Digital Commons Network^™

Studying Causal Inferences For Student Success In Higher Education, Prabesh Shrestha

Theses and Dissertations

Higher education institutions strive to support students in achieving their academic goals through targeted interventions and data-driven strategies. This study analyzed 19 variables, spanning student demographics, enrollment patterns, and institutional interventions, to identify predictors of students completing all enrolled courses in a semester. Using random forest, logistic regression, and bootstrap forest models, we assessed variable importance and derived causal estimates to determine their impact. Results from causal analysis indicate positive effects from session type, term load, referrals, kudos, flags, and scholarships, while negative impacts were associated with location, course delivery method, enrollment status, advisor contact, and registration interval. These findings …

Protein-Nucleic Acid Language Model-Assisted Design Of Precise And Compact Adenine Base Editor, Jingxuan Ren, Jiawei Yao, Qiuyu Cao, Yinuo Li, Yang Li, Ziyi Zhang, Xiyu Ge, Shengfang Wang, Yang Zhang, Xiaogang Wang, Xiaohui Zhang

Faculty, Staff and Students Publications

Adenine base editors (ABEs) are powerful tools for gene therapy. However, efficient version of ABEs (e.g. ABE8e) always induce excessive bystander and off-target editing events and are large in size, hindering their potential in clinical disease treatment. Here, we develop a pre-trained Protein-Nucleic Acid Constrained Language Model to design ABE8e with high activity, reduced editing window and decreased size. By further engineering, the smallest ABE8e- PNLM-pcABE- with a 27% size reduction, exhibits high activity, precise 3-nt editing window, and reduced off-target events near background level in HEK293T cells. Compared to ABE8e, PNLM-pcABE has up to 133.5-fold precision improvement in pathogenic …

Stakeholder Criteria For Trust In Artificial Intelligence-Based Computer Perception Tools In Health Care: Qualitative Interview Study, Ansh Rai, Meghan E Hurley, John Herrington, Eric A Storch, Casey J Zampella, Julia Parrish-Morris, Anika Sonig, Gabriel Lázaro-Muñoz, Kristin Kostick-Quenet

Faculty, Staff and Students Publications

Background: Computer perception (CP) technologies hold significant promise for advancing precision mental health care systems, given their ability to leverage algorithmic analysis of continuous, passive sensing data from wearables and smartphones (eg, behavioral activity, geolocation, vocal features, and ambient environmental data) to infer clinically meaningful behavioral and physiological states. However, successful implementation critically depends on cultivating well-founded stakeholder trust.

Objective: This study aims to investigate, across adolescents, caregivers, clinicians, and developers, the contingencies under which CP technologies are deemed trustworthy in health care.

Methods: We conducted 80 semistructured interviews with a purposive sample of adolescents (n=20) diagnosed with autism, Tourette …

Automated Detection Of Kinky Back In Broiler Chickens Using Optimized Deep Learning Techniques, Ramesh Bahadur Bist, Andi Asnayanti, Anh Dang Trieu Do, Yang Tian, Chaitanya Palleria, Dongyi Wang, Adnan Alrubaye

Food Science Faculty Publications and Presentations

The global poultry industry faces growing challenges from skeletal disorders, with Kinky Back (KB) significantly impacting broiler welfare and production. KB causes spinal deformities that reduce mobility, feed access, and increase mortality. It often remains undetected in early subclinical stages. Traditional KB diagnosis methods are slow and subjective, and highlighting the need for an automated and objective detection. This study develops a machine learning approach for detecting KB in broilers using image data. Male Cobb 500 broilers were raised under controlled conditions and monitored over 7 weeks using overhead 4K video cameras. Behavioral and posture data related to KB were …

Hidden Threats In Water: The Global Rise Of Emerging Contaminants, Baljit Singh, Abhijnan Bhat, Gayathree Thenuwara, Kamna Ravi, Azza Silotry Naik, Christine O’Connor, Furong Tian

Research Outputs: 2025-Present

The general spread of water safety awareness and enforcement often masks the escalating risks of emerging contaminants (ECs) that evade standard detection and monitoring techniques. Traditional monitoring infrastructures depend heavily on localized laboratory-based testing, which is expensive, time-consuming, and reliant on specialized infrastructure and skilled personnel. While specific types of ECs and detection technologies have been examined in numerous studies, a significant gap remains in compiling and commenting on this information in a concise framework that incorporates global impact and monitoring strategies. We aimed to compile and highlight the impact ECs have on global water safety and how advanced sensor …

Fully Integrated Slippage Detection System For Lower Limb Amputees, Christopher E. Miglio

Master's Theses

Lower limb amputees face significant challenges in maintaining a proper prosthetic fit, as improper fit can lead to slippage at the limb-socket interface, resulting in discomfort, pressure sores, and long-term musculoskeletal complications. To address this issue, a fully integrated slippage detection system was developed to monitor an amputee’s daily activities and slippage occurrences to understand their prosthetic fit over time. The system consists of a prosthetic sock embedded with Interlink 406 flexible piezoresistive force-sensing resistors (FSRs). The sock, worn directly on the residual limb, continuously measures pressure at the limb-socket interface. Sensor data from six FSRs is sampled at approximately …

Ai-Optimized Resource Management In Next-Gen Wireless Networks, Fatemeh Lotfi

All Dissertations

Next-generation wireless networks must deliver highly adaptive, scalable, and intelligent connectivity to satisfy the heterogeneous demands of emerging services, including enhanced mobile broadband, massive machine-type communications, and ultra reliable low latency applications. The Open Radio Access Network (O-RAN) paradigm has emerged as a key enabler of this vision, introducing openness, virtualization, and artificial intelligence (AI)-driven control into the RAN ecosystem. O-RAN’s disaggregated architecture facilitates multi-vendor interoperability and empowers intelligent management through the RAN Intelligent Controller (RIC). However, achieving real-time, autonomous, and generalized optimization in such a dynamic environment remains a significant challenge due to its distributed nature, non-stationary traffic, and …

Predicting Newsworthiness Of California Bill Discussions, Venkata Kedarnath Chaturvedula

Master's Theses

Among various California bill hearings, it is crucial to identify which hearings are most likely to receive media coverage, as this highlights the importance of the bill and the hearing, while also underscoring its societal impact. Digital Democracy, established at Cal Poly’s Institute for Advanced Technology and Public Policy, provides users access to a distinctive dataset and information about state legislative committee hearings, hearing transcripts, assets, bill information, etc.

This thesis presents a machine learning framework for predicting whether California legislative bills are likely to receive media coverage. Drawing on a dataset of legislative ”tip sheets” provided by Digital Democracy …

Exploring Synthetic Data Generation For Machine Learning And Statistical Analysis, Aditi S. Gajjar

Master's Theses

In the data-driven machine learning and statistical analysis world, real-world data availability is often constrained by privacy concerns, access restrictions, or resource limitations. To address these challenges, synthetic data provides a groundbreaking solution, enabling academics and data scientists to generate artificial datasets that mimic real-world data for model development and testing in a controlled, privacy-preserving environment. The syntheticDatasets package represents an R-based counterpart to Python's widely-used Scikit-learn dataset generators, adapted to meet the needs of statisticians, educators, and data scientists primarily working in the R programming ecosystem.

This thesis explores the design and implementation of the syntheticDatasets package, focusing on …

A Graph Backdoor Detection Method For Data Collection Scenarios, Xiaogang Xing, Ming Xu, Yujing Bai, Dongdong Yang

College of Communication and Media Scholarship and Creative Works

Data collection is an effective way to build a better Graph Neural Network (GNN) model, but it also makes it easy for attackers to implant backdoors into the model through data poisoning. In this work, we propose a backdoor detection method of graph for data collection scenarios (CGBD). Different from most existing backdoor detection methods of Neural Network (NN) models, especially the Deep Neural Network (DNN) models, the difference in predictions of backdoor samples in clean and backdoor models is exploited for backdoor detection in CGBD. Specifically, in the backdoor model, the backdoor samples with modified labels are predicted as …

Early Diabetes Prediction Using Machine Learning: A Comparative Study Of Classification Models, Ahmed Abdelqadir Bawazir

Theses

The current dissertation investigates the application of machine learning in diabetes prediction at early stages through a comparison of the performances of three classification models, including Logistic Regression, Decision Tree, and Random Forest. Inspired by the increased and spread cases of diabetes worldwide, the research objective is to facilitate early diagnosis by providing interpretable and accurate predictive models. Based on Pima Indians Diabetes Dataset containing 768 clinical records, the research implemented data preprocessing including KNN imputation and outlier processing, feature scaling, and formation of interaction features. Quantitative and comparative approach was made to train and test the models based on …

Unmasking Corruption And Bribery Using Predictive Analytics, Maha Salem

Theses

The initiative (Unmasking Corruption and Bribery Using Predictive Analytics) aims to identify employees in government sectors who commit corruption and bribery by addressing issues such as the lack of transparency, fairness, equality, and public trust, as well as weakened loyalty and poor reputation. This project focuses on data analytics and predictive modeling to detect suspicious or high-risk employee behaviors related to corruption and bribery. The main objective is to reduce these unethical activities in government sectors, strengthen integrity, and promote transparency and public trust through predictive capabilities. It also seeks to raise awareness about how serious and harmful these crimes …

Predicting Violent Crime Hotspots, Mohammad Alhammadi

Theses

This thesis presents a systematic literature review and an empirical demonstration focused on predicting violent-crime hotspots. Drawing on 50 studies published between 2010 and 2025, the review synthesises methodological developments across hotspot mapping, spatio-temporal modelling, risk terrain analysis, and machine-learning approaches. The review highlights a clear evolution from retrospective density maps to more dynamic, data-driven techniques, while also identifying persistent challenges related to data bias, temporal granularity, environmental context, fairness, and operational implementation. To complement the review, the thesis applies kernel density estimation (KDE) and three ensemble machine-learning models—Random Forest, Gradient Boosting, and XGBoost—to 769,680 geocoded violent-crime incidents recorded in …

The Impact Of The 2008 Financial Crisis On Crime And Cybercrime In The United States, Rashed Adel Alsuwaidi

Theses

This thesis will examine how the traditional crime and cy- were affected by the 2008 financial crisis. United States are also experiencing a rise in crime between 2005 and 2012. Based on the FBI data at the national level. The Internet Crime Complaint Center (IC3), Uniform Crime Reports and important economic indicators. The study, which involves tors, including unemployment, GDP, rates of foreclosures, and mortgage rates, is a combination. correlationbased interpretation supported by exploratory descriptive trend analysis. model-fit checks. The results indicate that contrary to the conventional expectations, violent and property crime also maintained their long-term reduction during the period …

Detecting Fraud In Police Reports Using Machine Learning And Natural Language Processing, Maryam Almarar

Theses

The paper explores how statistical analysis and machine learning can be used to identify the fraud patterns in the police reports. The study aims at establishing the most important predictive factors and indicators distinguishing fraudulent and valid cases with the use of structured data of police databases. The work was done in the background of the increase in financial fraud instances and the rising necessity of the introduction of automated detection systems in police departments. Police reports of the pastwere mined down to data and analyzed on SPSS 1, to carry out statistical operations. The sample was structured data which …

Predicting Residential Real Estate Prices In Dubai Using Integrated Data Analytics: A Machine Learning Approach, Mohammad Nasser

Theses

Dubai’s rapidly evolving real estate market attracts substantial global investment yet remains characterized by information asymmetry, pricing volatility, and fragmented data sources. This study examines how integrated data analytics and ensemble machine learning can be used not primarily for precise price prediction, but rather to identify and quantify the key behavioral and transactional drivers of residential property prices in Dubai. A unified dataset was constructed by integrating over 100,000 residential property transactions from the Dubai Land Department with macroeconomic indicators, web-scraped listing attributes, and sentiment measures derived from online reviews and social media discussions. Supervised learning models including Linear Regression, …

Predicting Loan Defaults: A Behavioral Scoring Approach For Portfolio Risk Monitoring In Banking And P2p Lending, Humaid Sultan Almarri

Theses

The critical challenge of lending institutions and peer-to-peer investors is to single out high-risk active borrowers in order to avoid defaults and corresponding losses. This thesis is devoted to the development of a data-driven approach to predict loan defaults, which balances predictive accuracy with interpretability and cost-sensitive evaluation. Using a large dataset of loans from LendingClub, we applied the CRISP-DM methodology, performing extensive data preprocessing and exploratory analysis before training several machine learning models-logistic regression, decision tree, random forest, and XGBoost-to classify loans as default or non-default. Class imbalance was addressed through resampling, and models were tuned via cross-validation with …

Deepfake Audio Detection, Rashed Alfalasi

Theses

The rise of deepfake audio technology has introduced a serious threat to information credibility, personal security, and media integrity. This thesis investigates the application of machine learning techniques for detecting synthetic audio through the analysis of acoustic features, including Mel-Frequency Cepstral Coefficients (MFCCs), spectral centroid, chroma_stft, and zero-crossing rate. The dataset used in this study was sourced from Kaggle and contains labeled samples of real and fake audio clips. The research aimed to train and evaluate multiple machine learning models—Support Vector Machines (SVM), Random Forest, XGBoost, Logistic Regression, and Neural Networks—to determine the most effective approach for deepfake audio classification. …

An Analytical Assessment Of Sentiment Analysis Trends And Methods Through Systematic Review And Topic Modeling, Chelsey Hill, Fatima Irshaidat, Marina Johnson, Jorge Fresneda

Department of Information Management and Business Analytics Faculty Scholarship and Creative Works

Sentiment Analysis (SA) applies Artificial Intelligence (AI) and Machine Learning (ML) techniques to identify and interpret opinions, emotions, and sentiment polarity in text data. This study presents a systematic literature review of SA research published between 2012 and 2024 using a dataset of 14,482 journal articles indexed in Scopus. The primary objective of this research is to provide a data-driven overview of the evolution of SA research, offering insights into methodological evolutions, emerging application areas, and the growing influence of AI in the field. The research findings reveal a multidisciplinary growth of SA research, with increasing contributions from Health Sciences …

Marsanywhere: Dataset And Cross-View Diffusion Model For Satellite-To-Ground View Synthesis With Mars Data, Benjamin T. Hinchliff

Master's Theses

Satellite-to-ground view synthesis aims to create a realistic ground view image from a corresponding satellite view image. This is a well-studied problem for street level imagery, with good results being achieved by using modern image synthesis techniques such as diffusion models. However, despite the public availability of satellite and ground level imagery on Mars, these techniques have yet to be applied to the domain due to difficulties in collating and processing the data into a usable form. We address this deficiency by creating a dataset consisting of ground view panorama imagery from the Perseverance rover, along with associated satellite view …

A Machine Learning Approach To Estimating Agricultural Return Flows: A Case Study Of The Twin Falls Canal Company In Idaho, Kendra Alise Figgins

Boise State University Theses and Dissertations

Idaho is the third largest water user in the United States due to its extensive agriculture industry (Murray, 2018). Irrigation has allowed farmers to take advantage of the warm climate and rich soils of the Eastern Snake River Plain to develop one of the nation’s most productive agricultural regions. This area now contains one-third of Idaho’s irrigated acres (Idaho Department of Water Resources, 2024). It also contains critical hydrologic connections between the Snake River and the Eastern Snake Plain Aquifer.

Farming practices in Idaho have gradually shifted over time. Warming temperatures and increased irrigation efficiency are allowing an expansion of …

Generalized Detection Of Animal Behavior Using Accelerometers, Alexander J. Arrieta

Master's Theses

Animal mounted sensors are becoming increasingly used to passively monitor both domestic and wild animals. Advances in lightweight accelerometer and GPS technology have allowed many animals to be fitted with high accuracy sensors for extended periods of time. This leads to new opportunities to study animal behavior without direct observation. However, interpreting the raw data is difficult due to the high volume and missing context of the information. Machine learning techniques excel at extracting information from raw data streams and are excellent candidates for processing the sensor data. However, due to large variance in how different animals execute the same …

Learning To Accelerate Tightening Of Convex Relaxations Of The Ac Optimal Power Flow Problem, Faith Cengil, Harsha Nagarajan, Russell Bent, Sandra Eksioglu, Burak Eksioglu

Industrial Engineering Faculty Publications and Presentations

We propose a novel machine learning (ML)-based approach to significantly reduce the run times of the optimality-based bound tightening (OBBT) algorithm for strengthening the convex relaxations of the non-convex Alternating Current Optimal Power Flow (AC-OPF) problem. While OBBT can yield near-global solutions via tight convex relaxations, its runtime remains a critical bottleneck on large-scale power grids. Our key contribution is a dynamic policy that selects smaller subsets of voltage magnitude and phase-angle difference variables for sequential bound tightening at every iteration of the OBBT algorithm. This ensures that the bound-tightening process remains adaptive, thereby circumventing the stalling in the optimality …

Integrating Dft And Machine Learning To Predict Structural Properties In High Entropy Alloys, Nathan Linton

All Dissertations

In the past decade, a paradigm shift in the design of metal alloys has been observed. These new alloys are commonly referred to as high entropy alloys (HEAs), multi-principal element alloys (MPEAs), or complex, concentrated alloys (CCAs). In contrast to conventional alloys, which consist of one main element (for example 80%) with other elements in small amounts, HEAs are made of four or more main elements ranging from 5 to 35% each element. Due to the large presence of multiple elements, HEAs have shown substantial material property improvements over conventional alloys such as steel. For example, they have high ductility …

Machine Learning-Based Detection Of Covert Data Exfiltration Via Electromagnetic Side-Channel Emissions From Computer Memory In Air-Gapped Systems, Oladapo Akintoye Ajike

All Theses

Air-gapped computer systems are physically isolated from unsecured networks. Though isolated, they remain vulnerable to covert data exfiltration via electromagnetic side channels and other covert-channel attacks. This research presents a comprehensive approach to detecting electromagnetic data exfiltration by establishing a controlled laboratory environment using low-cost, readily available hardware components. The study shows a proof-of-concept covert data transmission system that exploits electromagnetic emissions from computer memory access patterns through software-controlled Random Access Memory (RAM) operations.

The research methodology involved developing a C++ transmitter program that modulates CPU and memory-intensive operations to generate detectable electromagnetic signals at 100 MHz frequency, and implementing …

Integrating Gis And Machine Learning To Uncover Spatial, Temporal, And Contributing Factors To Bicycle Crashes, Precious Kamsiyo Ejikeme

All Theses

The recent drive for a cleaner environment has led to an increase in greener modes of transportation, including the use of bicycles. These emerging trends have led to an increase in bicycle-related crashes. According to the CDC, an estimated 1,150 cyclists were killed in 2023, with 120,000 sustaining non-fatal injuries. Transportation planners and agencies are under pressure to make cycling safer by providing dedicated bike lanes and improving existing infrastructure. To accomplish this, it is very important to understand which factors contribute to bicycle crash severity across different roadways.

Despite recent advancements in modeling bicycle crash severity, most studies rely …

Integrating Gis And Machine Learning To Uncover Spatial, Temporal, And Contributing Factors To Bicycle Crashes, Precious Kamsiyo Ejikeme

All Theses

The recent drive for a cleaner environment has led to an increase in greener modes of transportation, including the use of bicycles. These emerging trends have led to an increase in bicycle-related crashes. According to the CDC, an estimated 1,150 cyclists were killed in 2023, with 120,000 sustaining non-fatal injuries. Transportation planners and agencies are under pressure to make cycling safer by providing dedicated bike lanes and improving existing infrastructure. To accomplish this, it is very important to understand which factors contribute to bicycle crash severity across different roadways.

Despite recent advancements in modeling bicycle crash severity, most studies rely …

Development And Validation Of An Interpretable Risk Prediction Model For The Early Classification Of Thalassemia, Jin Xin Lai, Jia Wei Tang, Shan Shan Gong, Ming Xiong Qin, Yu Lu Zhang, Quan Fa Liang, Li Yan Li, Zhen Cai, Liang Wang

Research outputs 2022 to 2026

Thalassemia is an inherited blood disorder. Current diagnostic methods mainly rely on sophisticated equipment and specifically trained technicians. This study aims to identify and genotype thalassemia by applying machine learning (ML) algorithms to routine blood parameters. This study recruited a derivation cohort of 31,311 individuals from four independent hospitals and developed eight machine learning (ML) models for the purpose. The performance of these models was compared using a set of evaluation metrics. An additional cohort of 2000 patients was recruited for external validation to assess the generalization of the models. The results demonstrated that the categorical boosting (CatBoost) model exhibited …

Feature Extraction From Railroad Bearing Onboard Vibration Sensors Using Machine Learning Models, Diego Cantu

Theses and Dissertations

The University Transportation Center for Railway Safety (UTCRS) has developed an algorithm capable of identifying defective railroad bearings, determining damaged component(s) within, and quantifying severity of the defects. The defect-detection algorithm requires the operating speed as an input, which is not readily available in field operation of onboard sensors. Therefore, onboard sensors deployed in rail revenue service must rely on Global Positioning Systems (GPS) to obtain speed, which can be power-intensive and susceptible to signal interference. This study covers the development of a vibration-based model that extracts operating speed from wireless sensor data to enable fully autonomous onboard diagnostics. Signal …

Data-Driven Streamflow Forecasting In The Upper Colorado River Basin Using Spatio-Temporal Graph Networks, Akhila Akkala

All Graduate Theses and Dissertations, Fall 2023 to Present

Forecasting river flow is essential for managing water supplies, reducing flood risk, and supporting healthy ecosystems. In the Upper Colorado River Basin, much of the yearly water comes from melting snow. However, many traditional models struggle to capture how snowpack and river flow interact, especially across such a large and complex region.

This study uses a modern machine learning approach called a Spatio-Temporal Graph Neural Network (STGNN) to improve streamflow prediction. The model uses Snow Water Equivalent (SWE)—a measure of how much water is stored in the snowpack—along with river flow data. By treating each river gauge as part of …