Data Science Commons^™

Unlocking User Identity: A Study On Mouse Dynamics In Dual Gaming Environments For Continuous Authentication, Marcho Setiawan Handoko

All Graduate Theses, Dissertations, and Other Capstone Projects

With the surge in information management technology reliance and the looming presence of cyber threats, user authentication has become paramount in computer security. Traditional static or one-time authentication has its limitations, prompting the emergence of continuous authentication as a frontline approach for enhanced security. Continuous authentication taps into behavior-based metrics for ongoing user identity validation, predominantly utilizing machine learning techniques to continually model user behaviors. This study elucidates the potential of mouse movement dynamics as a key metric for continuous authentication. By examining mouse movement patterns across two contrasting gaming scenarios - the high-intensity "Team Fortress" and the low-intensity strategic …

Aircraft Damage Classification By Using Machine Learning Methods, Tüzün Tolga İnan

International Journal of Aviation, Aeronautics, and Aerospace

Safety is the most significant factor that affected incidents (non-fatal) and accidents (fatal) in civil aviation history related to scheduled flights. In the history of scheduled flights, the total incident and accident number until 2022 is 1988. In this study, 677 of them are taken into consideration since 11 September 2001. The purpose of this study is to reveal the factors that can classify type of aircraft damages such as none, minor and substantial in all-time incidents and accidents. ML algorithms with different configurations are applied for the classification process. The RFE and PCA are used to find the most …

A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu, Irene Moulitsas

Journal of Aviation/Aerospace Education & Research

This paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) models. Flight delays are a major issue in the airline industry, causing inconvenience to passengers and financial losses to airlines. The BiLSTM and LSTM models, powerful deep learning techniques, have shown promising results in a classification task. In this study, we collected a dataset from the United States (US) Bureau of Transportation Statistics (BTS) of flight on-time performance information and used it to train and test the BiLSTM and LSTM models. We set three criteria for selecting highly important features …

Developing A Data-Driven Statistical Model For Accurately Predicting The Superconducting Critical Temperature Of Materials Using Multiple Regression And Gradient-Boosted Methods, Emil Agbemade

Data Science and Data Mining

This study focuses on developing a statistical model for estimating the superconducting critical temperature (Tc) of materials using a data-driven strategy. The study analyzed 21,263 superconductors and used a combination of multiple regression and gradient-boosted models to make predictions. The analysis included a descriptive analysis of the distribution of Tc, feature selection using the Backwards selection method, and model diagnostics. The results showed that the gradient-boosted method outperformed the multiple linear regression method with an RMSE of 12.01 and an R² value of 88.23 after fine-tuning its hyperparameters. The study concludes that the gradient-boosted method is an effective approach …

A Linear Regression Model To Predict The Critical Temperature Of A Superconductor, Amir Alipour Yengejeh

Data Science and Data Mining

Since the superconductivity has been introduced, almost all studies in this area have been striving to predict the critical temperature ($T_{c}$) through the features extracted from the superconductor's chemical formula. In this study, thus, we are interested in exploring the linear association between $T_{c}$ and the related features.

Health Care Equity Through Intelligent Edge Computing And Augmented Reality/Virtual Reality: A Systematic Review, Vishal Lakshminarayanan, Aswathy Ravikumar, Harini Sriraman, Sujatha Alla, Vijay Kumar Chattu

Engineering Management & Systems Engineering Faculty Publications

Intellectual capital is a scarce resource in the healthcare industry. Making the most of this resource is the first step toward achieving a completely intelligent healthcare system. However, most existing centralized and deep learning-based systems are unable to adapt to the growing volume of global health records and face application issues. To balance the scarcity of healthcare resources, the emerging trend of IoMT (Internet of Medical Things) and edge computing will be very practical and cost-effective. A full examination of the transformational role of intelligent edge computing in the IoMT era to attain health care equity is offered in this …

Attending To The Cultures Of Data Science Work, Lindsay Poirier

Statistical and Data Sciences: Faculty Publications

This essay reflects on the shifting attention to the “social” and the “cultural” in data science communities. While recently the “social” and the “cultural” have been prioritized in data science discourse, social and cultural concerns that get raised in data science are almost always outwardly focused – applying to the communities that data scientists seek to support more so than more computationally-focused data science communities. I argue that data science communities have a responsibility to attend not only to the cultures that orient the work of domain communities, but also to the cultures that orient their own work. I describe …

Making Data-Driven Decisions For Investing In Restaurant Business: A Case Study Based On Zomato Dataset, Rachna Shah

All Graduate Theses, Dissertations, and Other Capstone Projects

In today’s fast-paced world, where time is a precious commodity, the ability to order a wide array of cuisines from the comfort of your home or office impacts your quality of life. With an increasing number of food delivery services, with just a few taps on the smartphone or clicks on the computer, we can enjoy the food we want. The importance of this convenience cannot be overstated, as it allows people to save time and effort that would otherwise be spent on cooking, grocery shopping, or dining out. As the food delivery system grows and develops, its economic framework …

Campus Safety Data Gathering, Classification, And Ranking Based On Clery-Act Reports, Walaa F. Abo Elenin

College of Graduate Studies: Theses & Dissertations

Most existing campus safety rankings are based on criminal incident history with minimal or no consideration of campus security conditions and standard safety measures. Campus safety information published by universities/colleges is usually conceptual/qualitative and not quantitative and are based-on criminal records of these campuses. Thus, no explicit and trusted ranking method for these campuses considers the level of compliance with the standard safety measures. A quantitative safety measure is important to compare different campuses easily and to learn about specific campus safety conditions.

In this thesis, we utilize Clery-Act reports of campuses to automatically analyze their safety conditions and generate …

Application Of Big Data Technology, Text Classification, And Azure Machine Learning For Financial Risk Management Using Data Science Methodology, Oluwaseyi A. Ijogun

College of Graduate Studies: Theses & Dissertations

Data science plays a crucial role in enabling organizations to optimize data-driven opportunities within financial risk management. It involves identifying, assessing, and mitigating risks, ultimately safeguarding investments, reducing uncertainty, ensuring regulatory compliance, enhancing decision-making, and fostering long-term sustainability. This thesis explores three facets of Data Science projects: enhancing customer understanding, fraud prevention, and predictive analysis, with the goal of improving existing tools and enabling more informed decision-making. The first project examined leveraged big data technologies, such as Hadoop and Spark, to enhance financial risk management by accurately predicting loan defaulters and their repayment likelihood. In the second project, we investigated …

Readiness For Transfer: A Mixed-Methods Study On Icu Transfers Of Care, Soo-Hoon Lee, Clarice Wee, Phillip Phan, Yanika Kowitlawakul, Chee-Kiat Tan, Amartya Mukhopadhyay

Management Faculty Publications

Objective Past studies on intensive care unit (ICU) patient transfers compare the efficacy of using standardised checklists against unstructured communications. Less studied are the experiences of clinicians in enacting bidirectional (send/receive) transfers. This study reports on the differences in protocols and data elements between receiving and sending transfers in the ICU, and the elements constituting readiness for transfer.

Methods Mixed-methods study of a 574-bed general hospital in Singapore with a 74-bed ICU for surgical and medical patients. Six focus group discussions (FGDs) with 34 clinicians comprising 15 residents and 19 nurses, followed by a structured questionnaire survey of 140 clinicians …

A Geospatial Analysis Of Crime Hotspots, Campus Safety Measures, And The Campus Community’S Perceived Safety At Murray State University, Rachel Stuckey

Murray State Theses and Dissertations

Murray State University (Murray State) is in far Western Kentucky. Murray State prides itself on being a safe campus for prospective students. In this study spatial analysis was used to analyze the relationship among crime hotspots, campus safety measures, and students’ perception of safety on campus the campus of Murray State. A survey was distributed to students to determine the areas of the campus where students feel safe and unsafe. Geographical Information Systems (GIS) were used to determine the location of crime hotspots over eight years (2014-2022) to measure the spatial relationship between logged crime hotspots and perceived safe and …

Fitting Time Series Models To Fisheries Data To Ascertain Age, Kathleen S. Kirch, Norou Diawara, Cynthia M. Jones

OES Faculty Publications

The ability of government agencies to assign accurate ages of fish is important to fisheries management. Accurate ageing allows for most reliable age-based models to be used to support sustainability and maximize economic benefit. Assigning age relies on validating putative annual marks by evaluating accretional material laid down in patterns in fish ear bones, typically by marginal increment analysis. These patterns often take the shape of a sawtooth wave with an abrupt drop in accretion yearly to form an annual band and are typically validated qualitatively. Researchers have shown key interest in modeling marginal increments to verify the marks do, …

Tenvr: Matlab-Based Toolbox For Environmental Research, Aleksandar I. Goranov, Rachel L. Sleighter, Dobromir A. Yordanov, Patrick G. Hatcher

Chemistry & Biochemistry Faculty Publications

With the advancements in science and technology, datasets become larger and more multivariate, which warrants the need for programming tools for fast data processing and multivariate statistical analysis. Here, the MATLAB-based Toolbox for Environmental Research "TEnvR" (pronounced "ten-ver") is introduced. This novel toolbox includes 44 open-source codes for automated data analysis from a multitude of techniques, such as ultraviolet-visible, fluorescence, and nuclear magnetic resonance spectroscopies, as well as from ultrahigh resolution mass spectrometry. Provided are codes for processing data (e.g., spectral corrections, formula assignment), visualization of figures, calculation of metrics, multivariate statistics, and automated work-up of large datasets. TEnvR allows …

Classification Of Darknet Traffic By Application Type, Shruti Sharma

Master's Projects

The darknet is frequently exploited for illegal purposes and activities, which makes darknet traffic detection an important security topic. Previous research has focused on various classification techniques for darknet traffic using machine learning and deep learning. We extend previous work by considering the effectiveness of a wide range of machine learning and deep learning technique for the classification of darknet traffic by application type. We consider the CICDarknet2020 dataset, which has been used in many previous studies, thus enabling a direct comparison of our results to previous work. We find that XGBoost performs the best among the classifiers that we …

An Incentivization Mechanism Towards Network-Slice As A Service, Yosha Mundhra

Master's Projects

Next-generation communication networks are expected to support many different types of services. Network slicing virtually divides one physical network into several virtual networks in order to accommodate diverse demands for network connections. Assuming multiple network domains are stitched together to provide end-to-end network slices for such diverse services, it is critical to consider the economic behaviors and revenue models of the stakeholders. This project analyzes the economic behaviors from the perspective of a Global Slice Coordinator (GSC) that receives a slice request and coordinates the end-to-end resource allocations across multiple network domains. The primary role of the GSC is to …

Assessing Spurious Correlations In Big Search Data, Jesse T. Richman, Ryan J. Roberts

Political Science & Geography Faculty Publications

Big search data offers the opportunity to identify new and potentially real-time measures and predictors of important political, geographic, social, cultural, economic, and epidemiological phenomena, measures that might serve an important role as leading indicators in forecasts and nowcasts. However, it also presents vast new risks that scientists or the public will identify meaningless and totally spurious ‘relationships’ between variables. This study is the first to quantify that risk in the context of search data. We find that spurious correlations arise at exceptionally high frequencies among probability distributions examined for random variables based upon gamma (1, 1) and Gaussian random …

Thinking Local With Original Data In Ai And Machine Learning Research, David G. Taylor, Robert Mccloud

WCBT Working Papers

Sacred Heart University spent significant funds to establish an AI lab. Initially there is no ongoing research and no real plan for a research agenda. This paper details how the Jack Welch College of Business and Technology created and implemented an active meaningful research plan. It involves two key elements: thinking local and using business connections to foster active, impactful research. Surrounding communities, business connections, area environment, and other Sacred Heart University departments all played a part. The research plan also identifies a specific issue in working with local and business contact sources: the AI researcher almost never gets data …

Utilizing Machine Learning In Healthcare In An Ethical Fashion, Nishka Ayyar

CMC Senior Theses

This thesis paper explores the ethical considerations surrounding the use of machine learning (ML) solutions in healthcare. The background section discusses the basics of machine learning techniques and algorithms, and the increasing interest in their utilization in the healthcare sector. The paper then reviews and critically analyzes four studies that highlight concerns related to using ML in healthcare, including issues of bias, privacy, accountability, and transparency. Based on the analysis of these studies, the paper presents several recommendations for addressing these concerns. The paper concludes with a discussion on the potential benefits of using machine learning technology in healthcare. Ultimately, …

The Daily Patterns Of Emergency Medical Events, Mary Elizabeth Helander, Margaret K. Formica, Dessa K. Bergen-Cico

Social Science - All Scholarship

This study examines population level daily patterns of time-stamped emergency medical service (EMS) dispatches to establish their situational predictability. Using visualization, sinusoidal regression, and statistical tests to compare empirical cumulative distributions, we analyzed 311,848,450 emergency medical call records from the U.S. National Emergency Medical Services Information System (NEMSIS) for years 2010 through 2022. The analysis revealed a robust daily pattern in the hourly distribution of distress calls across 33 major categories of medical emergency dispatch types. Sinusoidal regression coefficients for all types were statistically significant, mostly at the p < 0.0001 level. The coefficient of determination ($R^2$) ranged from 0.84 and 0.99 for all models, with most falling in the 0.94 to 0.99 range. The common sinusoidal pattern, peaking in mid-afternoon, demonstrates that all major categories of medical emergency dispatch types appear to be influenced by an underlying daily rhythm that is aligned with daylight hours and common sleep/wake cycles. A comparison of results with previous landmark studies revealed new and contrasting EMS patterns for several long-established peak occurrence hours--specifically for chest pain, heart problems, stroke, convulsions and seizures, and sudden cardiac arrest/death. Upon closer examination, we also found that heart attacks, diagnosed by paramedics in the field via 12-lead cardiac monitoring, followed the identified common daily pattern of a mid-afternoon peak, departing from prior generally accepted morning tendencies. Extended analysis revealed that the normative pattern prevailed across the NEMSIS data when re-organized to consider monthly, seasonal, daylight-savings vs civil time, and pre-/post- COVID-19 periods. The predictable daily EMS patterns provide impetus for more research that links daily variation with causal risk and protective factors. Our methods are straightforward and presented with detail to provide accessible and replicable implementation for researchers and practitioners.