Integrating Deep Traffic Prediction And Environmental Impact Assessment Using Noisy Real-World Data In Las Vegas,
2024
University of Nevada, Las Vegas
Integrating Deep Traffic Prediction And Environmental Impact Assessment Using Noisy Real-World Data In Las Vegas, Tarek Bin Zahid
UNLV Theses, Dissertations, Professional Papers, and Capstones
This thesis introduces an integrated framework for advanced traffic prediction and real-time emission estimation, designed to aid urban planning and environmental monitoring. Utilizing a graph-based transformer model, it predicts traffic conditions across the Las Vegas road network, drawing on spatial and temporal data from a large-scale sensor network. The study significantly expands the dataset from 26 to approximately 900 sensors, enhancing predictive accuracy and regional coverage. Inspired by masking techniques and strategies tailored to incomplete datasets, the model effectively handles real-world, noisy data without relying on resource-intensive imputation. Innovative training approaches enable robust traffic flow predictions despite missing or imperfect …
Tracking Joint Movement Using Optical Flow,
2024
University of Nevada, Las Vegas
Tracking Joint Movement Using Optical Flow, Isabella Paperno
UNLV Theses, Dissertations, Professional Papers, and Capstones
We developed an algorithm that aims to move us closer to detecting early signs of arthritis. The program processes and analyzes X-ray videos using coyote and dog cadavers as models to examine the range of motion around the hip and connecting joints using optical flow techniques that track motion and velocity. We focus on how optical flow techniques track embedded metal markers and verify accuracy through comparisons with XMALab (X-ray motion analysis lab). Once proven as an accurate alternative, the focus will switch to markerless tracking and become a proof-of-concept for optical flow to be used in place of XMALab, …
Mechanically Cost-Effective Approach For Bipedal Walking In Robots Using Instantaneous Collision Angle,
2024
University of Nevada, Las Vegas
Mechanically Cost-Effective Approach For Bipedal Walking In Robots Using Instantaneous Collision Angle, Smit R. Patel
UNLV Theses, Dissertations, Professional Papers, and Capstones
Humans, as bipedal locomotors, are effective at reducing the mechanical cost of transport (CoTmech) by adopting movement strategies and gaits that minimize energy expenditure for a given distance. By using different gaits at different speeds, leveraging their long spring-like tendons and muscle elasticity which store and release energy during movement, humans reduce the mechanical effort required for locomotion. Current locomotion solutions offered in bipedal robots, based on legacy walking and running gait models, are not great at energy efficiency unless walking at very low speeds. Additionally, the control system of robots, designed to ensure stability and adaptability, requires substantial resources, …
Tac-It An Affective Computing User Interface Design,
2024
Florida Institute of Technology
Tac-It An Affective Computing User Interface Design, Andrew Biron
Theses and Dissertations
TAC-IT affective computing user-interface design is an independent computer peripheral that is a tool to be utilized to obtain a user’s self-reported emotional state in real-time. Doctor Rosalind Picard first coined and used the term affective computing in her paper Affective Computing [Picard, R. (1995)]. Affective Computing is defined as the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. It is an interdisciplinary field spanning computer science, psychology, and cognitive science. Since that time, areas of research have expanded exponentially, and areas of interest include how to trigger emotions in a test …
Breaking The Procrastination Barrier,
2024
Florida Institute of Technology
Breaking The Procrastination Barrier, Bianca Ebanks
Theses and Dissertations
Procrastination is a common barrier to productivity, impacting individuals' ability to achieve goals, especially in academic and professional settings. This study investigates a mixed-methods online intervention combining Behavioral Analysis (BA) principles with mindfulness exercises to reduce procrastination. The aim was to develop a human-centered, personalized intervention that utilizes behavioral reminders and mindfulness techniques to address procrastination in 43 participants. Participants' procrastination levels were assessed using the Irrational Procrastination Scale (IPS), and interventions were tailored based on individual procrastination tendencies. Reminders were sent via email or text, with timing adjusted to participants’ specific needs. The intervention also included mindfulness exercises designed …
Prevalence Of Autism Spectrum Characteristics In Students Taking Undergraduate Computing Courses,
2024
University of Nebraska-Lincoln
Prevalence Of Autism Spectrum Characteristics In Students Taking Undergraduate Computing Courses, Rachel Michaela Mettenbrink
School of Computing: Dissertations, Theses, and Student Research
The incidence rate of autism spectrum condition (ASC) has increased significantly in recent decades, as awareness of the condition and its impacts increases amongst clinicians, parents, and the general population. Medical literature has proposed that there may be a relationship between ASC and participation in the computing field. This study tests for the prevalence of autism spectrum condition traits measured by delivering the Autism Spectrum Quotient (AQ) to a population of undergraduate computer science students. We examine the relationships between AQ scores and students taking undergraduate computer science classes, sex, socioeconomic status, and parents in the computing industry. Additionally, we …
Debtor Eligibility Prediction Using Deep Learning With Chatbot-Based Testing,
2024
Universitas Teknologi Yogyakarta, Indonesia
Debtor Eligibility Prediction Using Deep Learning With Chatbot-Based Testing, Reski Noviania, Enny Itje Sela, Luther Alexander Latumakulita, Steven R. Sentinuwo
Knowledge Engineering and Data Science
Predicting debtor eligibility is essential for effective risk management and minimizing lousy credit risks. However, financial institutions face challenges such as imbalanced data, inefficient feature selection, and limited user accessibility. This study combines Recursive Feature Elimination (RFE) and Deep Learning (DL) to improve prediction accuracy. It integrates a chatbot interface for user-friendly testing. RFE effectively identifies critical features, while the DL model achieves a validation accuracy of 97.62%, surpassing previous studies with less comprehensive methodologies. The chatbot's novel design not only ensures accessibility but also enhances user engagement through flexible input options, such as approximate values, enabling non experts to …
Manifold Learning And Undersampling Approaches For Imbalanced Class Sentiment Classification,
2024
IPB University, Indonesia
Manifold Learning And Undersampling Approaches For Imbalanced Class Sentiment Classification, L.M. Risman Dwi Jumansyah, Agus Mohamad Soleh, Utami Dyah Syafitri
Knowledge Engineering and Data Science
Movie reviews are crucial in determining a film's success by influencing audience decisions. Automating sentiment classification is essential for efficient public opinion analysis. However, it faces challenges such as high-dimensional data and imbalanced class distributions. This study addresses these issues by applying manifold learning techniques, Principal Component Analysis (PCA) and Laplacian Eigenmaps (LE) to reduce data complexity and undersampling strategies (Random Undersampling (RUS) and EasyEnsemble) to balance data and improve predictions for both sentiment classes. On reviews of The Raid 2: Berandal, EasyEnsemble achieved the highest average G-Mean of 0.694 using Term Frequency-Inverse Document Frequency (TF IDF) features with a …
Deep Learning Approach For Dental Anomalies X-Ray Imaging Using Yolov8,
2024
University Malaysia, Malaysia
Deep Learning Approach For Dental Anomalies X-Ray Imaging Using Yolov8, Amelia Ritahani Ismail, Md Salim Sadman Taseen
Knowledge Engineering and Data Science
Dental X-ray imaging is a critical diagnostic tool for identifying various dental anomalies. However, manual interpretation is time-consuming, prone to human error, and requires specialized expertise. Deep learning models, particularly object detection frameworks like YOLO, have demonstrated promising results in automating medical image analysis. This study aims to develop and evaluate a YOLOv8-based deep learning model for automated detection and classification of 14 dental anomaly categories, including Caries, Crowns, Fillings, Implants, and Periapical lesions. The proposed approach addresses limitations in previous YOLO versions by leveraging anchor-free detection and enhanced feature extraction for improved accuracy. The model was trained on a …
Optimal Strategy For Handling Unbalanced Medical Datasets: Performance Evaluation Of K-Nn Algorithm Using Sampling Techniques,
2024
Universitas Muslim Indonesia, Indonesia
Optimal Strategy For Handling Unbalanced Medical Datasets: Performance Evaluation Of K-Nn Algorithm Using Sampling Techniques, Yulita Salim, Aulia Putri Utami, Abdul Rachman Manga, Huzain Azis, Fadhila Tangguh Admojo
Knowledge Engineering and Data Science
This study addresses the critical role of medical image classification in enhancing healthcare effectiveness and tackling the challenges of imbalanced medical datasets. It focuses on optimizing classification performance by integrating Canny edge detection for segmentation and Hu-moment feature extraction and applying oversampling and undersampling techniques. Five diverse medical datasets were utilized, covering Alzheimer’s and Parkinson’s diseases, COVID-19, brain tumours, and lung cancer. The K-Nearest Neighbors (K-NN) algorithm was implemented to enhance classification accuracy, aiming to develop a more robust framework for medical image analysis. The evaluation, conducted using cross-validation, demonstrated notable improvements in key metrics. Specifically, oversampling significantly enhanced lung …
A Hierarchical Density-Based Spatial Clustering Of Applications With Noise (Hdbscan) Approach For Identifying Potential Villages In Buleleng Regency,
2024
Universitas Islam Indonesia, Indnonesia
A Hierarchical Density-Based Spatial Clustering Of Applications With Noise (Hdbscan) Approach For Identifying Potential Villages In Buleleng Regency, Dina Nur Amalina, Achmad Fauzan
Knowledge Engineering and Data Science
Buleleng Regency, located in Bali Province, possesses diverse village potential, including agricultural production and tourist attractions. However, this potential has not been fully optimized. Therefore, it is important to enhance village potential by clustering villages based on their specific characteristics to identify and prioritize those requiring special attention. This approach aims to promote equitable village development and reduce poverty levels. This study clusters villages in Buleleng Regency based on their potential using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) method. The data utilized in this study comprises village potential data obtained from the Buleleng Regency Statistics Office …
Classification Of Anxiety Levels Entering The World Of Work In Final Year Students Using The Neighbor Weighted K-Nearest Neighbor Method,
2024
Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia
Classification Of Anxiety Levels Entering The World Of Work In Final Year Students Using The Neighbor Weighted K-Nearest Neighbor Method, Awang Hendrianto Pratomo, Muhammad Fahmi Adam, Dessyanto Boedi Prasetyo
Knowledge Engineering and Data Science
This study evaluates the accuracy of the Neighbor Weighted K-Nearest Neighbor (NWKNN) method in classifying the anxiety levels of final-year students as they prepare to enter the workforce, particularly in cases of unbalanced data distribution. The system was developed using the prototype method, and NWKNN was applied to classify anxiety levels into low, medium, and high categories. Testing using the Confusion Matrix demonstrated strong performance, achieving an accuracy of 94% based on a dataset of 1009 students, with a 90:10 ratio of training to test data. The results indicate that NWKNN effectively provides classification input values, making it a reliable …
Comparative Analysis Of Bpnn And Lvq For Sundanese Character Recognition,
2024
Universitas Mulawarman, Indonesia
Comparative Analysis Of Bpnn And Lvq For Sundanese Character Recognition, Haviluddin Haviluddin, Herman Santoso Pakpahan, Dinda Izmya Nurpadillah, Hario Jati Setyadi, Medi Taruk, Rayner Alfred
Knowledge Engineering and Data Science
The Sundanese script (Aksara Sunda), an essential part of Sundanese cultural heritage, has been used since the 14th century AD. However, recognizing handwritten Sundanese characters remains challenging due to variations in individual writing styles. This study compares the performance of Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) for recognizing handwritten Sundanese vowel (Swara) characters. A dataset was collected from 15 individuals, each writing seven Sundanese vowel characters, which were then used for training and testing the recognition models. Experimental results show that BPNN outperforms LVQ, achieving a higher classification accuracy (95.23%), lower Mean Squared Error (MSE), and faster …
Dynamic Key-Based Privacy-Preserving Authentication Scheme For Internet Of Drones,
2024
University of Southern Mississippi
Dynamic Key-Based Privacy-Preserving Authentication Scheme For Internet Of Drones, Zain Chaudhary
Honors Theses
The Internet of Drones (IoD) proliferation has catalyzed transformative changes across various industries, from agriculture to urban management. However, expanding drone networks also presents significant security challenges concerning secure communication and authentication. This paper introduces a robust privacy-preserving key-based authentication scheme tailored explicitly for the IoD, utilizing a matrix key generated by Hierarchical Message Authentication Codes (HMAC) and the SHA-256 algorithm to address these vulnerabilities. Our system enhances security by ensuring each drone in the network can authenticate securely and reliably with a central unit, preventing unauthorized access and securing communications against common threats like eavesdropping and impersonation attacks. Our …
Improving Robustness Of Learning-Based Approaches In Autonomous Systems And Engineering Education,
2024
Florida Institute of Technology
Improving Robustness Of Learning-Based Approaches In Autonomous Systems And Engineering Education, Godwyll Aikins
Theses and Dissertations
This dissertation advances the development of robust learning-based approaches across two complementary domains: engineering education and autonomous systems. Through four studies, this research addresses critical challenges in preparing data-proficient engineers and developing reliable autonomous systems that can operate under uncertainty and incomplete information. The engineering education study examines how mechanical and aerospace engineering undergraduates conceptualize and develop data proficiency skills essential for modern engineering practice. Through interviews with 27 students, the research employs the How People Learn framework to analyze student perspectives on information literacy, data interpretation, and computational thinking. The findings inform pedagogical strategies for developing data proficiency in …
An Automated Design Flow From Synchronous Rtl To Optimized Layout Using Commercial Eda Tools For Multi-Threshold Null Convention Logic Circuits,
2024
University of Arkansas, Fayetteville
An Automated Design Flow From Synchronous Rtl To Optimized Layout Using Commercial Eda Tools For Multi-Threshold Null Convention Logic Circuits, Cole Harrington Sherrill
Graduate Theses and Dissertations
This work presents the first automated design flow from synchronous RTL to highly optimized layout for Multi-Threshold NULL Convention Logic (MTNCL) circuits. The developed synthesis flow overcomes many of the drawbacks of existing attempts and leverages the advanced optimization features provided by modern synthesis tools. The remaining timing race conditions native to the MTNCL architecture have been identified and thoroughly explored. Two sets of novel timing constraints were devised: the first responds to these race conditions, yielding highly reliable MTNCL circuits; the second directly targets the critical paths within MTNCL circuits, allowing the designer to optimize the target circuit for …
Causal Discovery In Time Series Data Using Deep Learning Techniques,
2024
University of Arkansas, Fayetteville
Causal Discovery In Time Series Data Using Deep Learning Techniques, Saima Zahin Farhana Absar
Graduate Theses and Dissertations
Causal structure learning from observational data has been an active field of research over the past decades. In the literature, different algorithms and models have been proposed, such as constrained-based methods and score-based methods including the emerging deep learning-based methods. However, most of the approaches apply to static and non-dynamic data only. In many applications, the data is temporal. For example, monitoring systems, weather surveillance systems, and stock data, to name but a few. Incorporating temporal information is an important extension of the causal discovery field. With the growth of observational data these days, the discovery of causal relationships from …
Heterogeneous Collaborative Robotics: Multi-Robot Navigation In Dynamic Environments,
2024
Florida Institute of Technology
Heterogeneous Collaborative Robotics: Multi-Robot Navigation In Dynamic Environments, Tyler Nicholas Raettig
Theses and Dissertations
Abstract: The challenges of multi-robot navigation in dynamic environments, focusing on uncertainties in obstacle complexities, partial observation, and the transition of policies from simulations to the real world. The proposed approach utilizes a deep reinforcement learning (DRL) framework enabling a Light Detection and Ranging (LiDAR)-equipped robot to communicate with a camera-equipped robot to achieve optimal paths despite their different sensors. The key contributions include the development of a cooperative architecture for information exchange between robots, a DRL-based framework for learning navigation policies, and a training mechanism based on dynamic randomization for enhanced real-world adaptability. Experimental validation using Gazebo simulations demonstrates …
E-Learning Management Technology Integration For Laboratory Usage,
2024
Department of Planning, Directorate of Private University Education, Ministry of Higher Education and Scientific Research, Baghdad, Iraq
E-Learning Management Technology Integration For Laboratory Usage, Mohammed Khaleel Hussein, Mohammed Ahmed Subhi, Saleh Mahdi Mohammed, Mayasah Al-Khateeb
Iraqi Journal for Computer Science and Mathematics
E-learning systems have transformed educational sectors and more interestingly, the use of these educational platforms for laboratory-based practices has been trending in the last couple of years. Laboratory-usage e-learning system has various tremendous benefits such as the skill of accessibility utilization and practical experimentation simulation. Several intelligent tasks still need to be tackled to make the most of it. One of the biggest problems is how to recreate hands-on experience in a virtual or remote environment. Some e-learning systems offer simulations and virtual experiments but may also require additional tactile feedback through interaction with physical equipment for this challenge, it …
The Significance Of Cannibalism, Panic, And Sanctuary In The Interactions Between Prey And Predator With A Stage Structure,
2024
Scientific Affairs Department, Al-Iraqia University, Baghdad, Iraq
The Significance Of Cannibalism, Panic, And Sanctuary In The Interactions Between Prey And Predator With A Stage Structure, Ahmed Sami Abdulghafour, Raid Kamel Naji
Iraqi Journal for Computer Science and Mathematics
This paper analyzes a novel prey-predator model that takes into account the predator's stage structure, cannibalism within the predator population, panicky behavior, and the existence of a sanctuary where the prey might hide from the predator. The Holling type II functional response is used in the predation process. The behavior of the identified fixed points of the proposed system has been closely analyzed. The analysis focuses on the local stability and potential bifurcations that could happen close to the system's fixed points. The Lyapunov function approach is used to investigate the fixed-point stability zone globally. Numerical simulations were run to …
