Engineering Commons^™

Feasibility Of Using Untreated And Treated Rice Straw Fibers As A Modifier For Hot Mix Asphalt, Aditya Chowdhury

Civil Engineering Theses

Flexible pavements are subject to progressive deterioration driven by repeated traffic loading, environmental exposure, and material degradation, which causes distress. This study presents an integrated research framework that combines laboratory performance evaluation of rice straw fiber (RSF)-modified hot mix asphalt with machine learning-based pavement distress prediction to advance sustainable pavements. Three plant-produced HMA mixtures were modified with untreated and alkali-treated RSF at dosage levels of 0.2%, 0.4%, and 0.6% by total mixture weight. FTIR spectroscopic analysis confirmed that alkali treatment induced significant chemical modifications to the fiber surface, increasing hydroxyl group availability and enhancing fiber–binder adhesion. Alkali-treated RSF consistently outperformed …

Explainable Multi-Horizon Wind Power Forecasting Via Aquila-Optimized Machine Learning Models, Mostafa A. Abdelnaby, Nahla B. Abdel-Hamid, Eman M. El-Gendy, Mahmoud M. Saafan

Mansoura Engineering Journal

The inherently unpredictable nature of wind energy necessitates the development of sophisticated forecasting models to ensure grid stability and optimal distribution. In this research, we propose a novel and systematic approach to wind power forecasting (WPF) across diverse timescales. This approach leverages the power of various machine learning (ML) models, metaheuristic hyperparameter optimization, and utilizes explainable artificial intelligence (XAI). The developed methodology is based on the Aquila optimizer (AO), capable of automatically adjusting different ML models for four different time periods (30 minutes, 6 hours, 24 hours, and 36 hours) on the data collected from the Gabal El-Zayt wind power …

Evaluating Traffic Safety And Geometric Characteristics Using Machine Learning Ensemble Techniques: A Case Study Of Egyptian Rural Multi-Lane Divided Roads, Sania R. Elagamy, Ahmed N. Awaad, Usama E. Shahdah, Sherif M. El-Badawy, Marwa E. Elbany, Eman K. Ali

Mansoura Engineering Journal

Crash prediction models are essential for evaluating traffic safety by analyzing crash occurrence, frequency, or severity. Recently, machine learning techniques have gained prominence in statistical regression modeling and data analysis. This study assesses the effectiveness of machine learning in predicting crashes on Egyptian rural multi-lane divided roads using limited regional data. Supervised machine learning ensemble techniques were applied to predict crash-prone segments (classification) and estimate the total number of crashes per segment (regression). A comparative analysis aims to identify the most suitable method. The Synthetic Minority Oversampling Technique (SMOTE) addressed data imbalance, while K-means Clustering (KC) enhanced regression model accuracy, …

Mosquito Classification And Explainability From Image Data Via Deep Learning Techniques, Farhat Binte Azam

USF Tampa Graduate Theses and Dissertations

According to the World Health Organization (WHO), mosquitoes are the deadliest animals on Earth, responsible for more human deaths annually than any other species. Mosquito-borne illnesses continue to pose severe risks to global health. In 2015 alone, there were an estimated 214 million malaria cases worldwide. Similarly, a 2016 report from the Centers for Disease Control and Prevention (CDC) revealed that Puerto Rico’s Department of Health received over 62,500 suspected cases of Zika, with 29,345 confirmed positive cases. In 2019, Southeast Asia experienced its worst dengue outbreak in recorded history. Of the approximately 4,500 mosquito species distributed across 34 genera, …

Effects Of Lossy Compression Data On Machine Learning Models, Max H. Faykus Iii

All Dissertations

Machine learning is a fundamental tool that is incorporated in every field across academia and other industries. Due to the large amount of data needed for training machine learning models, lossy compression plays a crucial role in storing data. Machine learning involves the use of algorithms and models to learn patterns in data. This allows the AI to make decisions without specific programming. On the other hand, compression utilizes encoding and decoding techniques to reduce the size of files. Compression is either lossy or lossless, lossy causes a loss of data while lossless preserves the data. This dissertation will explore …

Revealing Building Operating Carbon Dynamics For Multiple Cities, Winston Yap, Abraham Noah Wu, Clayton Miller, Filip Biljecki

Research Collection College of Integrative Studies

Achieving carbon neutrality is a critical yet elusive goal for many cities, hindered by limited understanding of the relationship between building emissions and their surroundings. To address this challenge, we present a generalizable open science framework that integrates building energy-consumption data, multi-modal geospatial inputs and graph deep learning to quantify building operating emissions and their links to urban form and socio-economic factors. Applying this approach to five cities with diverse climates and planning contexts—Melbourne, New York City (Manhattan), Seattle, Singapore and Washington DC—we demonstrate that our models explain 78.4% of the variation in building operating carbon emissions across cities, achieving …

Predicting Seizure Onset Zones From Interictal Intracranial Eeg Using Functional Connectivity And Machine Learning, Jared Pilet, Scott A. Beardsley, Chad Carlson, Christopher T. Anderson, Candida Ustine, Sean Lew, Wade Mueller, Manoj Raghavan

Biomedical Engineering Faculty Research and Publications

Functional connectivity (FC) analyses of intracranial EEG (iEEG) signals can potentially improve the mapping of epileptic networks in drug-resistant focal epilepsy. However, it remains unclear whether FC-based metrics provide additional value beyond established epilepsy biomarkers such as epileptic spikes and high-frequency oscillations (HFOs). Using interictal iEEG data from 26 patients, we estimated FC across eight frequency bands (4–290 Hz) using amplitude envelope correlation (AEC) and phase locking value (PLV). From the resulting FC-matrices, we estimated two graph metrics each to derive 32 FC-based features. We also extracted features related to spikes, HFOs, and power spectral densities (PSD). A trained support …

Predicting Crises On The African Frontier Stock Markets With Investor Sentiment Indicators: A Machine Learning Approach, David Korsah, Lord Mensah

Journal of International Technology and Information Management

This study examined the predictive ability of machine learning algorithms in identifying crises within African stock markets. The study employed seven distinct machine-learning models, analyzing historical stock prices from eight stock markets, three major sentiment indicators, and the exchange rates of local currencies against the US dollar, with each data spanning from May 1, 2007, to April 1, 2023. Extreme Gradient Boosting (XGBoost) emerged as the most effective algorithm for predicting crises. Historical stock prices and exchange rates were identified as the most critical features for prediction. On the sentiment side, investors’ perceptions of potential volatility on the S&P 500, …

Predicting Global Healthcare Supply Chain Delays: A Machine Learning Approach Leveraging Country-Level Logistics Metrics, Jeevan Sai Gali, Nima Molavi, Sepideh Alavi

Journal of International Technology and Information Management

In global healthcare logistics, ensuring the timely delivery of medical commodities is critical, particularly in low- and middle-income countries characterized by infrastructural limitations and operational uncertainties. This research introduces an advanced, data-driven predictive framework designed to forecast delivery delays by synthesizing granular, internal shipment-level data from the USAID Global Health Supply Chain Program (GHSC-PSM) with external country-level logistics capabilities indicators derived from the World Bank’s Logistics Performance Index (LPI). Rather than relying on retrospective trend analyses, this study employs machine learning algorithms such as Random Forest, XGBoost, Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP) to detect …

Development Of Satellite-Assisted Forecasting System For Vibrio Vulnificus Prevalence In Coastal Waters, Saber Aradpour

LSU Doctoral Dissertations

Vibrio vulnificus is a halophilic gamma proteobacterium that is autochthonous to coastal waters and is responsible for 50% of seafood-related deaths in the United States. This study presents a series of nowcasting and forecasting models for predicting vibrio vulnificus abundance in oysters by identifying the long-range dependence of vibrio vulnificus abundance on antecedent environmental conditions and detecting the environmental conditions using satellite remote sensing technology. It was discovered that vibrio vulnificus abundance exhibits a long-range dependence on antecedent environmental conditions which can be characterized by seven independent environmental predictors (stressors) including sea surface temperature (SST), water level (WL), sea surface …

Enhancement Of Rainfall Prediction And Satellite Precipitation Estimates: A Machine Learning Approach For United Arab Emirates, Faisal Baig

Dissertations

The accurate estimation of rainfall in arid regions presents a significant challenge due to sparse ground observations and complex atmospheric dynamics. This dissertation investigates various aspects of rainfall estimation and prediction in the United Arab Emirates (UAE), employing Satellite Precipitation Products (SPPs) and Machine Learning (ML) techniques. The research is structured around four key articles, each addressing different facets of the overarching theme. The first publication examines rainfall variability, consistency, and concentration in the UAE by comparing satellite precipitation products with rain gauge observations, highlighting the discrepancies between the two data sources. The second publication evaluates the performance of precipitation …

A Systematic Review Of Fourth Industrial Revolution Technologies In Smart Irrigation: Constraints, Opportunities, And Future Prospects For Sub-Saharan Africa, Joshua Wanyama, Erion Bwambale, Shafik Kiraga, Abia Katimbo, Prossie Nakawuka, Isa Kabenge, Isaac Oluk

Department of Agricultural and Biological Systems Engineering: Faculty Publications

The adoption of Fourth Industrial Revolution (4IR) technologies has revolutionized agricultural practices worldwide. However, their application in the context of sub-Saharan Africa remains a critical challenge. This study presents a systematic review that investigates the potential of 4IR technologies in smart irrigation. Sub- Saharan Africa faces multiple agricultural challenges, exacerbated by climate change, water scarcity, and inefficient irrigation practices. The need for sustainable, water-efficient, and data-driven irrigation systems is urgent to ensure food security and economic development in the region. This study addresses a crucial knowledge gap by assessing the constraints, opportunities, and prospects of implementing 4IR technologies for smart …

Design And Development Of Clinical Decision Support System For Breast Cancer Diagnosis Using Artificial Intelligence, Karthiga R

Theses and Dissertations

The prevalence of breast cancer in women worldwide is far higher than that of cancers of the lungs, brain, or liver. Increasing ageing populations and poor lifestyle habits among the general public, primarily in industrialized nations, are significant factors contributing to the rise in cancer-related mortality rates worldwide. Approximately one woman in every three will develop breast cancer. This research proposes several advanced computer methods for analyzing breast cancer images. This work analyses breast cancer in four imaging modalities: mammography, thermography, ultrasonography and histopathology.

Each modality has some limitations in diagnosing tumors in the breast region. Heavy dose in mammogram …

Insect Classification And Explainability From Image Data Via Deep Learning Techniques, Tanvir Hossain Bhuiyan

USF Tampa Graduate Theses and Dissertations

Since the dawn of the Industrial Revolution, humanity has always tried to make labor more efficient and automated, and this trend is only continuing in the modern digital age. With the advent of artificial intelligence (AI) techniques in the latter part of the 20th century, the speed and scale with which AI has been leveraged to automate tasks defy human imagination. Many people deeply entrenched in the technology field are genuinely intrigued and concerned about how AI may change many of the ways in which humans have been living for millennia. Only time will provide the answers. This dissertation is …

Applied Machine Learning In Development Of Geospatial Information Tools For Sustainable Groundwater Management, Saul Gallegos Ramirez

Theses and Dissertations

Groundwater plays an important role in sustainable water resource management. Globally, the development of groundwater resources for irrigation provides a stable water source and enhances food security. However, developing groundwater resources is difficult, requiring the collection, synthesis, analysis, and dissemination of information about groundwater in an accessible, effective manner so decision makers have the knowledge and tools to implement sustainable management strategies. In this dissertation, I present solutions to assist with the paucity of groundwater data. The first problem I explore is extending the Palmer Drought Severity Index to near present day. This dataset is crucial to current groundwater imputation …

An Analysis Of Groundwater Storage Loss In The Central Valley Using A Novel In Situ Method Compared To Grace-Derived Results, Michael David Stevens

Theses and Dissertations

Robust groundwater management is necessary to maintain long-term aquifer sustainability. Temporally and spatially inconsistent in situ data prevents robust groundwater resource evaluation. Data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission has been used to evaluate long-term, large-scale groundwater trends. However, the spatial resolution of GRACE data presents challenges for groundwater management in medium-sized aquifers like the Central Valley of California (CV). Other researchers have utilized GRACEderived data to evaluate groundwater storage in the CV, but they often make corrections due to what is referred to as the "leakage effect." We demonstrate a method for imputing gaps in …

Happiness And Policy Implications: A Sociological View, Sarah M. Kahl

Dissertations, Theses, and Capstone Projects

The World Happiness Report is released every year, ranking each country by who is “happier” and explaining the variables and data they have used. This project attempts to build from that base and create a machine learning algorithm that can predict if a country will be in a “happy” or “could be happier” category. Findings show that taking a broader scope of variables can better help predict happiness. Policy implications are discussed in using both big data and considering social indicators to make better and lasting policies.

Seabem: An Artificial Intelligence Powered Web Application To Predict Cover Crop Biomass, Aime Christian Tuyishime, Andrea Basche

Honors Program: Senior Projects (Public)

SEABEM, the Stacked Ensemble Algorithms Biomass Estimator Model, is a web application with a stacked ensemble of Machine Learning (ML) algorithms running on the backend to predict cover crop biomass for locations in Sub-Saharan. The SEABEM model was developed using a previously developed database of crop growth and yield that included site characteristics such as latitude, longitude, soil texture (sand, silt, and clay percentages), temperature, and precipitation. The goal of SEABEM is to provide global farmers, mainly small-scale African farmers, the knowledge they need before practicing and benefiting from cover crops while avoiding the expensive and time-consuming operations that come …

Project Leanness Score: A Machine Learning Approach, Julia Said

Theses and Dissertations

The construction industry is known to have several inadequacies in resource utilization leading to cost and schedule overruns. One of the popular recent methods that attempts to eliminate these inadequacies is lean construction principles, techniques and tools. Lean construction is a philosophy, backed with principles and tools, aiming at maximizing value, eliminating waste, optimizing efficiency, and seeking continuous improvement. Lean construction techniques (such as pull planning, just-in-time delivery, fail safe for quality, etc.) are widely researched and well developed. However, their implementation in construction sites is tricky as their success depends on several other factors such as the level of …

Subnational Map Of Poverty Generated From Remote-Sensing Data In Africa: Using Machine Learning Models And Advanced Regression Methods For Poverty Estimation, Lionel N. Hanke

Master's Theses

According to the 2020 poverty estimates from the World Bank, it is estimated that 9.1% - 9.4% of the global population lived on less than $1.90 per day. It is estimated that the Covid-19 pandemic further aggravated the issue by pushing more than 1% of the global population below the international poverty line of $1.90 per day (WorldBank, 2020). To provide help and formulate effective measures, poverty needs to be located as exact as possible. For this purpose, it was investigated whether regression methods with aggregated remote-sensing data could be used to estimate poverty in Africa. Therefore, five distinct regression …

Forecasting Vegetation Health In The Mena Region By Predicting Vegetation Indicators With Machine Learning Models, Sachi Perera, Wenzhao Li, Erik Linstead, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA …

Bacteria Analysis By Using A Supervised Machine Learning Algorithm Based On Droplet Microfluidics, Yulder Daniel Angarita

Electronic Theses and Dissertations

Sepsis is a major medical problem and massive resources have been invested in developing and evaluating alternative treatments. Statistics indicate that sepsis causes between one third and one half of all hospital deaths in the United States. Sepsis has a high impact on health care in the US, with direct sepsis costs in 2009 exceeding $15.4 billion. A research study found that a 1-hour delay in appropriate antimicrobial care resulted in a 7% - 10% rise in mortality. Several professional societies seek to reduce sepsis mortality by targeting the timely use of diagnostic tests and antimicrobial therapy. The diagnostic instruments …

Automatic Chest X-Rays Analysis Using Statistical Machine Learning Strategies, Hermann Yepdjio Nkouanga

All Master's Theses

Tuberculosis (TB) is a disease responsible for the deaths of more than one million people worldwide every year. Even though it is preventable and curable, it remains a major threat to humanity that needs to be taken care of. It is often diagnosed in developed countries using approaches such as sputum smear microscopy and culture methods. However, since these approaches are rather expensive, they are not commonly used in poor regions of the globe such as India, Africa, and Bangladesh. Instead, the well known and affordable chest x-ray (CXR) interpretation by radiologists is the technique employed in those places. Nevertheless, …

Applications Of Artificial Intelligence To Cryptography, Jonathan Blackledge, Napo Mosola

Articles

This paper considers some recent advances in the field of Cryptography using Artificial Intelligence (AI). It specifically considers the applications of Machine Learning (ML) and Evolutionary Computing (EC) to analyze and encrypt data. A short overview is given on Artificial Neural Networks (ANNs) and the principles of Deep Learning using Deep ANNs. In this context, the paper considers: (i) the implementation of EC and ANNs for generating unique and unclonable ciphers; (ii) ML strategies for detecting the genuine randomness (or otherwise) of finite binary strings for applications in Cryptanalysis. The aim of the paper is to provide an overview on …

Elm-Som: A Continuous Self-Organizing Map For Visualization, Renjie Hu, Venous Roshdibenam, Hans J. Johnson, Emil Eirola, Anton Akusok, Yoan Miche, Kaj Mikael Björk, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

This Paper Presents a Novel Dimensionality Reduction Technique: Elm-Som. This Technique Preserves the Intrinsic Quality of Self-Organizing Maps (Som): It is Nonlinear and Suitable for Big Data. It Also Brings Continuity to the Projection using Two Extreme Learning Machine (Elm) Models, the First One to Perform the Dimensionality Reduction and the Second One to Perform the Reconstruction. Elm-Som is Tested Successfully on Six Diverse Datasets. Regarding Reconstruction Error, Elm-Som is Comparable to Som While Bringing Continuity.

Computer-Aided Detection Of Pathologically Enlarged Lymph Nodes On Non-Contrast Ct In Cervical Cancer Patients For Low-Resource Settings, Brian M. Anderson, Laurence E. Court, Ann Klopp, Stephen F. Kry, Jennifer Johnson, Erik Cressman, Arvind Rao, Jinzhong Yang

Dissertations and Theses (Open Access)

The mortality rate of cervical cancer is approximately 266,000 people each year, and 70% of the burden occurs in Low- and Middle- Income Countries (LMICs). Radiation therapy is the primary modality for treatment of locally advanced cervical cancer cases. In the absence of high quality diagnostic imaging needed to identify nodal metastasis, many LMIC sites treat standard pelvic fields, failing to include node metastasis outside of the field and/or to boost lymph nodes in the abdomen and pelvis. The first goal of this project was to create a program which automatically identifies positive cervical cancer lymph nodes on non-contrast daily …