Physical Sciences and Mathematics Commons^™

Gr-461 Sudoku Solver Using Brute Force Algorithm With Backtracking Approach, Md Shamsul Alam, Md Jahirul Islam, Vyghni Sudha Kommineni, Jaswanthi Vellanki, Poka Sudheer

C-Day Computing Showcase

Sudoku is a fun game that challenges our brain to think logically. It only has numbers from 1 to 9 in a 9x9 matrix network where the nine numbers should not be repeated in the same column, row or each 3x3 submatrix. Although there are numerous methods to solve this problem, the most common method is the backtracking algorithm. So, we are using brute force algorithm to solve sudoku and then compare it with backtracking to ensure which algorithm gives best results.

Gr-512 A Fcfs Approach For Order Of Operations In Arithmetic Formalism In Programming Languages, Ethan K Hunt

C-Day Computing Showcase

This paper provides the description of my research project on the computational power a programming language can have that uses an arthritic model that has a first come first serve approach to the order of operations. The goal of such analysis is to verify if such a method of computation can enclose all basic arithmetic and algebraic expressions, the answer to which will help disclose the computational limitations of certain programming language frameworks.

Egr-518 A Multi-Model Approach For Detecting And Combating Fake News, Sujisha Devineni, Thrisandhya Bodakunta

C-Day Computing Showcase

Internet plays a vital role in our daily lives, we use it for various purposes and benefit from advancements in technology and social media. However, the same platforms which make global information exchange also promote spread of fake news,raising a significant threat. To resist this issue, fact checking has become important, leading to extensive research to identify fake news and deal problems arising with them. Our project’s mission is to find the most effective model for fake news detection. We explore different approaches and models, like BERT, Decision Trees, Logistic Regression, and Ada Boost classification and evaluate their performance by …

Uc-441 Finding The Limits Of Ai For Web Development In 2023, Jessica Chavez, Jackson A. Chastine, Joel R. Eve, Danish Khan, Alexandre D. Garcia

C-Day Computing Showcase

This Project explores the limits of artificial intelligence (AI) in web development, focusing on the year 2023. The study is conducted by AI Limits Team 1 from Kennesaw State University. The primary objective of the project is to harness the potential of ChatGPT 3.5, an advanced AI model, to create a fully functional Auction House Website. The achievements of the project include innovative web development, AI-generated content, and successful integration of AI into both frontend and backend aspects of web development. The research findings offer valuable insights into ChatGPT's proficiency in generating web application code and emphasize the importance of …

Eur-443 The Compression Connection: Ncd And Knn In Law Enforcement Text Analytics, Gabriel T. Gillott

C-Day Computing Showcase

Facing a deluge of digital records, law enforcement needs advanced data sorting systems. This project uses a new NLP model, blending compression algorithms and KNN, to categorize Cobb County police reports by mental health, behavioral, and drug issues—vital for efficient resource allocation. The model employs Normalized Compression Distance (NCD) to discern text similarities, enhancing analysis of varying report styles. Early tests show promise in label categorization, but generalizing remains challenging, marking future research directions. This NLP advancement could revolutionize data handling in public safety, aiming to surpass current classification standards.

Gc-427 Elevating Ai Research: Creating A Website For Kennesaw State University's Ai Lab, Shashank Gadhe

C-Day Computing Showcase

The project titled "Elevating AI Research: Creating a website for Kennesaw State University's AI Lab" is dedicated to developing an HTML5 Content Management System website for Kennesaw State University. This website, AILab.kennesaw.edu, serves as a dedicated platform to showcase lab facilities, ongoing projects, and cutting-edge research, with a focus on promoting global AI research and education. Our target audience encompasses university students, faculty, AI researchers, and organizations with an interest in AI innovation.Preliminary findings support our goal: engaging platforms showcasing AI Lab research effectively.The incorporation of admin access empowers university professors to customize content, thus enhancing adaptability and personalization. These …

Gc-447 Bio-Contribute, Tejaswi Divi, Kiranmayi Tandra, Sainath Reddy Bheemireddy, Chandrasekhar Reddy Bheemireddy, Sharanya Chinthakuntla

C-Day Computing Showcase

The "Bio-Contribute" challenge is an ambitious initiative aimed at revolutionizing the way facts are generated and shared in lifestyle sciences. Bio-Contribute encompasses various aspects, including design, development, and facts collection. It leverages Figma for innovation, ensuring a user-friendly and collaborative interface. The challenge has completed the frontend development section, permitting customers to carry out various movements, which include content advent and information seizure with the help of the GPT-3 era. This report gives an in-depth analysis of the venture's progression, strategies used, and initial outcomes, demonstrating its potential to convert statistics introduction and sharing in the discipline of lifestyles sciences.

Gc-511 Predicting Stock Prices Using Different Machine Learning And Deep Learning Models, Afnan Crystal, Rohith Sundar Jonnalagadda, Hrithik Singh Chandel

C-Day Computing Showcase

Our project focuses on the challenge of predicting the daily closing prices and stock movements of Amazon, one of the world's largest and most dynamic corporations. Amazon's stock prices are known for their unpredictability and are influenced by a multitude of intricate factors. Our project aims to provide accurate and reliable forecasts for Amazon's stock prices, going beyond mere predictions. The analysis employs a comprehensive approach, comparing the performance of three distinct machine learning and deep learning models: Linear Regression, Support Vector Machine (SVM), and Multi-Layered Perceptron (MLP) for financial time series data. The dataset we used spans from January …

Gr-397 Conceptualizing A Toc-Enhanced Chatbot: Pattern Recognition And Interaction, Sumaiya Tasneem, Sharon Elugoti, Chinni Cherrishma Reddy Aduri, Purna Pavan Kumar Kolli, Krishna Vamsi Anche

C-Day Computing Showcase

A chatbot is a software which is capable of communicating with human by using natural language processing. In our project, we plan to develop a Python-based chatbot that integrates theory of computation (TOC) concepts, including finite automata and regular expressions. The chatbot will interact with users, recognizing patterns and keywords in their inputs. We’ll begin by defining initial regular expressions for basic user interactions including greetings and inquiries.Future developments may enhance regular expressions and broaden the chatbot’s TOC-related capabilities, creating a versatile educational tool with practical TOC applications.

Gr-406 Federated Learning In Cardiac Diagnostics: Balancing Predictive Accuracy With Data Privacy In Heart Sound Classification, Sricharan Donkada

C-Day Computing Showcase

Cardiovascular diseases account for nearly a third of global deaths, posing a challenge that machine learning can help address. However, data privacy concerns hinder the direct application of conventional machine learning in this sensitive area. This paper explores Federated Learning (FL) as a decentralized strategy to mitigate these concerns by allowing for local data processing. FL's design ensures that only processed updates, not raw data, are shared with a central server, maintaining individual privacy. Our research assesses FL's practicality and effectiveness in predicting heart disease while adhering to ethical and legal norms. We build upon previous studies, such as Wanyong …

Gr-422 Simplified Named Entity Recognition Using Context Free Grammar, Sai Chandana Koganti, Varshini Yaganti, Vinesh Babu Yaganti, Surya Kiran Katragadda, Pavani Thulluru

C-Day Computing Showcase

Named Entity Recognition (NER) is a crucial component of natural language processing. Although Spacy is a popular tool for NER, it faces challenges in accurately identifying individual's names. In response, Context-Free Grammar (CFG) is introduced as a complementary solution to augment Spacy's NER functionality, with the specific objective of enhancing the precision of person name recognition. This project focuses on formulating CFG rules and applying them to a sample text, showcasing improved NER accuracy. By combining the strengths of Spacy and CFG, we aim to address the limitations of current NER systems, particularly in recognizing individual's names, and contribute to …

Gr-434 Phase Estimation’S Application In Qram, Ethan K Hunt

C-Day Computing Showcase

The paper proposes a new novel way of creating QRAM through quantum phase estimation. This is done by mapping a monotonically increasing sequence of natural numbers to a binary series and, ultimately, to a characteristic constant η which is then encoded as a phase in a quantum state. This process leverages quantum phase estimation, a fundamental quantum algorithm for finding the eigenvalues of a unitary operator which can be used as a form of QRAM in either Quantum or Hybrid models of computing

Egr-494 Using A Non-System Language To Implement An Optimized Round Robin Scheduling Algorithm, Francis E Nweke

C-Day Computing Showcase

The objective of this project is to create an optimized Round Robin scheduling algorithm in C# and examine the related performance metrics. In this study, I will evaluate performance by implementing the optimized model in a non-system language such as (C#). Our simulation would provide insight into the execution of many processes while taking into account arrival times, burst timings, and user-defined time quantum. We can interact with the simulator because it is a Graphical User Interface (GUI) software.

Gr-496 Cardiac Arrest Prediction Model, Vineeth Amsham, Sai Reddy Balaiah, Tamilkumar Subbarayakgounder

C-Day Computing Showcase

The "Cardiac arrest prediction model" project melds machine learning with healthcare to tackle heart disease. It aims to surpass current diagnostic tools that fail to catch early signs of cardiac events, often leading to high mortality. By developing an ML model that identifies early predictors of cardiac arrest, the project seeks to enable early interventions. Using supervised learning for its pattern recognition strength, the goal is to predict heart attacks accurately and thus, revolutionize preventative care and outcomes. This effort marks a leap in medical diagnostics and moves towards personalized healthcare, potentially saving countless lives and pioneering a new direction …

Gr-515 Developing A Conversational Chatbot Using Seq2seq Model With Tensorflow, Drashtee Parmar, Ruthvik R. Anugu

C-Day Computing Showcase

Sequence-to-Sequence (Seq2Seq) modeling, when paired with Long-Short-Term Memory (LSTM) units, has demonstrated significant potential in developing conversational chatbot capable of participating in text-based conversation and providing human-like responses.The Cornell Movie-Dialogs Corpus will be used to extract dialogues, preprocess the data, and then use the output to train the Seq2Seq model. Our contributions include exploring the application of LSTM for Natural Language Generation (NLG) and creating a comprehensive chatbot system. According to the results of the experiment, our method works well for coming up with thoughtful answers during a conversation.

Gr-519 Meditation As An Intervention To Improve Student Attention An Eeg Study Based On Machine Learning Prediction And Spectral Ratio Analysis, Sreekanth Gopi

C-Day Computing Showcase

This research aims to develop a machine learning model using EEG data to identify student inattention, serving as an early intervention tool. Attention deficit, influenced by social media, adversely affects student performance. ADHD, characterized by inattention, hyperactivity, and impulsivity, is linked to academic challenges. Early detection in academics is crucial. A Machine Learning model was designed, and trained on an attention dataset with 34 EEG recordings of young adults. The raw EEG data was pre-processed and filtered, ICA was applied, and spectral analysis was done. Guided meditation with music was developed as an intervention to improve attention. EEG recordings from …

Uc-395 Ccse Reservation Application, Nicholas Kelley, Tykira Carreker, Keith Mutisya, Aaron Hyatt

C-Day Computing Showcase

The CCSE department tasked us with creating an application to streamline the reservation of the rooms and equipment maintained by the college. To achieve this, we developed a front-end application created with PHP that allows users to view available resources, and request reservations for them. We created and maintained a back-end database configured with MySQL through phpMyAdmin to allow for the easy integration with the front end. Through the project we created new functions in the application with the user in mind, to create the best possible application to fit the needs of the CCSE department.

Uc-401 Website Hardening And Ethical Hacking, Alex L Liu, Samrat H Pandya, Kennedy A Sanchez, Kenny M Frontin, Komlan I Wogomebu

C-Day Computing Showcase

This project is to showcase a real life scenario of securing a theoretical business website on Red Hat Linux, Apache, MariaDB, and PHP hosted in a virtual machine. The project objective is for a team to research ways to secure the theoretical business website, develop and implement security policies, and perform a red/blue team exercise. This project is a way for a team to exercise ethical hacking in a closed environment to obtain experience.

Uc-426 Cybersecurity Park, Michael P. Leung, Kalil R. Masters, Robert Deason, Ian C. Skoonberg, Aiden D. Gohlke

C-Day Computing Showcase

We are presenting two additional modules to the Cyber Security Park, a long-running game project that is part of the realities lab. Whack-a-Malware is an arcade game where the player has to whack various malware, each with unique effects that mimic real-world malware. This includes Adware, Spyware, Ransomware, Computer Worms, and Trojan Horses. The player is equipped with two hammers that instantly destroy malware. But it goes on a three-second cooldown every time they destroy something. The player can reduce the cooldown by destroying adware as they are the main cause of slowing down the computer. Digital Footprints Private Investigator …

Uc-432 Ccse Reservation Application, Brandon R. Bunn, Ryan Kafka, Dina Levieva, Alyn Ovell, Olivia C. Venuti

C-Day Computing Showcase

This CCSE Reservation Application project is focused on creating a unified web application to simplify the reservation process for inventory devices and room time slots for faculty and staff at Kennesaw State University. This initiative combines two separate reservation systems into a single solution, encompassing a database, backend, and frontend. The database will store relevant information, the backend will handle data communication, and the frontend will provide a user-friendly interface for making reservations. Administrators will have access to an admin view for managing requests. The project has faced challenges, including resource constraints and team coordination issues. While the integration of …

Uc-492 Lotspotter, Julian Yankah, Henry Pham, Tripp Greene, Jonathan Perry, Ghislain Dongbou

C-Day Computing Showcase

The parking issue has quietly become the cause of a lot of stress for travelers and other regular users. It's nothing new that some people miss their flight and/or get late to other important meetings and appointments because they couldn’t locate an available parking lot. Not because there isn't available parking but because they don’t know where it is! What if there was some way to solve that? Introducing LotSpotter! An application built to detect and navigate to vacant parking spaces across the United States. It will leverage various technologies, including image processing, sensors, AI and mobile app development, to …

Uc-506 Underock Arena, Bryanna N Walker

C-Day Computing Showcase

The game I developed was meant to highlight the ways that game developers can utilize our mobile devices to create a casual game. Breaking down complex parts of an RPG battling game, I devised the most casual and mobile-friendly way for the player to battle with unique bugs against an AI enemy using only 3 simple buttons: attack, heal, and ultimate. In addition, because of my areas of study at Kennesaw State University, I wanted to use my artistic abilities in my game. I thought it would be interesting to see how traditional art looks in a mobile game on …

Uc-508 Memories: Echoes Of Resilience, Ethan J Hester, Joshua Tyler Padgett, Katelyn R Freeman, Sarah Crosby, Keith W Rhymer

C-Day Computing Showcase

Memories: Echoes of Resilience is a first-person narrative experience that takes players through different experiences of a boy with autism. The game aims to show how everyday interactions can be different to someone with autism. Experiences such as making friends in school, being in overstimulating environments, or managing adult life are experiences we want to simulate for the player. The player will be able to interact with the world around them but will also have to be aware of how their environment impacts them, so they don’t become overstimulated.

Ur-409 Enhancing Aircraft Electronic Warfare Testing With Automated Rf Spectrum Analysis, Anthony De Santiago, Matthew T. Morgan, Geonhyeong Kim, Jalon L. Bailey, Camille Reaves

C-Day Computing Showcase

Military test ranges utilize a variety of Radio Frequency (RF) threat systems, to assess the effectiveness of Electronic Warfare (EW) systems during flight tests. A component of this process involves monitoring RF transmissions. Traditionally, system engineers at Robins Airforce Base have manually analyzed video from spectrum analyzers to confirm properties of specific threat systems. To streamline this analysis, our team's aim was to develop an automated solution for RF spectrum analysis. We employed a custom YOLO V8 model to isolate the analyzer screen and used a novel combination of frame differencing, summing, and agglomerative clustering techniques to extract relevant properties …

Ur-484 Ai Detection – Chat Gpt, Wylder Everett, Daniel Mode, Paul Nguyen, Alfred Johnson, Bilal Khan

C-Day Computing Showcase

With the emergence of large language models (LLM) and Artificial Intelligence (AI) assistants like ChatGPT, accompanying tremendous potentials are critical challenges. Indeed, these assistant systems can provide quality information with conveniences. However, the generated contents are highly problematic being seemingly indistinguishable from that of human. The implication of this issue is severe in science, education, and domains that value original contents. With such motivation, this project addresses the task of identifying ChatGPT-synthesized texts with a focus on education, specifically, in short-answer questions. The goal of the project is to develop an AI technology that identifies synthesized texts by comparing such …

Lung Dosimetry Model Of Inhaled 222rn For Workers At Selected Building Material Factories In Erbil City, Iraq, Sardar Qader Othman, Ali Hassan Ahmed, Sarbaz Ibrahim Mohammed

Polytechnic Journal

Radon gas can cause lung harm, leading to extensive research on the biological effects of radon exposure in human lungs under various environmental conditions. The study estimates radon progeny doses for workers in Erbil city's building materials industries, focusing on alpha particle energy deposition, particle clearance, and lung deposition. Using a home condition of 1 Bq.m-3, radon progeny particle-absorbed doses in the trachea, bronchi, bronchioles, alveolar-interstitial sections, and lungs were determined in the first scenario. The 1 Bq.m-3 is replaced with the radon level for each factory to prepare its atmosphere in the second scenario. For the first scenario, the …

Electrofacies Analysis Using A Geostatistical Approach, Northern Iraq Case Study, Hussein S. Hussein

Polytechnic Journal

The distribution of petrophysical parameters is governed by lithology, hence understanding the spatial variation in lithology is essential for reservoir characterisation. This study points out the use of well logs to estimate the lithology of carbonate rocks (limestone, marly limestone, dolomite, and dolomitic limestone) found in Upper Cretaceous to Miocene formations at the Khabbaz Oil Field in Northern Iraq. Applying the multivariate regression technique to neutron, and density data enabled accurate lithology prediction. By using independent values (well log data) to predict a dependent value (lithology), this technique is a progression of regression analysis. The formations under study comprised a …

Migration In Edge Computing, Arshin Rezazadeh

Electronic Thesis and Dissertation Repository

Mobile IoT applications often require low response time and high bandwidth. These applications include virtual reality, augmented reality, and online gaming. Currently, most data processing is done in the cloud. However, for latency-sensitive applications, the latency may need to be reduced. Edge and fog computing can be used to place application services close to mobile devices to reduce latency. However, as mobile devices move, latency increases, which can be decreased by moving the service to a closer edge/fog server. This can be addressed by migrating services so that the mobile device can receive services from the new server. These services …

Assessment Of Green Spaces In Metro Nashville Public High Schools, Webster G. Andrews, Anna Lennon, Darlene Panvini

Science University Research Symposium (SURS)

Green spaces on and near school property have been correlated to student mental health and academic achievement. However, there is a gap in the literature investigating how quality and quantity of green space is related to socioeconomic status and school economic metrics in public high schools and the surrounding neighborhoods. Nashville public high schools located in higher socioeconomic areas and having greater financial resources were predicted to provide larger, higher quality green spaces. This study combined a quantitative analysis of neighborhood socioeconomic metrics, school socioeconomic metrics, and quantity of green space (assessed using iTree Canopy) with a qualitative field assessment …

The Role Of Myc-Egr1 Pathway In Colorectal Cancer Development And Its Relationship To Wnt Signaling, Lexi Chrysochoos, Erick Spears, Emily Abdoney

Science University Research Symposium (SURS)

Lexi Chrysochoos, Emily Abdoney, and Erick Spears

The Role of MYC-EGR1 Pathway in Colorectal Cancer Development and its Relationship to Wnt Signaling

Colorectal tumors show high levels of Wnt pathway activity, most often due to an initiating mutation in critical Wnt pathway components. Wnt pathway activation leads to b-catenin-driven expression of genes that generally stimulate cell cycle progression. One such Wnt pathway target gene is MYC, a well described oncogene involved in many cancers, including colorectal. MYC is known to stimulate proliferation but in certain cellular contexts, can stimulate apoptosis by stimulating the expression of EGR1. This is termed a …