, Samuel Onalaja, Eric Romero, Bosang Yun
SMU Data Science Review
This study investigates a comparison of classification models used to determine aspect based separated text sentiment and predict binary sentiments of movie reviews with genre and aspect specific driving factors. To gain a broader classification analysis, five machine and deep learning algorithms were compared: Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network Long-Short-Term Memory (RNN LSTM). The various movie aspects that are utilized to separate the sentences are determined through aggregating aspect words from lexicon-base, supervised and unsupervised learning. The driving factors are randomly assigned to various movie aspects and their impact tied to …
Clinical Diagnosis Support With Convolutional Neural Network By Transfer Learning, Spencer Fogleman, Jeremy Otsap, Sangrae Cho
Clinical Diagnosis Support With Convolutional Neural Network By Transfer Learning, Spencer Fogleman, Jeremy Otsap, Sangrae Cho
SMU Data Science Review
Breast cancer is prevalent among women in the United States. Breast cancer screening is standard but requires a radiologist to review screening images to make a diagnosis. Diagnosis through the traditional screening method of mammography currently has an accuracy of about 78% for women of all ages and demographics. A more recent and precise technique called Digital Breast Tomosynthesis (DBT) has shown to be more promising but is less well studied. A machine learning model trained on DBT images has the potential to increase the success of identifying breast cancer and reduce the time it takes to diagnose a patient, …
Using Machine Learning Methods To Predict The Movement Trajectories Of The Louisiana Black Bear, Daniel Clark, David Shaw, Armando Vela, Shane Weinstock, John Santerre, Joseph D. Clark
Using Machine Learning Methods To Predict The Movement Trajectories Of The Louisiana Black Bear, Daniel Clark, David Shaw, Armando Vela, Shane Weinstock, John Santerre, Joseph D. Clark
SMU Data Science Review
In 1992, the Louisiana black bear (Ursus americanus luteolus) was placed on the U.S. Endangered Species List. This was due to bear populations in Louisiana being small and isolated enough where their populations couldn’t intersect with other populations to grow. Interchange of individuals between subpopulations of bears in Louisiana is critical to maintain genetic diversity and avoid inbreeding effects. Utilizing GPS (Global Positioning System) data gathered from 31 radio-collared bears from 2010 through 2012, this research will investigate how bears traverse the landscape, which has implications for gene exchange. This paper will leverage machine learning tools to improve upon existing …
Machine Learning In The Health Industry: Predicting Congestive Heart Failure And Impactors, Alexandra Norman, James Harding, Daria Zhukova
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 …
Predicting Attrition - A Driver For Creating Value, Realizing Strategy, And Refining Key Hr Processes, Kevin Mendonsa, Maureen Stolberg, Vivek Viswanathan, Scott Crum
Predicting Attrition - A Driver For Creating Value, Realizing Strategy, And Refining Key Hr Processes, Kevin Mendonsa, Maureen Stolberg, Vivek Viswanathan, Scott Crum
SMU Data Science Review
Talent is the most important asset for every organization's success. While attrition (or churn) and turnover can refer to both employees and customers, this paper will focus on employee attrition only. Many organizations accept attrition as an inevitable cost of doing business and do nothing to adopt or implement mitigating strategies to combat it. World class companies on the other hand take deliberate measures to understand, control and mitigate attrition (turnover) at every stage. Unmitigated attrition can have a devastating effect on an organization's bottom line and market value. In addition, the “invisible" costs of low employee morale, reduced employee …
Machine Learning In Support Of Electric Distribution Asset Failure Prediction, Robert D. Flamenbaum, Thomas Pompo, Christopher Havenstein, Jade Thiemsuwan
Machine Learning In Support Of Electric Distribution Asset Failure Prediction, Robert D. Flamenbaum, Thomas Pompo, Christopher Havenstein, Jade Thiemsuwan
SMU Data Science Review
In this paper, we present novel approaches to predicting as- set failure in the electric distribution system. Failures in overhead power lines and their associated equipment in particular, pose significant finan- cial and environmental threats to electric utilities. Electric device failure furthermore poses a burden on customers and can pose serious risk to life and livelihood. Working with asset data acquired from an electric utility in Southern California, and incorporating environmental and geospatial data from around the region, we applied a Random Forest methodology to predict which overhead distribution lines are most vulnerable to fail- ure. Our results provide evidence …
Predicting National Basketball Association Success: A Machine Learning Approach, Adarsh Kannan, Brian Kolovich, Brandon Lawrence, Sohail Rafiqi
Predicting National Basketball Association Success: A Machine Learning Approach, Adarsh Kannan, Brian Kolovich, Brandon Lawrence, Sohail Rafiqi
SMU Data Science Review
In this paper, we present a machine learning based approach to projecting the success of National Basketball Association (NBA) draft prospects. With the proliferation of data, analytics have increasingly be- come a critical component in the assessment of professional and collegiate basketball players. We leverage player biometric data, college statistics, draft selection order, and positional breakdown as modelling features in our prediction algorithms. We found that a player's draft pick and their college statistics are the best predictors of their longevity in the National Basketball Association.
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 …
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 …