Computer Sciences Commons^™

Social Network Analysis Of A Biomedical Research Co-Authorship Network, Amrina Ferdous

Boise State University Theses and Dissertations

A social network analysis (SNA) is a graph-based method for visualizing social networks. Our study uses the SNA to analyze co-authorship patterns in National Institutes of Health (NIH) - Centers of Biomedical Research Excellence (COBRE), Idaho Institutional Development Award (IDeA), Network of Biomedical Research Excellence (INBRE), and Biomedical Research Infrastructure Network (BRIN) grants between 2001 and 2022. It is of interest to us to analyze the growth and expansion of research over time to look at the social networks of research Hubs, the social networks of potential research Hubs in the near future, and the social networks of potential leaders …

On The Effect Of Emotion Identification From Limited Translated Text Samples Using Computational Intelligence, Madiha Tahir, Zahid Halim, Muhmmad Waqas, Shanshan Tu

Research outputs 2022 to 2026

Emotion identification from text data has recently gained focus of the research community. This has multiple utilities in an assortment of domains. Many times, the original text is written in a different language and the end-user translates it to her native language using online utilities. Therefore, this paper presents a framework to detect emotions on translated text data in four different languages. The source language is English, whereas the four target languages include Chinese, French, German, and Spanish. Computational intelligence (CI) techniques are applied to extract features, dimensionality reduction, and classification of data into five basic classes of emotions. Results …

Cyber-Informed Engineering Of Industrial Control Systems By Prioritization Of High Consequence Events, Conflict Clause Learning Technique, And Autotuning Mechanisms For The Real-Time Monitoring Of Critical Processes, Chidi Ugo Agbo

Boise State University Theses and Dissertations

Industrial Control Systems (ICS) are systems employed to supervise, regulate, and control industrial processes and critical infrastructure. These critical systems require robust measures to protect them against potential safety and security violations. Ensuring the safety and security of ICS is a significant challenge facing nations and states today, necessitating the development of robust, dependable, and resilient ICS. Traditional cybersecurity and engineering practices continue to adopt an inefficient approach that treats security as an add-on element during the system design and development. In response, this dissertation builds on a novel theoretical approach known as Cyber-Informed Engineering (CIE) that leverages safety and …

Automated Materials Spectroscopy And Topology Optimizations Using Genetic Algorithms, Miu Lun Lau

Boise State University Theses and Dissertations

This dissertation will focus on the implementation and development of genetic algorithm software for increasing the productivity and analysis rate for spectrum analysis, topology optimization, and others. The software artifacts generated have resulted in a basic software framework called Neo. The framework has been further developed into different software for specific spectra methods such as EXAFS, Astrophysics, Nano-Indentation, and many more.

For GA implementation that relates to EXAFS, we have demonstrated the usage of GA to correctly identify chemical species presented, for Copper metal species, Technetium compounds, and in-situ of SnS2 batteries. The software artifact was able to generate accurate …

Toward Privacy-Preserving Electronic Voting With Ballot-Spoiling Using Blockchain, Yoonsu Ra

Boise State University Theses and Dissertations

Several voting techniques have been presented in recent years to address the security and privacy concerns associated with electronic voting. Some of these systems have used blockchain technology to reduce the requirement for centralized authority trust while maintaining voter privacy and ballot confidentiality. Existing methods, however, neglect to comply with government standards and therefore are unsuitable for real-world election settings. They often overlook essential attributes such as ballot spoiling and voter-to-ballot unlinkability, dismissing them as conflicting features. In this thesis, we introduce ORBIT, a cryptographic voting scheme that complies with regulations outlined in the Idaho state law while achieving receipt-freeness …

Improving Security Of Order-Preserving Encryption And Its Applications In Data Outsourcing, Ning Shen

Boise State University Theses and Dissertations

Encryption is an important tool to protect data confidentiality and privacy. One important practice of computation primitives is encrypting data with Order-Preserving Encryption (OPE). Order-Preserving Encryption is an encryption algorithm that allows the ciphertexts to preserve the same order of plaintext and it is very useful for range queries in databases and other applications. However, OPE has certain security vulnerabilities, such as it may leak sensitive information other than the ordering. Currently, the application of OPE is also limited, which is primarily used in the encryption of outsourced databases.

Due to the OPE’s security concern and limited usage, this dissertation …

Key Communication Technologies, Applications, Protocols And Future Guides For Iot-Assisted Smart Grid Systems: A Review, Md Ohirul Qays, Iftekhar Ahmad, Ahmed Abu-Siada, Md Liton Hossain, Farhana Yasmin

Research outputs 2022 to 2026

Towards addressing the concerns of conventional power systems including reliability and security, establishing modern Smart Grids (SGs) has been given much attention by the global electric utility applications during the last few years. One of the key advantageous of SGs is its ability for two-way communication and bi-directional power flow that facilitates the inclusion of distributed energy resources, real time monitoring and self-healing systems. As such, the SG employs a large number of digital devices that are installed at various locations to enrich the observability and controllability of the system. This calls for the necessity of employing Internet of Things …

Responsibility Gaps And Black Box Healthcare Ai: Shared Responsibilization As A Solution, Benjamin H Lang, Sven Nyholm, Jennifer Blumenthal-Barby

Center for Medical Ethics and Health Policy Staff Publications

As sophisticated artificial intelligence software becomes more ubiquitously and more intimately integrated within domains of traditionally human endeavor, many are raising questions over how responsibility (be it moral, legal, or causal) can be understood for an AI’s actions or influence on an outcome. So called “responsibility gaps” occur whenever there exists an apparent chasm in the ordinary attribution of moral blame or responsibility when an AI automates physical or cognitive labor otherwise performed by human beings and commits an error. Healthcare administration is an industry ripe for responsibility gaps produced by these kinds of AI. The moral stakes of healthcare …

Deep Learning With Effective Hierarchical Attention Mechanisms In Perception Of Autonomous Vehicles, Qiuxiao Chen

All Graduate Theses and Dissertations, Fall 2023 to Present

Autonomous vehicles need to gather and understand information from their surroundings to drive safely. Just like how we look around and understand what's happening on the road, these vehicles need to see and make sense of dynamic objects like other cars, pedestrians, and cyclists, and static objects like crosswalks, road barriers, and stop lines.

In this dissertation, we aim to figure out better ways for computers to understand their surroundings in the 3D object detection task and map segmentation task. The 3D object detection task automatically spots objects in 3D (like cars or cyclists) and the map segmentation task automatically …

Optimal Stopping Of Multi-Robot Exploration For Unknown, Bounded Environments, Trey D. Crowther

All Graduate Theses and Dissertations, Fall 2023 to Present

Limited resources and uncertainty pose a substantial problem for multi-robot exploration of unknown environments. This research paper looks to determine the optimal time to terminate robot exploration while maximizing information gathered. Whilst making this determination, the system's resources and capabilities must be taken into account. To see if our strategy works, we ran many simulations in varying environments. The results of this research are important for real-world uses like robot exploration, search and rescue missions, and automated surveillance. Determining when to stop exploring can help the system save resources, explore faster, and make better decisions.

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-405 Boosting Clickbait Detection Through Semantic Insights And Attention-Driven Neural Network, Lokesh Meesala

C-Day Computing Showcase

The digital age has witnessed an explosion of online content, making it increasingly challenging for users to differentiate between reliable information and clickbait, which is often misleading or sensationalized. Clickbait contributes to the spread of misinformation, phishing attacks, and illegal marketing practices, and manipulates users’ decisions. Even from a business standpoint a clickbait might not lead to a conversion, A user might land on the page by following a clickbait and get frustrated and close the page. Additionally, with the increase in the usage of large language models for content writing it is even more challenging for the general user …

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

Gr-453 Medical Records Summarization Using Prompt-Based Nlp, Rawan Masadeh, Nicholas S Servies

C-Day Computing Showcase

In this paper, we present an innovative Natural Language Processing (NLP) algorithm for summarizing medical records extracted from the MIMIC-IV dataset using state-of-the-art (SOTA) techniques in text summarization. The increasing volume of electronic health records (EHRs) demands efficient methods for extracting meaningful insights from these complex and extensive documents. Our algorithm leverages recent advancements in NLP, including transformer-based models, to automate summarizing medical records while preserving critical information. Our algorithm is trained and tested using the Medical Information Mart for Intensive Care (MIMIC)-IV database that provides critical care data for over 40,000 patients admitted to intensive care units at the …

Egr-490 Importance Of Food Recognition On Blood Glucose Monitoring, Afnan Crystal

C-Day Computing Showcase

Maintaining blood sugar under control requires eating a healthy and balanced diet, exercising, and adhering to medications. Dietary consumption must be under strict control for diabetic patients’ general health. Traditional techniques for monitoring dietary consumption include recollection and manual record-keeping, which can be tedious and prone to mistakes. However, automated technologies for maintaining records that make use of computer vision, such as food image recognition systems, can streamline chronic health management for diabetics. These solutions seek to efficiently track daily food intake and consequential calories to facilitate and encourage lifestyle improvements. With this goal in mind, we design a Machine …

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 …

Gc-444 It Course Profile Website, Manikanta Voruganti

C-Day Computing Showcase

Build a Dynamic course profile website for Bachelor of science in information technology courses, that display all the information regarding the course.

Gc-448 Project Title: Discover, Learn, And Protect: A Mobile App For Informal Stem Learning About Local Biodiversity And Environmental Issues., Elvin Mccray, David Y. Appah, Adedunmola Banu, Binh Tran, Zenya Tucker

C-Day Computing Showcase

Our team assignment for this project was to create a mobile application that offers informal STEM learning about local biodiversity and environmental issues. Dr. Ying Xie, Professor in the College of Computing and Software Engineering (CCSE) is the owner of this project, who also laid out required features and provided necessary information, guidance, and advice for the project development. The core function of this application is to empower users to explore, identify and gain insights into the plant and animal species native to their region. Leveraging the capabilities of their smartphone’s camera, users can effortlessly scan, record, or locate local …

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-469 A Simulation Model Of The Traffic Signal System Using Java, Lingtao Chen

C-Day Computing Showcase

A traffic signal controls the flow of traffic at the intersection of two or more roadways. The first system of traffic signals was installed in London, England, in 1868. In this project, I will develop a simulation model for the traffic signal system using Java. The model will simulate the traffic signal system at a single four-way intersection. Also, I will compare the system performance with different input parameters, such as the number of vehicles and the cycle length, using various performance metrics, such as average waiting time and average sojourn time.

Euc-472 Biomimetic Remote-Controlled Vehicle, Jonathan Ridley, Kian Brown

C-Day Computing Showcase

The goal of this project is smoothly integrating instinctual concepts of control into devices beyond the body. It is essentially an attempt to extend the body without any complex prior training. To do this, we have developed a both a Bluetooth connection between hand movements and the motors of a multifaceted vehicle. Furthermore, the hand movements will be tracked using both an accelerometer and gyroscope found in the common hobbyist tool Arduino nano. Logging this data and processing it through the Bluetooth communication system, the intention is to provide real-time updates to the vehicle’s motors that ultimately sync the intentions …

Uc-491 Spectrum Analysis Cli Tool, Vitor B. Santos, Trey Redden, Sam Corella, Christopher Flores Santos, Jonathan Glennon

C-Day Computing Showcase

The Spectrum Analysis CLI Tool takes in .mp4 recordings of a Spectrum Analyzer, converts them programmatically into values the application can understand and outputs this data into a .csv file. This file can be parsed/filtered by the user with commands during upload of the .mp4 recording, or anytime after the recording has been processed.

Ur-510 Exploring The Impact Of Wavelength In Non-Invasive Blood Glucose Monitoring, John E. Oakley, Tahsin Kazi

C-Day Computing Showcase

Diabetes and metabolic diseases are some of the most crucial health issues of the 21st century. Monitoring blood glucose, the lead indicator of these diseases is a cumbersome process of constantly drawing blood or using subcutaneous needles. However, new technologies have emerged for non-invasive blood glucose monitoring that uses spectroscopy, which involves emitting light and capturing patient data with cameras. These new devices remove the cost of multiple tests, reduce the risk of skin conditions, and create more patient-friendly solutions. However, the hardware variables of these devices have not been tested thoroughly. One such avenue is via laser wavelength, which …

Gc-417 It Curriculum Success Portal, Alexander Thacker, Cedric Boakye-Danquah, Damola Adeyemo, Abdoulaye Ndiaye, Lydia Asante

C-Day Computing Showcase

In this project, we built a curriculum and course web portal to have all curriculum and course information in one place with easy search and browse interfaces. Currently course data and information are scattered in various places. These include essential information like course description, learning outcomes, sample syllabus, offering schedule and history. It also includes curriculum development information for department use, such as coordinator, developer, revision schedule, open learning materials. We collected all sources of IT course information and built a database to integrate the data in one place. Through this, we can build a complete profile of a course …

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.

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 …

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-412 Ecoedconnect, Vidhi Dave, Mythili Jayaraman, Manikanta Reddy Anugu, Rohini Paithanker, Neharika Beeram

C-Day Computing Showcase

An inventive educational platform called EcoEdConnect provides high school students with various opportunities to investigate biodiversity and environmental issues. By adjusting to each user's needs and choices, the web app offers a customized educational experience such as quizzes, experiments, videos, blogs, articles, etc. The project's first analysis, methodology, and early conclusions are presented in this document. It shows the several phases of the project, such as the introduction modules, practical experiments, discussions, blog, final assessment, and presentation, among other things. The application customizes the material and complexity according to the user's inclinations. Students' knowledge of biodiversity and environmental issues and …

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 …

Egc-442 Sars-Cov-2 Spike And Ace2 Protein-Protein Interactions Database, Jovanny Duran Salgado, Durga Narayana Varma Addepalli, Travis Meeks, Pooja Venkata Ramana Adapa, Divya Sri Ambati

C-Day Computing Showcase

SARS-CoV-2 protein interactions are essential for viral replication and pathogenesis. To better understand these interactions, we have created a database using AWS (Amazon Web Services) to store data extracted from protein simulations. This database can be used to study the structure and function of SARS-CoV-2 proteins and their interactions with each other and with host cell proteins.