Computer Engineering Commons^™

A Strategic Audit Of Microsoft Azure, Lee Fitchett

Honors Program: Senior Projects (Public)

This paper looks at Microsoft Azure's current strategies and proposes possible options for the future. It looks at several competitors and explores how Azure will affect and react to Microsoft’s vision.

Distributed Wireless Algorithms For Rfid Systems: Grouping Proofs And Cardinality Estimation, Vanya D. Cherneva

LSU Doctoral Dissertations

The breadth and depth of the use of Radio Frequency Identification (RFID) are becoming more substantial. RFID is a technology useful for identifying unique items through radio waves. We design algorithms on RFID-based systems for the Grouping Proof and Cardinality Estimation problems.

A grouping-proof protocol is evidence that a reader simultaneously scanned the RFID tags in a group. In many practical scenarios, grouping-proofs greatly expand the potential of RFID-based systems such as supply chain applications, simultaneous scanning of multiple forms of IDs in banks or airports, and government paperwork. The design of RFID grouping-proofs that provide optimal security, privacy, and …

Machine Translation With Image Context From Mandarin Chinese To English, Brooke E. Johnson

Theses and Dissertations

Despite ongoing improvements in machine translation, machine translators still lack the capability of incorporating context from which source text may have been derived. Machine translators use text from a source language to translate it into a target language without observing any visual context. This work aims to produce a neural machine translation model that is capable of accepting both text and image context as a multimodal translator from Mandarin Chinese to English. The model was trained on a small multimodal dataset of 700 images and sentences, and compared to a translator trained only on the text associated with those images. …

Hyper-Parameter Optimization Of A Convolutional Neural Network, Steven H. Chon

Theses and Dissertations

In the world of machine learning, neural networks have become a powerful pattern recognition technique that gives a user the ability to interpret high-dimensional data whereas conventional methods, such as logistic regression, would fail. There exists many different types of neural networks, each containing its own set of hyper-parameters that are dependent on the type of analysis required, but the focus of this paper will be on the hyper-parameters of convolutional neural networks. Convolutional neural networks are commonly used for classifications of visual imagery. For example, if you were to build a network for the purpose of predicting a specific …

A Shared-Memory Algorithm For Updating Single-Source Shortest Paths In Large Weighted Dynamic Networks, Sriram Srinivasan

UNO Student Research and Creative Activity Fair

In the last decade growth of social media, increased the interest of network algorithms for analyzing large-scale complex systems. The networks are highly unstructured and exhibit poor locality, which has been a challenge for developing scalable parallel algorithms. The state-of-the-art network algorithms such as Prim's algorithm for Minimum Spanning Tree, Dijkstra's algorithm for Single Source Shortest Path and ISPAN algorithm for detecting strongly connected components are designed and optimized for static networks. The networks which change with time i.e. the dynamic networks such as social networks, the above-mentioned approaches can only be utilized if they are recomputed from scratch each …

Mobility-Based Models For Advancing Diagnostic/Predictive Healthcare, Elham Rastegari

UNO Student Research and Creative Activity Fair

Functional ability has been always considered as one of the important determining factors of individuals’ health and quality of life. Traditional movement analysis systems require expensive facilities and frequent visits for patients to specialized laboratories. Portability and affordability of wearable sensors along with their improved accuracy and capability of monitoring movement during daily activities make them a potential alternative for analyzing mobility patterns for clinical and health assessment purposes. Wearable-based movement data, when combined with other relevant clinical or laboratory data, could enhance evidence-based healthcare and data-driven Clinical Decision Support Systems (CDSS). Utilizing the data from wearable devices, many researchers …

Machine Shop Instruction Tool, John Sullivan, Junfei Yu, Tao Wang, Yuteng Mei

ENGS 89/90 Reports

This team has developed a digital learning solution to supplement the machine shop’s training curriculum that students will be able to access remotely anytime and anywhere. This solution will improve how efficiently the machine tools are taught, reduce the time needed to educate each student, and possibly give more students time to learn in the machine shop. In addition, the team has delivered a code repository and documentation to the machine shop. In the future, this solution can be extended to other machines and courses at Thayer. Students will be able to apply their knowledge to their future engineering projects.

My Baseball Collection App, Nicolas A. Parra

Computer Science and Software Engineering

My Baseball Collection is an iOS application that aims to simplify the management and expansion of physical baseball trading card collections. The app allows users to digitize their baseball card collection by uploading images of cards they possess, creating a wishlist of cards they are seeking, and viewing the collections and wishlists of other users. This project seeks to provide quality of life improvements to those within the baseball card trading community and to further facilitate communication and trading in an online world.

Applications Of Supervised Machine Learning In Autism Spectrum Disorder Research: A Review, Kayleigh K. Hyde, Marlena N. Novack, Nicholas Lahaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead

Engineering Faculty Articles and Research

Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as …

Finding Truth In Fake News: Reverse Plagiarism And Other Models Of Classification, Matthew Przybyla, David Tran, Amber Whelpley, Daniel W. Engels

SMU Data Science Review

As the digital age creates new ways of spreading news, fake stories are propagated to widen audiences. A majority of people obtain both fake and truthful news without knowing which is which. There is not currently a reliable and efficient method to identify “fake news”. Several ways of detecting fake news have been produced, but the various algorithms have low accuracy of detection and the definition of what makes a news item ‘fake’ remains unclear. In this paper, we propose a new method of detecting on of fake news through comparison to other news items on the same topic, as …

Comparative Study Of Sentiment Analysis With Product Reviews Using Machine Learning And Lexicon-Based Approaches, Heidi Nguyen, Aravind Veluchamy, Mamadou Diop, Rashed Iqbal

SMU Data Science Review

In this paper, we present a comparative study of text sentiment classification models using term frequency inverse document frequency vectorization in both supervised machine learning and lexicon-based techniques. There have been multiple promising machine learning and lexicon-based techniques, but the relative goodness of each approach on specific types of problems is not well understood. In order to offer researchers comprehensive insights, we compare a total of six algorithms to each other. The three machine learning algorithms are: Logistic Regression (LR), Support Vector Machine (SVM), and Gradient Boosting. The three lexicon-based algorithms are: Valence Aware Dictionary and Sentiment Reasoner (VADER), Pattern, …

Sr Education Group, A Leading Education Research Publisher, Ranked Nova Southeastern University (Nsu) Within Their 2019 Lists Of Best Online Colleges, Nova Southeastern University

College of Computing and Engineering News Archive

SR Education Group, a leading education research publisher, ranked Nova Southeastern University (NSU) within their 2019 lists of best online colleges. The group recognized NSU’s College of Computing and Engineering for its Master of Science in Computer Science and Engineering program, ranking it 13 out of 19 in “Best Online Master's in Computer Science Programs.” The college was also ranked 6 out of 8 for “Best Online Master's in Information Technology (IT) Degrees.”

Building A Classification Model Using Affinity Propagation, Christopher R. Klecker

College of Graduate Studies: Theses & Dissertations

Regular classification of data includes a training set and test set. For example for Naïve Bayes, Artificial Neural Networks, and Support Vector Machines, each classifier employs the whole training set to train itself. This thesis will explore the possibility of using a condensed form of the training set in order to get a comparable classification accuracy. The technique explored in this thesis will use a clustering algorithm to explore with data records can be labeled as exemplar, or a quality of multiple records. For example, is it possible to compress say 50 records into one single record? Can a single …

Car Image Classification Using Deep Neural Networks, Mingchen Li

Honors Theses

Image classification is widely used in many fields of study. Deep neural networks are proven to be effective classifier structure due to its massive parameters and training capability. This paper outlines the development of Deep Neural Network in recent years and applied them on a Car image data set in order to compare their performances.

The Evaluation Of An Android Permission Management System Based On Crowdsourcing, Pulkit Rustgi

Theses and Dissertations

Mobile and web application security, particularly concerning the area of data privacy, has received much attention from the public in recent years. Most applications are installed without disclosing full information to users and clearly stating what they have access to. This often raises concerns when users become aware of unnecessary information being collected or stored. Unfortunately, most users have little to no technical knowledge in regard to what permissions should be granted and can only rely on their intuition and past experiences to make relatively uninformed decisions. DroidNet, a crowdsource based Android recommendation tool and framework, is a proposed avenue …

Exploring Age-Related Metamemory Differences Using Modified Brier Scores And Hierarchical Clustering, Chelsea Parlett-Pelleriti, Grace C. Lin, Masha R. Jones, Erik Linstead, Susanne M. Jaeggi

Engineering Faculty Articles and Research

Older adults (OAs) typically experience memory failures as they age. However, with some exceptions, studies of OAs’ ability to assess their own memory functions—Metamemory (MM)— find little evidence that this function is susceptible to age-related decline. Our study examines OAs’ and young adults’ (YAs) MM performance and strategy use. Groups of YAs (N = 138) and OAs (N = 79) performed a MM task that required participants to place bets on how likely they were to remember words in a list. Our analytical approach includes hierarchical clustering, and we introduce a new measure of MM—the modified Brier—in order to adjust …

Machine Learning And Neural Networks For Real-Time Scheduling, Daniel Hureira, Christian Vartanian

Recent Advances in Real-Time Systems

This paper aims to serve as an efficient survey of the processes, problems, and methodologies surrounding the use of Neural Networks, specifically Hopfield-Type, in order to solve Hard-Real-Time Scheduling problems. Our primary goal is to demystify the field of Neural Networks research and properly describe the methods in which Real-Time scheduling problems may be approached when using neural networks. Furthermore, to give an introduction of sorts on this niche topic in a niche field. This survey is derived from four main papers, namely: “A Neurodynamic Approach for Real-Time Scheduling via Maximizing Piecewise Linear Utility” and “Scheduling Multiprocessor Job with Resource …

Shaping The Future Of Trusted Digital Identity: The Idef Registry With Health Information Use Cases, Noreen Y. Whysel

Publications and Research

No abstract provided.

Improvement Of The Material’S Mechanical Characteristics Using Intelligent Real Time Control Interfaces In Hfc Hardening Process, Florentin Smarandache, Luige Vladareanu, Mihaiela Iliescu, Victor Vladareanu, Alexandru Gal, Octavian Melinte, Adrian Margean

Branch Mathematics and Statistics Faculty and Staff Publications

The paper presents Intelligent Control (IC) Interfaces for real time control of mechatronic systems applied to Hardening Process Control (HPC) in order to improvement of the material’s mechanical characteristics. Implementation of IC laws in the intelligent real time control interfaces depends on the particular circumstances of the models characteristics used and the exact definition of optimization problem. The results led to the development of the IC interfaces in real time through Particle Swarm Optimization (PSO) and neural networks (NN) using off- line the regression methods.

Hometracker: A Household Information Feedback System For Food/Energy/Water Metabolism, Nichole Mackey

Dissertations, Master's Theses and Master's Reports

The Food, Energy and Water Conscious (FEWCON) project seeks to understand how food, energy and water (FEW) as independent resources within households are connected. In the main study of the project, intervention messages that link household FEW consumption to equivalent climate consequences are pushed to the households. The goal of the FEWCON study is to determine potential intervention messages that influence household FEW consumption behavior.

A key component of the FEWCON study is a web application named HomeTracker (Household Metabolism Tracker) which collects FEW consumption data within households, then uses this data to select consumption-specific feedback to the homeowners. To …

Towards A Fault-Tolerant, Scheduling Methodology For Safety-Critical Certified Information Systems, Jian Lin

Journal of International Technology and Information Management

Today, many critical information systems have safety-critical and non-safety-critical functions executed on the same platform in order to reduce design and implementation costs. The set of safety-critical functionality is subject to certification requirements and the rest of the functionality does not need to be certified, or is certified to a lower level. The resulting mixed-criticality systems bring challenges in designing such systems, especially when the critical tasks are required to complete with a timing constraint. This paper studies a problem of scheduling a mixed-criticality system with fault tolerance. A fault-recovery technique called checkpointing is used where a program can go …

Microcontroller Based Granular Urea Application Attachment For Rice Transplanter, Md Towfiqur Rahman, Md Monjurul Alam, Md Mosharraf Hossain, Muhammad Rashed Al Mamun

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Transplanting and fertilizer application for rice production in Bangladesh are tedious, time consuming and laborious task, and mostly done manually. Mechanical transplanting of rice becoming popular in the country in recent years and few machines have been developed for granular urea deep placement, however, having some limitations. Placing granular urea precisely along with rice transplanting, an attempt was under taken to design and fabricate an electronic control granular urea applicator to be attach with a 4-row walk behind type rice transplanter. Fabrication of the electronic granular urea applicator was done in the workshop of the Department of Farm Power and …

Integrating Multi-Source Weather Data For Deep Learning, Haidar A. Alanbari Mr

Dissertations and Theses

Big Data has been playing a major role in the domain of Deep Learning applications as many companies and institutions continue to find solutions and extract certain trends in fields of climate change, weather forecasting and meteorology. This project extracts weather events data from multiple data sources that are supported by National Centers for Environmental information (NCEI) [1] and Amazon Web Services (AWS) [2]. Data sources include Next-Generation NEXRAD [3] Doppler radar reflectivity, GOES-16 [4] multi-channel satellite imagery and NCEI [1] storm events. Then, it integrates and refines data in proper formats to be fed to the open-source Detectron [5] …

Genet-Cnv: Boolean Implication Networks For Modeling Genome-Wide Co-Occurrence Of Dna Copy Number Variations, Salvi Singh

Graduate Theses, Dissertations, and Problem Reports (ETD)

Lung cancer is the leading cause of cancer-related death in the world. Lung cancer can be categorized as non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). NSCLC makes up about 80% to 85% of lung cancer cases diagnosed, whereas SCLC is responsible for 10% to 15% of the cases. It remains a challenge for physicians to identify patients who shall benefit from chemotherapy. In such a scenario, identifying genes that can facilitate therapeutic target discoveries and better understanding disease mechanisms and their regulation in different stages of lung cancer, remains an important topic of research.

In this …

Security Bug Report Classification Using Feature Selection, Clustering, And Deep Learning, Tanner D. Gantzer

Graduate Theses, Dissertations, and Problem Reports (ETD)

As the numbers of software vulnerabilities and cybersecurity threats increase, it is becoming more difficult and time consuming to classify bug reports manually. This thesis is focused on exploring techniques that have potential to improve the performance of automated classification of software bug reports as security or non-security related. Using supervised learning, feature selection was used to engineer new feature vectors to be used in machine learning. Feature selection changes the vocabulary used by selecting words with the greatest impact on classification. Feature selection was able to increase the F-Score across the datasets by increasing the precision. We also explored …

Kidney Ailment Prediction Under Data Imbalance, Ranaa Mahveen

Graduate Theses, Dissertations, and Problem Reports (ETD)

Chronic Kidney Disease (CKD) is the leading cause for kidney failure. It is a global health problem affecting approximately 10% of the world population and about 15% of US adults. Chronic Kidney Diseases do not generally show any disease specific symptoms in early stages thus it is hard to detect and prevent such diseases. Early detection and classification are the key factors in managing Chronic Kidney Diseases.

In this thesis, we propose a new machine learning technique for Kidney Ailment Prediction. We focus on two key issues in machine learning, especially in its application to disease prediction. One is related …

Investigation And Development Of Exhaust Flow Rate Estimation Methodologies For Heavy-Duty Vehicles, Chakradhar Reddy Vardhireddy

Graduate Theses, Dissertations, and Problem Reports (ETD)

Exhaust gas flow rate from a vehicle tailpipe has a great influence on emission mass rate calculations, as the emission fractions of individual gases in the exhaust are calculated by using the measured exhaust flow rate. The development of high-end sensor technologies and emission pollutant measurement instruments, which can give instantaneous values of volume concentration of pollutants flowing out of the engine are gaining importance because of their ease of operation. The volume concentrations measured can then be used with the instantaneous exhaust flow rate values to obtain mass flow rates of pollutants.

With the recent promulgation of real world …

Eaglebot: A Chatbot Based Multi-Tier Question Answering System For Retrieving Answers From Heterogeneous Sources Using Bert, Muhammad Rana

College of Graduate Studies: Theses & Dissertations

This paper proposes to tackle Question Answering on a specific domain by developing a multi-tier system using three different types of data storage for storing answers. For testing our system on University domain we have used extracted data from Georgia Southern University website. For the task of faster retrieval we have divided our answer data sources into three distinct types and utilized Dialogflow's Natural Language Understanding engine for route selection. We compared different word and sentence embedding techniques for making a semantic question search engine and BERT sentence embedding gave us the best result and for extracting answer from a …

Trapping Aco Applied To Mri Of The Heart, Shannon Lloyd Birchell

UNF Graduate Theses and Dissertations

The research presented here supports the ongoing need for automatic heart volume calculation through the identification of the left and right ventricles in MRI images. The need for automated heart volume calculation stems from the amount of time it takes to manually processes MRI images and required esoteric skill set. There are several methods for region detection such as Deep Neural Networks, Support Vector Machines and Ant Colony Optimization. In this research Ant Colony Optimization (ACO) will be the method of choice due to its efficiency and flexibility. There are many types of ACO algorithms using a variety of heuristics …

On Learning And Visualizing Lexicographic Preference Trees, Ahmed S. Moussa

UNF Graduate Theses and Dissertations

Preferences are very important in research fields such as decision making, recommendersystemsandmarketing. The focus of this thesis is on preferences over combinatorial domains, which are domains of objects configured with categorical attributes. For example, the domain of cars includes car objects that are constructed withvaluesforattributes, such as ‘make’, ‘year’, ‘model’, ‘color’, ‘body type’ and ‘transmission’.Different values can instantiate an attribute. For instance, values for attribute ‘make’canbeHonda, Toyota, Tesla or BMW, and attribute ‘transmission’ can haveautomaticormanual. To this end,thisthesis studiesproblemsonpreference visualization and learning for lexicographic preference trees, graphical preference models that often are compact over complex domains of objects built of …