Data Science Commons^™

A Machine Learning Approach For Survival Analysis Of Transplanted Kidneys Based On Donors’ And Recipients’ Factors., Alain Edward Despeignes

Theses and Dissertations

Over seven thousand people on average die each year in the United States waiting for an organ transplant due to the shortage of donated organs. With this alarming concern, efforts from the health organizations like the United Network Organ Sharing (UNOS) and government officials have considered avenues to remedy this distress, one of which is to investigate the characteristics among donors and recipients that affects the longevity of donated organs. The goal of this project is to investigate the survival time of transplanted kidneys from 1987 to 2018 with regards to the donors’ and the recipients’ characteristics including gender, ethnicity, …

Anomaly Detection Using Unsupervised Machine Learning Algorithms: A Simulation Study, Edmund F. Agyemang

School of Mathematical & Statistical Sciences Faculty Publications

This study presents a comprehensive evaluation of five prominent unsupervised machine learning anomaly detection algorithms: One-Class Support Vector Machine (One-Class SVM), One-Class SVM with Stochastic Gradient Descent (SGD), Isolation Forest (iForest), Local Outlier Factor (LOF), and Robust Covariance (Elliptic Envelope). Through systematic analysis on a synthetically simulated dataset, the study assessed each algorithm’s predictive performance using accuracy, precision, recall, and F1 score specifically for outlier detection. The evaluation reveals that One-Class SVM, Isolation Forest, and Robust Covariance are more effective in identifying outliers in the synthetic simulated dataset, with Isolation Forest slightly outperforming the other algorithms in terms of balancing …

De-Identification Is Not Enough: A Comparison Between De-Identified And Synthetic Clinical Notes, Atiquer Rahman Sarkar, Yao-Shun Chuang, Noman Mohammed, Xiaoqian Jiang

Faculty, Staff and Student Publications

For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alternative. Recent successes with numerical and tabular data generative models and the breakthroughs in large generative language models raise the question of whether synthetically generated clinical notes could be a viable alternative to real notes for research purposes. In this work, we demonstrated that (i) de-identification of real clinical notes does not protect records against a membership inference attack, (ii) proposed a novel approach to generate synthetic clinical notes using the current state-of-the-art large language models, (iii) evaluated …

Digital Humanities: Using Computational Methods On Literature To Understand Human-Water Relations, Ariel Yang

Cybersecurity Undergraduate Research Showcase

By using computational techniques to analyze literature, deeper insights can be gained into human-water relationships across different historical and cultural contexts. Natural Language Processing (NLP) and other data science methods can explore applications of traditional ecological knowledge (TEK) and underlying emotions or beliefs in literature to help understand sustainability. Protecting this sensitive cultural data through ethical applications can further secure future implementations of policies, urban planning, and environmental relationships.

Holistic Correlation Measure For Enhanced Encapsulation Of Trait Heterogeneity And Discovery Of Co-Expression, Zachary Valleroy

Theses

Large-scale, high-dimensional data analyses can be computationally prohibitive due to combinatorial explosion of the search space for finding complex patterns; a viable alternative is network modeling for abstraction and quantifying intrinsic data associations. Prominent network analysis methods furnish frameworks for model synthesis and validation but rely on standard correlation measures impaired by semi-supervised biases, latent heterogeneity, and uneven discretization techniques. Here we investigate a holistic measure for encapsulating data heterogeneity for enhanced efficacy of revealing complex patterns through network analysis. Our unique correlation metric, K-medoids Utility for Duo Original Similarities (Kudos), exhaustively factors real-valued analyte data to compute …

Dynamic Knowledge Elicitation: Leveraging Student Feedback For Improved Language Model Distillation, Reuven Muller

Master's Theses

Large Language Models (LLMs) have significantly advanced the field of natural language processing but remain resource-intensive and impractical for many organizations. Specialist models offer a viable alternative, often developed through Knowledge Distillation (KD) techniques. However, traditional KD methods rely on predefined static datasets to elicit knowledge from the teacher model, failing to dynamically address the weaknesses of the student model during training. This research introduces two novel methods for adaptive knowledge elicitation: Feedback-Driven Question Generation and Agent-Based Targeted Question Generation. These methods iteratively expand the training dataset based on the student model’s performance, leveraging a teacher model to generate targeted …

Benefits And Challenges Of Constructing Low-Altitude Air Route Network Infrastructure For Developing Low-Altitude Economy, Xiaohan Liao, Chenchen Xu, Huping Ye

Bulletin of Chinese Academy of Sciences (Chinese Version)

Low-altitude airspace is a resource that needs to be fully explored, and the low-altitude economy is a new type of economic activity resulted from low-altitude airspace exploration and utilization. As the primary players of low-altitude flight activities, the commercialization and wide applications of unmanned aerial vehicles (UAVs) are promoting the prosperity of the low-altitude economy. A low-altitude air route network is an effective means to ensure the safe and efficient operations of a large number of UAVs, and it is also a new key infrastructure hosting low-altitude traffic. The history shows that the investment on public transport infrastructure as a …

Prospect And Problem Analysis Of Industry Data Application In Livestock And Poultry Breeding, Yiran Chen, Zhuqing Xiong, Jiaogen Zhou, Quan Wang, Jiancheng Shu, Yinfa Yan, Lanlin Yang, Zemeng Feng, Benhai Xiong, Yulong Yin

Bulletin of Chinese Academy of Sciences (Chinese Version)

Livestock and poultry breeding is a pillar industry in China. The massive data in livestock and poultry breeding is a valuable resource. The market-oriented utilization of livestock and poultry breeding data plays an important role in improving industry standards, increasing industry profits, and driving the development of the entire industry chain. Currently, based on the demand for marketization of livestock and poultry breeding data, the application of new generation information technologies such as artificial intelligence and the Internet of Things in the process of livestock and poultry breeding to collect breeding process data, after de-sensitization and de-classification, through cloud computing, …

Promote Deep Integration Of Real Economy And Digital Economy, Qinmin Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Deep integration of digital technology, industrial development, and data resources empowers development of the digital economy, constantly opens up new tracks, creates new momentum, and establishes new advantages, becoming a new powerful engine for global economic and social development. China attaches great importance to the high-quality development of the digital economy. This study discusses the practical experience of vigorously promoting digital industrialization and industrial digitization, promoting the deep integration of digital technology and the real economy, and accelerating the construction of a network power and digital China. It also proposes countermeasures and suggestions to further promote the efficient empowerment of …

Reflections On Strengthening Digital Public Goods Institutions To Promote Construction Of Open Source Innovation Ecosystem, Chao Zhang, Ze Feng, Kaihua Chen, Qigang Zhu

Bulletin of Chinese Academy of Sciences (Chinese Version)

The digital public goods institutions are important foundations for promoting the development of an open source innovation ecosystem, which helps to break the key limitations faced by China’s open source innovation ecosystem, such as the lack of unity in technology routes and concentration of R&D power. Based on discussing the significance of improving digital public goods institutions, this study reviews the experience of major countries in the world in building digital public goods institutions and analyzes the prominent problems faced by the construction of China’s digital public goods institutions. This study points out that China’s open source innovation ecosystem faces …

Status Quo Of Large-Scale Models, Risks And Challenges, And Recommended Countermeasures, Le Cheng, Yang Xiao

Bulletin of Chinese Academy of Sciences (Chinese Version)

Large-scale models (large models) are not only central to technological innovation, but also deeply entwined with national security, economic transformation, and social governance. This study examines the status quo of large-model development, identifies the key risks and challenges, and proposes response strategies, aiming to provide theoretical and policy insights for China’s navigations in global artificial intelligence (AI) competition and advances technological innovation. The research indicates that competition in the large-model market is fierce, while the industry is gradually consolidating. Competition in large models between China and the United States has escalated into a form of geopolitical contest. From a technical …

Construction Of Data Factor Circulation Law System In The United States And Its Reflection For China, Zihan Lin, Youmei Ma, Feng Guo

Bulletin of Chinese Academy of Sciences (Chinese Version)

As a pioneer country in the global digital economy, the United States has established a relatively complete system for the circulation of data elements. The focus is on government data openness, with the United States forming a legislative system for data openness at both federal and state levels to ensure data freedom and openness. In the field of personal data trading, the United States has established a relatively relaxed legal environment and data brokers to promote data circulation and trading. At present, China is actively promoting the market-oriented allocation reform of data elements, building a multi-level data element market, and …

Video Label Refinement And Temporal Localization Using Motion Signal Patterns, Jennifer Piane

College of Computing and Digital Media Dissertations

Performing video analysis for activity recognition presents challenges beyond classification, including obtaining class labels and performing temporal localization. One such challenge is precisely labeling a video with class labels having the exact start and end frames of an activity - a difficult task for a human to perform. Moreover, the task of annotating a video at any level of precision can quickly become tedious, impacting the attentiveness of the annotator and resulting in class label errors. Temporally localizing an activity within a video presents a second challenge. This dissertation investigates novel signal and image processing methods for motion features extracted …

Assessing Economic Losses With Covid-19 Integrated Models: A Retrospective Analysis, Timothy Robin Teng, Elvira De Lara-Tuprio, Joselito T. Sescon, Cymon Kayle Lubangco, Rolly Czar Joseph T. Castillo, Mark Anthony C. Tolentino, Maria Regina Justina E. Estuar, Lenard Paulo V. Tamayo, Christian E. Pulmano

Mathematics Faculty Publications

The COVID-19 pandemic led to a global crisis that forced governments to implement restrictive measures to control the spread of the disease. Although these restrictions, such as community quarantines, played a pivotal role in stabilizing healthcare systems, they also caused forced closures of various economic sectors that resulted in huge societal costs and severely impacted the marginalized. In order to understand and quantify the economic impact of the COVID-19 pandemic in the Philippines, an integrated modeling approach, which combined an epidemiological compartmental model and an economic model, was utilized. The evolving nature of COVID-19 required continuous updating of the integrated …

Seshaiyer: Data-Driven Machine Learning Framework To Predict Dynamics Of Infectious Diseases Incorporating Human Behavior, Alonso Gabriel Ogueda Oliva, Dr. Padmanabhan Seshaiyer

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Competitive Conquest: Charting The Climb To Pokémon Supremacy, Robert Dilworth

BCoE Publications

This manuscript presents a comprehensive exploration of optimizing Pokémon gameplay through data-driven methodologies, aimed at enhancing competitive performance in high-stakes environments. In the first section, we introduce a robust Pokémon teambuilding algorithm that leverages statistical analysis of championship-winning compositions. By employing multiple linear regression techniques, we predict team performance based on critical factors such as Base Stat Totals (BSTs) and various coverage types. This integration of data science principles into Pokémon strategy underscores the importance of offensive capabilities over defensive considerations, ultimately contributing to advancements in teambuilding strategies. Our proficiency in R programming facilitated the development of an efficient codebase …

Enhancing Data Standards To Advance Translation In Spinal Cord Injury, Vanessa K. Noonan, Suzanne Humphreys, Fin Biering-Sørensen, Susan Charlifue, Yuying Chen, James D. Guest, Linda A. T. Jones, Jennifer French, Eva Widerström-Noga, Vance P. Lemmon, Allen W. Heinemann, Jan M. Schwab, Aaron A. Phillips, Marzieh M. Rizi, John L. K. Kramer, Catherine R. Jutzeler, Abel Torres-Espin

Department of Physical Therapy Faculty Papers

Data standards are available for spinal cord injury (SCI). The International SCI Data Sets were created in 2002 and there are currently 27 freely available. In 2014 the National Institute of Neurological Disorders and Stroke developed clinical common data elements to promote clinical data sharing in SCI. The objective of this paper is to provide an overview of SCI data standards, describe learnings from the traumatic brain injury (TBI) field using data to enhance research and care, and discuss future opportunities in SCI. Given the complexity of SCI, frameworks such as a systems medicine approach and Big Data perspective have …

The Wallet And The Gut: Forecasting The 2024 Presidential Election With A State-By-State Adaptation Of The Time-For-Change Model, Simeon A. Betapudi, Hadassah Betapudi

Science University Research Symposium (SURS)

This study adapts Abramowitz's Time-for-Change model to a state-level framework to forecast the 2024 U.S. presidential election. The Time-for-Change model’s focus on the popular vote has become less relevant in recent years, given the growing divergence between popular vote outcomes and electoral college results. Our model addresses these issues by adapting the original Time-for-Change predictors (presidential approval rating, GDP, and time in office) to the state level. Using data from five election cycles (2004–2020), we employ an Ordinary Least Squares (OLS) regression to predict incumbent two-party vote share. Unlike the original model, state-level GDP and incumbency duration were found to …

Ipydisp V2 Alias Dudutracker: A Web-Based Version, Komi Mensah Agboka, Elfatih M. Abdel-Rahman, Samira A. Mohamed, Sunday Ekesi

All Peer-Reviewed Publications

This study presents the updated version v2 of IpyDisp named DuduTracker which improves on the window-only-requirement of IpyDisp. The updated version is web-based that can be used in alternative operating systems like Ubuntu, Mac, Linux, and others. The update's effectiveness was also evaluated using a survey involving a diverse range of users including students, data analysts, and academic researchers from different age groups, geographical locations, and computer literacy levels. Areas for future enhancement were identified, primarily focused on making the software responsive to various screen types and improving certain interface aspects.

Question Answering For Electronic Health Records: Scoping Review Of Datasets And Models, Jayetri Bardhan, Kirk Roberts, Daisy Zhe Wang

Faculty, Staff and Student Publications

Background: Question answering (QA) systems for patient-related data can assist both clinicians and patients. They can, for example, assist clinicians in decision-making and enable patients to have a better understanding of their medical history. Substantial amounts of patient data are stored in electronic health records (EHRs), making EHR QA an important research area. Because of the differences in data format and modality, this differs greatly from other medical QA tasks that use medical websites or scientific papers to retrieve answers, making it critical to research EHR QA.

Objective: This study aims to provide a methodological review of existing works on …

Key Technology Selection And Countermeasures To Promote Full Lifecycle Data Governance, Yuyao Feng, Hongyun Zhang, Pengfei Wang, Jianping Li, Zongben Xu

Bulletin of Chinese Academy of Sciences (Chinese Version)

In recent years, the digital economy, driven by data as a critical element, has developed rapidly. Nevertheless, China’s progress in data factorization and valorization is still at a preliminary stage. The data governance system remains underdeveloped, with numerous challenges and technical issues arising in the full lifecycle governance of data, including supply, circulation, application, and security protection. Against this backdrop, this study analyzes the primary technical bottlenecks encountered during the modernization of China’s data governance framework. By employing bibliometric analysis, patent data analysis, Delphi surveys, and expert opinions, a critical technology list to support the modernization of data governance in …

Review Of Current Trends In Information Technology Concerning Phonetic Similarity”, Zaid Rajih Mohammed, Ahmed H. Aliwy

Al-Bahir

With the increasing availability of textual information in various languages via the Internet in homes and companies through Internet and intranet services, there is an urgent need for the technologies and tools necessary to process this information, phonetic representation, and voice interaction. For example voice to voice machine translation need to phonetic mapping and similarity among the languages especially for names and foreign words. This one example of the importance of phonetic mapping and similarity. This article aims to describe, in detail, the recent surge in interest and advancements in phonetic similarity (PS), phonetic representation, and phonetic mapping researches. PS …

Characterizing The Progression From Mild Cognitive Impairment To Dementia: A Network Analysis Of Longitudinal Clinical Visits, Muskan Garg, Sara Hejazi, Sunyang Fu, Maria Vassilaki, Ronald C Petersen, Jennifer St Sauver, Sunghwan Sohn

Faculty, Staff and Student Publications

Background: With the recent surge in the utilization of electronic health records for cognitive decline, the research community has turned its attention to conducting fine-grained analyses of dementia onset using advanced techniques. Previous works have mostly focused on machine learning-based prediction of dementia, lacking the analysis of dementia progression and its associations with risk factors over time. The black box nature of machine learning models has also raised concerns regarding their uncertainty and safety in decision making, particularly in sensitive domains like healthcare.

Objective: We aimed to characterize the progression of health conditions, such as chronic diseases and neuropsychiatric symptoms, …

Undergraduate Data Literacy In Engineering: A Collaborative Approach, Amber Gruszeczka, Nicole Galloway

Libraries Faculty & Staff Presentations

Data literacy is increasingly crucial to research across disciplines, and librarians are diversifying their skills to encourage students’ self-reliance when interacting with data. Two librarians discuss a collaboration across library departments to design instruction and materials to bring the fundamentals of data literacy to a freshman engineering course.

Tradeoffs Of Generalization, Kyra M. Abrams, Peter T. Darch

I-GUIDE Forum

Models used in geospatial data science are often built and optimized for a specific local context, such as a particular location at a point in time. However, upon publication, these models may be generalized beyond this context, reused in research simulating or predicting other times and places. Without sufficient information or documentation, bias embedded in these models can in turn result in bias in the reuser’s research outputs. Drawing on a long-term qualitative case study of aging dams researchers and developers of models used by these researchers, we find significant documentation gaps. We combine a literature-based genealogy with interviews with …

M3t-Lm: A Multi-Modal Multi-Task Learning Model For Jointly Predicting Patient Length Of Stay And Mortality, Junde Chen, Qing Li, Feng Liu, Yuxin Wen

Engineering Faculty Articles and Research

Ensuring accurate predictions of inpatient length of stay (LoS) and mortality rates is essential for enhancing hospital service efficiency, particularly in light of the constraints posed by limited healthcare resources. Integrative analysis of heterogeneous clinic record data from different sources can hold great promise for improving the prognosis and diagnosis level of LoS and mortality. Currently, most existing studies solely focus on single data modality or tend to single-task learning, i.e., training LoS and mortality tasks separately. This limits the utilization of available multi-modal data and prevents the sharing of feature representations that could capture correlations between different tasks, ultimately …

Course-Skill Atlas: A National Longitudinal Dataset Of Skills Taught In U.S. Higher Education Curricula, Alireza Javadian Sabet, Sarah H. Bana, Renzhe Yu, Morgan R. Frank

Economics Faculty Articles and Research

Higher education plays a critical role in driving an innovative economy by equipping students with knowledge and skills demanded by the workforce. While researchers and practitioners have developed data systems to track detailed occupational skills, such as those established by the U.S. Department of Labor (DOL), much less effort has been made to document which of these skills are being developed in higher education at a similar granularity. Here, we fill this gap by presenting Course-Skill Atlas – a longitudinal dataset of skills inferred from over three million course syllabi taught at nearly three thousand U.S. higher education institutions. To …

Discrete Time Series Forecasting Of Hive Weight, In-Hive Temperature, And Hive Entrance Traffic In Non-Invasive Monitoring Of Managed Honey Bee Colonies: Part I, Vladimir A. Kulyukin, Daniel Coster, Aleksey V. Kulyukin, William Meikle, Milagra Weiss

Computer Science Faculty and Staff Publications

From June to October, 2022, we recorded the weight, the internal temperature, and the hive entrance video traffic of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, AZ, USA. The weight and temperature were recorded every five minutes around the clock. The 30 s videos were recorded every five minutes daily from 7:00 to 20:55. We curated the collected data into a dataset of 758,703 records (208,760–weight; 322,570–temperature; 155,373–video). A principal objective of Part I of our investigation was to use the curated dataset to investigate …

2024 Gateway Magazine, College Of Computing, Michigan Technological University

College of Computing Annual Magazines

Table of Contents

• 50 Years of Computer Science at Michigan Tech
• Data Science for a Changing Planet
• Healthcare Transformed
• Mechatronics Matters
• Powered by Michigan Tech Talent
• Esports: Bringing Everything Great about Sports to More People
• The Michigander Scholars Program: Electrifying Careers in Michigan
• College of Computing News

Using Machine Learning To Predict State Compliance With International Legal Obligations For Registration Of Space Objects: Comparative Performance Of Logistic Regression And Dense Neural Network Models, Jonathan K. Sawmiller

Student Publications

Approximately 12% of satellites and other objects launched into outer space have not been registered with the United Nations (UN) as required by international law. To predict whether States will register a launched space object and understand what factors influence a registration decision, data from a UN online index of space objects was used to train and select the best machine learning model. After preparation, the dataset had 1938 datapoints with 11 features, with categorical features simplified and converted to binary.

Multiple variations of classical logistic regression models were compared to multiple variations of dense neural network models. The best …