Engineering Commons^™

Docai, Riley Badnin, Justin Brunings

Computer Science and Software Engineering

DocAI presents a user-friendly platform for recording, transcribing, summarizing, and classifying doctor-patient consultations. The application utilizes AssemblyAI for conversational transcription, and the user interface allows users to either live-record consultations or upload an existing MP3 file. The classification process, powered by 'ml-classify-text,' organizes the consultation transcription into SOAP (Subjective, Objective, Assessment, and Plan) format – a widely used method of documentation for healthcare providers. The result of this development is a simple yet effective interface that effectively plays the role of a medical scribe. However, the application is still facing challenges of inconsistent summarization from the AssemblyAI backend. Future work …

Decoding Usage And Adoption Behavior Of The Low-Carbon Transportation Market: An Ai-Driven Exploration, Vuban Chowdhury

Graduate Theses and Dissertations

The transportation sector stands as a significant contributor to greenhouse gas emissions in the United States, with its environmental impact steadily escalating over the past few decades. This has prompted government agencies to facilitate the adoption and usage of low-carbon transportation (LCT) options as alternatives to fossil-fuel-powered transportation. LCTs include modes of transportation that minimize the overall carbon footprint of the transportation sector by relying on energy sources that are environmentally sustainable. These sustainable transportation options have also garnered significant interest in the transportation research community. For government agencies and researchers alike, a comprehensive understanding of the adoption and usage …

Ti-6al-4v Β Phase Selective Dissolution: In Vitro Mechanism And Prediction, Michael A Kurtz

All Dissertations

Retrieval studies document Ti-6Al-4V β phase dissolution within total hip replacement systems. A gap persists in our mechanistic understanding and existing standards fail to reproduce this damage. This thesis aims to (1) elucidate the Ti-6Al-4V selective dissolution mechanism as functions of solution chemistry, electrode potential and temperature; (2) investigate the effects of adverse electrochemical conditions on additively manufactured (AM) titanium alloys and (3) apply machine learning to predict the Ti-6Al-4V dissolution state. We hypothesized that (1) cathodic activation and inflammatory species (H₂O₂) would degrade the Ti-6Al-4V oxide, promoting dissolution; (2) AM Ti-6Al-4V selective dissolution would occur …

Artificial Intelligence Integration In Construction: A Cal Poly Curriculum Assessment, Carter James Hahn

Construction Management

In a rapidly evolving industry where AI enhances efficiency and safety, questions are being raised on whether it should be introduced in the classroom. This research project is with the intention of understanding if and how artificial intelligence should be incorporated into Cal Poly’s construction management curriculum. The research combines a literature review establishing AI's relevance in construction project management with a survey-based methodology to gauge industry perspectives. Today, the global construction market witnesses a surge in AI adoption in its planning, construction, and O&M phases. In turn, the industry has benefited from its application in mitigation of risks, improved …

Smartphone Based Object Detection For Shark Spotting, Darrick W. Oliver

Master's Theses

Given concern over shark attacks in coastal regions, the recent use of unmanned aerial vehicles (UAVs), or drones, has increased to ensure the safety of beachgoers. However, much of city officials' process remains manual, with drone operation and review of footage still playing a significant role. In pursuit of a more automated solution, researchers have turned to the usage of neural networks to perform detection of sharks and other marine life. For on-device solutions, this has historically required assembling individual hardware components to form an embedded system to utilize the machine learning model. This means that the camera, neural processing …

Online Aircraft System Identification Using A Novel Parameter Informed Reinforcement Learning Method, Nathan Schaff

Doctoral Dissertations and Master's Theses

This thesis presents the development and analysis of a novel method for training reinforcement learning neural networks for online aircraft system identification of multiple similar linear systems, such as all fixed wing aircraft. This approach, termed Parameter Informed Reinforcement Learning (PIRL), dictates that reinforcement learning neural networks should be trained using input and output trajectory/history data as is convention; however, the PIRL method also includes any known and relevant aircraft parameters, such as airspeed, altitude, center of gravity location and/or others. Through this, the PIRL Agent is better suited to identify novel/test-set aircraft.

First, the PIRL method is applied to …

Cyber-Human Systems, Space Technologies, And Threats, Randall K. Nichols, Candice M. Carter, Jerry V. Drew Ii, Max Farcot, John-Paul Hood, Mark J. Jackson, Peter D. Johnson, Siny Joseph, Saeed Kahn, Wayne D. Lonstein, Robert Mccreight, Trevor W. Muehlfelder, Hans C. Mumm, Carter Diebold, Juole J.C.H. Ryan, Suzanne M. Sincavage, William Solfer, John Toebes

NPP eBooks

CYBER-HUMAN SYSTEMS, SPACE TECHNOLOGIES, AND THREATS is our eighth textbook in a series covering the world of UASs / CUAS/ UUVs / SPACE. Other textbooks in our series are Space Systems Emerging Technologies and Operations; Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD); Disruptive Technologies with applications in Airline, Marine, Defense Industries; Unmanned Vehicle Systems & Operations On Air, Sea, Land; Counter Unmanned Aircraft Systems Technologies and Operations; Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets, 2nd edition; and Unmanned Aircraft Systems (UAS) in the Cyber …

Ai-Enabled Modeling And Monitoring Of Data-Rich Advanced Manufacturing Systems, Abdullah Al Mamun

Theses and Dissertations

The infrastructure of cyber-physical systems (CPS) is based on a meta-concept of cybermanufacturing systems (CMS) that synchronizes the Industrial Internet of Things (IIoTs), Cloud Computing, Industrial Control Systems (ICSs), and Big Data analytics in manufacturing operations. Artificial Intelligence (AI) can be incorporated to make intelligent decisions in the day-to-day operations of CMS. Cyberattack spaces in AI-based cybermanufacturing operations pose significant challenges, including unauthorized modification of systems, loss of historical data, destructive malware, software malfunctioning, etc. However, a cybersecurity framework can be implemented to prevent unauthorized access, theft, damage, or other harmful attacks on electronic equipment, networks, and sensitive data. The …

Generative Neural Network-Based Defense Methods Against Cyberattacks For Connected And Autonomous Vehicles, M Sabbir Salek

All Dissertations

The rapid advancement of communication and artificial intelligence technologies is propelling the development of connected and autonomous vehicles (CAVs), revolutionizing the transportation landscape. However, increased connectivity and automation also present heightened potential for cyber threats. Recently, the emergence of generative neural networks (NNs) has unveiled a myriad of opportunities for complementing CAV applications, including generative NN-based cybersecurity measures to protect the CAVs in a transportation cyber-physical system (TCPS) from known and unknown cyberattacks. The goal of this dissertation is to explore the utility of the generative NNs for devising cyberattack detection and mitigation strategies for CAVs. To this end, the …

Rebranding Originality For The Age Of Ai, Jason Gulya

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

"Originality" has been a longstanding focal point within the college classroom, with students being encouraged to embrace creativity and boldness. The traditional view of originality, relying solely on one's wit and imagination, has lost its effectiveness in the present era. The concept of learning has undergone a significant transformation, no longer resembling the isolated ivory tower of the past where individuals would immerse themselves in books, hoping to be inspired. Instead, modern learning has become more social and collaborative. Students compare and contrast class material with online resources, engaging in conversations, both in person and virtually, to solidify their understanding. …

Predicting Corrosion Damage In The Human Body Using Artificial Intelligence: In Vitro Progress And Future Applications Applications, Michael A. Kurtz, Ruoyu Yang, Mohan S. R. Elapolu, Audrey C. Wessinger, William Nelson, Kazzandra Alaniz, Rahul Rai, Jeremy L. Gilbert

Publications

Artificial intelligence (AI) is used in the clinic to improve patient care. While the successes illustrate the impact AI can have, few studies have led to improved clinical outcomes. A gap in translational studies, beginning at the basic science level, exists. In this review, we focus on how AI models implemented in non-orthopedic fields of corrosion science may apply to the study of orthopedic alloys. We first define and introduce fundamental AI concepts and models, as well as physiologically relevant corrosion damage modes. We then systematically review the corrosion/AI literature. Finally, we identify several AI models that may be Preprint …

Extending The Convolution In Graph Neural Networks To Solve Materials Science And Node Classification Problems, Steph-Yves Mike Louis

Theses and Dissertations

The usage of graph to represent one's data in machine learning has grown in popularity in both academia and the industry due to its inherent benefits. With its flexible nature and immediate translation to real life observed objects, graph representation had a considerable contribution in advancing the state-of-the-art performance of machine learning in materials.

In this dissertation proposal, we discuss how machines can learn from graph encoded data and provide excellent results through graph neural networks (GNN). Notably, we focus our adaptation of graph neural networks on three tasks: predicting crystal materials properties, nullifying the negative impact of inferior graph …

Insect Classification And Explainability From Image Data Via Deep Learning Techniques, Tanvir Hossain Bhuiyan

USF Tampa Graduate Theses and Dissertations

Since the dawn of the Industrial Revolution, humanity has always tried to make labor more efficient and automated, and this trend is only continuing in the modern digital age. With the advent of artificial intelligence (AI) techniques in the latter part of the 20th century, the speed and scale with which AI has been leveraged to automate tasks defy human imagination. Many people deeply entrenched in the technology field are genuinely intrigued and concerned about how AI may change many of the ways in which humans have been living for millennia. Only time will provide the answers. This dissertation is …

Exploring Machine Learning In Deep Foundation And Soil Classification Application, Mohammad Moontakim Shoaib

LSU Master's Theses

The applicability of several Machine Learning (ML) models was explored in this research to predict the ultimate capacity and load-settlement behavior of axially loaded single-driven piles from Cone Penetration Test (CPT) data. Additionally, a common CPT-based soil behavior type (SBT) classification system was reproduced using those ML models. Eighty static pile load tests and corresponding CPT data close to those pile locations were collected from 34 sites in Louisiana for the deep foundation application. On the other hand, 70 CPT soundings were taken in 14 different parishes across Louisiana for the soil classification application. Specifically, tree-based ML models such as …

A Graph-Based Approach For Adaptive Serious Games, Nidhi G. Patel

Theses and Dissertations

Traditional education systems are based on the one-size-fits-all approach, which lacks personalization, engagement, and flexibility necessary to meet the diverse needs and learning styles of students. This encouraged researchers to focus on exploring automated, personalized instructional systems to enhance students’ learning experiences. Motivated by this remark, this thesis proposes a personalized instructional system using a graph method to enhance a player’s learning process by preventing frustration and avoiding a monotonous experience. Our system uses a directional graph, called an action graph, for representing solutions to in-game problems based on possible player actions. Through our proposed algorithm, a serious game integrated …

Robotic Cotton Harvesting With A Multi-Finger End-Effector: Research, Design, Development, Testing, And Evaluation, Hussein Gharakhani

Theses and Dissertations

Cotton is harvested with large and heavy machines that are very efficient but have some disadvantages. They can harvest the crop only once at the end of the growing season. Since cotton bolls do not mature uniformly, the early opened bolls expose their fiber to weather for extended periods, reducing lint quality. In addition, the machines can also compact the soil, reducing water and fertilizer usage efficiencies and crop yields in the following years. Robotic cotton harvesting offers a promising solution to these issues. Smaller robotic harvesters could go to the field multiple times during the season to pick cotton …

Smart-Insect Monitoring System Integration And Interaction Via Ai Cloud Deployment And Gpt, Ahmed Moustafa

Computer Science and Computer Engineering Undergraduate Honors Theses

The Insect Detection Server was developed to explore the deployment and integration of an Artificial Intelligence model for Computer Vision in the context of insect detection. The model was developed to accurately identify insects from images taken by camera systems installed on farms. The goal is to integrate the model into an easily accessible, cloud-based application that allows farmers to analyze automatically uploaded images containing groups of insects found on their farms. The application returns the bounding boxes and the detected classes of insects whenever an image is captured on-site, enabling farmers to take appropriate actions to address the issue …

A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb

Masters Theses

One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded …

Improving Inference Speed Of Perception Systems In Autonomous Unmanned Ground Vehicles, Bradley Selee

All Theses

Autonomous vehicle (AV) development has become one of the largest research challenges in businesses and research institutions. While much research has been done, autonomous driving still requires extensive amounts of research due to its immense, multi-factorial difficulty. Autonomous vehicles rely on many complex systems to function, make accurate decisions, and, above all, provide maximum safety. One of the most crucial components of autonomous driving is the perception system.

The perception system allows the vehicle to identify its surroundings and make accurate, but safe, decisions through the use of computer vision techniques like object detection, image segmentation, and path planning. Due …

Implementation Of A Pre-Assessment Module To Improve The Initial Player Experience Using Previous Gaming Information, Rafael David Segistan Canizales

Electronic Thesis and Dissertation Repository

The gaming industry has become one of the largest and most profitable industries today. According to market research, the industry revenues will pass $200 Billion and are expected to reach another $20 Billion in 2024. With the industry growing rapidly, players have become more demanding, expecting better content and quality. This means that game studios need new and innovative ways to make their games more enjoyable. One technique used to improve the player experience is DDA (Dynamic Difficulty Adjustment). It leverages the current player state to perform different adjustments during the game to tune the difficulty delivered to the player …

Beirut Arab University - Faculty Of Engineering - Newsletter Issue 0, Faculty Of Engineering, Beirut Arab University

Engineering Newsletters

No abstract provided.

An Artificial Intelligence Approach To Fatigue Crack Length Estimation From Acoustic Emission Signals, Shane T. Ennis

Theses and Dissertations

As in service aircraft begin to age and fatigue, a method for evaluating the operational life they are currently operating under and have remaining comes into question. Structural health monitoring is (SHM) is a popular method of structural analysis with growing interest in the aerospace industry. SHM is capable of damage assessment and structural life estimations.

The ultimate goal of the research presented in this thesis is to develop a methodology of classifying the length of a fatigue crack though the use of machine learning. The thesis has three major chapters as described below.

The first chapter deals with the …

Using Statistics, Computational Modelling And Artificial Intelligence Methods To Study And Strengthen The Link Between Kinematic Impacts And Mtbis, Andrew Luke Mcconnell Patterson

Electronic Thesis and Dissertation Repository

Mild traumatic brain injuries (mTBIs) are frequently occurring, yet poorly understood, injuries in sports (e.g., ice hockey) and other physical recreation activities where head impacts occur. Helmets are essential pieces of equipment used to protect participants’ heads from mTBIs. Evaluating the performance of helmets to prevent mTBIs using simulations on anatomically accurate computational head finite element models is critically important for advancing the development of safer helmets. Advancing the level of detail in, and access to, such models, and their continued validation through state-of-the-art brain imaging methods and traditional head injury assessment procedures, is also essential to improve safety. The …

An Evaluation Of A Computational Technique For Measuring The Embeddedness Of Sustainability In The Curriculum Aligned To Aashe-Stars And The United Nations Sustainable Development Goals, Philippe Lemarchand, Cormac H. Macmahon Dr, Mick Mckeever, Philip Owende

Articles

Introduction: SDG 4.7 mandates university contributions to the United Nations (UN) Sustainable Development Goals (SDGs) through their education provisions. Hence, universities increasingly assess their curricular alignment to the SDGs. A common approach to the assessment is to identify keywords associated with specific SDGs and to analyze for their presence in the curriculum. An inherent challenge is associating the identified keywords as used in the diverse set of curricular contexts to relevant sustainability indicators; hence, the urgent need for more systematic assessment as SDG implementation passes its mid-cycle.

Method: In this study, a more nuanced technique was evaluated with notable capabilities …

An Artificial Intelligence Tool For The Selection Of Delay Analysis Technique In Construction, Mostafa Farouk

Theses and Dissertations

The increasing complexity and magnitude of projects impose greater impact of delays on stakeholders. Construction delays are a major source of disputes in construction projects. Since a construction project depends on interactions and shared responsibilities among parties, research works were directed toward identifying delay causes, quantifying their impacts, and proposing ways to deal with them. Several delay analysis techniques (DATs) are available, but when applied to the same project’s delays provide different results. Thus, the selection of the DAT to use in evaluating delays becomes vital. Reviewing the literature, it has been realized that often there are disagreements, which lead …

Exploring The Application Of Chatgpt In Mechanical Engineering Education, Joan Puig-Ortiz, Rosa Pàmies-Vilà, Lluïsa Jordi Nebot

Practice Papers

The use of language models such as ChatGPT in the field of engineering has gained popularity in recent years due to their ability to assist engineers in their projects and tasks. In this study, we evaluated the effectiveness of ChatGPT in supporting students' learning in the Mechanism and Machine Theory (MMT) subject. The study involved participants who were asked to interact with ChatGPT to obtain concept clarification and factual information related to MMT.

Our results show that the majority of participants were familiar with ChatGPT and had used it for academic or technical questions. They also found it easy to …

A Framework For Teaching Machine Learning For Engineers, Lauren Singelmann, Jacob Covarrubias

Practice Papers

As machine learning and artificial intelligence become increasingly prevalent in our day-to-day lives, there becomes an even greater need for literacy in machine learning for those outside of the computer science domain. This work proposes a conceptual framework for teaching machine learning to engineering students with the goal of developing the knowledge and skills needed to apply machine learning techniques to engineering problems.

Many machine learning courses in computer science, math, and statistics focus on the theoretical basis of machine learning algorithms and assessment. This framework takes a fundamentally different approach by creating a course structure for machine learning practitioners …

Challenge-Based Learning And Constructive Alignment: A Challenge For Information Systems’ Educators, Joao Moreira, Wallace Ugulino, Marcos Machado, Luís Ferreia Pires

Practice Papers

Challenge-Based Learning (CBL) is an emerging approach to the design of education activities known for its benefits in fostering student engagement and, consequently, positively affecting their learning outcomes. For the educator, the ’challenge in the challenge’ is to guarantee that the CBL-based education design follows certain regulations, like ensuring proper curriculum coverage with Constructive Alignment. This challenge becomes particularly difficult to address in the field of Information Systems, within Computer Science, where multiple practices can be followed to solve the same problem. This is even more challenging when CBL is applied at course-level, where the curriculum of the course focuses …

Ai Usage In Development, Security, And Operations, Maurice Ayidiya

Walden Dissertations and Doctoral Studies

Artificial intelligence (AI) has become a growing field in information technology (IT). Cybersecurity managers are concerned that the lack of strategies to incorporate AI technologies in developing secure software for IT operations may inhibit the effectiveness of security risk mitigation. Grounded in the technology acceptance model, the purpose of this qualitative exploratory multiple case study was to explore strategies cybersecurity professionals use to incorporate AI technologies in developing secure software for IT operations. The participants were 10 IT professionals in the United States with at least 5 years of professional experience working in DevSecOps and managing teams of at least …

Probability Expressions In Ai Decision Support: Impacts On Human+Ai Team Performance, Elias Spinn

Dissertations

AI decision support systems aim to assist people in highly complex and consequential domains to make efficient, effective, and high-quality decisions. AI alone cannot be guaranteed to be correct in these complex decision tasks, and a human is often needed to ensure decision accuracy. The ambition is for these human+ AI teams to perform better together than either would individually. To realise this, decision makers must trust their AI partners appropriately, knowing when to rely on their recommendations and when to be sceptical. However, research has shown that decision makers often either mistrust and underutilise these systems, or trust them …