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Articles 1 - 30 of 70
Full-Text Articles in Engineering
Development And Evaluation Of A Modeling Platform For Evaluating Immunotherapeutic Efficacy In The Tumor Microenvironment., Dylan Andrew Goodin
Development And Evaluation Of A Modeling Platform For Evaluating Immunotherapeutic Efficacy In The Tumor Microenvironment., Dylan Andrew Goodin
Electronic Theses and Dissertations
The tumor microenvironment (TME) represents the complex outcome of numerous tumor, stromal, and immune interactions, and whose composition can significantly affect treatment response. Particularly, immunotherapeutic efficacy is subject to multiple tumor-specific TME interactions that may be difficult to evaluate/predict clinically. Mathematical modelling has been formulated to evaluate specific aspects of the TME, including vasculature, ECM deposition, and immune-tumor interactions. However, the computational challenge of simulating multiple TME interactions has led to sacrificing varying degrees of model generalizability and clinical relevance. This work describes increased computational performance of a 3D continuum model that simulates tumor tissue, ECM, and vasculature using a …
Novel Breath Collection Techniques For Detection Of Covid-19., James Morris
Novel Breath Collection Techniques For Detection Of Covid-19., James Morris
Electronic Theses and Dissertations
Volatile Organic Compounds (VOC) generated endogenously in the human body can be used to detect diseases that induce oxidative stress and inflammation. Breath analysis has been used for the detection of diseases such as COPD, Depression, Lung Cancer and most recently COVID-19. Methods such as Exhaled Breath Condensate (EBC), Sorbent Tubes, Solid Phase Microextraction (SPME), and silicon microreactors have shown considerable capability in extracting and concentrating trace VOC’s present in human breath. Silicon microreactors functionalized with capture agents to derivatize carbonyl compounds are effective but the overall maximum flow rate of breath sample possible during analysis is low, making direct …
Interrogating Autism From A Multidimensional Perspective: An Integrative Framework., Mohamed T. Ali
Interrogating Autism From A Multidimensional Perspective: An Integrative Framework., Mohamed T. Ali
Electronic Theses and Dissertations
Autism Spectrum Disorder (ASD) is a condition characterized by social and behavioral impairments, affecting approximately 1 in every 44 children in the United States. Common symptoms include difficulties in communication, interpersonal interactions, and behavior. While symptoms can manifest as early as infancy, obtaining an accurate diagnosis may require multiple visits to a pediatric specialist due to the subjective nature of the assessment, which may yield varying scores from different specialists. Despite growing evidence of the role of differences in brain development and/or environmental and/or genetic factors in autism development, the exact pathology of this disorder has yet to be fully …
Metabolomic Differentiation Of Tumor Core And Edge In Glioma., Mary E. Baxter
Metabolomic Differentiation Of Tumor Core And Edge In Glioma., Mary E. Baxter
Electronic Theses and Dissertations
Glioma is one of the most aggressive forms of brain cancer. It has been shown that the microenvironments differ significantly between the core and edge regions of glioma tumors. This study obtained metabolomic profiles of glioma core and edge regions using paired glioma core and edge tissue samples from 27 human patients. Data was acquired by performing liquid-liquid metabolite extraction and 2DLC-MS/MS on the tissue samples. In addition, a boosted generalized linear machine learning model was employed to predict the metabolomic profiles associated with O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation.
A panel of 66 metabolites was found to be statistically significant …
Design, Determination, And Evaluation Of Gender-Based Bias Mitigation Techniques For Music Recommender Systems, Sunny Shrestha
Design, Determination, And Evaluation Of Gender-Based Bias Mitigation Techniques For Music Recommender Systems, Sunny Shrestha
Electronic Theses and Dissertations
The majority of smartphone users engage with a recommender system on a daily basis. Many rely on these recommendations to make their next purchase, download the next game, listen to the new music or find the next healthcare provider. Although there are plenty of evidence backed research that demonstrates presence of gender bias in Machine Learning (ML) models like recommender systems, the issue is viewed as a frivolous cause that doesn’t merit much action. However, gender bias poses to effect more than half of the population as by default ML systems are designed to cater to a cisgender man. This …
Application Of Big Data Technology, Text Classification, And Azure Machine Learning For Financial Risk Management Using Data Science Methodology, Oluwaseyi A. Ijogun
Application Of Big Data Technology, Text Classification, And Azure Machine Learning For Financial Risk Management Using Data Science Methodology, Oluwaseyi A. Ijogun
Electronic Theses and Dissertations
Data science plays a crucial role in enabling organizations to optimize data-driven opportunities within financial risk management. It involves identifying, assessing, and mitigating risks, ultimately safeguarding investments, reducing uncertainty, ensuring regulatory compliance, enhancing decision-making, and fostering long-term sustainability. This thesis explores three facets of Data Science projects: enhancing customer understanding, fraud prevention, and predictive analysis, with the goal of improving existing tools and enabling more informed decision-making. The first project examined leveraged big data technologies, such as Hadoop and Spark, to enhance financial risk management by accurately predicting loan defaulters and their repayment likelihood. In the second project, we investigated …
Predictions Of The Dynamic Complex Modulus Of Non-Conventional Asphalt Concrete Using Machine Learning Techniques, Annie Benson
Predictions Of The Dynamic Complex Modulus Of Non-Conventional Asphalt Concrete Using Machine Learning Techniques, Annie Benson
Electronic Theses and Dissertations
The complex dynamic modulus (|E*|) is a characterization property that defines the stiffness of an asphalt mixture. The dynamic modulus can be found through lab testing or predictions. Since lab testing can be time-consuming and expensive, the prediction method can be used as an alternative method. While a statistical method has been traditionally used for the |E*| prediction such as the Witczak’s predictive equations, machine learning (ML) is recently emerging as an alternative way that |E*| predictions can be made. This research attempted to predict the |E*| using several ML techniques including linear regression, support vector machines (SVM), decision trees, …
Iot In Smart Communities, Technologies And Applications., Muhammad Zaigham Abbas Shah Syed
Iot In Smart Communities, Technologies And Applications., Muhammad Zaigham Abbas Shah Syed
Electronic Theses and Dissertations
Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing …
Nucleate Boiling Under Different Gravity Values: Numerical Simulations & Data-Driven Techniques., Sandipan Banerjee
Nucleate Boiling Under Different Gravity Values: Numerical Simulations & Data-Driven Techniques., Sandipan Banerjee
Electronic Theses and Dissertations
Nucleate boiling is important in nuclear applications and cooling applications under earth gravity conditions. Under reduced gravity or microgravity environment, it is significant too, especially in space exploration applications. Although multiple studies have been performed on nucleate boiling, the effect of gravity on nucleate boiling is not well understood. This dissertation primarily deals with numerical simulations of nucleate boiling using an adaptive Moment-of-Fluid (MoF) method for a single vapor bubble (water vapor or Perfluoro-n-hexane) in saturated liquid for different gravity levels. Results concerning the growth rate of the bubble, specifically the departure diameter and departure time have been provided. The …
Cloud-Based Machine Learning And Sentiment Analysis, Emmanuel C. Opara
Cloud-Based Machine Learning And Sentiment Analysis, Emmanuel C. Opara
Electronic Theses and Dissertations
The role of a Data Scientist is becoming increasingly ubiquitous as companies and institutions see the need to gain additional insights and information from data to make better decisions to improve the quality-of-service delivery to customers. This thesis document contains three aspects of data science projects aimed at improving tools and techniques used in analyzing and evaluating data. The first research study involved the use of a standard cybersecurity dataset and cloud-based auto-machine learning algorithms were applied to detect vulnerabilities in the network traffic data. The performance of the algorithms was measured and compared using standard evaluation metrics. The second …
Surrogate Modeling Of Fluid Flows With Physics-Aware Graph Neural Networks, Emanuel Raad
Surrogate Modeling Of Fluid Flows With Physics-Aware Graph Neural Networks, Emanuel Raad
Electronic Theses and Dissertations
Graph neural networks provide a framework for learning on unstructured data, such as meshes used for solving Computational Fluid Dynamics problems. However, current applications do not take advantage of known physical laws in the training process. This thesis addresses that gap by introducing graph convolution layers to calculate the divergence and gradient operator. The convolutions are valid on any 2D or 3D graph storing spatial data, and can be added to existing graph architectures. Using these convolutions, the residuals of the conservation of mass and momentum equations are computed and minimized through a physics-aware loss function. Two classical fluid dynamics …
Classification Of Electropherograms Using Machine Learning For Parkinson’S Disease, Soroush Dehghan
Classification Of Electropherograms Using Machine Learning For Parkinson’S Disease, Soroush Dehghan
Electronic Theses and Dissertations
Parkinson’s disease (PD) is a neurodegenerative movement disorder that progresses gradually over time. The onset of symptoms in people who are suffering from PD can vary from case to case, and it depends on the progression of the disease in each patient. The PD symptoms gradually develop and exacerbate the patient’s movements throughout time. An early diagnosis of PD could improve the outcomes of treatments and could potentially delay the progression of this disorder and that makes discovering a new diagnostic method valuable. In this study, I investigate the feasibility of using a machine learning (ML) approach to classify PD …
Above-Ground Biomass Estimation In Oat Breeding Nurseries Using Uav-Based Multispectral Data, Rakshya Dhakal
Above-Ground Biomass Estimation In Oat Breeding Nurseries Using Uav-Based Multispectral Data, Rakshya Dhakal
Electronic Theses and Dissertations
No abstract provided.
Machine Learning Based Prediction Of Reinforced Concrete Members’ Shear Friction Capacity, Farah Mohamed
Machine Learning Based Prediction Of Reinforced Concrete Members’ Shear Friction Capacity, Farah Mohamed
Electronic Theses and Dissertations
Shear friction theory describes the mechanisms by which shear forces are transferred across concrete-to-concrete interfaces. Shear transfer across a plane involves a complex interaction of several phenomena, such as concrete surface condition and cohesion, concrete strength, and steel reinforcement strength and reinforcement ratio. Existing empirical equations for shear friction have been developed using limited sample data sets;thus, their accuracy is limited to the range covered by the data. In order to overcome this limitation, the present thesis proposes two machine learning models drawn from an extensive database to predict the shear friction capacity in reinforced concrete (RC) with a high …
Machine Learning Models For Lodi Indices., Lucas A. Bruns
Machine Learning Models For Lodi Indices., Lucas A. Bruns
Electronic Theses and Dissertations
Two indices published monthly by the Logistics and Distribution Institute (LoDI) predict changes in logistics and distribution activity levels nationally and regionally and are useful for organizations when planning projects and expenses. This research validates the current linear regression model, updates the index conversion method, and introduces machine learning models.
New source data are introduced to the models to validate the current linear regression model and a comparative analysis verifies that the current source data are robust. A rolling average is used for index conversion in place of a fixed reference month to reflect recent changes in employment levels.
Three …
Machine Learning Approaches For Lung Cancer Diagnosis., Ahmed Mahmoud Ahmed Shaffie
Machine Learning Approaches For Lung Cancer Diagnosis., Ahmed Mahmoud Ahmed Shaffie
Electronic Theses and Dissertations
The enormity of changes and development in the field of medical imaging technology is hard to fathom, as it does not just represent the technique and process of constructing visual representations of the body from inside for medical analysis and to reveal the internal structure of different organs under the skin, but also it provides a noninvasive way for diagnosis of various disease and suggest an efficient ways to treat them. While data surrounding all of our lives are stored and collected to be ready for analysis by data scientists, medical images are considered a rich source that could provide …
Multi-Style Explainable Matrix Factorization Techniques For Recommender Systems., Olurotimi Nugbepo Seton
Multi-Style Explainable Matrix Factorization Techniques For Recommender Systems., Olurotimi Nugbepo Seton
Electronic Theses and Dissertations
Black-box recommender system models are machine learning models that generate personalized recommendations without explaining how the recommendations were generated to the user or giving them a way to correct wrong assumptions made about them by the model. However, compared to white-box models, which are transparent and scrutable, black-box models are generally more accurate. Recent research has shown that accuracy alone is not sufficient for user satisfaction. One such black-box model is Matrix Factorization, a State of the Art recommendation technique that is widely used due to its ability to deal with sparse data sets and to produce accurate recommendations. Recent …
Wind Turbine Parameter Calibration Using Deep Learning Approaches, Rebecca Mccubbin
Wind Turbine Parameter Calibration Using Deep Learning Approaches, Rebecca Mccubbin
Electronic Theses and Dissertations
The inertia and damping coefficients are critical to understanding the workings of a wind turbine, especially when it is in a transient state. However, many manufacturers do not provide this information about their turbines, requiring people to estimate these values themselves. This research seeks to design a multilayer perceptron (MLP) that can accurately predict the inertia and damping coefficients using the power data from a turbine during a transient state. To do this, a model of a wind turbine was built in Matlab, and a simulation of a three-phase fault was used to collect realistic fault data to input into …
Unobtrusive Assessment Of Student Engagement Levels In Online Classroom Environment Using Emotion Analysis, Sasirekha Anbusegaran
Unobtrusive Assessment Of Student Engagement Levels In Online Classroom Environment Using Emotion Analysis, Sasirekha Anbusegaran
Electronic Theses and Dissertations
Measuring student engagement has emerged as a significant factor in the process of learning and a good indicator of the knowledge retention capacity of the student. As synchronous online classes have become more prevalent in recent years, gauging a student's attention level is more critical in validating the progress of every student in an online classroom environment. This paper details the study on profiling the student attentiveness to different gradients of engagement level using multiple machine learning models. Results from the high accuracy model and the confidence score obtained from the cloud-based computer vision platform - Amazon Rekognition were then …
Computational Techniques In Medical Image Analysis Application For White Blood Cells Classification., Omar Dekhil
Computational Techniques In Medical Image Analysis Application For White Blood Cells Classification., Omar Dekhil
Electronic Theses and Dissertations
White blood cells play important rule in the human body immunity and any change in their count may cause serious diseases. In this study, a system is introduced for white blood cells localization and classification. The dataset used in this study is formed by two components, the first is the annotation dataset that will be used in the localization (364 images), and the second is labeled classes that will be used in the classification (12,444 images). For the localization, two approaches will be discussed, a classical approach and a deep learning based approach. For the classification, 5 different deep learning …
Satellite Constellation Deployment And Management, Joseph Ryan Kopacz
Satellite Constellation Deployment And Management, Joseph Ryan Kopacz
Electronic Theses and Dissertations
This paper will review results and discuss a new method to address the deployment and management of a satellite constellation. The first two chapters will explorer the use of small satellites, and some of the advances in technology that have enabled small spacecraft to maintain modern performance requirements in incredibly small packages.
The third chapter will address the multiple-objective optimization problem for a global persistent coverage constellation of communications spacecraft in Low Earth Orbit. A genetic algorithm was implemented in MATLAB to explore the design space – 288 trillion possibilities – utilizing the Satellite Tool Kit (STK) software developers kit. …
Facial Action Unit Detection With Deep Convolutional Neural Networks, Siddhesh Padwal
Facial Action Unit Detection With Deep Convolutional Neural Networks, Siddhesh Padwal
Electronic Theses and Dissertations
The facial features are the most important tool to understand an individual's state of mind. Automated recognition of facial expressions and particularly Facial Action Units defined by Facial Action Coding System (FACS) is challenging research problem in the field of computer vision and machine learning. Researchers are working on deep learning algorithms to improve state of the art in the area. Automated recognition of facial action units has man applications ranging from developmental psychology to human robot interface design where companies are using this technology to improve their consumer devices (like unlocking phone) and for entertainment like FaceApp. Recent studies …
Applying Artificial Intelligence To Medical Data, Shaikh Shiam Rahman
Applying Artificial Intelligence To Medical Data, Shaikh Shiam Rahman
Electronic Theses and Dissertations
Machine learning, data mining, and deep learning has become the methodology of choice for analyzing medical data and images. In this study, we implemented three different machine learning techniques to medical data and image analysis. Our first study was to implement different log base entropy for a decision tree algorithm. Our results suggested that using a higher log base for the dataset with mostly categorical attributes with three or more categories for each attribute can obtain a higher accuracy. For the second study, we analyzed mental health data tuning the parameters of the decision tree (splitting method, depth and entropy). …
Automated Recognition Of Facial Affect Using Deep Neural Networks, Behzad Hasani
Automated Recognition Of Facial Affect Using Deep Neural Networks, Behzad Hasani
Electronic Theses and Dissertations
Automated Facial Expression Recognition (FER) has been a topic of study in the field of computer vision and machine learning for decades. In spite of efforts made to improve the accuracy of FER systems, existing methods still are not generalizable and accurate enough for use in real-world applications. Many of the traditional methods use hand-crafted (a.k.a. engineered) features for representation of facial images. However, these methods often require rigorous hyper-parameter tuning to achieve favorable results.
Recently, Deep Neural Networks (DNNs) have shown to outperform traditional methods in visual object recognition. DNNs require huge data as well as powerful computing units …
An Explainable Recommender System Based On Semantically-Aware Matrix Factorization., Mohammed Sanad Alshammari
An Explainable Recommender System Based On Semantically-Aware Matrix Factorization., Mohammed Sanad Alshammari
Electronic Theses and Dissertations
Collaborative Filtering techniques provide the ability to handle big and sparse data to predict the ratings for unseen items with high accuracy. Matrix factorization is an accurate collaborative filtering method used to predict user preferences. However, it is a black box system that recommends items to users without being able to explain why. This is due to the type of information these systems use to build models. Although rich in information, user ratings do not adequately satisfy the need for explanation in certain domains. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less …
Modeling And Counteracting Exposure Bias In Recommender Systems., Sami Khenissi
Modeling And Counteracting Exposure Bias In Recommender Systems., Sami Khenissi
Electronic Theses and Dissertations
Recommender systems are becoming widely used in everyday life. They use machine learning algorithms which learn to predict our preferences and thus influence our choices among a staggering array of options online, such as movies, books, products, and even news articles. Thus what we discover and see online, and consequently our opinions and decisions, are becoming increasingly affected by automated predictions made by learning machines. Similarly, the predictive accuracy of these learning machines heavily depends on the feedback data, such as ratings and clicks, that we provide them. This mutual influence can lead to closed-loop interactions that may cause unknown …
Clustering Heterogeneous Autism Spectrum Disorder Data., Mariem Boujelbene
Clustering Heterogeneous Autism Spectrum Disorder Data., Mariem Boujelbene
Electronic Theses and Dissertations
Autism spectrum disorder (ASD) is a developmental disorder that affects communication and behavior. Several studies have been conducted in the past years to develop a better understanding of the disease and therefore a better diagnosis and a better treatment by analyzing diverse data sets consisting of behavioral surveys and tests, phenotype description, and brain imagery. However, data analysis is challenged by the diversity, complexity and heterogeneity of patient cases and by the need for integrating diverse data sets to reach a better understanding of ASD. The aim of our study is to mine homogeneous groups of patients from a heterogeneous …
Applied Deep Learning In Orthopaedics, William Stewart Burton Ii
Applied Deep Learning In Orthopaedics, William Stewart Burton Ii
Electronic Theses and Dissertations
The reemergence of deep learning in recent years has led to its successful application in a wide variety of fields. As a subfield of machine learning, deep learning offers an array of powerful algorithms for data-driven applications. Orthopaedics stands to benefit from the potential of deep learning for advancements in the field. This thesis investigated applications of deep learning for the field of orthopaedics through the development of three distinct projects.
First, algorithms were developed for the automatic segmentation of the structures in the knee from MRI. The resulting algorithms can be used to accurately segment full MRI scans in …
Supervised Machine Learning Techniques For Short-Term Load Forecasting, Harish Amarasundar
Supervised Machine Learning Techniques For Short-Term Load Forecasting, Harish Amarasundar
Electronic Theses and Dissertations
Electric Load Forecasting is essential for the utility companies for energy management based on the demand. Machine Learning Algorithms has been in the forefront for prediction algorithms. This Thesis is mainly aimed to provide utility companies with a better insight about the wide range of Techniques available to forecast the load demands based on different scenarios. Supervised Machine Learning Algorithms were used to come up with the best possible solution for Short-Term Electric Load forecasting. The input Data set has the hourly load values, Weather data set and other details of a Day. The models were evaluated using MAPE and …
Landmine Detection Using Semi-Supervised Learning., Graham Reid
Landmine Detection Using Semi-Supervised Learning., Graham Reid
Electronic Theses and Dissertations
Landmine detection is imperative for the preservation of both military and civilian lives. While landmines are easy to place, they are relatively difficult to remove. The classic method of detecting landmines was by using metal-detectors. However, many present-day landmines are composed of little to no metal, necessitating the use of additional technologies. One of the most successful and widely employed technologies is Ground Penetrating Radar (GPR). In order to maximize efficiency of GPR-based landmine detection and minimize wasted effort caused by false alarms, intelligent detection methods such as machine learning are used. Many sophisticated algorithms are developed and employed to …