Data Science Commons^™

A New Approach To Generate Combinatorial Patterns In Logical Analysis Of Data And Its Application To Predict College Retention, Salihah Ahmed E. Jaafari

Theses and Dissertations

Student retention and degree completion remain central challenges for higher-education institutions, with significant implications for student success, institutional effectiveness, and public accountability. While advances in predictive analytics have enabled earlier identification of students at risk of withdrawal, many commonly used machine learning approaches suffer from limited interpretability, constraining their practical usefulness for advising, intervention, and policy decision making. This dissertation addresses the problem of predicting student persistence by developing and evaluating optimization based, interpretable classification models within the Logical Analysis of Data (LAD) framework. Building on existing LAD formulations, this research introduces two novel pattern generation models, the Best Term …

An Ensemble Classifier For Ordinal Outcomes In High-Dimensional Genomics Data, Heranga K. Rathnasekara, Sinjini Sikdar

Mathematics & Statistics Faculty Publications

Analysis of genomics data for predicting disease outcomes is a fast-growing field in medical research. There often exist categorical, specifically, ordinal outcomes that need to be predicted based on genomic profiles. This has led to recent development of some high-dimensional ordinal classification methods that can address the large dimensionality of the genomic covariate set. These high-dimensional ordinal models tend to vary widely in their performance depending on the data they are applied to and the evaluation criteria used. In this article, we outline an ensemble ordinal classifier that integrates different ordinal modeling approaches through bootstrap-based model evaluation, multi-metric performance assessment, …

A Machine Learning Analysis Of Factors Leading To Major League Baseball Postseason Berths, Chase S. Foster

Undergraduate Honors Theses

Machine learning is a method that employs statistical algorithms to identify patterns and make predictions from data. This study applies machine learning techniques to analyze data from Major League Baseball (MLB) teams between 1998 and 2024, with the goal of determining which factors strongly influence a team's likelihood of reaching the postseason and in accurately predicting the teams that do and do not qualify for the postseason. Data exploration and unsupervised machine learning methods such as clustering were used to identify underlying patterns in team performance metrics and determine potential significant contributors to team success. Many different supervised learning methods …

Spotify Recommender Using Content Filtering, Colin M. Anderson

SPARK Symposium Presentations

This project is a music recommender system that analyzes Spotify data to establish relationships between songs by analyzing their musical components. A user can receive recommendations through two methods. Firstly, the user can select a song from the database that they enjoy, and the system will provide them with a list of recommendations, as well as predict the genre of the song that they have entered. Secondly, the user can set personalized values for various musical features (i.e. energy, danceability). In either case, by selecting a number 1 to 5 recommendations that the user would like, they will get that …

Deep Learning-Based Ensemble Two-Step Classification Of Medical Images Using Cnn Architectures And Ensemble Methods, Noreliz Alorico

Master's Theses or Doctor of Nursing Practice

Breast cancer remains one of the most common cancers amongst women globally. Early detection is crucial for improving survival rates. While mammography is widely used and an effective imaging technique, it can sometimes yield false positive or false negatives. Mammogram interpretation is highly operator-dependent, introducing variability and the potential for diagnostic errors. Additionally, mammographic images have limitations, such as low contrast in breast tissue and overlapping structures that can obscure lesions or mimic abnormalities. These limitations can lead to unnecessary biopsies or delayed diagnosis. These challenges highlight the needs for advanced and data driven diagnostic tools to support and enhance …

From Data To Insight: A Machine Learning Approach In Classifying Dairy Cow Productivity Level And Identifying Important Influencing Variables, Fatkhurokhman Fauzi, Achmad Fauzan, Rhendy K P Widiyanto, Khairil Anwar Notodiputro, Bagus Sartono

Knowledge Engineering and Data Science

Identifying influential predictor variables is crucial for enhancing model interpretability in supervised classification. This study applies Permutation Variable Importance (PVI), a model-agnostic approach, to evaluate variable relevance after model fitting. Using data from the 2024 Indonesia Dairy Cow Productivity Survey, this research investigates five classification techniques: (1) Support Vector Machine (SVM), (2) Neural Network (NN), (3) k-Nearest Neighbors (kNN), (4) Naïve Bayes Classifier (NB), and (5) Logistic Regression (LR), to identify which method(s) yield the best performance based on evaluation metrics such as accuracy, sensitivity, and specificity. PVI is employed to identify the most influential predictor variables within the best-performing …

Leveraging Transformer Models For Genre Classification, Andreea C. Craus, Ben Berger, Yves Hughes, Hayley Horn

SMU Data Science Review

As the digital music landscape continues to expand, the need for effective methods to understand and contextualize the diverse genres of lyrical content becomes increasingly critical. This research focuses on the application of transformer models in the domain of music analysis, specifically in the task of lyric genre classification. By leveraging the advanced capabilities of transformer architectures, this project aims to capture intricate linguistic nuances within song lyrics, thereby enhancing the accuracy and efficiency of genre classification. The relevance of this project lies in its potential to contribute to the development of automated systems for music recommendation and genre-based playlist …

Comparison Of Classification Methodologies Using Convolutional Neural Networks In A Dataset Of Plant Leaf Diseases., Ruairi O’Donohoe

ICT

This project investigates the impact of classification methodology selection on the performance of four Convolutional Neural Network (CNN) models applied to a multi-label image dataset. The dataset consists of plant leaf images with one or more diseases. Two classification methodologies—multi-label and multi-class—are compared based on their model performance metrics. It was hypothesised that multi-label classification would perform better, but the results show that although multi-label models performed better for Loss and Accuracy metrics, they underperformed in terms of the F1 score, which is considered a more appropriate metric for this task. This surprising result refutes the initial hypothesis. Transfer learning …

A Review Of Emerging Sensor Technologies For Tank Inspection: A Focus On Lidar And Hyperspectral Imaging And Their Automation And Deployment, Sergio Pallas Enguita, Chung-Hao Chen, Samuel Kovacic

Electrical & Computer Engineering Faculty Publications

This paper reviews various sensor technologies for tank inspection, focusing on Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) as advanced solutions for corrosion detection. These technologies are evaluated alongside traditional methods such as ultrasonic, electromagnetic, and thermographic inspections. This review highlights their potential to enhance inspection accuracy, reduce the limitations of manual inspection, and support integrated data analysis for comprehensive asset management. Additionally, this paper proposes a pathway for automating these techniques to streamline inspection processes and improve implementation in practical applications.

Comparison Of Support Vector Machine (Svm), K-Nearest Neighbor (K-Nn), And Stochastic Gradient Descent (Sgd) For Classifying Corn Leaf Disease Based On Histogram Of Oriented Gradients (Hog) Feature Extraction, Firdaus Solihin, Muhammad Syarief, Eka Mala Sari Rochman, Aeri Rachmad

Elinvo (Electronics, Informatics, and Vocational Education)

Image classification involves categorizing an image's pixels into specific classes based on their unique characteristics. It has diverse applications in everyday life. One such application is the classification of diseases on corn leaves. Corn is a widely consumed staple food in Indonesia, and healthy corn plants are crucial for meeting market demands. Currently, disease identification in corn plants relies on manual checks, which are time-consuming and less effective. This research aims to automate disease identification on corn leaves using the Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) with K=2, and Stochastic Gradient Descent (SGD) algorithms. The classification process utilizes the …

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 …

Reu-Deim Classification Of Hispanic Voters In Hispanic Groups Using Name And Zip Code Data In Palm Beach, Florida, Kamila Soto-Ortiz

Beyond: Undergraduate Research Journal

When it comes to registering to vote, Hispanic voters can only register as “Hispanic” in the “Race/Ethnicity” category, causing difficulties when analyzing voting trends amongst the Hispanic community. Upon the recent idea that not all Hispanic Groups vote the same, the goal is to create a model that can possibly identify a voter’s Hispanic Group with the information provided on the public Florida voter file. This is accomplished using name and zip code data for all voters in Palm Beach, Florida. This paper will explore the model implemented, its findings and limitations. Palm Beach, Florida, is met with low confidence …

Exploring The Impact Of Students Demographic Attributes On Performance Prediction Through Binary Classification In The Kdp Model, Issah Iddrisu, Peter Appiahene, Obed Appiah, Inusah Fuseini

Knowledge Engineering and Data Science

During the course of this research, binary classification and the Knowledge Discovery Process (KDP) were used. The experimental and analytical capabilities of Rapid Miner's 9.10.010 instructional environment are supported by five different classifiers. Included in the analysis were 2334 entries, 17 characteristics, and one class variable containing the students' average score for the semester. There were twenty experiments carried out. During the studies, 10-fold cross-validation and ratio split validation, together with bootstrap sampling, were used. It was determined whether or not to use the Random Forest (RF), Rule Induction (RI), Naive Bayes (NB), Logistic Regression (LR), or Deep Learning (DL) …

Optimizing Random Forest Algorithm To Classify Player's Memorisation Via In-Game Data, Akmal Vrisna Alzuhdi, Harits Ar Rosyid, Mohammad Yasser Chuttur, Shah Nazir

Knowledge Engineering and Data Science

Assessment of a player's knowledge in game education has been around for some time. Traditional evaluation in and around a gaming session may disrupt the players' immersion. This research uses an optimized Random Forest to construct a non-invasive prediction of a game education player's Memorization via in-game data. Firstly, we obtained the dataset from a 3-month survey to record in-game data of 50 players who play 4-15 game stages of the Chem Fight (a test case game). Next, we generated three variants of datasets via the preprocessing stages: resampling method (SMOTE), normalization (min-max), and a combination of resampling and normalization. …

Domain Specific Analysis Of Privacy Practices And Concerns In The Mobile Application Market, Fahimeh Ebrahimi Meymand

LSU Doctoral Dissertations

Mobile applications (apps) constantly demand access to sensitive user information in exchange for more personalized services. These-mostly unjustified-data collection tactics have raised major privacy concerns among mobile app users. Existing research on mobile app privacy aims to identify these concerns, expose apps with malicious data collection practices, assess the quality of apps' privacy policies, and propose automated solutions for privacy leak detection and prevention. However, existing solutions are generic, frequently missing the contextual characteristics of different application domains. To address these limitations, in this dissertation, we study privacy in the app store at a domain level. Our objective is to …

Analyzing Relationships With Machine Learning, Oscar Ko

Dissertations, Theses, and Capstone Projects

Procedurally, this project aims to take a dataset, analyze it, and offer insights to the audience in an easy-to-digest format. Conceptually, this project will seek to explore questions like: “Do couples that meet through online dating or dating apps have higher or lower quality relationships?”, “Can any features in this dataset help predict how a subject would rate their relationship quality?”, and “What other insights can I derive from using machine learning for exploratory analysis?” The intended audience for this project is anyone interested in romantic relationships or machine learning.

The dataset is from a Stanford University survey, “How Couples …

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 …

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 Explainable Deep Learning Prediction Model For Severity Of Alzheimer's Disease From Brain Images, Godwin O. Ekuma

Graduate Theses/Dissertations

Deep Convolutional Neural Networks (CNNs) have become the go-to method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. The effectiveness of deep learning models are hampered by limited data sets, skewed class distributions, and the undesirable "black box" of neural networks, which decreases their understandability and usability in precision medicine applications. This thesis addresses the challenge of building an explainable deep learning model for a clinical application: predicting the severity of Alzheimer's disease (AD). AD …

Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Classification Of Pixel Tracks To Improve Track Reconstruction From Proton-Proton Collisions, Kebur Fantahun, Jobin Joseph, Halle Purdom, Nibhrat Lohia

SMU Data Science Review

In this paper, machine learning techniques are used to reconstruct particle collision pathways. CERN (Conseil européen pour la recherche nucléaire) uses a massive underground particle collider, called the Large Hadron Collider or LHC, to produce particle collisions at extremely high speeds. There are several layers of detectors in the collider that track the pathways of particles as they collide. The data produced from collisions contains an extraneous amount of background noise, i.e., decays from known particle collisions produce fake signal. Particularly, in the first layer of the detector, the pixel tracker, there is an overwhelming amount of background noise that …

Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, Aswini Thota, Ololade Awodipe, Rashmi Patel

SMU Data Science Review

Since the pandemic started, researchers have been trying to find a way to detect COVID-19 which is a cost-effective, fast, and reliable way to keep the economy viable and running. This research details how chest X-ray radiography can be utilized to detect the infection. This can be for implementation in Airports, Schools, and places of business. Currently, Chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonia. Different pre-trained algorithms were fine-tuned and applied to the images to train the model and the best model obtained was fine-tuned InceptionV3 model …

A Machine Learning Approach To Revenue Generation Within The Professional Hair Care Industry, Alexander K. Sepenu, Linda Eliasen

SMU Data Science Review

The cosmetic and beauty industry continues to grow and evolve to satisfy its patrons. In the United States, the industry is heavily science-driven, innovative, and fast-paced, suggesting that to remain productive and profitable, companies must seek smart alternatives to their current modus operandi or risk losing out on this multi-billion-dollar industry to fierce competition. In this paper, the authors seek to utilize machine learning models such as clustering and regression to improve the efficiency of current sales and customer segmentation models to help HairCo (pseudonym for confidentiality), a professional hair products manufacturer, strategize their marketing and sales efforts for revenue …

Split Classification Model For Complex Clustered Data, Katherine Gerot

Honors Program: Senior Projects (Public)

Classification in high-dimensional data has generated tremendous interest in a multitude of fields. Data in higher dimensions often tend to reside in non-Euclidean metric space. This prevents Euclidean-based classification methodologies, such as regression, from reliably modeling the data. Many proposed models rely on computationally-complex embedding to convert the data to a more usable format. Others, namely the Support Vector Machine, rely on kernel manipulation to implicitly describe the "feature space" to arrive at a non-linear decision boundary. The proposed methodology in this paper seeks to classify complex data in a relatively computationally-simple and explainable manner.

Eeg Signals Classification Using Lstm-Based Models And Majority Logic, James A. Orgeron

College of Graduate Studies: Theses & Dissertations

The study of elecroencephalograms (EEGs) has gained enormous interest in the last decade with the increase of computational power and availability of EEG signals collected from various human activities or produced during medical tests. The applicability of analyzing EEG signals ranges from helping impaired people communicate or move (using appropriate medical equipment) to understanding people's feelings and detecting diseases.

We proposed new methodology and models for analyzing and classifying EEG signals collected from individuals observing visual stimuli. Our models rely on powerful Long-Short Term Memory (LSTM) Neural Network models, which are currently the state of the art models for performing …

Classifying Blood Glucose Levels Through Noninvasive Features, Rishi Reddy

Graduate Theses, Dissertations, and Problem Reports (ETD)

Blood glucose monitoring is a key process in the prevention and management of certain chronic diseases, such as diabetes. Currently, glucose monitoring for those interested in their blood glucose levels are confronted with options that are primarily invasive and relatively costly. A growing topic of note is the development of non-invasive monitoring methods for blood glucose. This development holds a significant promise for improvement to the quality of life of a significant portion of the population and is overall met with great enthusiasm from the scientific community as well as commercial interest. This work aims to develop a potential pipeline …

Stress Classification Using Deep Learning With 1d Convolutional Neural Networks, Abdulrazak Yahya Saleh, Lau Khai Xian

Knowledge Engineering and Data Science

Stress has been a major problem impacting people in various ways, and it gets serious every day. Identifying whether someone is suffering from stress is crucial before it becomes a severe illness. Artificial Intelligence (AI) interprets external data, learns from such data, and uses the learning to achieve specific goals and tasks. Deep Learning (DL) has created an impact in the field of Artificial Intelligence as it can perform tasks with high accuracy. Therefore, the primary purpose of this paper is to evaluate the performance of 1D Convolutional Neural Networks (1D CNNs) for stress classification. A Psychophysiological stress (PS) dataset …

Face Images Classification Using Vgg-Cnn, I Nyoman Gede Arya Astawa, Made Leo Radhitya, I Wayan Raka Ardana, Felix Andika Dwiyanto

Knowledge Engineering and Data Science

Image classification is a fundamental problem in computer vision. In facial recognition, image classification can speed up the training process and also significantly improve accuracy. The use of deep learning methods in facial recognition has been commonly used. One of them is the Convolutional Neural Network (CNN) method which has high accuracy. Furthermore, this study aims to combine CNN for facial recognition and VGG for the classification process. The process begins by input the face image. Then, the preprocessor feature extractor method is used for transfer learning. This study uses a VGG-face model as an optimization model of transfer learning …

Per-Pixel Cloud Cover Classification Of Multispectral Landsat-8 Data, Salome E. Carrasco, Torrey J. Wagner, Brent T. Langhals

Faculty Publications

Random forest and neural network algorithms are applied to identify cloud cover using 10 of the wavelength bands available in Landsat 8 imagery. The methods classify each pixel into 4 different classes: clear, cloud shadow, light cloud, or cloud. The first method is based on a fully connected neural network with ten input neurons, two hidden layers of 8 and 10 neurons respectively, and a single-neuron output for each class. This type of model is considered with and without L2 regularization applied to the kernel weighting. The final model type is a random forest classifier created from an ensemble of …

Machine Learning Approaches To Dribble Hand-Off Action Classification With Sportvu Nba Player Coordinate Data, Dembe Stephanos

Electronic Theses and Dissertations

Recently, strategies of National Basketball Association teams have evolved with the skillsets of players and the emergence of advanced analytics. One of the most effective actions in dynamic offensive strategies in basketball is the dribble hand-off (DHO). This thesis proposes an architecture for a classification pipeline for detecting DHOs in an accurate and automated manner. This pipeline consists of a combination of player tracking data and event labels, a rule set to identify candidate actions, manually reviewing game recordings to label the candidates, and embedding player trajectories into hexbin cell paths before passing the completed training set to the classification …