Computer Engineering Commons^™

Towards An Iot-Enabled Digital Earth For Sdgs: The Data Quality Challenge, Msb Syed, Paula Kelly, Paul Stacey, Damon Berry

Articles

Digital Earth (DE), a technology offering real-time visualisation of Earth's processes, has shown promising results in aiding decision-making for a sustainable world, raising awareness about individual impacts on our planet, and supporting the United Nations Sustainable Development Goals (UN SDGs) agenda. However, both DE and SDGs face a common obstacle: Data Quality (DQ). This review investigates the challenge of DQ in the context of DE for SDGs and explores how IoT can address this challenge and extend the reach of DE to support SDGs. Furthermore, the study discusses three core aspects; first, the potential of IoT as a data source …

Mouasla: Integrating Iot And Ai For An Intelligent Trans-Portation Payment System, Hany El-Ghaish Dr., Haitham Darweesh

Journal of Engineering Research

Smart payment systems have emerged as vital components of global public transportation, offering passengers a more efficient and convenient fare payment method. The Mouasla system addresses traditional payment limitations through IoT devices and AI-backed backend services. Features of Mouasla It employs RFID smart card and IoT features from the device to ensure all components such as a card reader function, driver functions, charging units function, and payment are combined with this system alongside a mobile application for quick access backend services. Each passenger dataset is analyzed by an AI-powered backend service to provide insight that can be used to improve …

A Parallel Methodology For Early Fake News Detection Based On Hybrid Features On Social Media, Asmaa Mohemed Elsaieed Dr

Journal of Engineering Research

The increased use of social media platforms has made it easier to publish and distribute news items, but it has also opened up new opportunities for distributing fake news. Fake news is information that has been written with the goal of misleading or deceiving readers. As a result, there is a need for efficient false news identification tools where the information can be gathered from the text of posts or from publicly available social data (such as user information or feedback on articles or the social network). The detection of fake news in its early stages is a major challenge. …

Early Autism Detection Using Machine Learning Techniques: A Review, Shaimaa Fouad Sharabash, Hany Ali Elghaish

Journal of Engineering Research

Abstract- This article provides a comprehensive literature review on technology-based interventions for Autism Spectrum Disorder (ASD). It emphasizes the challenges in early detection and treatment of ASD, highlighting the spectrum nature of the disorder. The review discusses traditional diagnostic strategies such as behavioural observations, developmental screening and medical testing and goes on to explore advanced machine learning and deep learning models, including SVM, k-nearest neighbours, decision tree and LSTM, for predicting ASD characteristics in toddlers and children. Additionally, recent techniques employing more than ten strategies for ASD detection are summarized and various datasets used in early detection are described. The …

Digital Assessments For Children And Adolescents With Adhd: A Scoping Review, Franceli L. Cibrian, Elissa M. Monteiro, Kimberley D. Lakes

Engineering Faculty Articles and Research

Introduction: In spite of rapid advances in evidence-based treatments for attention deficit hyperactivity disorder (ADHD), community access to rigorous gold-standard diagnostic assessments has lagged far behind due to barriers such as the costs and limited availability of comprehensive diagnostic evaluations. Digital assessment of attention and behavior has the potential to lead to scalable approaches that could be used to screen large numbers of children and/or increase access to high-quality, scalable diagnostic evaluations, especially if designed using user-centered participatory and ability-based frameworks. Current research on assessment has begun to take a user-centered approach by actively involving participants to ensure the development …

M3t-Lm: A Multi-Modal Multi-Task Learning Model For Jointly Predicting Patient Length Of Stay And Mortality, Junde Chen, Qing Li, Feng Liu, Yuxin Wen

Engineering Faculty Articles and Research

Ensuring accurate predictions of inpatient length of stay (LoS) and mortality rates is essential for enhancing hospital service efficiency, particularly in light of the constraints posed by limited healthcare resources. Integrative analysis of heterogeneous clinic record data from different sources can hold great promise for improving the prognosis and diagnosis level of LoS and mortality. Currently, most existing studies solely focus on single data modality or tend to single-task learning, i.e., training LoS and mortality tasks separately. This limits the utilization of available multi-modal data and prevents the sharing of feature representations that could capture correlations between different tasks, ultimately …

2024 Gateway Magazine, College Of Computing, Michigan Technological University

College of Computing Annual Magazines

Table of Contents

• 50 Years of Computer Science at Michigan Tech
• Data Science for a Changing Planet
• Healthcare Transformed
• Mechatronics Matters
• Powered by Michigan Tech Talent
• Esports: Bringing Everything Great about Sports to More People
• The Michigander Scholars Program: Electrifying Careers in Michigan
• College of Computing News

Development Of Brain Tumor Detection And Feature Extraction Through Deep Learning Approach, Sivapathi A

Theses and Dissertations

As the body's central control system, the human brain is susceptible to a wide variety of disorders, including tumors characterized by abnormal cell growth. It is imperative to detect these tumors as early as possible to plan effective treatment and improve patient outcomes. By using contemporary medical imaging methods, this research seeks to improve the accuracy and efficiency of brain tumor detection through the careful preprocessing and analysis of images, particularly Magnetic Resonance Imaging (MRI) [1]. To provide context for the subsequent research efforts, the challenges inherent in brain tumor detection are discussed comprehensively, including segmentation accuracy, small lesion detection, …

Pig Butchering In Cybersecurity: A Modern Social Engineering Threat, Sharon L. Burton, Pamela D. Moore

Publications

Pig butchering is an escalating cybersecurity threat that exploits social engineering to build trust and execute financial fraud. The relevance of this research problem lies in the growing incidence and sophistication of these scams, which have severe financial and psychological impacts on victims. The main purpose of this research is to uncover the methods used in pig butchering scams and their impact on individuals and businesses. The research focuses on digital platforms such as social media, dating apps, and professional networking sites, chosen for their wide user bases and the ease of establishing personal connections. The study period encompasses recent …

Instructional Systems Design: The Diffusion And Adoption Of Technology: (Volume 2), Cassandra Celaya (Author), Pamela J. Downing (Author), Jessica Shifflett (Author), Debbie Gdula (Author), Tracie Barr (Author), Miguel Ramlatchan (Author & Editor)

University Administration Bookshelf

Instructional designers, instructional systems designers, and other educational technologists are, by their nature, innovators. These professionals apply and extend the applied science of learning, systems, communication, and instructional design theory to help students learn. Technology in some capacity is used to make the connections between subject matter experts, teachers, instructors, and their learners. It is common for instructional designers to seek new tools, techniques, and innovations for the improvement of learning, access, quality, and student satisfaction. However, the adoption and diffusion of new educational technology and innovation is a complex process that depends on many variables. Understanding these processes and …

2024 (Fall) Ensi Informer Magazine, Morehead State University. Engineering Sciences Department

ENSI Informer Magazine Archive

The ENSI Informer Magazine published in the fall of 2024.

Wearable Sensing Systems And Data Analytics For Pressure Sensing Socket Prostheses, Stacey Le, Mio Nakagawa

College of Engineering Summer Undergraduate Research Program

Prosthetics have been widely used as the primary solution for lower limb amputations, but residual limb volume fluctuations have posed challenges to the effectiveness and comfortability of these devices. In this project, we aim to observe pressure distribution patterns in the prosthetic socket during gait using sensing technology and investigate the performance of different machine learning algorithms on determining good or bad fit.

Machine Learning-Based Stress Detection Using Smartphones And Wearable Sensors, Robert Burns, Kassandra Martinez-Mejia

College of Engineering Summer Undergraduate Research Program

Stress and mental health have been considered increasing global concerns in modern society, including some of the illnesses responsible for a large proportion of comorbidities and deaths worldwide, such as depression and cardiovascular disease. Recently, smartphones, smartwatches, and smart wristbands have become an integral part of our lives and have reached widespread usage [1]. This raised the question of whether we can detect and prevent stress with smartphones and wearable sensors wirelessly. The challenge of such an approach is that the weak ECG signal is prone to errors, and the battery life of the smart devices is limited. In this …

Deep-Learning Based Microstructure Reconstruction And Generation, Cameron J. Maloney, Lucas Taliaferro

College of Engineering Summer Undergraduate Research Program

Characterizing the microstructural behavior of materials is crucial for understanding their properties and performance. Traditional imaging methods, such as optical microscopy and electron microscopy, are effective but costly and time-consuming. Computational approaches can reduce costs and time while expanding the accessibility of microstructural analysis through the generation of new microstructure images. Traditional computational approaches, namely descriptor-based approaches, are slow but effective in low-data scenarios. Modern approaches use machine learning (ML), which is faster but often requires a lot of data to approach the performance of descriptor-based methods. This research leverages a special data-efficient Generative Adversarial Network (GAN) architecture to artificially …

Exploration Of Esp32 Vulnerabilities And Malware, Charles T. Moreno

College of Engineering Summer Undergraduate Research Program

The rapid expansion of the Internet of Things (IoT) has revolutionized industries by enabling connected smart devices to monitor, communicate, and automate tasks in real-time. Central to the functioning of many IoT systems are microcontrollers like the ESP32, a versatile and low-cost microcontroller known for its integrated Wi-Fi and Bluetooth capabilities. The ESP32's powerful processing, energy efficiency, and adaptability make it a popular choice for IoT applications ranging from smart home devices to industrial automation. However, as IoT adoption grows, so too do the security challenges posed by vulnerabilities in these devices, particularly within ESP32-based systems. For this project, I …

Optimizing Resume Authenticity And Ats Compatibility With Llm Feedback Integration, Katie He, Justin Lau

College of Engineering Summer Undergraduate Research Program

Resume generation using Large Language Models (LLMs) like ChatGPT is becoming increasingly popular for automating the creation of customized resumes, but significant user modification is often required before submission. Common issues include poor alignment with job descriptions, inflated qualifications, and lack of authenticity, which undermine the effectiveness of LLM-generated resumes. This project addresses these challenges by integrating feedback from Applicant Tracking Systems (ATS) to guide LLMs in producing resumes that accurately reflect an applicant’s qualifications and better align with job-specific requirements. By optimizing the model's output through ATS feedback, the project aims to create more authentic, tailored, and ATS-compatible resumes, …

Advanced Grasping Sensor Technologies For Autonomous Robotic Apple Harvesting Using Tactile Data And Cnns, Chris Bae

College of Engineering Summer Undergraduate Research Program

This research investigates how to achieve an optimal grasp of an apple using a four-finger soft robotic grasper equipped with force-resistive sensors. Specifically, we sought to determine whether a convolutional neural network (CNN) could accurately classify the grasper's state and recommend adjustments ("in," "out," or "good" grasp) based on tactile data from the sensors. Spatiotemporal tactile images were developed from the sensors and fed into our CNN, achieving near 100% accuracy on unseen test data. This work suggests that CNN-based processing of tactile images can be a powerful tool for real-time control of soft robotic grippers.

Incorporation Of Gnss Technology For Water-Level Instruments, Armaan S. Oberai, Toma Grundler, Serena B. Lee, Stefan A. Talke

College of Engineering Summer Undergraduate Research Program

Our goal was to take an existing water-level measuring embedded system and upgrade the GNSS module for the purpose of getting the elevation of the device within an error of 1 cm. Main milestones for the project included deploying a successful field test at a known survey point, using software-based post processing to improve the GNSS solution point, and implementing robust hardware and software for the water-level embedded system such that it was easily scalable.

Devices And Dining: A Cross-Cultural Analysis Of Mobile Device Use In Italian And American Restaurants, O'Malley Jenkins

Senior Theses

The field of technology ethics has seen increasing growth and interest over the years. Many have begun to consider the impacts of technology’s use and whether it is being employed in a healthy manner by users. However, there has been limited investigation into whether the ways in which technology use varies across cultures. This study begins to address this gap by researching Italian and American device usage in fast food settings. Non-participant naturalistic observations were used to record data for 89 Italian and 88 American customers of fast food restaurants. Analysis of the collected data indicates that Americans use their …

Hardware Control Unit For Trusted Program Verification System, Jake Owen Alt

Master's Theses

Trust in the underlying hardware is the foundational step towards trusting the correctness and integrity of a software application. However, verifying that today's extremely complex processors work exactly as intended has not been feasible, as evidenced by several recent hardware bugs. Trustworthy, formally verified processors currently forego intricate performance enhancements such as out-of-order execution, hampering them substantially versus their less secure counterparts.

The Containment Architecture with Verified Output (CAVO) system solves this problem by isolating the host system and requiring the result of each instruction to be validated by a small, trusted hardware module called the Sentry. Any transmissions to …