Physical Sciences and Mathematics Commons^™

Suicide Ideation Detection Using Social Media Data And Ensemble Machine Learning Model, Erol Kina, Jin Ghoo Choi, Abid Ishaq, Rahman Shafique, Monica Gracia Villar, Eduardo Silva Alvarado, Isabel De La Torre Diez, Imran Ashraf

Research outputs 2022 to 2026

Identifying the emotional state of individuals has useful applications, particularly to reduce the risk of suicide. Users’ thoughts on social media platforms can be used to find cues on the emotional state of individuals. Clinical approaches to suicide ideation detection primarily rely on evaluation by psychologists, medical experts, etc., which is time-consuming and requires medical expertise. Machine learning approaches have shown potential in automating suicide detection. In this regard, this study presents a soft voting ensemble model (SVEM) by leveraging random forest, logistic regression, and stochastic gradient descent classifiers using soft voting. In addition, for the robust training of SVEM, …

A Systematic Review Of Audio Deepfake Detection Techniques For Digital Investigation, Mahra Alnaqbi, Richard Adeyemi Ikuesan

All Works

Deepfake technology has been driven by advanced machine learning and revolutionized multimedia creation by synthesizing hyper-realistic content. It includes images, videos, and audio. While its creative applications in entertainment and accessibility are significant, the technology also poses critical risks, especially in fraud, disinformation, and identity theft. Audio deepfakes are a subset of this phenomenon that replicate human voices with enhanced precision, mimicking tone, accent, and subtle vocal nuances. This has raised concerns in security-sensitive domains like voice authentication and forensic investigations. This systematic literature review (SLR) adopts PRISMA guidelines to explore the state-of-the-art in audio deepfake detection. It examines existing …

Image And Metadata-Driven Personality Inference For Career Recommendation: A Social Media-Based Ai Framework For Adolescents, Heba Ismail, Maryam Alhefeiti, Ashraf Khalil

All Works

This study presents a novel AI-based framework that leverages Instagram image and metadata analysis to infer Big Five personality traits and deliver personalized career recommendations for high school students in the UAE. Addressing the limitations of traditional recommender systems that rely on self-reported questionnaires or text, the proposed approach uses multimodal visual features—including profile metrics, HSV color patterns, semantic image labels, and texture analysis—to enable a non-intrusive, scalable personalization method. A pilot study involving data from 30 student accounts served as a proof of concept. Correlation analysis identified profile and HSV features as the most predictive, and four machine learning …

Machine Learning Models For Estimating The Consumed And Remaining Useful Life Of Haul Trucks In An Open-Pit Mine In Peru, Marco Cotrina, Jairo Marquina, Mario Sandoval, Jose Mamani, Johnny Ccatamayo

Journal of Sustainable Mining

The purpose of this study was to develop a machine learning-based model to predict the consumed useful life and estimate the remaining useful life of haul trucks in an open-pit mining operation in Peru. A comparative analysis of multiple machine learning models was conducted, including multiple linear regression (MLR), random forest + PSO, support vector regression (SVR), gradient boosting machine (GBM), decision tree + PSO, and artificial neural networks (ANN-MLP). The models were evaluated using performance metrics such as R², RMSE, and MAE, selecting the optimal model to estimate the remaining useful life based on a theoretical lifespan …

Understanding The Role Of Data Science Applications In Soil And Water Health, Payton Davis, Debabrata Sahoo, Dara Park, Brook Russell

Forestry and Natural Resources

Data science is an emerging field that can be incorporated into many disciplines, including environmental science. Data science can provide valuable information from data, enhancing the understanding of systems and environments. This publication is intended to give an overview of the different aspects of data science and how data science can be leveraged into and applied to soil and water health.

Investigating The Characteristics Of Ch4 Adsorption In Coal Reservoirs Based On Interpretable Machine Learning, Chen Ruibin, Zhou Sandong, Zhang Weixin, Yan Detian, Wang Hua

Coal Geology & Exploration

Objective Accurately predicting methane (CH₄) adsorption capacity in coal reservoirs is critical to the efficient exploration and exploitation of coalbed methane (CBM). Existing studies largely rely on experimental data from a small number of samples and theoretical adsorption models such as Langmuir. However, these studies exhibit time-consuming and laborious processes, as well as conclusions with limited generalizability, posing challenges to their widespread applications across different basins. Methods Based on 1150 sets of experimental data on CH₄ adsorption in coals, this study investigated five machine learning models, namely artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor …

Intelligent Identification Of Deep Coal Macrolithotypes Based On Weighted Ensemble In Machine Learning: A Case Study Of The Shenmu Area, Ordos Basin, Yao Yuhao, Yang Zhaobiao, Huang Daojun, Wang Jianan, Dong Guodong, Chen Yilin, He Jinxian, Zhang Shang

Coal Geology & Exploration

Objective Coal macrolithotype represents a key geological property in the exploration and recovery of deep coalbed methane (CBM) resources, and its accurate identification is of great significance for selecting the optimal geological and engineering sweet-spot intervals. However, conventional coal macrolithotype identification relies heavily on core observation, suffering from prohibitively high costs since core data are extremely valuable and thus feature a small sample size. Meanwhile, conventional log-based threshold methods face challenges in effectively characterizing the complex boundaries between coal macrolithotypes.Methods This study investigated the No. 8 coal seam of the Benxi Formation, Shenmu area, eastern Ordos Basin. Based on …

Non-Gradient Quaternion Training Matrix Modifications For Color-Image Distillation, Tahsin Shahnewaz, Megdam Ahmed Chowdhury, Nikolay Metodiev Sirakov

Student Publications

This paper develops a new multi-stage image distillation method that combines two well known techniques. In the first stage, our method creates a matrix from all training images. In the next stage, it adapts a modified principal component analysis (M-PCA) approach to transform the training matrix. In the third stage, Singular Value Decomposition (SVD) fur ther refines the training-image matrix through low-rank reconstruction and controlled row selection. In the fourth stage, rotation of small 2 ×2 matrix blocks on the entire left singular matrix is conducted. The upper m (user-selected number) rows of the reconstructed matrix are selected and transformed …

From Data To Victory: The Race For Analytic Superiority In Warfare, Robert Grossman, Emily Goldman

Joint Force Quarterly

Artificial intelligence technologies have reached a tipping point after decades of development. They are diffusing widely across defense and national security applications. Twenty-first century warfighters rely on analytic models in all systems, at all echelons, and in all domains. As more powerful models built on ever larger data sets become ubiquitous, militaries are in a new competition to deploy artificial intelligence. Operational art must embrace “analytic superiority.” This is the operational advantage from collecting and ingesting data, building robust models and computing infrastructure, deploying the models into operational systems, and denying adversaries' ability to do the same

This article explains …

The Application Of Machine Learning And Deep Learning On Demand Forecasting Across Time-Critical Industries: A Systematic Review, Asmaa Seyam, Sujith Samuel Mathew, May El Barachi, Cheng Zhang, Jun Shen

All Works

The applications of machine learning and deep learning in demand forecasting have attracted increasing attention, as they offer remarkable predictive capabilities that help automate forecasting processes and achieve higher accuracy. While numerous review studies have examined solutions within specific industries, there is a lack of comprehensive literature review investigating these solutions across different sectors. Therefore, this study overviews machine learning and deep learning applications in demand forecasting across time-critical industries, including power, tourism, water, transportation, and food. A two-tier classification framework is proposed to categorize demand forecasting studies by both application industry and methodological architecture. In addition, the most popular …

Exposing And Addressing Machine Learning Brittleness Through Constraint Solving, Muyeed Ahmed

Dissertations

Machine Learning (ML) implementations are fundamentally brittle: nondeterministic, inconsistent, and prone to overfitting; however, constraint solving can be used to systematically expose, quantify, and address this brittleness.

This dissertation first establishes that widely-used implementations of popular ML algorithms are nondeterministic (producing different outputs on the same input, across different runs) and inconsistent (different implementations of the same algorithm producing different outputs on the same input). This is more prevalent in Unsupervised Learning (UL) implementations where, due to the lack of a ground truth, subtle execution errors can go unnoticed and are difficult to verify. Nondeterminism and inconsistency also introduce security …

Predictive Modeling Of Heavy Metal Pollution And Ecological Risk For Sustainable Water Quality Management In The Ny–Nj Harbor System, Md Shahnul Islam, Sara Naveed, Huan Feng, Tapos Kumar Chakraborty

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Evaluating and forecasting surface water quality is essential for protecting aquatic ecosystems and improving water resource management. This study introduces a novel paradigm that integrates machine learning (ML) with the potential ecological risk index (PERI) to dynamically forecast, rather than statically assess, ecological risks from heavy metal contamination in an urban estuarine environment. Surface water samples from the Lower Passaic River in New Jersey, USA, were analyzed for copper (Cu), lead (Pb), and mercury (Hg) across multiple sites and sampling campaigns. Concentrations ranged from 3.1 to 42.6 µg/L for Cu, 1.8 to 25.4 µg/L for Pb, and 0.12 …

Machine Learning For Functional Outcome Prediction After Vestibular Schwannoma Surgery: A Systematic Review And Diagnostic Test Accuracy Meta-Analysis, Shiva Nischal, Shaan Patel, Musa China, Kush Kale, Yi Hein Chai, Santosh Guru, William Muirhead, Patrick Grover

Department of Neurosurgery Faculty Papers

PURPOSE: Machine learning (ML) models have been increasingly applied to predict postoperative facial nerve dysfunction and hearing preservation after vestibular schwannoma (VS) surgery. However, reported performance varies substantially, and the overall diagnostic accuracy and clinical reliability of these models remain uncertain. We conducted a systematic review and diagnostic test accuracy meta-analysis to characterise the current state and methodological readiness of ML-based prediction of these outcomes.

METHODS: PubMed, Embase, and CENTRAL were searched from inception to February 2026. Studies evaluating ML-based prediction of facial nerve function or hearing preservation following VS surgery were included. Diagnostic performance metrics were pooled using random-effects …

Aqqd: Annotated Quranic Qira’At Dataset, Linda Smail, Mohammed Lataifeh, Md Sohazur Islam Sozib, Arthur Diniz De Souza

All Works

AQQD (Annotated Quranic Qira'at Dataset) is an open audio dataset of Quranic recitations annotated across canonical Qira'at styles. The dataset is designed to support research in machine learning, speech and audio processing, computational linguistics, and Quranic studies. The current release contains 24,183 WAV audio files from 309 reciters and covers 70 selected Quranic Surahs segmented into representative verses and phonetic variation points. Of these, 23,111 recordings were collected from publicly available sources, including official reciter websites, the Midad repository, MP3Quran, and verified YouTube channels, while an additional controlled subset of 1,072 recordings was obtained from a single reciter recorded as …

Physics-Guided Deep Learning For Predictive Modeling Of Spatiotemporal Dynamical Systems, Niharika Deshpande

Engineering Management & Systems Engineering Theses & Dissertations

Many physical and networked systems evolve under continuously changing spatial and temporal conditions. Transportation networks respond to fluctuating demand, atmospheric fields reorganize as storms intensify, and coastal response depends on localized forcing pathways. Modeling such systems requires learning formulations that adapt to evolving structure, operate on irregular geometries, and provide interpretable measures of predictive uncertainty. This dissertation develops a physics-guided spatiotemporal learning framework designed for structured dynamical systems whose governing interactions are neither static nor Euclidean. The central premise is that spatial relationships in these systems are dynamic and geometry-dependent. To represent this behavior, system states are modeled on time-varying …

A Data-Driven Nutrient Density Scoring Framework For Beef Using Principal Component Analysis, Teja Vuppala

All Graduate Theses and Dissertations, Fall 2023 to Present

Beef is one of the most nutrient-rich foods in the human diet, providing high-quality protein, iron, omega-3 fatty acids, B vitamins, and a wide range of other compounds important to health. However, current nutrition scoring systems used on food labels were designed to compare different foods to one another — for example, beef versus broccoli — and do not work well for judging the nutritional quality of different beef samples relative to each other. A grass-fed steak and a conventionally-finished steak can carry nearly identical Nutrition Facts panels while differing substantially in their content of omega-3 fatty acids, vitamins, and …

Ai-Driven Segmentation And Volumetric Response Modeling Of Liver Regions To Radiotherapy, Aashish Chandra Gupta

Dissertations and Theses (Open Access)

In liver-directed radiotherapy (RT), liver regions receiving higher doses typically undergo atrophy while contralateral/adjacent lower-dose regions may exhibit compensatory hypertrophy through regeneration of healthy tissue. Optimizing the RT plan to promote regional hypertrophy while minimizing the risk of developing atrophy has the potential to enhance post-RT liver function and long-term survivorship. However, current clinical practice largely relies on global liver dose-volume metrics during RT-planning, which may obscure favorable dose-response correlation and limit actionable guidance for clinicians. Therefore, we hypothesized that post-RT regional liver response is governed by a combination of region-specific dose-volume and patient clinical features, and that these responses …

Advancements In Modern Seismic Monitoring: Integrating Novel And Traditional Methods For Earthquake Detection, Characterization, And Structural Imaging, Marc Adrian Garcia

Open Access Theses & Dissertations

Modern seismic monitoring has been transformed by machine-learning methods that detect and locate earthquakes at scales manual analysis cannot reach. This dissertation develops, validates, and applies such workflows across three settings that span the range of modern monitoring problems: a major subduction-zone aftershock sequence, the tectonic questions that sequence can answer, and an urban region without any local monitoring at all. First, I construct a high-resolution catalog for the aftershock sequence of the September 8, 2017, Mw 8.2 Tehuantepec, Mexico earthquake, by integrating deep-learning phase detection with established location and relocation methods. The resulting catalog of 11,374 relocated earthquakes is …

Unsupervised Learning For Minimum Error Adaptive Sampling Of Atmospheric Vertical Temperature Profiles, Alejandro Medina

Open Access Theses & Dissertations

Uncrewed aerial systems (UAS) collect atmospheric data on fixed schedules, and endurance limits make blind searching costly. This thesis develops an unsupervised representation that summarizes a multi-decade radiosonde archive into a compact library of atmospheric states, giving a UAS an expectation of the column before it flies. The method standardizes both axes of a profile against the sounding's own surface conditions, which makes the representation independent of season and of station elevation. Applied to 27,270 soundings from Norman, Oklahoma, over the lowest 1.5 km of the atmosphere, 12 representative profiles reconstruct the record to within 1.0 °C of mean absolute …

A Machine Learning Approach For Water Quality Assessment In The Lower Rio Grande Valley Watershed, Saika Nowshin Nowrin, Chu-Lin Cheng, Jungseok Ho, Jinwoo An, Fatemeh Nazari

Civil Engineering Faculty Publications

Water quality analysis plays an essential role in maintaining the health and sustainability of river ecosystems, especially in semi-arid regions like the Arroyo Colorado Watershed in South Texas. Since the river is a vital source of water supply for local communities, agriculture, and wildlife, it faces significant challenges and pollution from land use changes, climate variation, and agricultural runoff. Continuous monitoring and assessment of water quality parameters and their temporal variability are essential to ensure the drinking water supply and aquatic ecosystem health. However, comprehensive laboratory-based water quality investigations are often constrained by higher costs, logistical complexity, and limited manpower. …

Stylometric And Formal Patterns In The Scholarly Impact Of Scientific Literature, Joshua Ange, Eric Godat, Rajani Sudan

SMU Journal of Undergraduate Research

Scientific communication is typically tied to promoting public engagement and interest in science, increasing scientific literacy, and playing an essential role in policymaking. The success of public communication of scientific findings is largely associated with secondary characteristics of research (e.g. the style of writing and presentation), rather than the primary content or research quality. But it is unclear to what extent the success of scientific literature intended for working scientists is influenced by those same secondary characteristics. Does the writing style of scientific articles impact their success in academic spheres? In this study, we explore the stylometric and formal characteristics …

A Holistic Approach For Workforce Scheduling And Routing, Kerem Can Manalp, Ansel Kaplan Erol, Kutluhan Erol, Cem Evrendi̇lek

Turkish Journal of Electrical Engineering and Computer Sciences

The workforce scheduling and routing problem (WSRP) involves assigning tasks across multiple locations while accounting for varying travel times, service durations, time windows, and skill requirements in a wide range of industries, from healthcare to telecommunications. This paper presents a mixed-integer programming model for the WSRP that balances the trade-off between cost and customer satisfaction using a score-generation function and subsequently evaluates the trade-off between solution quality and computation time for several algorithms on well-known datasets. We demonstrate that our model effectively balances cost, service-level agreement satisfaction, and task priorities while providing high-quality solutions in a timely manner. Observing that …

Coupled-Pendulum Modeling In An Ode Class: An Assignment On Fourier-Initialized Gradient Descent In Machine Learning, Huy Truong, Andrew Bennett

CODEE Journal

As data-driven methods are increasingly used in science and engineering, students benefit from learning to integrate machine learning techniques with traditional mathematical modeling. We present a hands-on extra-credit assignment for an undergraduate ordinary differential equations (ODE) course that enables students to compare classical analytical methods with data-driven approaches on the same physical system. Using a coupled-pendulum system---two pendulums connected by a spring---with real experimental data acquired via video tracking of a real physical setup, students work through three models in a guided Jupyter notebook with all code provided. First, they fit a neural network with Fourier features as a purely …

Data-Driven Characterization Of Counties In The Prison Industrial Complex Using Clustering Analysis, Riley N. Tuccio

Capstone Projects

This project investigates the complex relationship between counties that house prisons in the United States and the rurality associated with them. The central research question explores how both county characteristics, such as variables corresponding to cost of living and demographics of a county, and prison characteristics, such as programming available to inmates and staffing levels, differ across the census-designated rural-urban distinctions. Furthermore, the study examines whether modern data science methods can more accurately define and distinguish these characteristics, providing a nuanced understanding of the Prison Industrial Complex (PIC) and its manifestation across various American communities. The motivation for this research …

Anomaly Management In Unmanned Aerial Vehicles: A Systematic Literature Review, Ivan Tan Wei Han, Christopher M. Poskitt, Lingxiao Jiang, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in safety-critical applications such as logistics, surveillance, disaster response, and urban air mobility. While their autonomy enables powerful capabilities, it also introduces vulnerabilities due to hardware faults, software defects, communication failures, and adversarial interference. This survey presents a comprehensive review of research studies closely related to UAV anomalies published between 2015 and 2025, covering 111 papers from academic and industrial sources. We introduce a unified five-pillar taxonomy—anomaly generation, prevention, detection, recovery, and analysis—that organizes existing work across the full anomaly management lifecycle. In contrast to prior surveys that focus primarily on detection algorithms, …

Hsv-1 Us3 Hijacks Conserved Actin Regulatory Complexes To Drive F-Actin Remodeling, Md Imran Hossain, Md Arifuzzaman, Md Mehedi Hasan, Seung Jong Park, Leila Rahimian, Ojasvi Dutta, Vladimir Chouljenko, Harikrishnan Mohan, Reza Ghavimi, Konstantin G. Kousoulas

Computer Science Faculty Research & Creative Works

The herpes simplex virus 1 (HSV-1) US3 is a multifunctional serine/threonine kinase that promotes HSV-1 replication and spread. But its role and the mechanisms by which US3 regulates actin cytoskeletal remodeling remain poorly defined. We combined flow cytometry, confocal microscopy, immunoprecipitation-mass spectrometry (IP-MS), protein complex mapping, and machine learning to characterize US3-mediated F-actin dynamics. Flow cytometry and confocal microscopy showed that wild-type HSV-1 induces significant F-actin remodeling, while the ΔUS3 mutant displays F-actin levels comparable to uninfected cells, identifying US3 as a key regulator. IP-MS identified 47 high-confidence US3 interactors enriched in conserved actin regulatory complexes, including Arp2/3 nucleation machinery, …

Statistical Machine Learning For Calibrating Climate Model Wind Fields., Augustine Ouru

Graduate Theses and Dissertations

This thesis investigates statistical calibration methods for wind data from Global Climate Models (GCMs), with emphasis on wind speed and wind direction. The analysis uses 30 years of daily observed and GCM wind records from 9 spatial locations in the South East USA. Wind is represented in polar coordinates, but also often modeled in Cartesian coordinates. Four calibration models are compared: a marginal wind speed model, an independent Cartesian model. The calibration models are based on semi-parametric quantile regression, a flexible conditional density estimator fitted using neural networks. The neural network objective functions are optimized using maximum likelihood estimation (MLE) …

Survey On Learning-Based Dynamic Fault Localization: From Traditional Machine Learning To Large Language Models, Chunyan Liu, Yan Lei, Huan Xie, Jinping Wang, Yue Yu, David Lo

Research Collection School Of Computing and Information Systems

Learning-based dynamic fault localization techniques play a crucial role in the field of software engineering. These techniques dynamically execute test cases to meticulously extract useful knowledge from the execution information in the program, with the aim of identifying fault locations by leveraging machine learning, deep learning, and large language models. Currently, there is already a flourishing body of research that is intensely focused on learning-based dynamic fault localization. Research literature can be categorized into two main aspects for learning-based dynamic fault localization: data-based enhancements (i.e., the datasets) and model-based enhancements (i.e., the suspiciousness algorithms). Thus, we conduct an extensive literature …

Research On Mechanical Properties Of Concrete At High Temperatures Based On Machine Learning, Liu Junhua, Liu Bin, Cao Haifeng, Liu Zhiguang, Li Zhiyong

Journal of China & Foreign Highway

Structural safety is directly affected by the mechanical properties of concrete at high temperatures. Firstly, based on the existing compression and tension test data of concrete at high temperatures, the Abaqus finite element software was adopted for numerical simulation reproduction, and the reliability of the simulation method was verified. Secondly, by simulating the uniaxial tension-compression and confining pressure tests of normal concrete with different strength grades under high temperatures of 20‒800 ℃, the influence rules of temperature on the compressive strength, splitting tensile strength, elastic modulus, and stress ‒ strain relationship of concrete were elucidated. Finally, based on three commonly …

Advances In Research On Methods For Intelligent Identification Of Seismic Facies, Liu Xingye, Yu Peilin, He Hengjun

Coal Geology & Exploration

Background The intelligent identification of seismic facies can significantly improve the efficiency of sedimentary system characterization and hydrocarbon reservoir interpretation. However, influenced by factors such as non-stationary geological bodies, high costs of sample labeling, and limited training samples, conventional methods for intelligent identification are generally insufficient to achieve high identification accuracy and widespread application concurrently. Advances This study presents a systematic review of three types of technologies for the intelligent identification of seismic facies, namely unsupervised, supervised, and semi-supervised learning, with each type including deep learning methods. The three technological types are comparatively verified using 3D seismic data from a …