Physical Sciences and Mathematics Commons^™

Embedding Information Literacy Practices In An Upper Division Chemistry Lab Class At A University In The United States Of America, Christopher Randles, Matilynn Lamm, Katy A. Miller

Faculty Scholarship and Creative Works

This poster discusses the implementation of information literacy into an upper division inorganic chemistry lab and the strategies employed to develop students’ information literacy in the laboratory course.

Machine Learning Approaches For Cyberbullying Detection, Roland Fiagbe

Data Science and Data Mining

Cyberbullying refers to the act of bullying using electronic means and the internet. In recent years, this act has been identifed to be a major problem among young people and even adults. It can negatively impact one’s emotions and lead to adverse outcomes like depression, anxiety, harassment, and suicide, among others. This has led to the need to employ machine learning techniques to automatically detect cyberbullying and prevent them on various social media platforms. In this study, we want to analyze the combination of some Natural Language Processing (NLP) algorithms (such as Bag-of-Words and TFIDF) with some popular machine learning …

Predicting Superconducting Critical Temperature Using Regression Analysis, Roland Fiagbe

Data Science and Data Mining

This project estimates a regression model to predict the superconducting critical temperature based on variables extracted from the superconductor’s chemical formula. The regression model along with the stepwise variable selection gives a reasonable and good predictive model with a lower prediction error (MSE). Variables extracted based on atomic radius, valence, atomic mass and thermal conductivity appeared to have the most contribution to the predictive model.

Xgboost Hyperberd Model Using Steam Platform, Yuh-Haur Chen

Data Science and Data Mining

This project investigates game pricing strategies in the Steam market using an XGBoost model, drawing motivation from Professor Xie's lecture, and presenting findings through a density plot that delineates two primary pricing strategies. A free-to-play approach, indicated by a significant hot spot, is adopted by developers focusing on post-purchase revenues through DLC, aesthetic purchases, and in-game transactions. This sailing strategy includes community-centric developers aiming to distribute their games for player engagement rather than profit.

The project illustrates the effectiveness of advanced modeling techniques in handling complex datasets, with significant predictive accuracy reflected by a reduced MSE from 0.3472 to 0.1397. …

Combating Cyberbullying On Social Media: A Machine Learning Approach With Text Analysis On Twitter, Amir Alipour Yengejeh

Data Science and Data Mining

The popularity of the electronic mobile devices along with social media as well as networking websites have been tremendously increased in the recent year. Most people around the world daily engage in the variety of cyberspace additives. Even though the users can take most advantages of these system such as exchange the idea and information, being sociable, and enjoyments, they might be faced with such adverse behaviors such as toxicity, bullying, extremism, and cruelty. The recent statistics reports that such mentioned behaviors has been noticeably grown on the cyberspace such that can threaten the individuals and even any community. Thus, …

Advancing Cancer Classifcation Through Machine Learning Analysis Of Rna-Seq Gene Expression Data, Emil Agbemade, Amina Issoufou Anaroua, Dimitri Bamba

Data Science and Data Mining

This study delves into the classifcation of various cancer types using the RNA-Seq (HiSeq) PANCAN dataset from the UCI Machine Learning Repository, which encompasses a rich collection of gene expression data across multiple tumor samples. To improve cancer diagnosis and treatment, our methodology confronts the challenges inherent in high-dimensional datasets, such as the Hughes Effect and the Curse of Dimensionality, through innovative feature selection methods and machine learning approaches. A key component of our strategy includes the use of tree-based algorithms, particularly Random Forest, to refine the dataset to seventy genes of utmost relevance for tumor classifcation, and the application …

Bootstrap Regression For Investigating Macroeconomics Factors Affecting Usa Home Prices, Benedict Kongyir, Emil Agbemade

Data Science and Data Mining

This study investigates the impact of macroeconomic indicators on US home prices, underscoring the importance of understanding these dynamics due to their signifcant socioeconomic consequences. Utilizing a dataset from Kaggle, originally collected by FRED, the research examines variables like the Consumer Price Index, Population, Unemployment, GDP, Stock Prices, Income, and Mortgage Rate to discern their efect on housing market fuctuations. The analysis identifes multicollinearity among predictors, necessitating a shift from traditional multiple linear regression to a more robust bootstrap regression method due to violations of parametric assumptions. Key fndings reveal that Real Disposable Income is a signifcant predictor of home …

Optimizing Ai With Advanced Data Structuring: A Comparative Analysis Of K-Means And Gmm Clustering Techniques, Amir Alipour Yengejeh

Data Science and Data Mining

This study presents a detailed comparison of Kmeans and Gaussian Mixture Model (GMM) clustering algorithms, illustrating their unique capabilities and limitations across various synthetic datasets. By utilizing metrics such as the Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI), the research provides nuanced insights into how these algorithms handle datasets with varying structures and complexities. For instance, while both K-means and GMM show robust performance on well-separated clusters, GMM demonstrates a distinct advantage in scenarios with overlapping clusters or unbalanced data distributions. Conversely, K-means excels in identifying clear, distinct groupings, highlighting its utility in simpler clustering contexts. This study …

Design Of Stormwater Bmps For Surface And Groundwater Protection Based On Site-Scale Soil Properties: Phase I, Kelly Kibler, Lisa Chambers, Melanie Beazley

Florida DOT

Much of Earth’s nutrient cycling takes place in soils. Characteristics of soils control physical, chemical, and biological processes that determine rates of nutrient fluxes, storage, or transformation. As remediation of excess nutrients in stormwater runoff is one function of stormwater Best Management Practices (BMPs), the soil profile constitutes one of the most important factors of BMP design. Variation observed in BMP effectiveness (e.g., why one BMP design works effectively in one place and not another) can often be explained by variations in the soil profile, either through direct means or by a soil’s influence on hydraulics of stormwater flow through …

Linear Regression With Regularization On The Genetic Architecture Of Maize Flowering Time, Roland Fiagbe

Data Science and Data Mining

Over a century, the maize crop has been one of the most important crop species that is targeted for genetic investigations and experiments. One of the major experiments that have been a topic of interest is crossing inbred lines to produce better offspring through a process called heterosis. Crossing the inbred lines create numerous SNP markers that determine the time to male flowering. This project seeks to explore the SNP markers to select the most relevant ones for predicting time to male flowering using linear regression with regularization methods due to the fact that p > n in our dataset. Various …

A Recommender System For Movie Ratings With Matrix Factorization Algorithm, Amir Alipour Yengejeh

Data Science and Data Mining

Nowadays, a Recommender System is a technology
that aims to predict preferences based on the user’s selections.
These systems are applied in numerous fields, such as movies,
music, news, books, research articles, search queries, social tags,
and various products. In this study, we use this potential tool to
predict the ratings of users’ preferences in MovieLens datasets. To
do so, we applied the matrix factorization algorithm and calculate
the RMSE as our evaluation metric. The results represent that
RMSE estimated for the train and test set are 0.83 and 0.93 that
are very close one another. This results indicates that …

Genome-Wide Association Study Of The Maize Crop By The Lasso Regression Analysis, Amir Alipour Yengejeh

Data Science and Data Mining

The accurate estimation of the male flowering period in Maize crops is key for the prediction crop fertility. The recent scientific investigations has shown that the genetic single nucleotic polymorphism (SNP) can contribute in this regard. The genomewide association study (GWAS) is employed to generate these attributes (SNP). But it caused a high-dimensional data in which 4,981 observations with 7,389 SNP attributes. Hence, in this study, we used the penalized regression approach with the least absolute shrinkage and selection operator (Lasso) to reduce the dataset. In this regard, we set the regularization parameter to 0.21. It resulted in a set …

Analysis Of Credit Approval By Decision Tree, Amir Alipour Yengejeh

Data Science and Data Mining

Nowadays, machine learning algorithms are com-
monly used by the financial institutions or bankers to evaluate
the applications’ requires for credit card. In this study, we used
the decision tree algorithm to predict credit card approval based
on the other associated features applicants like age, employment
status, Education Level, etc. Our results shows that the applicants’
Prior Default and Debt, and Employed have more contribution
in the credit card approval.

Movie Recommender System Using Matrix Factorization, Roland Fiagbe

Data Science and Data Mining

Recommendation systems are a popular and beneficial field that can help people make informed decisions automatically. This technique assists users in selecting relevant information from an overwhelming amount of available data. When it comes to movie recommendations, two common methods are collaborative filtering, which compares similarities between users, and content-based filtering, which takes a user’s specific preferences into account. However, our study focuses on the collaborative filtering approach, specifically matrix factorization. Various similarity metrics are used to identify user similarities for recommendation purposes. Our project aims to predict movie ratings for unwatched movies using the MovieLens rating dataset. We developed …

Variable Selection And Regression Analysis, Emil Agbemade

Data Science and Data Mining

One of the most valuable crop species, maize, has been the subject of genetic study and experimentation for more than a century. However, species that share similarities and differences across a wide spectrum have developed astonishing adaptations as a result of small changes throughout time. Because it is usual practice to determine the genotypes of thousands of single nucleotide polymorphism (SNP) markers for thousands of patients, the data set we are dealing with has an issue with small n and large p. The result of this is that there are noticeably more predictor factors than responder variables. The original data …

Predicting Heart Disease Using Tree-Based Model, Emil Agbemade

Data Science and Data Mining

The paper presents a study on the use of machine learning algorithms for the prediction of heart disease, which is the leading cause of death worldwide. The study focuses on the use of decision tree algorithms, which have the advantage of considering a large number of risk factors. The heart disease data set was obtained from the UCI Machine Learning Repository and was analyzed using a decision tree classifier. The data set had 6 missing data points, which were deleted, leaving 279 instances for analysis. One-hot-encoding was performed on categorical variables with more than two responses. The decision tree classifier …

Classification Of Adult Income Using Decision Tree, Roland Fiagbe

Data Science and Data Mining

Decision tree is a commonly used data mining methodology for performing classification tasks. It is a tree-based supervised machine learning algorithm that is used to classify or make predictions in a path of how previous questions are answered. Generally, the decision tree algorithm categorizes data into branch-like segments that develop into a tree that contains a root, nodes, and leaves. This project seeks to explore the decision tree methodology and apply it to the Adult Income dataset from the UCI Machine Learning Repository, to determine whether a person makes over 50K per year and determine the necessary factors that improve …

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.

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 …

Analyzing The Impact Of Health, Economic, And Demographic Factors On Life Expectancy: A Comparative Study Of Developed And Developing Countries, Mahyar Alinejad

Data Science and Data Mining

This study presents a comprehensive analysis of three prominent machine learning regression models—Random Forest, XGBoost, and Support Vector Machine (SVM)—in the context of predictive analysis. Leveraging a carefully curated dataset, we explore the impact of various hyperparameters on model performance through an exhaustive tuning process. The Random Forest and XGBoost models exhibit robust predictive capabilities, with the former revealing notable insights through feature importance visualization. Additionally, SVM, optimized via GridSearchCV, demonstrates competitive performance. Evaluation metrics, including Mean Squared Error and R-squared, facilitate a thorough comparison of model efficacy. Results highlight nuanced strengths and weaknesses, informing practitioners on the suitability of …

Silent Agony: Automated Detection Of Ethnic And Religious Cyberbullying Using Machine Learning, Emil Agbemade

Data Science and Data Mining

The use of electronic mobile devices, social media, and networking websites has increased tremendously in recent years. Despite the advantages of these systems, such as exchanging ideas and information, being sociable, and providing entertainment, users may encounter adverse behaviors like toxicity, bullying, extremism, and cruelty. The prevalence of such behaviors has grown significantly in cyberspace, posing a threat to individuals and communities. To address this issue, there is a high demand for automated cyberbullying detection systems. Machine learning algorithms have been widely used to build such systems by classifying and detecting cyberbullying. In this study, we employed popular machine learning …

Machine Learning-Based Approaches For Predicting The Critical Temperature Of Superconductor, Pradip Dhakal

Data Science and Data Mining

This paper focuses on utilizing multiple linear regression, lasso regression, and extreme gradient boosting algorithms to predict the critical temperature of the superconductor. The model will be evaluated using the mean square error and adjusted R-squared values, and the best model will be recommended for future work related to this study.

Variable Selection Using Lasso And Elastic Net Regression On High Dimensional Genetic Architecture Data Of Maize Flowering Time, Pradip Dhakal

Data Science and Data Mining

Variable selection is one of the key components in the machine learning area. This method reduces the unwanted and redundant predictors in the model, which prevents the overfitting situation. Since the model contains few significant predictors, the model is less likely to learn the trend from the noise. Further, the time to train the model reduces when we have only a few valuable variables.

Hydrodynamic Limitations To Mangrove Seedling Retention In Subtropical Estuaries, Kelly M. Kibler, Christian Pilato, Linda Walters, Melinda Donnelly, Jyotismita Taye

Flow-biota Interaction and Natural Infrastructure Design

Mangrove forest sustainability hinges upon propagule recruitment and seedling retention. This study evaluates biophysical limitations to mangrove seedling persistence by measuring anchoring force of two mangrove species (Rhizophora mangle and Avicennia germinans). Anchoring force was measured in 362 seedlings via lateral pull-tests administered in mangrove forests of two subtropical estuaries and in laboratory-based experiments. Removal mechanism varied with seedling age: newly-established seedlings failed due to root pull-out while seedlings older than 3 months failed by root breakage. Anchoring force of R. mangle seedlings was consistently and significantly greater than A. germinans (GLM: p = 0.002), however force to …

Generational Harmony Saves The World: How The Power Of Generation Theory, Gen Z Youth, And Activism Can Mitigate The Climate Crisis, Ryan Hill

Undergraduate Scholarship and Creative Works

This paper explores generations as a concept for understanding and explaining the relationship between major sociohistorical events and societal members, posits generation succession as a way in which long-term social change occurs, compares and contrasts the perceptions of the major generations (Baby Boomers, Gen X, Millennials) created by popular media and scholarly research, illuminates characterizations of the youngest and still-emerging Gen Z, discusses what major sociohistorical events during the time of their adolescence have folded Gen Z into a distinct group with a common generational consciousness, outlines how anthropogenic climate change is a real phenomenon with harmful consequences already affecting …

Faculty-Librarian Cooperation For Virtual Stem Based Courses: Creating Successful Learning Experiences For Undergraduate Students At Ucf, Sandy Avila, Nicole Lapeyrouse

Faculty Scholarship and Creative Works

This conference presentation was delivered at the Special Libraries Association annual virtual conference on August 12, 2021.

Session Abstract

In this session, University of Central Florida Chemistry Faculty, Dr. Nicole Lapeyrouse and Science Librarian, Sandy Avila will discuss their transition from in-person instruction to online learning during the pandemic and how their professional collaboration developed instructional resources for student success. Prior to COVID-19, a fruitful cooperative relationship was already in place, but quickly having to pivot to remote teaching and learning allowed for additional opportunities for unique experiences to be created. Those include use of open-educational e-texts, interactive online software …

Anticipating Widespread Augmented Reality: Insights From The 2018 Ar Visioning Workshop, Gregory F. Welch, Gerd Bruder, Peter Squire, Ryan Schubert

Faculty Scholarship and Creative Works

In August of 2018 a group of academic, government, and industry experts in the field of Augmented Reality gathered for four days to consider potential technological and societal issues and opportunities that could accompany a future where AR is pervasive in location and duration of use. This report is intended to summarize some of the most novel and potentially impactful insights and opportunities identified by the group.

Our target audience includes AR researchers, government leaders, and thought leaders in general. It is our intent to share some compelling technological and societal questions that we believe are unique to AR, and …

Analysis Literatures Of Machine Learning And Neural Networks For Real Time Scheduling, Phong Nguyenho, Mark Nguyen

Recent Advances in Real-Time Systems

Real time scheduling problems are present in every aspect of software development. An optimized real time scheduling scheme would determine the performance of an operating system. There are many different approaches that real time scheduling researchers developed to tackle scheduling problems in many computer systems that have great important roles in keeping our modern society running smoothly. Neural-network real time scheduling is one of those approaches that can solve many computer scheduling problems. As computing technology advanced, more and more real time scheduling problems arise that need new solutions to keep up with the demand of faster computer systems. In …

There's More Than $ Involved When It Comes To Understanding Costs Of Fracking, Ali P. Gordon

UCF Forum

Several of society’s next grand challenges relate to the production of electrical energy.

How Did We Wind Up In Such An Unlikely Universe?, Michael Bass

UCF Forum

Not long ago the Public Broadcasting Service program NOVA presented an episode called “The Great Math Mystery.” It dealt with the many ways mathematical relationships and special numbers crop up when investigating the physical and natural world.