Computer Engineering Commons^™

Predicting Hazardous Near-Earth Objects Using Machine Learning For Planetary Defense, John Costa (Student), Lily Popova Zhuhadar (Mentor)

Posters-at-the-Capitol

Predicting Hazardous Near-Earth Objects Using Machine Learning for Planetary Defense

This research develops a machine learning model to classify Near-Earth Objects (NEOs) as hazardous or non-hazardous based on their physical and orbital characteristics, leveraging NASA's dataset of certified NEOs. NEOs, including asteroids and comets, often pass within close proximity to Earth, and while most pose no threat, some have the potential for catastrophic impacts. By using predictive models such as decision trees and random forests, this study aims to prioritize resources for monitoring and mitigation of high-risk objects. The model incorporates key features like velocity, diameter, and proximity to Earth …

Enhancing Smartphone Authentication By Integrating Decision-Making Model With Touch Pressure, Finger Location Data, And Advanced Cybersecurity Techniques, Maytham M. Hamood, Moceheb Lazam Shuwandy, Rawan Adel Fawzi Alsharida

Iraqi Journal for Computer Science and Mathematics

Smartphone authentication methods face significant challenges in achieving high accuracy, robustness, and usability within cybersecurity applications. Traditional methods, such as passwords and biometric recognition, often lack adaptability and are prone to high false-positive rates, impacting security and user acceptance. This study presents a novel hybrid approach incorporating machine learning (ML) and the Analytic Hierarchy Process (AHP) in a framework to facilitate decision-making abilities and improve smartphone authentication. A novel dataset was constructed based on 3D touch sensor data (pressure levels and spatial dynamics) collected from 20 participants performing tasks per task over sessions, where AHP was used to rank/choose relevant …

Indoor Localization With Ensemble Machine Learning Via Visible Light Communication Channels, Alzahraa M. Ghonim, Wessam M. Salama

Journal of Engineering Research

An indoor localization system based on received signal strength, visible light communication (VLC) and several machine learning approaches is proposed in this paper. Our proposed framework is divided into two strategies. The first one is consisting of gathering our dataset based on MATLAB software to create indoor VLC channel model. While the second phase is depending on training the gained dataset using ensemble machine learning models. Specifically, random forest, decision tree and gradient boosting models. In order to evaluate the robustness of the proposed framework, several evaluation metrics are applied, specifically, training time, testing time, classification accuracy (CA), area under …

An Integrated Approach To Enhance The Performance Of Rainfall Forecasting By Leveraging Stacking Based Machine Learning And Deep Learning Techniques, Umamaheswari P

Theses and Dissertations

Rainfall forecasting is critical for a variety of reasons, the most important of which is the substantial impact it has on many sectors of the community and the environment. It helps farmers with planting schedules, crop choices and irrigation techniques, all of which directly impact food production and agricultural yields. Rainfall forecasting is also vital in sectors such as hydroelectric power generation, since knowledge about water availability is essential for electricity generation. Accurate rainfall forecasts play very important roles in disaster planning and flood control. They enable authorities to take precautionary measures and, where necessary, plan for the evacuation of …

Sentiment Analysis For Stock Market Prediction Using Machine Learning Techniques, Rajendiran P

Theses and Dissertations

Sentiment analysis has become one of the most important procedures to predict the stock market behaviour according to the customer reviews about a particular topic such as news, movie, event, and remarks related to the product. Due to the huge number of reviews generated from the customer, for analyzing information in an accurate manner. In order to detect general view of product, sentiment analysis technique is performed. Lately, the majority of research works is designed for Sentiment analysis by application of an organization and ranking techniques. But it suffers less exactness of the accurate classification of the customer reviews.

The …

A Survey On Food Ingredient Substitutions, Hyunwook Kim, Revathy Venkataramanan, Amit P. Sheth

Publications

Diet plays a crucial role in managing chronic conditions and overall well-being. As people become more selective about their food choices, finding recipes that meet dietary needs is important. Ingredient substitution is key to adapting recipes for dietary restrictions, allergies, and availability constraints. However, identifying suitable substitutions is challenging as it requires analyzing the flavor, functionality, and health suitability of ingredients. With the advancement of AI, researchers have explored computational approaches to address ingredient substitution. This survey paper provides a comprehensive overview of the research in this area, focusing on five key aspects: (i) datasets and data sources used to …

Enhancing Accuracy In Predicting Continuous Values Through Regression, Ahmed Aljuboori, M. M. A. Abdulrazzq

Iraqi Journal for Computer Science and Mathematics

Enhancing the accuracy in predicting continuous values remains a significant challenge, especially when dealing with imbalanced data and choosing appropriate models. Regression techniques are widely used in data mining, and machine learning fields for this purpose. However, the traditional algorithms struggle to achieve high accuracy because of the limitations in dealing with complex data and imbalanced distribution. This study addresses these gaps by proposing a new framework that evaluates multiple regression models using the Boston House Pricing Dataset (BHD). The examined models involve simple linear, multiple linear, Polynomial, Lasso, Ridge, Random Forest, Keras and Gradient Boosting regression. The models are …

Enabling Technologies For Web 3.0: A Comprehensive Survey, Md Arif Hassan, Mohammad Behdad Jamshidi, Bui Duc Manh, Nam H Chu, Chi-Hieu Nguyen, Nguyen Quang Hieu, Cong T Nguyen, Dinh Thai Hoang, Diep N Nguyen, Nguyen Van Huynh, Mohammad Abu Alsheikh, Eryk Dutkiewicz

Staff Scholarship - Australia & Dubai

Web 3.0 represents the next stage of Internet evolution, aiming to empower users with increased autonomy, efficiency, quality, security, and privacy. This evolution has the potential to democratize content access by leveraging advancements in cutting-edge enabling technologies. In this paper, we conduct an in-depth survey of enabling technologies in the context of Web 3.0, such as blockchain, semantic web, 3D interactive web, Metaverse, Virtual Reality (VR) and Augmented Reality (AR), Internet of Things (IoT) technology, and their roles in shaping Web 3.0. We commence by providing a comprehensive background of Web 3.0, including its concept, basic architecture, potential applications …

Interfaces Gráficas Y Género: Impacto En Discursos Normativos, Marco V. Ferruzca, Paulo C. Portilla, Juan Villegas, Román A. Mora

GDI. Revista de investigación de Género, Diseño e Innovación

El diseño de interfaces gráficas de usuario generalmente considera una reflexión sobre propiedades vinculadas a aspectos visuales como el color, las imágenes, la tipografía, etc. Asimismo, algunos aspectos del usuario se ponen también a consideración como su edad, cultura, experiencia, género, entre otros. Sin embargo, la noción de género sólo se reduce a un binarismo para definir si la interfaz gráfica encaja en la categoría mujer u hombre. Muy pocos investigadores se han detenido a profundizar en el discurso heteronormativo que puede desprenderse de la interpretación de significado de una interfaz gráfica como consecuencia de la interrelación entre usuariogénerocomputadora. El …

Enhancing Multi-Robot Slam: Centralized Lidar-Based Loop Closure Detection Approach, Basma Ahmed Jalil, Ibraheem Kasim Ibraheem

Iraqi Journal for Computer Science and Mathematics

The loop closure detection is crucial for global mapping and route correction in multi-robot simultaneous localization and mapping (SLAM). However, including loop closure detection algorithms in MR-SLAM increases the computational complexity and the required resources on the robot board and at the base station. In this paper, An Enhanced Multi-Robot Fast Localization Odometry and Mapping (EMR-FLOAM) to deal with computation complexity issue. The EMR-FLOAM algorithm addresses computational complexity and resource requirements by utilizing a two-stage non-iterative distortion compensation technique, resulting in optimized code and accelerated localization and map construction processes. The simulation of the proposed work has been tested on …

Evaluating The Effect Of Students' Behavioural Intention To Use Social Media For Collaborative Learning, Nur Shamsiah Abdul Rahman, Noor Azida Sahabudin

Iraqi Journal for Computer Science and Mathematics

With the rise of social media technologies, investigating the use of social media for learning has become all the more important. However, far too little research has been conducted to investigate factors that contribute towards students’ attitude and behavioural intention to use social media for collaborative learning in Malaysian higher education. This study aims to examine the determinants that influence students’ attitude and behaviour intention to use social media for collaborative learning by applying the Theory Acceptance Model (TAM) and Unified Theory of Acceptance and Usage of Technology (UTAUT). 243 respondents participated in this study. The Structural Equation modelling (SEM) …

Exploring The Potential Of Large Language Models For Assisting With Mental Health Diagnostic Assessments: The Depression And Anxiety Case, Kaushik Roy, Harshul Surana, Darssan Eswaramoorthi, Yuxin Zi, Vedant Palit, Ritvik Garimella, Amit Sheth

Publications

Large language models (LLMs) are increasingly attracting the attention of healthcare professionals for their potential to assist in diagnostic assessments, which could alleviate the strain on the healthcare system caused by a high patient load and a shortage of providers. For LLMs to be effective in supporting diagnostic assessments, it is essential that they closely replicate the standard diagnostic procedures used by clinicians. In this paper, we specifically examine the diagnostic assessment processes described in the Patient Health Questionnaire-9 (PHQ-9) for major depressive disorder (MDD) and the Generalized Anxiety Disorder-7 (GAD-7) questionnaire for generalized anxiety disorder (GAD). We investigate various …

An Evaluation Of Reinforcement Learning Algorithms In Video Game Development, Isaac Lockwood

Masters Theses & Specialist Projects

Reinforcement Learning (RL) has demonstrated substantial promise for creating adaptive, responsive AI in complex environments such as video games. Yet despite growing academic interest, industry adoption remains limited due to computational overhead, reward-design challenges, and unpredictable AI behaviors. This thesis investigates how RL algorithms—specifically Advantage Actor-Critic (A2C), Deep Q-Network (DQN), and Proximal Policy Optimization (PPO)—can be applied to three different genres of video games. Those being first-person shooter (fps), fighting, and strategy.

Through a combination of scenario-based experimentation and comprehensive analysis, this work explores the feasibility and design considerations crucial for integrating RL-driven AI into commercial games. Key factors examined …

Dashar: An Implementation Of Augmented Reality Technology For Automotive Applications, Trevor D. Brown

Masters Theses & Specialist Projects

Since the advent of the modern automobile, manufacturers have provided means of tracking various critical data points associated with automobile operation, with the most prominent and standardized method being the instrument cluster. These data points include, but are not limited to, automobile speed, engine speed, fuel level, oil temperature, radiator (water) temperature, and battery charge. While this data is updated in real-time as the automobile is running, traditional instrument clusters cannot be modified or adjusted to the automobile driver’s needs, unless extensive after-market modifications are made. These modifications can be expensive, and require great understanding of the automobile’s assembly.

Alongside …

Very Large Scale Robotics Path Planning With Centroidal Voronoi Tessellation, Xu (James) Gao

Theses, Dissertations and Capstones

Swarm robotics, also referred to as very large-scale robotics (VLSR), has emerged as a transformative approach for addressing complex tasks that are infeasible for single-robot systems. Applications range from environmental monitoring and disaster response to large-scale agricultural and industrial operations. However, as the number of robots in a swarm increases, so do the challenges associated with motion control, energy efficiency, and scalability. These challenges necessitate innovative solutions that balance microscopic robot behaviors with macroscopic system-level objectives.

In this thesis, we address these challenges by building upon existing research [40], which introduced novel methods for optimizing swarm robotics systems using macroscopic …

Numerical And Experimental Investigation Of The Dynamic Behavior Of Vibrating Niti Guidewire, Graham Gavin

Conference Papers

Minimally invasive interventions often utilise slender wires to access and navigate to target sites. These guidewires act as rails, facilitating access for other therapies. In endovascular procedures, the guidewires facilitate angioplasty balloons, stenting and other dilation procedures. Due to their small profile and flexibility, endovascular guidewires have limited ability to transfer forces to their tip and as a result cannot penetrate hard calcified blockages, commonly referred to as chronic total occlusions (CTOs) [1-2]. One method proposed to overcome this limitation is the application of vibrations to the wire. The vibrations are transmitted along the length of the wire to the …

Evaluation Of The Crossing Forces Of A Vibrating Guidewire In Contact With Model Calcified Materials, A. Rezaei, Graham Gavin, S. Raut

Conference Papers

Peripheral arterial disease (PAD) affects millions globally, with over 21 million cases reported in the U.S. in 2020. It occurs when leg arteries become narrowed or blocked, leading to complications like critical limb ischemia, where blood flow is severely restricted. Standard treatments, such as bypass surgery and endovascular procedures, aim to restore blood flow. However, advanced chronic total occlusions with calcified plaques, often prevent flexible guidewires crossing into the true lumen of the distal vessel, accounting for approximately 80% of procedural failure. The use of high-frequency (20- 50 kHz) mechanical vibrations (0-50 μm) delivered to the distal tip of specialist …

Ai In Higher Ed, Where Are We Now?: Insights From The 2025 Educause Ai Landscape Study, Angela Neria, Jeff Burns

Posters

Curious about how higher education is really using AI? Wondering what’s next for AI policies, workforce impacts, and leadership strategies? The 2025 EDUCAUSE AI Landscape Study has the answers! Based on fresh data from institutions across higher ed, this study highlights key trends, challenges, and opportunities in AI adoption. Stop by our poster session to get a quick snapshot of where AI stands today—and where it’s headed. Let’s talk about what these findings mean for PSU and the future of AI in higher education!

Explaining Deep Learning-Based Anomaly Detection In Energy Consumption Data By Focusing On Contextually Relevant Data, Mohammad Noorchenarboo, Katarina Grolinger

Electrical and Computer Engineering Publications

Detecting anomalies in energy consumption data is crucial for identifying energy waste, equipment malfunction, and overall, for ensuring efficient energy management. Machine learning, and specifically deep learning approaches, have been greatly successful in anomaly detection; however, they are black-box approaches that do not provide transparency or explanations. SHAP and its variants have been proposed to explain these models, but they suffer from high computational complexity (SHAP) or instability and inconsistency (e.g., Kernel SHAP). To address these challenges, this paper proposes an explainability approach for anomalies in energy consumption data that focuses on context-relevant information. The proposed approach leverages existing explainability …

Kolmogorov–Arnold Recurrent Network For Short Term Load Forecasting Across Diverse Consumers, Muhammad Umair Danish, Katarina Grolinger

Electrical and Computer Engineering Publications

Load forecasting plays a crucial role in energy management, directly impacting grid stability, operational efficiency, cost reduction, and environmental sustainability. Traditional Vanilla Recurrent Neural Networks (RNNs) face issues such as vanishing and exploding gradients, whereas sophisticated RNNs such as Long Short- Term Memory Networks (LSTMs) have shown considerable success in this domain. However, these models often struggle to accurately capture complex and sudden variations in energy consumption, and their applicability is typically limited to specific consumer types, such as offices or schools. To address these challenges, this paper proposes the Kolmogorov–Arnold Recurrent Network (KARN), a novel load forecasting approach that …