Digital Commons Network^™

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 …

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, …

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 …

Energy-Efficient Routing Protocols In Wireless Sensor Networks For Iot Applications: A Machine Learning Approach Using Xgbm And Color Harmony Algorithm, Subhieh El-Salhi, Bashar Igried, Sari Awwad

Mesopotamian Journal of CyberSecurity

Energy efficiency is a central constraint in wireless sensor networks (WSNs) used for Internet of Things (IoT) applications because sensor nodes have limited batteries, processing capacity, and communication bandwidth. This study combines eXtreme Gradient Boosting (XGBM) with the Color Harmony Algorithm (CHA) to predict energy-efficient paths and optimize model and routing parameters. The analysis uses 58,847 validated network-event records generated with NS-3 and a custom Python simulator; the Intel Berkeley Research Lab sensor trace was used only as an external range check. In a 50-run simulated evaluation, the proposed configuration showed an 18% reduction in energy consumption, a 22% increase …

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.

Hybrid Deep-Machine Learning For Butterfly Species Image Classification, Maha A. Rajab, Wisal Hashim Abdulsalam, Firas A. Abdullatif, Tole Sutikno

Baghdad Science Journal

Classifying butterfly species is crucial in biodiversity studies and environmental monitoring. However, manual classification is often a laborious process that requires specialized expertise and is prone to error, especially when species have similar visual characteristics. To address these drawbacks, this paper presents a hybrid approach that combines machine learning with deep learning for feature extraction. To enhance the visibility of important features, preprocessing techniques such as background removal and binarization are applied to butterfly images. Feature extraction was performed using the SqueezeNet convolutional neural network, pretrained on the ImageNet dataset. By discarding the final classification layer, the network produced discriminative …

Quantum-Enhanced And Machine Learning Framework For High-Accuracy Fatigue Life Prediction And Structural Analysis Of Connecting Rod, P. H. J. Venkatesh, P. Seema Rani

Babylonian Journal of Mechanical Engineering

The connecting rod is a critical mechanical component that transmits reciprocating motion from the piston to rotational motion in the crankshaft. Although connecting rods are traditionally manufactured from aluminum alloys, titanium alloys, or alloy steels, the demand for higher power-to-weight ratios has motivated the exploration of alternative materials supported by advanced computational validation methods. This study proposes a multilayer optimization framework for identifying the most suitable material for the connecting rod of a 110 cc four-stroke petrol engine. The component was modelled in Pro/ENGINEER, and high-fidelity Finite Element Analysis (FEA) was performed in ANSYS to evaluate the von Mises stress, …

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 …

Leakage-Safe Boosting Ensembles With Shap Explainability For Alzheimer's Disease Risk Prediction: A Rigorous Multi-Classifier Benchmark On Global Structured Clinical Data, Hadeer Mahmoud, Fady Salama

Sustainable Machine Intelligence Journal

Alzheimer's disease (AD) is a major public health challenge, and scalable non-invasive risk-stratification tools are needed. We benchmarked 11 machine-learning classifiers on a publicly available global structured Alzheimer's prediction dataset (N = 74,283; 24 predictors) using leakage-safe sklearn/imblearn pipelines. Four resampling strategies were assessed with stratified cross-validation; preprocessing included imputation, IQR-based outlier capping, feature engineering, one-hot encoding, and standardization. The leading models underwent HalvingRandomSearchCV optimization, followed by soft-voting and stacking ensembles. Performance was evaluated on a held-out 20% test set and interpreted with SHAP, permutation importance, and built-in feature importance. No resampling was selected in Stage A (composite score = …

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 …

Insect Monitoring Without Pitfalls: Seven Steps For Robust Insect Sensing Systems, Jamie Alison, Luca Pegoraro, Jarrett Blair, Yuval Cohen, Birgen Haest, Jacob Idec, Jacob Kamminga, Jenna Lawson, Meng Li, Leandro Aparecido Do Nascimento, Charlotte L. Outhwaite, Benjamin Rutschmann, Maximilian Sittinger, Mariana Abarca, Et Al

Biological Sciences: Faculty Publications

1. Data shortages fuel controversy about an ongoing insect biodiversity crisis. Insects are immensely diverse and functionally critical for ecosystems, yet data on their trends remain patchy and biased. Sensors, ranging from camera traps and acoustic recorders to weather radar stations, are set to transform data collection in entomol- ogy. Meanwhile, AI models that extract biological information from sensors are improving at a startling rate.

2. Realising the potential of automated monitoring means progressing from proof- of-concept studies to scalable insect sensing systems. However, stakeholders face severe operational challenges when adopting a growing suite of sensors, models and protocols for …

Cmsi Translations #35: How To Leverage The "Butterfly Effect" To Cause The Collapse Of "Taiwan Independence" Elements?, Su Ming

CMSI Translations

Intelligent Tools for Designing Operational Concepts: Intelligent Agents Innovating Mechanisms for Victory

If a large model is likened to a machine, interacting via conversation is akin to a worker manually operating that machine. Although machine tools have significantly reduced human workloads, “manual operation” still entails challenges: demanding skill requirements for operators; difficulty in resolving complex, specialized tasks through conversation alone; difficulty in standardizing and reusing repetitive workflows; susceptibility to hallucinations; and the time- and labor-intensive nature of verifying results. To this end, the Department of Theoretical Innovation Research has proposed a technical roadmap for programming large models. The underlying concept …

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 …

Research Status And Prospects Of Monitoring Technology For Large-Span Cable-Stayed Bridges Based On Machine Learning, Liu Guoliang, Liu Guokun, Yan Donghuang, Wang Wenxi, Wang Qishun

Journal of China & Foreign Highway

Machine learning and intelligent optimization algorithms have been increasingly applied to construction and health monitoring of long-span cable-stayed bridges. Based on the construction history of cable-stayed bridges both domestically and internationally, an overview of the origin and development process of cable-stayed bridges was provided. Firstly, from the perspective of the entire life cycle of bridges, bridge monitoring was divided into construction period monitoring and operation period monitoring. The applications of mainstream construction monitoring methods in large cable-stayed bridge projects were elaborated, and the specific composition of bridge health monitoring systems was clarified. Secondly, the basic principles of several machine learning …

Effective Traffic Routing Under Disastrous Events Through Machine Learning, Eric Itemuagbor

All Theses

Disastrous events such as hurricanes, wildfires, and floods routinely disrupt transportation networks, preventing emergency response, evacuation, and the delivery of critical supplies. Conventional optimization-based routing methods offer strong theoretical guarantees but cannot scale and are often too slow to support time-sensitive decision-making during a disaster, while prior machine learning approaches to disaster routing have largely prioritized prediction accuracy over inference speed and data efficiency. This thesis investigated whether lightweight machine learning models can deliver both competitive routing accuracy and superior inference speed when trained on limited data, addressing a practical gap between existing research and real-world deployment needs.

The road …

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 …

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 …

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 …

A Hybrid Machine Learning-Based Feasibility Prediction Of 3d Mechanical Designs Using Scalar And Geometric Features, Md Mohsin Uddin Fahim

Open Access Theses & Dissertations

The computational bottleneck of structural feasibility screening frequently hinders the transition from a digital 3D model to a physically manufactured component. Traditionally, engineers have relied on either overly rigid heuristic constraint checks or computationally exhaustive finite element simulations. To address this inefficiency, this thesis proposes and validates a hybrid machine-learning framework to predict the structural manufacturability of 3D mechanical designs. Moving beyond the conventional reliance on isolated scalar parameters, the proposed methodology extracts and integrates both scalar manufacturing constraints (e.g., tolerance, minimum feature thickness) and spatial geometric descriptors (e.g., bounding volume, aspect ratio) directly from STL mesh data. Utilizing a …

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 …

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 …

Integrating Ai-Based Electricity Demand Forecasting With Solar Grid Planning To Enhance Sustainability And Reliability, Anas Thamer Mustafa, Omar Sharaf Al-Deen Al-Yozbaky

AUIQ Technical Engineering Science

Proper electricity-demand forecasting is essential for reliable power-system planning, particularly in urban networks facing rapid demand growth and transformer overloading. However, many previous studies have treated load forecasting and renewable-energy integration as separate tasks, which limits their usefulness for practical planning. This study develops an integrated forecasting–planning framework that links AI-based electricity-demand forecasting with photovoltaic (PV) system design and transformer-loading assessment. The framework is applied to real daily data from the Al-Intisar 132/33 kV substation in Mosul, Iraq, covering electrical load, temperature, population, and date-related variables for the period 2022–2024. Fourteen forecasting models from four methodological categories were evaluated: machine-learning …

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 …

Privacy-Preserving Intrusion Detection For The Internet Of Medical Things Using Ensemble And Federated Learning, Theyab Alsolami

Electronic Theses and Dissertations 2020 - Present

The rapid proliferation of the Internet of Medical Things (IoMT) has transformed healthcare by enabling continuous monitoring, intelligent diagnostics, and data-driven clinical decision-making. However, this increased connectivity has significantly expanded the attack surface of healthcare systems, exposing sensitive patient data and critical medical devices to cyber threats such as intrusion and data exfiltration attacks. Ensuring both strong security and strict privacy preservation in IoMT environments remains a fundamental and unresolved challenge.

This dissertation investigates the design and evaluation of robust and privacy-preserving intrusion detection systems (IDS) for IoMT networks using advanced machine learning techniques. The research first examines the effectiveness …