Data Science Commons^™

Using Case-Level Context To Classify Cancer Pathology Reports, Shang Gao, Mohammed Alawad, Noah Schaefferkoetter, Lynne Penberthy, Xiao-Cheng Wu, Eric B. Durbin, Linda Coyle, Arvind Ramanathan, Georgia Tourassi

Kentucky Cancer Registry Faculty Publications

Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence-for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We …

An Application Of Machine Learning To Explore Relationships Between Factors Of Organisational Silence And Culture, With Specific Focus On Predicting Silence Behaviours, Stephen Barrett Dr

Articles

Research indicates that there are many individual reasons why people do not speak up when confronted with situations that may concern them within their working environment. One of the areas that requires more focused research is the role culture plays in why a person may remain silent when such situations arise. The purpose of this study is to use data science techniques to explore the patterns in a data set that would lead a person to engage in organisational silence. The main research question the thesis asks is: Is Machine Learning a tool that Social Scientists can use with respect …

Sensor Data Analysis In Smart Buildings, Manuel A. Mane Penton

Publications and Research

Data analysis and Machine Learning are destined to evolve the current technology infrastructure by solving technology and economy demands present mainly in developed cities like New York. This research proposes a machine learning (ML) based solution to alleviate one of the main issues that big buildings such as CUNY campuses have, that is the waste of energy resources. The analysis of data coming from the readings of different deployed sensors such as CO2, humidity and temperature can be used to estimate occupancy in a specific room and building in general. The outcome of this research established a relationship between the …

Using Data Mining To Identify The Most Influential Factors In Training Results, Xiaoqing Wu, Daanial Ahmad

Publications and Research

Data Science is used as a tool to find hidden facts in the data. We want to find out what factors such as ‘AGE’, ‘TAX’, ‘PUPIL-TEACHER RATIO’, ‘PER-CAPITA INCOME’ contribute the most to housing prices. To answer this question, we studied the dataset of “Boston Houses Prices”. By applying the Lasso Regression (a Data Mining Technique) on the data set of “Boston Houses Prices” we identified the influential factors in the linear model. As a conclusion we found that there were six inputs which contributed the most to the prices of houses and those inputs are as follow: (i) CRIM-per …

Development Of Fully Balanced Ssfp And Computer Vision Applications For Mri-Assisted Radiosurgery (Mars), Jeremiah Sanders

Dissertations and Theses (Open Access)

Prostate cancer is the second most common cancer in men and the second-leading cause of cancer death in men. Brachytherapy is a highly effective treatment option for prostate cancer, and is the most cost-effective initial treatment among all other therapeutic options for low to intermediate risk patients of prostate cancer. In low-dose-rate (LDR) brachytherapy, verifying the location of the radioactive seeds within the prostate and in relation to critical normal structures after seed implantation is essential to ensuring positive treatment outcomes.

One current gap in knowledge is how to simultaneously image the prostate, surrounding anatomy, and radioactive seeds within the …

Cornac: A Comparative Framework For Multimodal Recommender Systems, Aghiles Salah, Quoc Tuan Truong, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Cornac is an open-source Python framework for multimodal recommender systems. In addition to core utilities for accessing, building, evaluating, and comparing recommender models, Cornac is distinctive in putting emphasis on recommendation models that leverage auxiliary information in the form of a social network, item textual descriptions, product images, etc. Such multimodal auxiliary data supplement user-item interactions (e.g., ratings, clicks), which tend to be sparse in practice. To facilitate broad adoption and community contribution, Cornac is publicly available at https://github.com/PreferredAI/cornac, and it can be installed via Anaconda or the Python Package Index (pip). Not only is it well-covered by unit tests …

Philosophical Perspectives, Jochen Albrecht

Publications and Research

This entry follows in the footsteps of Anselin’s famous 1989 NCGIA working paper entitled “What is special about spatial?” (a report that is very timely again in an age when non-spatial data scientists are ignorant of the special characteristics of spatial data), where he outlines three unrelated but fundamental characteristics of spatial data. In a similar vein, I am going to discuss some philosophical perspectives that are internally unrelated to each other and could warrant individual entries in this Body of Knowledge. The first one is the notions of space and time and how they have evolved in …

Finding Trends In Big City Health Issues With Data Visualization, Shridhar Kulkarni

Dissertations and Theses

In recent years, data visualization has become one of the most effective tools to understand and identify unseen features of the large datasets available. An open source data set available for health issues for big cities across the United States was obtained. There are numerous indicators presented in the dataset including Demographics, Chronic Health Diseases, Social and Economic Factors, Food Safety, Mortality Rates, Cancer and Life Expectancy Rates. The dataset encompassed myriad of demographics as well as specific data for a number of US cities. The data was explored in different methods in Data points in terms of the demographic …

Storage Management Strategy In Mobile Phones For Photo Crowdsensing, En Wang, Zhengdao Qu, Xinyao Liang, Xiangyu Meng, Yongjian Yang, Dawei Li, Weibin Meng

Department of Computer Science Faculty Scholarship and Creative Works

In mobile crowdsensing, some users jointly finish a sensing task through the sensors equipped in their intelligent terminals. In particular, the photo crowdsensing based on Mobile Edge Computing (MEC) collects pictures for some specific targets or events and uploads them to nearby edge servers, which leads to richer data content and more efficient data storage compared with the common mobile crowdsensing; hence, it has attracted an important amount of attention recently. However, the mobile users prefer uploading the photos through Wifi APs (PoIs) rather than cellular networks. Therefore, photos stored in mobile phones are exchanged among users, in order to …

Subsurface Analytics: Contribution Of Artificial Intelligence And Machine Learning To Reservoir Engineering, Reservoir Modeling, And Reservoir Management, Shahab D. Mohaghegh

Faculty & Staff Scholarship

Subsurface Analytics is a new technology that changes the way reservoir simulation and modeling is performed. Instead of starting with the construction of mathematical equations to model the physics of the fluid flow through porous media and then modification of the geological models in order to achieve history match, Subsurface Analytics that is a completely AI-based reservoir simulation and modeling technology takes a completely different approach. In AI-based reservoir modeling, field measurements form the foundation of the reservoir model. Using data-driven, pattern recognition technologies; the physics of the fluid flow through porous media is modeled through discovering the best, most …

Feature Extraction And Analysis Of Binaries For Classification, Micah Flack

Annual Research Symposium

The research project, Feature Extraction and, Analysis of Binaries for Classification, provides an in-depth examination of the features shared by unlabeled binary samples, for classification into the categories of benign or malicious software using several different methods. Because of the time it takes to manually analyze or reverse engineer binaries to determine their function, the ability to gather features and then instantly classify samples without explicitly programming the solution is incredibly valuable. It is possible to use an online service; however, this is not always viable depending on the sensitivity of the binary. With Python3 and the Pefile library, we …

A Data Scientist Looks At Covid-19 Part I: Local And National Statistics, Anthony Breitzman

College of Science & Mathematics Departmental Research

No abstract provided.

Data Science Meets Compliance, Christian Clarke

Petersheim Academic Exposition

No abstract provided.

Design Principles Influencing Secondary School Counselors' Satisfaction Of A Decision-Support System, Kodey S. Crandall

Masters Theses & Doctoral Dissertations

In the current era of accountability, secondary school counselors are expected to use data to drive program decision-making, identify and implement evidence-based interventions to create systemic change, and utilize emerging technology. Research shows it is difficult for school counselors to meet any of these expectations. A decision support system (DSS) is a technology that takes minimal effort to learn and can assist in decision-making processes. This design science research builds and evaluates an IT artifact, a decision-support system, in an attempt to solve the problems facing school counselors. To develop this system, four design principles (system usefulness, interface quality, information …

Digital Forensic Readiness: An Examination Of Law Enforcement Agencies In The State Of Maryland, James B. Mcnicholas Iii

Masters Theses & Doctoral Dissertations

Digital forensic readiness within the law enforcement community, especially at the local level, has gone mostly unexplored. As a result, a current lack of data exists that examines the digital forensic readiness of individual agencies, the possibility of proximity relationships, and correlations between readiness and backlogs. This quantitative, crosssectional research study sought to explore these issues by focusing on the state of Maryland. The study resulted in the creation of a digital forensic readiness scoring model that was then used to assign digital forensic readiness scores to thirty (30) of the one-hundred-forty-one (141) law enforcement agencies throughout Maryland. It was …

The Effectiveness Of Transfer Learning Systems On Medical Images, James Boit

Masters Theses & Doctoral Dissertations

Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. Current deep learning (DL) algorithms, specifically convolutional neural networks are increasingly becoming the methodological choice for most medical image analysis. However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned …

The Influence Of Peer And Parental Norms On First-Generation College Students’ Binge Drinking Trajectories, Graham T. Diguiseppi, Jordan P. Davis, Matthew K. Meisel, Melissa A. Clark, Mya L. Roberson, Miles Q. Ott, Nancy P. Barnett

Statistical and Data Sciences: Faculty Publications

Introduction: First-generation college students are those whose parents have not completed a four-year college degree. The current study addressed the lack of research on first-generation college students’ alcohol use by comparing the binge drinking trajectories of first-generation and continuing-generation students over their first three semesters. The dynamic influence of peer and parental social norms on students’ binge drinking frequencies were also examined. Methods: 1342 college students (n = 225 first-generation) at one private University completed online surveys. Group differences were examined at Time 1, and latent growth-curve models tested the association between first-generation status and social norms (peer descriptive, peer …

Surviving Under The Reign Of El Niño Southern Oscillation: An Analysis Of The Effects Of Extreme El Niño Events On The Oceanographic And Biological Environment Of The Galápagos Islands, Ava Mcilvaine

Independent Study Project (ISP) Collection

El Niño Southern Oscillation (ENSO) is commonly known as the atmospheric and oceanographic powerhouse of the Southern Pacific Ocean. From phytoplankton to apex predators, ENSO controls the stability of species’ populations within this biodiverse ocean environment. El Niño’s 9-15 month alteration of the heat storage in the tropical Pacific drastically shifts the temperature, nutrient, and circulation gradient its marine life is accustomed to. A single degree change in the ocean’s surface layer temperature can have large consequences for marine species, and El Niño is commonly associated with Sea Surface Temperature Anomalies (SSTAs) between 2°- 6° Celsius. The potential danger El …

An Apex Predator In Peril In The Western Lowlands Of Ecuador: Mapping The Population Distribution Of Harpy Eagles (Harpia Harpyja) In A Highly Deforested Region, Samuel Zhang

Independent Study Project (ISP) Collection

The Harpy Eagle (Harpia harpyja) is a highly threatened bird of prey in Ecuador. While they are already elusive in the Ecuadorian Amazon, they are even lesser known in the coastal lowlands, and their existence is threatened by rapid deforestation. This study mapped their potential distribution by examining satellite images to find intact humid forest, their ideal habitat. Habitat areas were quantified using ImageJ. The only sites found to be adequate for sustaining Harpy Eagle populations were the primary forests in the vicinities of Reserva Ecológica Mache Chindul and Reserva Ecológica Cotacachi Cayapas. The two reserves are expected to be …

A Semi-Automatic Integrated Framework For Non-English Sentiment Lexicons, Abdullah Kaity Mohammed Salem

Student Works (2020-2029)

There has been significant growth in social media networks in the last few years. Posting opinions and messages on social networking websites has become a popular activity on the Internet. The data sources are necessary for business intelligence and market analytics, as human opinions form a major indicator of human desires and behaviour. This has resulted in the development of a new study field called sentiment analysis. This includes the analysis, evaluation and interpretation of the opinions with the help of text mining and Natural Language Processing (NLP) processes, for identifying the text polarity, as positive, neutral or negative. It …

High Performance And Machine Learning Algorithms For Brain Fmri Data, Taban Eslami

Dissertations

Brain disorders are very difficult to diagnose for reasons such as overlapping nature of symptoms, individual differences in brain structure, lack of medical tests and unknown causes of some disorders. The current psychiatric diagnostic process is based on behavioral observation and may be prone to misdiagnosis.

Noninvasive brain imaging technologies such as Magnetic Resonance Imaging (MRI) and functional Magnetic Resonance Imaging (fMRI) make the process of understanding the structure and function of the brain easier. Quantitative analysis of brain imaging data using machine learning and data mining techniques can be advantageous not only to increase the accuracy of brain disorder …

On The Robustness Of Cascade Diffusion Under Node Attacks, Alvis Logins, Yuchen Li, Panagiotis Karras

Research Collection School Of Computing and Information Systems

How can we assess a network's ability to maintain its functionality under attacks? Network robustness has been studied extensively in the case of deterministic networks. However, applications such as online information diffusion and the behavior of networked public raise a question of robustness in probabilistic networks. We propose three novel robustness measures for networks hosting a diffusion under the Independent Cascade (IC) model, susceptible to node attacks. The outcome of such a process depends on the selection of its initiators, or seeds, by the seeder, as well as on two factors outside the seeder's discretion: the attack strategy and the …

Two (Or More) Viruses In One Bat: A Systematic Quantitative Literature Review Of Viral Coinfection In Bats, Eli J. Kaufman

Independent Study Project (ISP) Collection

Viral coinfection is an important topic in pathogen dynamics, and can increase viral shedding and change disease outcomes. As bats are carriers of important zoonoses, such as the SARS coronaviruses, rabies, and other deadly viruses, knowing more about their coinfection dynamics is important. This quantitative systematic literature review sought to show how many papers reported bat viral coinfections, and created three databases. The first database, the SQLR database was based on searches for coinfections. The second database, the Astrovirus database was to determine how much of the literature was being missed by examining a single viral family more in depth …

Neural Network Pruning For Ecg Arrhythmia Classification, Isaac E. Labarge

Master's Theses

Convolutional Neural Networks (CNNs) are a widely accepted means of solving complex classification and detection problems in imaging and speech. However, problem complexity often leads to considerable increases in computation and parameter storage costs. Many successful attempts have been made in effectively reducing these overheads by pruning and compressing large CNNs with only a slight decline in model accuracy. In this study, two pruning methods are implemented and compared on the CIFAR-10 database and an ECG arrhythmia classification task. Each pruning method employs a pruning phase interleaved with a finetuning phase. It is shown that when performing the scale-factor pruning …

Designing A Smart Internet Of Things Solution For Point Of Use Water Filtration Management System In Residential, Commercial And Public Settings, Tristan Lim, Hwee-Pink Tan, Chin Sin Ong, Rahul Belani, Siddhant S. K. Agrawal

Research Collection School Of Computing and Information Systems

The use of water filtration Point-of-Use (POU) systems are extensive, ranging from water dispensers in public estates, to household POU water systems. Manufacturers typically recommend filtration cartridges to be changed (i) after their useful life, or (ii) when the water flow volume have exceeded certain capacity, whichever is earlier. However, filtration mechanisms are typically not changed with sufficient regularity. Overused filters can result in negative health effects, over and above the deterioration and loss of filtration benefits of the POU water system. Presently most existing water purification systems do not have smart connected Internet of Things (IoT) means of informing …

Predictive Task Assignment In Spatial Crowdsourcing: A Data-Driven Approach, Yan Zhao, Kai Zheng, Yue Cui, Han Su, Feida Zhu, Xiaofang Zhou

Research Collection School Of Computing and Information Systems

With the rapid development of mobile networks and the widespread usage of mobile devices, spatial crowdsourcing, which refers to assigning location-based tasks to moving workers, has drawn increasing attention. One of the major issues in spatial crowdsourcing is task assignment, which allocates tasks to appropriate workers. However, existing works generally assume the static offline scenarios, where the spatio-temporal information of all the workers and tasks is determined and known a priori. Ignorance of the dynamic spatio-temporal distributions of workers and tasks can often lead to poor assignment results. In this work we study a novel spatial crowdsourcing problem, namely Predictive …

Efficient Model-Data Integration For Flexible Modeling, Parameter Analysis & Visualization, And Data Management, Angela Gregory, Chao Chen, Rui Wi, Sarah Miller, Sajjad Ahmad, John W. Anderson, Hays Berrett, Karl Benedict, Dan Cadol, Sergiu M. Dascalu, Donna Delparte, Lynn Fenstermaker, Sarah Godsey, Frederick C. Harris Jr., James P. Mcnamara, Scott W. Tyler, John Savickas, Luke Sheneman, Mark Stone, Matthew A. Turner

Civil and Environmental Engineering and Construction Faculty Research

Due to the complexity and heterogeneity inherent to the hydrologic cycle, the modeling of physical water processes has historically and inevitably been characterized by a broad spectrum of disciplines including data management, visualization, and statistical analyses. This is further complicated by the sub-disciplines within the water science community, where specific aspects of water processes are modeled independently with simplification and model boundary integration receiving little attention. This can hinder current and future research efforts to understand, explore, and advance water science. We developed the Virtual Watershed Platform to improve understanding of hydrologic processes and more generally streamline model-data integration and …

The Analytics Managers Ultimate Guide For Working With Universities, Robert J. Mcgrath

Faculty Publications

The challenges organizations are having related to finding (and retaining) deep analytical talent did not materialize out of thin air…or overnight. Analytics and Data science – and the role of the analytics professional – has evolved over the last several decades and has been fueled by our ability to capture and process increasingly larger and more complex variations of data and our desire to gain increasingly granular insights to fuel innovation and creativity. While many organizations recognize that a partnership with a university can be a resource to many of these challenges, the best way to start a conversation with …

Development Of An International Data Repository And Research Resource: The Prospective Studies Of Acute Child Trauma And Recovery (Pact/R) Data Archive, Nancy Kassam-Adams, Justin A. Kenardy, Douglas L. Delahanty, Meghan L. Marsac, Richard Meiser-Stedman, Reginald D. V. Nixon, Markus A. Landolt, Patrick A. Palmieri

Pediatrics Faculty Publications

Background: Studies that identify children after acute trauma and prospectively track risk/protective factors and trauma responses over time are resource-intensive; small sample sizes often limit power and generalizability. The Prospective studies of Acute Child Trauma and Recovery (PACT/R) Data Archive was created to facilitate more robust integrative cross-study data analyses.

Objectives: To (a) describe creation of this research resource, including harmonization of key variables; (b) describe key study- and participant-level variables; and (c) examine retention to follow-up across studies.

Methods: For the first 30 studies in the Archive, we described study-level (design factors, retention rates) and participant-level …

Identification And Description Of Potentially Influential Social Network Members Using The Strategic Player Approach, Miles Q. Ott, Sara G. Balestrieri, Graham Diguiseppi, Melissa A. Clark, Michael Bernstein, Sarah Helseth, Nancy P. Barnett

Statistical and Data Sciences: Faculty Publications

Background: Diffusion of innovations theory posits that ideas and behaviors can be spread through social network ties. In intervention work, intervening upon certain network members may lead to intervention effects “diffusing” into the network to affect the behavior of network members who did not receive the intervention. The strategic players (SP) method, an extension of Borgatti’s Key Players approach, is used to balance the (sometimes) opposing goals of spreading the intervention to as many members of the target group as possible, while preventing the spread of the intervention to others. Objectives: We sought to test whether members of the SP …