Engineering Commons^™

Double Cone Flow Field Reconstruction Between Mach 4 And 12 Using Machine Learning Techniques, Trevor A. Toros

Theses and Dissertations

No abstract provided.

Developing A Fake News Identification Model With Advanced Deep Languagetransformers For Turkish Covid-19 Misinformation Data, Mehmet Bozuyla, Akin Özçi̇ft

Turkish Journal of Electrical Engineering and Computer Sciences

The massive use of social media causes rapid information dissemination that amplifies harmful messages such as fake news. Fake-news is misleading information presented as factual news that is generally used to manipulate public opinion. In particular, fake news related to COVID-19 is defined as 'infodemic' by World Health Organization. An infodemic is a misleading information that causes confusion which may harm health. There is a high volume of misinformation about COVID-19 that causes panic and high stress. Therefore, the importance of development of COVID-19 related fake news identification model is clear and it is particularly important for Turkish language from …

Using Google Trends As A Proxy For Occupant Behavior To Predict Building, Chun Fu, Clayton Miller

Research Collection College of Integrative Studies

In recent years, the availability of larger amounts of energy data and advanced machine learning algorithms has created a surge in building energy prediction research. However, one of the variables in energy prediction models, occupant behavior, is crucial for prediction performance but hard-to-measure or time-consuming to collect from each building. This study proposes an approach that utilizes the search volume of topics (e.g., education or Microsoft Excel) on the Google Trends platform as a proxy of occupant behavior and use of buildings. Linear correlations were first examined to explore the relationship between energy meter data and Google Trends search terms …

Recent Advances In Electrochemical Kinetics Simulations And Their Applications In Pt-Based Fuel Cells, Ji-Li Li, Ye-Fei Li, Zhi-Pan Liu

Journal of Electrochemistry

Theoretical simulations of electrocatalysis are vital for understanding the mechanism of the electrochemical process at the atomic level. It can help to reveal the in-situ structures of electrode surfaces and establish the microscopic mechanism of electrocatalysis, thereby solving the problems such as electrode oxidation and corrosion. However, there are still many problems in the theoretical electrochemical simulations, including the solvation effects, the electric double layer, and the structural transformation of electrodes. Here we review recent advances of theoretical methods in electrochemical modeling, in particular, the double reference approach, the periodic continuum solvation model based on the modified Poisson-Boltzmann …

Age Specific Models To Capture The Change In Risk Factor Contribution By Age To Short Term Primary Ischemic Stroke Risk, Elizabeth Hunter, John D. Kelleher

Articles

Age is one of the most important risk factors when it comes to stroke risk prediction. However, including age as a risk factor in a stroke prediction model can give rise to a number of difficulties. Age often dominates the risk score, and also not all risk factors contribute proportionally to stroke risk by age. In this study we investigate a number of common stroke risk factors, using Framingham heart study data from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center to determine if they appear to contribute proportionally by age to a stroke risk score. As we …

Application Of Nonlinear Time Series And Machine Learning Algorithms For Forecasting Groundwater Flooding In A Lowland Karst Area, Bidroha Basu, Patrick Morrissey, Laurence Gill

Publications

In karst limestone areas interactions between ground and surface waters can be frequent, particularly in low lying areas, linked to the unique hydrogeological dynamics of that bedrock aquifer. In extreme hydrological conditions, however, this can lead to wide-spread, long-duration flooding, resulting in significant cost and disruption. This study develops and compares a nonlinear time-series analysis based nonlinear autoregressive model with exogenous variables (NARX), machine learning based near support vector regression as well as a linear time-series ARX model in terms of their performance to predict groundwater flooding in a lowland karst area of Ireland. The models have been developed upon …

Prediction Of Soil Water Content And Electrical Conductivity Using Random Forest Methods With Uav Multispectral And Ground-Coupled Geophysical Data, Yunyi Guan, Katherine R. Grote, Joel Schott, Kelsi Leverett

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

The volumetric water content (VWC) of soil is a critical parameter in agriculture, as VWC strongly influences crop yield, provides nutrients to plants, and maintains the microbes that are needed for the biological health of the soil. Measuring VWC is difficult, as it is spatially and tempo-rally heterogeneous, and most agricultural producers use point measurements that cannot fully capture this parameter. Electrical conductivity (EC) is another soil parameter that is useful in agricul-ture, since it can be used to indicate soil salinity, soil texture, and plant nutrient availability. Soil EC is also very heterogeneous; measuring EC using conventional soil sampling …

An Intelligent Distributed Ledger Construction Algorithm For Iot, Charles Rawlins, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Blockchain is the next generation of secure data management that creates near-immutable decentralized storage. Secure cryptography created a niche for blockchain to provide alternatives to well-known security compromises. However, design bottlenecks with traditional blockchain data structures scale poorly with increased network usage and are extremely computation-intensive. This made the technology difficult to combine with limited devices, like those in Internet of Things networks. In protocols like IOTA, replacement of blockchain's linked-list queue processing with a lightweight dynamic ledger showed remarkable throughput performance increase. However, current stochastic algorithms for ledger construction suffer distinct trade-offs between efficiency and security. This work proposed …

Exploring The Concept Of The Digital Educator During Covid-19, Fernando Jimenez, Gracia Sanchez, Jose Palma, Luis Miralles-Pechuán, Juan A. Botia

Articles

T In many machine learning classification problems, datasets are usually of high dimensionality and therefore require efficient and effective methods for identifying the relative importance of their attributes, eliminating the redundant and irrelevant ones. Due to the huge size of the search space of the possible solutions, the attribute subset evaluation feature selection methods are not very suitable, so in these scenarios feature ranking methods are used. Most of the feature ranking methods described in the literature are univariate methods, which do not detect interactions between factors. In this paper, we propose two new multivariate feature ranking methods based on …

Assisting End-Users In Creating Chatbots By Improving Training Data, Aparna Roy, Chris Egersdoerfer

Summer REU Program

No abstract provided.

Human Interaction With Fake News, Autumn Woodson, Sampath Jayarathna (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

No abstract provided.

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 …

Supervised Machine Learning Techniques Applied To Low-Cost Air Quality Sensor Suites, Peter Wahman

All Undergraduate Theses and Capstone Projects

Low-cost PM sensors have garnered interest for their ability to reduce the cost of investigating PM concentrations in both indoor and outdoor spaces. They perform well in high concentration lab testing with correlation coefficients greater than 0.9. In real-world applications, the correlation coefficients drop significantly because of sensing floors and adverse ambient conditions. There are plenty of supervised machine learning techniques that aim to correct the measurements ranging from linear regression to more advanced neural networks and random forests. This work aims to use those more complicated techniques to adjust the measurements using other data sets gathered by a sensor …

Improving Or Operations Using Machine Learning Techniques, Tannaz Khaleghi

Wayne State University Dissertations

Recently, health care related studies are being widely conducted by researchers using unique and efficient techniques to increase system profitability, quality of care, and patient satisfaction. Surgery department is considered as the hospital's engine, and cost of surgical services has a huge impact on the overall profitability of the hospital. This thesis proposes novel approaches to improve the efficiency of surgical services by using machine learning concepts.

In the first part, this research investigates the prediction of the surgery durations and Current Procedural Terminology (CPT) Codes. Accurate prediction of the surgery duration will improve the utilization of indispensable surgical resources …

Machine Learning In Requirements Elicitation: A Literature Review, Cheligeer Cheligeer, Jingwei Huang, Guosong Wu, Nadia Bhuiyan, Yuan Xu, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords …

Integrating Remote Sensing And Model-Based Datasets In A Machine Learning Model To Map Global Subsidence Associated With Groundwater Withdrawal, Md Fahim Hasan

Masters Theses

"Quantifying groundwater storage loss is becoming increasingly essential globally due limited availability of this major hydrologic component and its long recharge time. Groundwater overdraft gives rises to multiple adverse impacts including land subsidence and permanent groundwater storage loss. In absence of spatially dense monitoring network, publicly available in-situ data, and uniform monitoring strategies, it is challenging to assess the sustained losses from overexploitation of this resource. Remote sensing based techniques have the capacity to fill this gap to increase our groundwater monitoring capacities. Exploring the interrelation between groundwater pumping and land subsidence using remote sensing datasets can be a very …

Cloud-Based Machine Learning And Sentiment Analysis, Emmanuel C. Opara

College of Graduate Studies: Theses & 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 …

Honey-Cnt Based Resistive Switching Device For Neuromorphic Computing Applications, Md Mehedi Hasan Tanim, Abdi Yamil Vicenciodelmoral, Zoe Templin, Xinghui Zhao, Feng Zhao

Electrical and Computer Engineering Faculty Research & Creative Works

Modern computing applications increasingly rely on technologies in artificial intelligence, machine learning, and big data analytics. These applications often demand more powerful and energy-efficient hardware. Resistive switching random access memory (ReRAM) has emerged as a promising solution to satisfy both the storage and computing needs. In this paper, a natural organic honey film embedded with carbon nanotube (CNT) was fabricated into a resistive switching device, and the resistive switching behaviors were investigated. Endurance test results show the cycle-to-cycle variation of set and reset voltages. On/Off ratio in retention test was found to be in the order of ~105 which proves …

A Dnn-Ensemble Method For Error Reduction And Training Data Selection In Dnn Based Modeling, Ling Zhang, Da Li, Jiayi He, Bhyrav Mutnury, Bo Pu, Xiao Ding Cai, Chulsoon Hwang, Jun Fan, James L. Drewniak, Er Ping Li

Electrical and Computer Engineering Faculty Research & Creative Works

Deep neural networks (DNNs) have been widely adopted in modeling electromagnetic compatibility (EMC) problems, but the training data acquisition is usually time-consuming through various simulators. This paper presents a powerful approach using an ensemble of DNN s to effectively reduce the training data size in DNN-based modeling problems. A batch of training data with the largest uncertainties is selected using active learning through the variance among the ensemble of DNNs. Subsequently, a greedy sampling algorithm is applied to select a data subset using diversity. Thus, the proposed method can achieve both uncertainty and diversity in data selection. By averaging the …

Moisture Content Prediction In Polymer Composites Using Machine Learning Techniques, Partha Pratim Das, Monjur Morshed Rabby, Vamsee Vadlamudi, Rassel Raihan

Institute of Predictive Performance Methodologies (IPPM-UTARI)-Archive

The principal objective of this study is to employ non-destructive broadband dielectric spectroscopy/impedance spectroscopy and machine learning techniques to estimate the moisture content in FRP composites under hygrothermal aging. Here, classification and regression machine learning models that can accurately predict the current moisture saturation state are developed using the frequency domain dielectric response of the composite, in conjunction with the time domain hygrothermal aging effect. First, to categorize the composites based on the present state of the absorbed moisture supervised classification learning models (i.e., quadratic discriminant analysis (QDA), support vector machine (SVM), and artificial neural network-based multilayer perceptron (MLP) classifier) …

Temporal Bagging: A New Method For Time-Based Ensemble Learning, Göksu Tüysüzoğlu, Derya Bi̇rant, Volkan Kiranoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

One of the main problems associated with the bagging technique in ensemble learning is its random sample selection in which all samples are treated with the same chance of being selected. However, in time-varying dynamic systems, the samples in the training set have not equal importance, where the recent samples contain more useful and accurate information than the former ones. To overcome this problem, this paper proposes a new time-based ensemble learning method, called temporal bagging (T-Bagging). The significant advantage of our method is that it assigns larger weights to more recent samples with respect to older ones, so it …

Dynamic Instance-Wise Decision-Making For Machine Learning, Yasitha Warahena Liyanage

Legacy Theses & Dissertations (2009 - 2024)

In a typical supervised machine learning setting, the predictions on all test instances are based on a common subset of features discovered during model training. However, using a different subset of features that are most informative for each test instance individually may improve not only the quality of prediction but also the overall interpretability of the model. To this end, in this dissertation, we study the problem of optimizing the trade-off between instance-level sparsity and the quality of prediction using a dynamic instance-wise decision-making approach. Specifically, this approach sequentially reviews features one at a time for each data instance given …

Assessing Machine Learning Utility In Predicting Hydrologic And Nitrate Dynamics In Karst Agroecosystems, Timothy Mcgill

Theses and Dissertations--Biosystems and Agricultural Engineering

Seasonal hypoxia in the Gulf of Mexico and harmful algal blooms experienced in many inland freshwater bodies is partially driven due to excessive nitrogen loading seen from agricultural watersheds. Within the Mississippi/Atchafalaya River Basin, many areas are underlain with karst features, and efforts to reduce nitrogen contributions from these areas have had varying success, due to lacking a complete understanding of nutrient dynamics in karst agricultural systems. To improve the understanding of nitrogen cycling in these systems, 35 months of high resolution in situ water quality and atmospheric data were collected and fed into a two-hidden layer extreme learning machine …

Detecting User Emotions From Audio Conversations With The Smart Assistants, Sunanda Guha

Graduate Theses/Dissertations

With the proliferation of smart home devices like Google Home or Amazon Alexa, significant research endeavors are being carried out to improve the user experience while interacting with these smart assistants. One such dimension in this endeavor is ongoing research on successful emotion detection from short voice commands used in smart home environment. Besides facial expression and body language, etc., speech plays a pivotal role in the classification of emotions when it comes to smart home application. Upon successful implementation of accurate emotion recognition, the smart devices will be able to intelligently and empathetically suggest appropriate actions based on the …

Self-Vernier Effect-Assisted Optical Fiber Sensor Based On Microwave Photonics And Its Machine Learning Analysis, Chen Zhu, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Optical Vernier Effect Has Been Recently Demonstrated as a Tool to Enhance the Sensitivity of Optical Fiber Interferometric Sensors and Has Become a Hot Topic in the Last Few Years. the Generation of the Vernier Effect Relies on the Superposition of Interferograms of Two Interferometers (A Sensing One and a Reference One) with Marginally Different Optical Path Differences (OPDs), Where an Amplitude Modulation-Like Signal is Sustained in the Output Spectrum. the Vernier Modulation Envelope Exhibits Significantly Magnified Sensitivity in Response to External Perturbations, compared to the Individual Sensing Interferometer, Providing a New Route to New Generations of Ultra-Sensitive Optical Fiber …

Above-Ground Biomass Estimation In Oat Breeding Nurseries Using Uav-Based Multispectral Data, Rakshya Dhakal

Electronic Theses and Dissertations

Conventional methods of phenotyping for above-ground biomass under field conditions are time consuming, costly, and labor intensive. Unmanned Aerial Vehicles (UAV) offer a rapid and non-destructive approach for collecting high temporal and spatial resolution imagery of multiple field plots. Vegetation indices (VIs) can be derived from those imagery and have been used broadly for high-throughput biomass estimation, however, they suffer from spectrum saturation issues at high canopy density. In addition, VIs only reflect the canopy spectral information and fail to incorporate the 3-dimensional (3D) canopy structure and architecture information. It also ignores the spatial relationship of pixels to each other …

Framework For The Evaluation Of Perturbations In The Systems Biology Landscape And Inter-Sample Similarity From Transcriptomic Datasets — A Digital Twin Perspective, Mariah Marie Hoffman

Dissertations and Theses

One approach to interrogating the complexities of human systems in their well-regulated and dysregulated states is through the use of digital twins. Digital twins are virtual representations of physical systems that are descriptive of an individual's state of health, an object fundamentally related to precision medicine. A key element for building a functional digital twin type for a disease or predicting the therapeutic efficacy of a potential treatment is harmonized, machine-parsable domain knowledge. Hypothesis-driven investigations are the gold standard for representing subsystems, but their results encompass a limited knowledge of the full biosystem. Multi-omics data is one rich source of …

Nondestructive Multivariate Classification Of Codling Moth Infested Apples Using Machine Learning And Sensor Fusion, Nader Ekramirad

Theses and Dissertations--Biosystems and Agricultural Engineering

Apple is the number one on the list of the most consumed fruits in the United States. The increasing market demand for high quality apples and the need for fast, and effective quality evaluation techniques have prompted research into the development of nondestructive evaluation methods. Codling moth (CM), Cydia pomonella L. (Lepidoptera: Tortricidae), is the most devastating pest of apples. Therefore, this dissertation is focused on the development of nondestructive methods for the detection and classification of CM-infested apples. The objective one in this study was aimed to identify and characterize the source of detectable vibro-acoustic signals coming from CM-infested …

Design, Development And Benchmarking Of Machine Learning Algorithms In Biomedical Applications, Qi Sun

Theses and Dissertations--Computer Science

Machine learning algorithms are becoming the most effective methods for knowledge discovery from high dimensional datasets. Machine learning seeks to construct predictive models through the analysis of large-scale heterogeneous data. While machine learning has been widely used in many domains including computer vision, natural language processing, product recommendation, its application in biomedical science for clinical diagnosis and treatment is only emerging. However, the wealthy amount of data in the biomedical domain offers not only challenges but also opportunities for machine learning. In this dissertation, we focus on three biomedical applications from vastly different domains to understand the opportunities and challenges …

Image Geo-Localization With Cross-Attention, Connor Greenwell

Theses and Dissertations--Computer Science

The problem of estimating the location from which un-geotagged photographs were captured has been well studied by the computer vision community in recent years. The central proposal of this thesis is to define a common framework within which existing approaches can be constructed and evaluated, and to introduce a new method under this framework which uses cross-attention between the query image and a database of satellite imagery with known geotags. Our experiments fit within three broad categories: 1) evaluating the ability of image localization approaches to generalize to unseen regions; 2) examining performance changes under various reference database resolutions, scales, …