Digital Commons Network^™

Computational Nanoscale Investigations Of Structure, Dynamics, And Reactivity At Solid–Liquid Interfaces, Golam Azom

LSU Doctoral Dissertations

Interfaces where two or more condensed phases meet govern phenomena central to catalysis, electrochemical energy conversion, separations, and environmental remediation. Their structural heterogeneity and dynamic nature make them challenging to probe experimentally, but computer simulations provide molecular-level insights into processes that remain inaccessible to experiments. This dissertation employs ab initio molecular dynamics (AIMD), classical molecular dynamics, and machine-learned interatomic potentials to investigate the structure, dynamics, and proton transport at graphene oxide (GO)–liquid interfaces.

The first project explores aqueous GO interfaces across a spectrum of oxidation states that extends from highly oxidized sheets to fully reduced graphene. Deep neural network force …

Face Recognition Using Optimized Multi-Task Cascaded Cnn In Artificial Intelligence Systems, Sami Abduljabbar Rashid, Mustafa Maad Hamdi, Salah Ayad Jassim, Lukman Audah, Baraa Saad Abdulhakeem, Mohammed Salah Abood, Ahmed Adil Nafea

Baghdad Science Journal

Improving the accuracy level of face recognition system is still an open research area where it consists of certain limitations to attain maximum accuracy. In earlier days, several algorithms are developed using machine learning and deep learning models, but still, they need improvement in terms of accuracy. For that purpose, in this article a novel CNN model called the optimized multi cascaded CNN (OPT-MTC-CNN) in the artificial intelligence system is developed, which helps to overcome the accuracy issues in face recognition system. The algorithms that are incorporated in this model are Fully Convolutional Network (FCN), Convolutional Neural Network (CNN), and …

Green Carbon Dots In The Era Of Ai: Sustainable Synthesis, Intelligent Drug Delivery, Advanced Diagnostics, And Bioimaging, Emi̇ne Sezer, Fulden Ulucan Karnak, Si̇nan Akgöl

Turkish Journal of Biology

Background/aim: Green carbon dots (GCDs) are a rapidly developing class of nanomaterials that are revolutionizing various scientific disciplines due to their unique optical properties, low toxicity, and sustainable synthesis. This review offers a comprehensive roadmap for the field, emphasizing the synergy between GCDs and artificial intelligence (AI).

Materials and methods: We begin by detailing the sustainable synthesis of GCDs, highlighting green chemistry principles and the transformative role of AI in optimizing their production. Subsequently, we explore the critical characterization of GCDs, including their structural, optical, and biocompatibility assessment. The core of this study explores the diverse biomedical applications of GCDs, …

A Systematic Review Of Machine Learning In Heart Disease Prediction, Tathagat Banerjee, İshak Paçal

Turkish Journal of Biology

Background/aim: Cardiovascular diseases (CVDs) are a leading cause of global mortality, prompting the need for advanced predictive tools. While machine learning (ML) offers a powerful solution, there are significant challenges to clinical translation. This systematic review synthesizes the current state of ML in heart disease prediction, evaluating algorithmic performance, data utilization, and key translational challenges.

Materials and methods: Following PRISMA guidelines, a systematic search of literature published up to 2025 was conducted. From an initial pool of over 2500 records, a rigorous screening process yielded 65 studies for in-depth qualitative synthesis.

Results: Analysis showed that ensemble learning models dominate prediction …

A Deep Learning Approach: The Impact Of Sentiment Analysis Of Bangladeshi Workers Over The World, Md Raihanul Islam Tomal, Tanveer Kader, Kohbalan Moorthy, Mazlina Abdul Majid

Baghdad Science Journal

In today's globalized world, the pursuit of better opportunities has led to a significant increase in immigration and foreign labor. This trend is particularly evident in Bangladesh, where many workers migrate annually to destinations such as Malaysia, Qatar, Saudi Arabia, and the UAE. Understanding the sentiments and emotions of these workers is crucial to preventing crimes, suicides, political unrest, and other security issues in host countries. As maximum workers usually come from rural Bangladeshi communicate they use local dialect for conversations and very popular widely used local dialect known as Pabnaia. This study did not identify any prior research specifically …

Dramatic Biases In Terrestrial Nitrogen Fixation In Earth System Models Revealed By Natural Isotope Signatures, Maoyuan Fang, Shushi Peng, Philippe Ciais, Daniel S. Goll, Benjamin Z. Houlton, Ying-Ping Wang, Yilong Wang, Pan Liu, Joshua B. Fisher, Pierre Regnier

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Biological nitrogen fixation (BNF) is the primary input of new reactive nitrogen to natural terrestrial ecosystems. However, this flux is poorly constrained due to its unclear drivers and associated control mechanisms. Here, we extend the existing theory of nitrogen (N) isotope mass balance to estimate BNF rates and then use a Bayesian approach to constrain the BNF rates in natural terrestrial ecosystems by using measurements of natural N-isotope ratios (δ¹⁵N) in plants (δ_P) and soil (δ_S). Together with pairwise δ_P and δ_S measurements from 18 forest sites covering diverse climates and thousands …

Visible Image-Based Machine Learning For Identifying Abiotic Stress In Sugar Beet Crops, Seyed Reza Haddadi, Masoumeh Hashemi, Richard C. Peralta, Masoud Soltani

Plants, Soils and Climate Student Research

Previous researches have proved that the synchronized use of inexpensive RGB images, image processing, and machine learning (ML) can accurately identify crop stress. Four Machine Learning Image Modules (MLIMs) were developed to enable the rapid and cost-effective identification of sugar beet stresses caused by water and/or nitrogen deficiencies. RGB images representing stressed and non-stressed crops were used in the analysis. To improve robustness, data augmentation was applied, generating six variations on each image and expanding the dataset from 150 to 900 images for training and testing. Each MLIM was trained and tested using 54 combinations derived from nine canopy and …

Integration Of An Artificial Intelligence–Based Autism Diagnostic Device Into The Echo Autism Primary Care Workflow: Prospective Observational Study, Kristin Sohl, Erik Linstead, Kelianne Heinz, Elia Eiroa Lledo, Alicia Brewer Curran, Melissa Mahurin, Valeria Nanclares-Nogués, Carmela Salomon, Minda Seal, Sharief Taraman

Engineering Faculty Articles and Research

Background: Pediatric specialist shortages and rapidly rising autism prevalence rates have compelled primary care clinicians to consider playing a greater role in the autism diagnostic process. The ECHO Autism: Early Diagnosis Program (EDx) prepares clinicians to screen, evaluate, differentiate, diagnose, and provide longitudinal care for children with autism in primary care settings. Canvas Dx is a prescription-only Software as a Medical Device designed to support clinical diagnosis or rule out of autism, including in primary care settings. It is authorized by the Food and Drug Administration for use, in conjunction with clinical judgment, in 18‐ to 72-month-olds with indicators …

Machine Learning-Guided Engineering Of Galactose Oxidase For Enhanced Activity On Non-Galactose Carbohydrate Substrates, Julia Gordijo, Jose Barraza Chavez, Xuebin Feng, Prof. Emma Master, Benjamin Sanchez

Enzyme Engineering XXVIII

Please click Additional Files below to see the full abstract

Generalizing Yield Prediction And Evaluating The Common Factors Influencing The Models, Xiaoyu Zhang, Sunoj Shajahan

Symposium of Digital Agriculture

This study combines Sentinel-2 satellite images with yield monitoring data from fields to evaluate yield predictions from spatial, temporal, and modeling dimensions. Combining the Matern-Kriging method, vegetation index and machine learning models, four key aspects were studied: the influence of spatial resolution on ground truth interpolation, the influence of spatial proximity of training fields, single year and multi-year prediction performance, as well as the influence of spatial smoothing for yield map. Random forest is used as the main predictive model. The results show that a smaller Kriging grid size retains more spatial details, while larger grid leads to detail loss. …

User Behaviour Prediction In E-Commerce Using Logistic Regression, Wei-Wen Lee, Noramiza Hashim, Shaymaa Al-Juboori

School of Engineering, Computing and Mathematics

From a psychological perspective, human behaviourreflectsunderlyingthoughtsand decision-making patterns, for example, consumer behaviour may correlate with the purchase decisions. In the fast-evolving e-commerce industry, predicting user behaviouris essential for enhancing marketing strategies, improving customer experiences, and increasing sales. However, traditional heuristic (e.g.market basket analysis) approaches to analysebuyer behaviour are often rigid and fail to adapt to complex consumer interactions. This research work develops a predictive model that analysesuser behaviour based on data such as historical purchasing patterns and demographic attributes. Based on a review of previous studies, Logistic Regression (LR) is utilized as theprimarymachine learning algorithm to estimate the likelihood of …

Toward Durable 3d Printed Structures, David A. Lange, Chenyue Shen

International Conference on Durability of Concrete Structures

Durability of 3-D printable concrete begins with control of flow and consolidation of the fresh material. 3D printed concrete needs to be fluid-like for pumping and extrusion but solid-like during placement to support the printed structure. High quality 3D printed materials must have excellent consolidation and a low volume of trapped voids. In practice, achieving this balance is challenging. This research addresses these challenges by leveraging machine learning and numerical simulation to model the fresh behavior of cement-based materials for 3-D printing. It begins with rheological characterisation to understand how mixture design factors influence rheological properties, laying the foundation for …

Integration Of Uav And Remote Sensing Data For Early Diagnosis And Severity Mapping Of Diseases In Maize Crop Through Deep Learning And Reinforcement Learning, Jerry Gao, Krinal Gujarati, Meghana Hegde, Padmini Arra, Sejal Gupta, Neeraja Buch

Faculty Research, Scholarly, and Creative Activity

Highlights: What are the main findings? A framework combining UAV, satellite, and weather data to detect crop diseases. Ensemble deep learning and reinforcement learning improve classification and severity mapping. What is the implication of the main finding? Enables near real-time, scalable crop disease management to reduce yield losses. Early detection and hotspot mapping optimize water and pesticide use for sustainability. Accurate and timely prediction of diseases in water-intensive crops is critical for sustainable agriculture and food security. AI-based crop disease management tools are essential for an optimized approach, as they offer significant potential for enhancing yield and sustainability. This study …

Improved Streamflow Forecasting Through Swe-Augmented Spatio-Temporal Graph Neural Networks, Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

Computer Science Student Research

Streamflow forecasting in snowmelt-dominated basins is essential for water resource planning, flood mitigation, and ecological sustainability. This study presents a comparative evaluation of statistical, machine learning (Random Forest), and deep learning models (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Spatio-Temporal Graph Neural Network (STGNN)) using 30 years of data from 20 monitoring stations across the Upper Colorado River Basin (UCRB). We assess the impact of integrating meteorological variables—particularly, the Snow Water Equivalent (SWE)—and spatial dependencies on predictive performance. Among all models, the Spatio-Temporal Graph Neural Network (STGNN) achieved the highest accuracy, with a Nash–Sutcliffe Efficiency (NSE) of 0.84 …

Foundations Of Artificial Intelligence In Healthcare Diagnostics: A Systematic Survey, Raghad Tariq Al-Hassani

Al-Esraa University College Journal for Engineering Sciences

Artificial Intelligence (AI) is becoming the cornerstone of the future of healthcare diagnostics, that has to ability to change the healthcare diagnostic landscape in terms of diagnostic accuracy, speed, and availability. This systematic review investigates the basic methods, tools, applications, and challenges involved in the integration of AI in diagnostic medicine. It emphasizes the using of machine learning models, deep learning networks (e.g., CNNs), NLP for clinical documentation, and smart computing infrastructures, such as edge device and IoMT. They are making possible real-time, data-driven decision making that is already at human-expert-level performance or, in some cases, even better (in the …

Towards Autonomous Energy Management: Machine Learning For Effective Auditing And Optimization, Sameh O. Abdellatif, Sherif Ashraf, Mira Mohsen

Electrical Engineering

This study presents a fully automated procedure for energy management and auditing, applicable to a diverse range of residential and commercial loads, leveraging machine learning techniques across three key phases: load classification, benchmarking, and smart monitoring. The model effectively categorizes energy loads based on consumption patterns, establishes performance benchmarks through historical data analysis, and employs real-time monitoring to identify inefficiencies and predict future energy usage. Evaluating the model through four distinct case studies demonstrates its capability to optimize energy consumption in a techno-economic manner, achieving significant energy savings of 34.73 MWh/year for essential loads in Egypt, 215.67 MWh/year for HVAC …

Theoretical Investigation And Practical Validation Of Ai Based Fault Detection In Bjt Amplifier Circuits, Sameh O. Abdellatif, Kyrillos Selim, Ahmad Alaa, Ola Hassan

Electrical Engineering

Analog circuits are essential to electronic systems due to their variety of critical functions. Catastrophic faults, such as short- and open-circuit faults, can stop the circuit from functioning. Meanwhile, parametric faults such as drift due to temperature fluctuations, noise in components, and power supply issues cause the circuit to operate beyond its range. Such faults can harm the circuit and its constituent parts. Therefore, it is crucial to identify and address the issues brought on by these faults. Common traditional fault diagnosis methods that rely on manual testing and visual assessment are lacking in complex electronic systems and faults. Such …

Nonlinear Design Scaling Of Electric Machines Based On Hybrid De And Meta-Modeling Application To Synchronous Motors With Combined Pm Stator And Reluctance Rotor Excitation, Oluwaseun A. Badewa, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents an innovative method for nonlinear scaling of electric machines by integrating machine learning (ML)-based meta-modeling with a differential evolution (DE) algorithm. The technique is applied to high-performance combined-excitation synchronous electric motors which exhibit highly nonlinear characteristics, making performance scaling challenging. The proposed approach employs an ML meta-model trained on data obtained from finite element analysis (FEA), utilizing an experimentally validated model for nonlinear scaling and performance prediction at different power ratings. The accuracy of the meta-model in capturing the nonlinear relationships between design parameters and motor performance is first assessed using metrics such as R-squared (R2) and …

Design Optimization And Scaling Of Coreless Afpm Machines Using Hybrid Fea-Based Differential Evolution And Machine Learning, Matin Vatani, David R. Stewart, Donovin D. Lewis, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents a machine learning (ML) based design framework for the fast and accurate optimization of coreless axial flux permanent magnet (AFPM) machines. Although the absence of magnetic cores eliminates material nonlinearity, the design process remains highly nonlinear due to the complex influence of geometric parameters. To overcome the computational challenges of finite element analysis (FEA)-based optimization, a series of multi-objective differential evolution (MODE) optimizations were conducted across various machine sizes at constant power output. The resulting design data was used to train an artificial neural network (ANN), enabling rapid prediction of machine performance without the need for repeated …

Deep Learning For Hate Speech Detection: A Comparative Study, Jitendra Singh Malik, Hezhe Qiao, Guansong Pang, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media. Numerous methods have been developed for the task, including a recent proliferation of deep-learning based approaches. A variety of datasets have also been developed, exemplifying various manifestations of the hate-speech detection problem. We present here a largescale empirical comparison of deep and shallow hate-speech detection methods, mediated through the three most commonly used datasets. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. We particularly focus our analysis on measures of practical …

Solar Power Forecasting Using Deep Learning Techniques, Fatma Abdulrahman

Theses

The growing utilization of solar energy and other renewable energy sources has heightened the demand for a proper forecasting model that enables efficient control of the energy supply as well as grid reliability. In this work, we concentrate about the use of novel techniques in ML models, namely Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), to try to forecast power output in a solar-powered plant using past weather and power output records. Production of solar energy is naturally volatile because it is dependent on the weather conditions and is problematic in nature when integrated into power grids. Conventional …

Machine Learning For Dissolved Oxygen Prediction In The Manawatu River Catchment, New Zealand, Imokhai T. Tenebe, Eunice O. Babatunde, Nkpa M. Ogarekpe, Egbe Etu Etu, Joshua Emakhu, Kindness E. Etu, Obiora B. Ezeudu, Oluwarotimi S. Oladele

Faculty Research, Scholarly, and Creative Activity

Uncontrolled anthropogenic activities have increased river pollution, resulting in poor water quality and a growing need for continuous monitoring, which is time-consuming and costly. This study evaluated the accuracy of machine learning (ML) models in predicting dissolved oxygen (DO) levels, aiming to reduce field monitoring efforts and minimize the number of measured parameters. Regression models (Huber, Linear, Ridge), Bagging methods (Extra Trees, Random Forest, Decision Trees), and Boosting techniques (Gradient Boosting, Light Gradient Boosting, AdaBoost) were tested using data from three sites in the Manawatu Catchment, New Zealand. Data spanning 1989–2014 included 12 water quality variables and was split into …

Machine Learning Classification Of Eeg Responses To Pain-Related Vs Non-Pain-Related Stimulus In Preterm Infants, Lojain Hamwi, Hang Du, Sara Jasim, Xiaogang Wang, Vibhuti Shah, Carol Cheng, Lorenzo Fabrizi, Maria Fitzgerald, Judith Meek, Nicole Racine, Ian Stedman, Rebecca Pillai Riddell

Michigan Tech Publications

INTRODUCTION: Unmanaged pain in preterm infants can lead to long-term developmental consequences. Current pain assessment methods lack specificity, resulting in possible pain mismanagement in Neonatal Intensive Care Units (NICUs). This study explores the application of machine learning (ML) to differentiate between pain-related and non-pain-related cortical activity in preterm infants. OBJECTIVE: To evaluate the performance of ML models in distinguishing cortical EEG activity during a painful procedure in preterm infants across different postmenstrual ages (PMAs). METHODS: This observational study was conducted from June 2015 to May 2024 at Mount Sinai Hospital in Toronto, Canada, and University College London Hospital, United Kingdom. …

Fast, Slow, And Metacognitive Thinking In Ai, M. Bergamaschi Ganapini, M. Campbell, F. Fabiano, L. Horesh, J. Lenchner, A. Loreggia, N. Mattei, F. Rossi, Biplav Srivastava, K. B. Venable

Faculty Publications

Inspired by the ”thinking fast and slow” cognitive theory of human decision making, we propose a multi-agent cognitive architecture (SOFAI) that is based on ”fast”/”slow” solvers and a metacognitive module. We then present experimental results on the behavior of an instance of this architecture for AI systems that make decisions about navigating in a constrained environment. We show that combining the two decision modalities through a separate metacognitive function allows for higher decision quality with less resource consumption compared to employing only one of the two modalities. Analyzing how the system achieves this, we also provide evidence for the emergence …

Machine-Learning-Assisted Discovery Of Lattice Dynamics Signatures Of Sodium Superionic Conductors, Ogheneyoma Maxwell Aghoghovbia

Theses and Dissertations

Sodium superionic conductors are key to the development of all-solid-state sodium batteries. Discovery of new superionic conductors has traditionally relied on insights from material defect chemistry and the transition/hopping theory, while the role of lattice vibrations, i.e., phonons, remains underexplored. We identify key lattice dynamics signatures that govern ionic conductivity by analyzing the phonon mean squared displacement (MSD) of Na⁺ ions. By high-throughput screening of a dataset of 3903 Na-containing structures, we establish a strong positive correlation between phonon MSD and diffusion coefficients, providing a quantitative correlation between lattice dynamics and ion transport. To accelerate this discovery, we incorporate …

Investigating Machine Learning Algorithms To Classify Label-Free Images Of Pancreatic Neuroendocrine Neoplasms, Noelle Daigle, Shuyuan Guan, Suzann Duan, Thomas G Knapp, Eung-Joo Lee, Ali Azhdarinia, Sukhen C Ghosh, Solmaz Aghaamiri, Servando Hernandez Vargas, Naruhiko Ikoma, Jeannelyn Estrella, Martin J Schnermann, Tobias Else, Michelle Kang Kim, Juanita L Merchant, Travis W Sawyer

The Brown Foundation: Institute of Molecular Medicine

Significance: Pancreatic neuroendocrine neoplasms (PNENs) are an uncommon cancer whose incidence rate has increased dramatically in recent years. Surgery is the only potentially curative treatment, which relies on both preoperative tumor localization and postoperative margin definition using histopathological examination for decision making. If pathology could be automated, valuable time and resources could be saved.

Aim: In this study, we investigate the ability of machine learning (ML) with handcrafted features, as well as deep learning, to classify label-free microscopy images of PNENs as a first step toward automated pathology of such tumors.

Approach: Patient samples of two different preparation types were …

Optimization Of A Mobile Imaging System To Aid In Evaluating Patients With Oral Lesions In A Dental Care Setting, Ruchika Mitbander, Jennifer Carns, Richard A Schwarz, Sean Anderson, Geethanjali Vipulanandan, Miriam Hernandez, Amelia E Hartzell, Jun Jiao, Ilona Rutkowska, Safia Durab, Sahar Sadeghi, Ashlee D Clayton, Nancy Bass, Loganayaki Anandasivam, Rachel A Giese, Ann M Gillenwater, Nadarajah Vigneswaran, Rebecca Richards-Kortum

Faculty, Staff and Student Publications

Significance: Oral cancer is often diagnosed at an advanced stage even though the oral cavity is easily accessible for examination. There is a need for tools to support early detection and help non-expert clinicians make appropriate referral recommendations.

Aim: We aim to evaluate a mobile detection of oral cancer (mDOC) smartphone-based autofluorescence imaging system by collecting data in a representative, low-prevalence population and optimizing a referral algorithm suitable for deployment in community dental settings.

Approach: Data were collected from patients at a community dental clinic using the mDOC system, which captures white light and autofluorescence images. A multi-input deep learning …

Rapid Discrimination Of Mycobacterium Tuberculosis And Non-Tuberculous Mycobacteria Disease Via Interpretive Machine Learning Analysis Of Routine Laboratory Tests, Jia Wei Tang, Xue Song Xiong, Ting Ting Huang, Yu Lu Zhang, Lin Fei Yao, Wen Wen Zhang, Yun Yun Xie, Quan Fa Liang, Zhi Xuan Tan, Kun Jiang, Xin Liu, Liang Wang

Research outputs 2022 to 2026

Objectives Rapid discrimination of infections caused by Mycobacterium tuberculosis (MTB) and non-tuberculous mycobacteria (NTM) is crucial in clinical settings. Despite overlapping clinical and radiological features, the two require markedly different therapeutic approaches and public health responses. Current laboratory methods are time-consuming and complex, underscoring the urgent need for a simple and efficient diagnostic tool to inform public health decision-making. Methods Demographic, haematological and biochemical data were collected from two hospitals in Jiangsu province, China, between December 2018 and October 2024. A total of 400 patients were included in the training cohort, with 66 patients used for external validation. Six machine …

Leveraging Machine Learning And Causal Inference For Loan Default Prediction, Luca Guida

Doctoral Dissertations and Master's Theses

This research explores a systematic application of machine learning techniques combined with causal inference to predict loan defaults in peer-to-peer lending. Accurately forecasting loan defaults is crucial for mitigating financial risk and optimizing lending strategies. This analysis is based on multiple datasets of loan applications spanning over a decade, containing detailed financial and credit information about borrowers. Beginning with extensive Exploratory Data Analysis (EDA) coupled with scaling strategies, the research identifies key trends in loan performance across a large number of factors, such as interest rates or borrower creditworthiness, and one objective is to determine from the many available predictors …

Fairer Public Benefit In Copyright Law, Amanda Levendowski Tepski

Georgetown Law Faculty Publications and Other Works

In 1966, a court considered expressly whether a secondary use of copyrighted works served a public benefit. While public benefit has become a subfactor of the fair use doctrine, it remains undefined, uncodified, and undertheorized. Courts often invoked public benefit in pivotal cases involving novel technologies, from home video recorders to digital libraries to algorithms--but they did not need to. After the recent Supreme Court decision in Google v. Oracle, however, public benefit is unavoidable: The Court stated that “we must take into account the public benefits the copying will likely produce.” A qualitative analysis of key public benefit cases …