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Uc-6 Covid-19 Data Analysis - Regression, Noah Druss 2021 Kennesaw State University

Uc-6 Covid-19 Data Analysis - Regression, Noah Druss

C-Day Computing Showcase

Covid-19 has been arguably the most impactful event in the past century. SARS-Cov-2 is a viral respiratory illness discovered in late 2019 that has spread to almost every country in the world. It has directly or indirectly affected just about everybody in the world greatly, causing over 117 million cases and 2.59 million deaths as of March 2021. This project has focused on the use of different types of linear regression to both analyze and predict Covid-19 infection data based on different features. First, simple linear regression was used to predict total deaths based on infections both globally and ...


Uc-62 Machine Learning: Twitter Bots In Disguise, Matthew Joseph Scheer, Nicolas Vasquez, James C Andersen, Joshua Tiangco, Justin Van, Cody R Walicek, Daniel Rimmel 2021 Kennesaw State University

Uc-62 Machine Learning: Twitter Bots In Disguise, Matthew Joseph Scheer, Nicolas Vasquez, James C Andersen, Joshua Tiangco, Justin Van, Cody R Walicek, Daniel Rimmel

C-Day Computing Showcase

This project was designed to help fight against misinformation spread by bots(computers), the goal assigned to us was to find and inform Twitter users of bots that follow and are being followed by the user.Advisors(s): Dr. Reza PariziTopic(s): Artificial IntelligenceSWE 4724


Ur-41 The Accessibility Of The Mobile Gaming Platform For The Visually Impaired, Christian Thomas Jansen 2021 Kennesaw State University

Ur-41 The Accessibility Of The Mobile Gaming Platform For The Visually Impaired, Christian Thomas Jansen

C-Day Computing Showcase

The motivation for this project is to research mobile gaming interfaces with the goal of conceptualizing practices in game design that would create more accessible interfaces for the visual impairment community. Thus far, the project has focused on practices that mobile game designers can use to make their games more accessible to the visually impaired. These includes the use of plain text rather than graphics to be scannable by screen readers, the inclusion of audio-oriented support and instruction, the use of contrasting colors to make options more recognizable to those with partial visual impairments, and the implementation of game mechanics ...


Ur-46 Breastnet;, Cora L Meador, Ryan Deem 2021 Kennesaw State University

Ur-46 Breastnet;, Cora L Meador, Ryan Deem

C-Day Computing Showcase

In the United states, 13% of women are diagnosed with breast cancer in their lifetime, and it is the second leading cause of death by cancer in women. Early detection and screening can result in an increase of life expectancy by 10 years on average. Unfortunately, breast cancer can be challenging to detect, since it can appear anywhere in the breast. Cancer that is detected in its early stages can give patients more options and save thousands of dollars in medical costs. Some of the most recent developments in computer science and machine learning are in the biomedical field, especially ...


Ur-48 Using Semantic Segmentation In A Convoluted Neural Network For Vocal Localization In Music, Trevor E Stanca, Trinite, Noah 2021 Kennesaw State University

Ur-48 Using Semantic Segmentation In A Convoluted Neural Network For Vocal Localization In Music, Trevor E Stanca, Trinite, Noah

C-Day Computing Showcase

I. PROJECT OVERVIEW A. Research Question In this project, the question was asked: ”Is there an easier way to extract vocals from music?” Many other works are able to extract vocals with Deep Neural Networks using Multitask Learning, which are large and take a long time to train. To rival this, we wish to present a method to identify vocals with a Convolutional U-Network (U-Net) for Semantic Segmentation of audio files. B. Project Description This project differs from other works by identifying vocal locations by converting audio files in Short Time Fourier Transforms(STFT), and treating them as images in ...


Ur-63 Low Cost High Impact Fall Detection At The Edge, Dylan SIRNA, Noah Sage 2021 Kennesaw State University

Ur-63 Low Cost High Impact Fall Detection At The Edge, Dylan Sirna, Noah Sage

C-Day Computing Showcase

ML models have become more accurate, powerful and portable in recent years, the purpose of this project is to explore how these advances can be applied towards fall detection for less cost than before possible. This project explores the application of micro controllers which have become cheaper and stronger along with emerging machine learning models that can be trained on a traditional computer with greater resources and then port the model to be interpreted on a micro-controller such as a raspberry pi. These two factors lead to the reason to revisit the problem of fall detection, a problem that plagues ...


Ur-65 Cnn Cifar Image Identification, Matteo L Staciarine 2021 Kennesaw State University

Ur-65 Cnn Cifar Image Identification, Matteo L Staciarine

C-Day Computing Showcase

Reducing the learning rate of a CNN can positively affect the validation accuracy of a machine learning model. Dropping out nodes from different layers can further delay overfitting from happening. Validation loss decreases over more epochs, but it must be cut when it reaches its minimum value.Advisors(s): Dr. Dan LoTopic(s): Artificial Intelligence


Uc-7 Software Engineer – Clarity Llc, Amy Mullins 2021 Kennesaw State University

Uc-7 Software Engineer – Clarity Llc, Amy Mullins

C-Day Computing Showcase

Clarity makes an app called CaptionMate that does closed captions for phone calls.. During the internship, a website was made to visualize metrics that are collected on users such as calls made, minutes used, time active, region, age, theme, font, and platform used. Bar charts are used to show minutes and calls used on days of the week. 100% bar charts are used to show how much a day contributes to the usage of the app; a user contributes to minutes, calls and platform usage; and show calls incoming vs. outgoing. Line graphs were created to show growth in app ...


Uc-37 Interactive Pdf File Editing For Online Classes, David J Hall, Chris J Stubbs, Justin Masters, Dalton G Parker, Rosendo Lopez 2021 Kennesaw State University

Uc-37 Interactive Pdf File Editing For Online Classes, David J Hall, Chris J Stubbs, Justin Masters, Dalton G Parker, Rosendo Lopez

C-Day Computing Showcase

This system aims to create an interactive environment for teachers to view/grade/edit student submission in virtual classes. Objectives for this project are to create independent component or logic model that includes the following functions. This component should be integrated with a .net core application easily. -Upload pdf files to the system and save files to the server; -Record audio online and save audio to the system; also, the audio can be played online; -Upload and play video or video link (YouTube); -Split file. When uploading a PDF file, the system will allow to split or crop the file ...


Uc-59 Analyzing Concentration Levels In Online Learning With Facial Values, Elliott J Witherell, Jakeira Askew, Jonathan R Dicks, Steven C McGuire, Jacob A Walton 2021 Kennesaw State University

Uc-59 Analyzing Concentration Levels In Online Learning With Facial Values, Elliott J Witherell, Jakeira Askew, Jonathan R Dicks, Steven C Mcguire, Jacob A Walton

C-Day Computing Showcase

Can deep learning models accurately predict whether an individual is focused or distracted on a task in order to improve learning efficiency? In the context of online learning with the use of a webcam, this project is aimed at detecting concentration levels of students to potentially assist with improving learning efficiency. Machine learning technologies have been utilized to evaluate students’ facial expression and eye movements to identify whether a student is focused or distracted. The machine learning branch that is employed is a supervised learning model. This supervised learning model makes predictions based on given input features. A total of ...


Gc-54 Covid-19 Mortality Prediction Using Machine Learning Techniques, Lindsay Schirato, Kennedy Makina, Dwayne Flanders 2021 Kennesaw State University

Gc-54 Covid-19 Mortality Prediction Using Machine Learning Techniques, Lindsay Schirato, Kennedy Makina, Dwayne Flanders

C-Day Computing Showcase

In late 2019, SARS-CoV2 also known as COVID-19 was first identified in the city of Wuhan, China. This virus can infect a person and without showing any signs of sickness, can spread of COVID-19 unknowingly. The World Health Organization declared it a global pandemic in March 2020 because of its far-reaching effects in every part of the world. Scientists have been working to leverage technology to prevent spread, detection and vaccine development. With machine learning, models can predict which patient will most likely have a higher mortality rate. Using WEKA, a machine learning tool and a data set based on ...


Ur-60 Video-To-Video Synthesis With Semantically Segmented Video, Aydan Mufti, Jordan S Hasty 2021 Kennesaw State University

Ur-60 Video-To-Video Synthesis With Semantically Segmented Video, Aydan Mufti, Jordan S Hasty

C-Day Computing Showcase

Our project involves studying the usage of generative adversarial networks (GANs) to translate semantically segmented video to photo-realistic video in a process known as video-to-video synthesis. The model is able to learn a mapping from semantically segmented masks to real-life images which depict the corresponding semantic labels. To achieve this, we employ a conditional GAN-based learning method that produces output conditionally based on the source video to be translated. Our model is capable of synthesizing a translated video, given semantically labeled video, that resembles real video by accurately replicating low-frequency details from the source.Advisors(s): Dr. Mohammed AledhariTopic(s ...


Ur-66 Image Segmentation With Machine Learning, Kedar A Johnson 2021 Kennesaw State University

Ur-66 Image Segmentation With Machine Learning, Kedar A Johnson

C-Day Computing Showcase

An experiment-based analysis of the performance of machine learning algorithms in image segmentation. The experiment is organized to test three experimental groups representing supervised, unsupervised and reinforcement machine learning. The three experimental groups are exposed to three datasets of images for training and testing. They’re performance results are recorded and compared for a statistically significant difference in mean performance values. These results are assumed to identify a trend in differences in performance if a statistically significant difference in performance statistics is discovered between any of the three groups. This experiment will follow a quasi-experimental design because of the absence ...


Uc-69 Team 10b Bchain, Jonathan D Lashgari, Carlos A Diaz, Jeffery Erhunse, Caleb T Goff, Giang T Nguyen 2021 Kennesaw State University

Uc-69 Team 10b Bchain, Jonathan D Lashgari, Carlos A Diaz, Jeffery Erhunse, Caleb T Goff, Giang T Nguyen

C-Day Computing Showcase

BChain is a new P2P file sharing system that is fully private, anonymous, globally self-verifying, and utilizes an automatic peer-maintained network of trust in data, accomplished through new methods of routing content over the whole network, encrypted, rather than per torrent download. Verification is done by adding file metadata to a blockchain giving the network consistent knowledge of each file it can transfer, and how to verify file received against the network. This enables a policy of zero trust against peers. This system is implemented by an app that interfaces with the network using the protocol, using it for upload ...


Cognitive Radio Spectrum Sensing And Prediction Using Deep Reinforcement Learning, Syed Qaisar Jalil, Mubashir Husain Rehmani, Stephan Chalup 2021 The Universit of Newcastle

Cognitive Radio Spectrum Sensing And Prediction Using Deep Reinforcement Learning, Syed Qaisar Jalil, Mubashir Husain Rehmani, Stephan Chalup

Preprints

In this paper, we propose to use deep reinforcement learning (DRL) for the task of cooperative spectrum sensing (CSS) in a cognitive radio network. We selected a recently proposed offline DRL method called conservative Q-learning (CQL) due to its ability to learn complex data distributions efficiently. The task of CSS is performed as follows. Each secondary user (SU) performs local sensing and using CQL algorithm, determines the presence of licensed user for current and k-1 future timeslots. These results are forwarded to the fusion centre where another CQL algorithm is operating that generates a global decision for the current and ...


A Lightweight And Explainable Citation Recommendation System, Juncheng Yin 2021 The University of Western Ontario

A Lightweight And Explainable Citation Recommendation System, Juncheng Yin

Electronic Thesis and Dissertation Repository

The increased pressure of publications makes it more and more difficult for researchers to find appropriate papers to cite quickly and accurately. Context-aware citation recommendation, which can provide users suggestions mainly based on local citation contexts, has been shown to be helpful to alleviate this problem. However, previous works mainly use RNN models and their variance, which tend to be highly complicated with heavy-weight computation. In this paper, we propose a lightweight and explainable model that is quick to train and obtains high performance. Our model is based on a pre-trained sentence embedding model and trained with triplet loss. Quantitative ...


Student Academic Conference, Caitlin Brooks 2021 Minnesota State University Moorhead

Student Academic Conference, Caitlin Brooks

Student Academic Conference

No abstract provided.


Exploring Ai And Multiplayer In Java, Ronni Kurtzhals 2021 Minnesota State University Moorhead

Exploring Ai And Multiplayer In Java, Ronni Kurtzhals

Student Academic Conference

I conducted research into three topics: artificial intelligence, package deployment, and multiplayer servers in Java. This research came together to form my project presentation on the implementation of these topics, which I felt accurately demonstrated the various things I have learned from my courses at Moorhead State University. Several resources were consulted throughout the project, including the work of W3Schools and StackOverflow as well as relevant assignments and textbooks from previous classes. I found this project relevant to computer science and information systems for several reasons, such as the AI component and use of SQL data tables; but it was ...


Poker Chip Calculator Application, Ryan Illies 2021 Minnesota State University Moorhead

Poker Chip Calculator Application, Ryan Illies

Student Academic Conference

Application to help start up in person poker games with friends.


Viability Of Consumer Grade Hardware For Learning Computer Forensics Principles, Lazaro A. Herrera 2021 Nova Southeastern University

Viability Of Consumer Grade Hardware For Learning Computer Forensics Principles, Lazaro A. Herrera

Journal of Digital Forensics, Security and Law

We propose utilizing budget consumer hardware and software to teach computer forensics principles and for non-case work, research and developing new techniques. Consumer grade hardware and free / open source software is more easily accessible in most developing markets and can be used as a first purchase for education, technique development and even when developing new techniques. These techniques should allow for small forensics laboratories or classroom settings to have the tooling and framework for trying existing forensics techniques or creating new forensics techniques on consumer grade hardware. We'll be testing how viable each individual piece of hardware is as ...


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