Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Artificial Intelligence and Robotics (26)
- Theory and Algorithms (17)
- Engineering (16)
- Other Computer Sciences (16)
- Data Science (13)
-
- Medicine and Health Sciences (12)
- Social and Behavioral Sciences (12)
- Library and Information Science (10)
- Numerical Analysis and Scientific Computing (10)
- Graphics and Human Computer Interfaces (9)
- Life Sciences (9)
- Mathematics (9)
- Applied Mathematics (8)
- Databases and Information Systems (7)
- Software Engineering (6)
- Statistics and Probability (5)
- Computer Engineering (4)
- Discrete Mathematics and Combinatorics (4)
- Information Security (4)
- Arts and Humanities (3)
- Bioinformatics (3)
- Electrical and Computer Engineering (3)
- Materials Science and Engineering (3)
- Mechanical Engineering (3)
- Medical Specialties (3)
- Physics (3)
- Art and Design (2)
- Biomedical Engineering and Bioengineering (2)
- Keyword
-
- Machine Learning (11)
- Machine learning (10)
- Academic libraries (7)
- Classification (6)
- Natural Language Processing (6)
-
- Natural language processing (6)
- Artificial intelligence (5)
- Interface Design (4)
- Transactional Log Analysis (4)
- Database (3)
- Deep learning (3)
- Graph theory (3)
- NLP (3)
- Support vector machines (3)
- Artificial Intelligence (2)
- Bioinformatics (2)
- Colony of Prehending Entities (2)
- Computer Science (2)
- Computer science (2)
- Data Mining (2)
- Deep Learning (2)
- EEG (2)
- Epidemiology (2)
- Epilepsy (2)
- Feature selection (2)
- Fuzzy logic (2)
- Image recognition (2)
- Image segmentation (2)
- Interface design (2)
- Internet (2)
- Publication Year
- Publication
-
- Theses and Dissertations (103)
- VCU Libraries Faculty and Staff Publications (7)
- Biology and Medicine Through Mathematics Conference (5)
- VCU Libraries Faculty and Staff Presentations (3)
- Computer Science Publications (2)
-
- Statistical Sciences and Operations Research Data (2)
- Statistical Sciences and Operations Research Publications (2)
- Summer REU Program (2)
- Undergraduate Research Posters (2)
- Chemical and Life Science Engineering Publications (1)
- Journal of Mathematics and Science: Collaborative Explorations (1)
- Pharmacotherapy and Outcomes Science Publications (1)
- Wright Center for Clinical and Translational Research Works (1)
- Publication Type
Articles 1 - 30 of 132
Full-Text Articles in Computer Sciences
A Comparative Analysis Of Source Identification Algorithms, Pablo A. Curiel
A Comparative Analysis Of Source Identification Algorithms, Pablo A. Curiel
Biology and Medicine Through Mathematics Conference
No abstract provided.
Introducing Flexible Assessment Into A Computer Networks Course: A Case Study, Joe Meehean
Introducing Flexible Assessment Into A Computer Networks Course: A Case Study, Joe Meehean
Journal of Mathematics and Science: Collaborative Explorations
With overall positive results and limited drawbacks, I have adapted modern pedagogical techniques to address a common difficulty encountered when teaching a computer networks course. Due to the tiered nature of the skills taught in the course, students often fail unnecessarily. Using mastery learning, competency-based education, and specifications grading as a foundation, I have developed a course that allows students with varied skills and abilities to pass. The heart of this approach is the flexible assessment of programming assignments which eliminates due dates and allows students to have their work graded and regraded without penalty. Flexible assessment also defines an …
Stealthy Control Logic Attacks And Defense In Industrial Control Systems, Adeen Ayub
Stealthy Control Logic Attacks And Defense In Industrial Control Systems, Adeen Ayub
Theses and Dissertations
Industrial control systems (ICS) play a crucial role in monitoring and managing critical infrastructure, including nuclear plants, oil and gas pipelines, and power grid stations. Programmable logic controllers (PLCs) are a fundamental component of ICS, directly interfacing with physical processes and implementing control logic programs that govern operations. Due to their significance in controlling critical infrastructure, PLCs often become prime targets for attackers seeking to disrupt these systems. Exploitable vulnerabilities in PLCs render them susceptible to such attacks. While many attacks on PLCs leave a large footprint in network traffic and are detectable by intrusion detection systems (IDS), this dissertation …
Adaptive Multi-Label Classification On Drifting Data Streams, Martha Roseberry
Adaptive Multi-Label Classification On Drifting Data Streams, Martha Roseberry
Theses and Dissertations
Drifting data streams and multi-label data are both challenging problems. When multi-label data arrives as a stream, the challenges of both problems must be addressed along with additional challenges unique to the combined problem. Algorithms must be fast and flexible, able to match both the speed and evolving nature of the stream. We propose four methods for learning from multi-label drifting data streams. First, a multi-label k Nearest Neighbors with Self Adjusting Memory (ML-SAM-kNN) exploits short- and long-term memories to predict the current and evolving states of the data stream. Second, a punitive k nearest neighbors algorithm with a self-adjusting …
Adaptable And Trustworthy Machine Learning For Human Activity Recognition From Bioelectric Signals, Morgan S. Stuart
Adaptable And Trustworthy Machine Learning For Human Activity Recognition From Bioelectric Signals, Morgan S. Stuart
Theses and Dissertations
Enabling machines to learn measures of human activity from bioelectric signals has many applications in human-machine interaction and healthcare. However, labeled activity recognition datasets are costly to collect and highly varied, which challenges machine learning techniques that rely on large datasets. Furthermore, activity recognition in practice needs to account for user trust - models are motivated to enable interpretability, usability, and information privacy. The objective of this dissertation is to improve adaptability and trustworthiness of machine learning models for human activity recognition from bioelectric signals. We improve adaptability by developing pretraining techniques that initialize models for later specialization to unseen …
Towards Effective Developer Communication In Open Source Software Via Emotional Awareness, Mia Mohammad Imran
Towards Effective Developer Communication In Open Source Software Via Emotional Awareness, Mia Mohammad Imran
Theses and Dissertations
Emotions play an integral yet understudied role in open-source software development, profoundly shaping critical collaborative processes such as knowledge sharing, decision-making, and team dynamics. However, accurately detecting and analyzing emotions in developer communications poses significant challenges due to the lack of visual and auditory cues in text-based interactions. This dissertation investigates techniques to enhance the understanding and modeling of emotions within the textual artifacts of open-source projects. We conduct an extensive evaluation of existing emotion classification tools using a novel dataset of annotated GitHub comments. An error analysis reveals deficiencies in handling implicit emotional expressions and figurative language. We demonstrate …
Graph Coloring Reconfiguration, Reem Mahmoud
Graph Coloring Reconfiguration, Reem Mahmoud
Theses and Dissertations
Reconfiguration is the concept of moving between different solutions to a problem by transforming one solution into another using some prescribed transformation rule (move). Given two solutions s1 and s2 of a problem, reconfiguration asks whether there exists a sequence of moves which transforms s1 into s2. Reconfiguration is an area of research with many contributions towards various fields such as mathematics and computer science.
The k-coloring reconfiguration problem asks whether there exists a sequence of moves which transforms one k-coloring of a graph G into another. A move in this case is a type …
Enhancing Neuromorphic Computing With Advanced Spiking Neural Network Architectures, Paolo Gabriel Alejandro Cachi Delgado
Enhancing Neuromorphic Computing With Advanced Spiking Neural Network Architectures, Paolo Gabriel Alejandro Cachi Delgado
Theses and Dissertations
This dissertation proposes ways to address current limitations of neuromorphic computing to create energy-efficient and adaptable systems for AI applications. It does so by designing novel spiking neural networks architectures that improve their performance. Specifically, the two proposed architectures address the issues of training complexity, hyperparameter selection, computational flexibility, and scarcity of neuromorphic training data. The first architecture uses auxiliary learning to improve training performance and data usage, while the second architecture leverages neuromodulation capability of spiking neurons to improve multitasking classification performance. The proposed architectures are tested on Intel's Loihi2 neuromorphic chip using several neuromorphic datasets, such as NMIST, …
Material Extrusion-Based Additive Manufacturing: G-Code And Firmware Attacks And Defense Frameworks, Haris Rais
Material Extrusion-Based Additive Manufacturing: G-Code And Firmware Attacks And Defense Frameworks, Haris Rais
Theses and Dissertations
Additive Manufacturing (AM) refers to a group of manufacturing processes that create physical objects by sequentially depositing thin layers. AM enables highly customized production with minimal material wastage, rapid and inexpensive prototyping, and the production of complex assemblies as single parts in smaller production facilities. These features make AM an essential component of Industry 4.0 or Smart Manufacturing. It is now used to print functional components for aircraft, rocket engines, automobiles, medical implants, and more. However, the increased popularity of AM also raises concerns about cybersecurity. Researchers have demonstrated strength degradation attacks on printed objects by injecting cavities in the …
Innovations In Drop Shape Analysis Using Deep Learning And Solving The Young-Laplace Equation For An Axisymmetric Pendant Drop, Andres P. Hyer
Innovations In Drop Shape Analysis Using Deep Learning And Solving The Young-Laplace Equation For An Axisymmetric Pendant Drop, Andres P. Hyer
Theses and Dissertations
Axisymmetric Drop Shape Analysis (ADSA) is a technique commonly used to determine surface or interfacial tension. Applications of traditional ASDA methods to process analytical technologies are limited by computational speed and image quality. Here, we address these limitations using a novel machine learning approach to analysis. With a convolutional neural network (CNN), we were able to achieve an experimental fit precision of (+/-) 0.122 mN/m in predicting the surface tension of drop images at a rate of 1.5 ms^-1 versus 7.7 s^-1, which is more than 5,000 times faster than the traditional method. The results are validated on real images …
Face Anti-Spoofing And Deep Learning Based Unsupervised Image Recognition Systems, Enoch Solomon
Face Anti-Spoofing And Deep Learning Based Unsupervised Image Recognition Systems, Enoch Solomon
Theses and Dissertations
One of the main problems of a supervised deep learning approach is that it requires large amounts of labeled training data, which are not always easily available. This PhD dissertation addresses the above-mentioned problem by using a novel unsupervised deep learning face verification system called UFace, that does not require labeled training data as it automatically, in an unsupervised way, generates training data from even a relatively small size of data. The method starts by selecting, in unsupervised way, k-most similar and k-most dissimilar images for a given face image. Moreover, this PhD dissertation proposes a new loss function to …
Development Of Tangible Code Blocks For The Blind And Visually Impaired, Hyun Woo Kim
Development Of Tangible Code Blocks For The Blind And Visually Impaired, Hyun Woo Kim
Theses and Dissertations
The fields of Science, Technology, Engineering, and Mathematics (STEM) have been growing at an accelerating rate in recent times. Knowing how to program has become one key skill for entering all of these STEM fields. However, many students find programming difficult. The block based programming language, Scratch, was specifically designed to lower hurdles to learning how to program for sighted students. Unfortunately, although very effective and widely used in K12 classrooms, Scratch, similar to other block based languages, is inaccessible to students who are blind and visually impaired (BVI). This thesis is part of a larger project to make the …
Computer-Based Scaffolding In Computer Science Education, Rebecca Trinh, Simone Levy
Computer-Based Scaffolding In Computer Science Education, Rebecca Trinh, Simone Levy
Summer REU Program
No abstract provided.
A Study On Developing Novel Methods For Relation Extraction, Darshini Mahendran
A Study On Developing Novel Methods For Relation Extraction, Darshini Mahendran
Theses and Dissertations
Relation Extraction (RE) is a task of Natural Language Processing (NLP) to detect and classify the relations between two entities. Relation extraction in the biomedical and scientific literature domain is challenging as text can contain multiple pairs of entities in the same instance. During the course of this research, we developed an RE framework (RelEx), which consists of five main RE paradigms: rule-based, machine learning-based, Convolutional Neural Network (CNN)-based, Bidirectional Encoder Representations from Transformers (BERT)-based, and Graph Convolutional Networks (GCNs)-based approaches. RelEx's rule-based approach uses co-location information of the entities to determine whether a relation exists between a selected entity …
Temporal Disambiguation Of Relative Temporal Expressions In Clinical Texts Using Temporally Fine-Tuned Contextual Word Embeddings., Amy L. Olex
Theses and Dissertations
Temporal reasoning is the ability to extract and assimilate temporal information to reconstruct a series of events such that they can be reasoned over to answer questions involving time. Temporal reasoning in the clinical domain is challenging due to specialized medical terms and nomenclature, shorthand notation, fragmented text, a variety of writing styles used by different medical units, redundancy of information that has to be reconciled, and an increased number of temporal references as compared to general domain texts. Work in the area of clinical temporal reasoning has progressed, but the current state-of-the-art still has a ways to go before …
Learning Robot Motion From Creative Human Demonstration, Charles C. Dietzel
Learning Robot Motion From Creative Human Demonstration, Charles C. Dietzel
Theses and Dissertations
This thesis presents a learning from demonstration framework that enables a robot to learn and perform creative motions from human demonstrations in real-time. In order to satisfy all of the functional requirements for the framework, the developed technique is comprised of two modular components, which integrate together to provide the desired functionality. The first component, called Dancing from Demonstration (DfD), is a kinesthetic learning from demonstration technique. This technique is capable of playing back newly learned motions in real-time, as well as combining multiple learned motions together in a configurable way, either to reduce trajectory error or to generate entirely …
Nlp@Vcu: Crop Characteristic Extraction Framework, Cora Lewis, Bridget Mcinnes, Getiria Onsongo
Nlp@Vcu: Crop Characteristic Extraction Framework, Cora Lewis, Bridget Mcinnes, Getiria Onsongo
Summer REU Program
We developed a crop characteristic extraction framework. Starting from a custom SpaCy named entity recognition model, we added pre-trained word embeddings and a part-of-speech based entity expansion post-processing step. Then, we implemented an evaluation framework that functioned as a 5-fold cross validation wrapper for SpaCy custom training. Preliminary results showed improvement in the extraction framework after these additions.
Smart City Management Using Machine Learning Techniques, Mostafa Zaman
Smart City Management Using Machine Learning Techniques, Mostafa Zaman
Theses and Dissertations
In response to the growing urban population, "smart cities" are designed to improve people's quality of life by implementing cutting-edge technologies. The concept of a "smart city" refers to an effort to enhance a city's residents' economic and environmental well-being via implementing a centralized management system. With the use of sensors and actuators, smart cities can collect massive amounts of data, which can improve people's quality of life and design cities' services. Although smart cities contain vast amounts of data, only a percentage is used due to the noise and variety of the data sources. Information and communication technology (ICT) …
Universal Design In Bci: Deep Learning Approaches For Adaptive Speech Brain-Computer Interfaces, Srdjan Lesaja
Universal Design In Bci: Deep Learning Approaches For Adaptive Speech Brain-Computer Interfaces, Srdjan Lesaja
Theses and Dissertations
In the last two decades, there have been many breakthrough advancements in non-invasive and invasive brain-computer interface (BCI) systems. However, the majority of BCI model designs still follow a paradigm whereby neural signals are preprocessed and task-related features extracted using static, and generally customized, data-independent designs. Such BCI designs commonly optimize narrow task performance over generalizability, adaptability, and robustness, which is not well suited to meeting individual user needs. If one day BCIs are to be capable of decoding our higher-order cognitive commands and conceptual maps, their designs will need to be adaptive architectures that will evolve and grow in …
Computational Analysis Of Drug Targets And Prediction Of Protein-Compound Interactions, Sina Ghadermarzi
Computational Analysis Of Drug Targets And Prediction Of Protein-Compound Interactions, Sina Ghadermarzi
Theses and Dissertations
Computational prediction of compound-protein interactions generated a substantial amount of interest in the recent years owing to the importance of the knowledge of these interaction for drug discovery and drug repurposing efforts. Research suggests that the currently known drug targets constitute only a fraction of a complete set of drug targets, limiting our ability to identify suitable targets to develop new drugs or to repurpose current drugs for new diseases. These efforts are further thwarted by our limited knowledge of protein-drug (and more generally protein-compound) interactions, where only a subset of drug targets is typically known for the currently used …
A Deep Learning U-Net For Detecting And Segmenting Liver Tumors, Vidhya Cardozo
A Deep Learning U-Net For Detecting And Segmenting Liver Tumors, Vidhya Cardozo
Theses and Dissertations
Visualization of liver tumors on simulation CT scans is challenging even with contrast-enhancement, due to the sensitivity of the contrast enhancement to the timing of the CT acquisition. Image registration to magnetic resonance imaging (MRI) can be helpful for delineation, but differences in patient position, liver shape and volume, and the lack of anatomical landmarks between the two image sets makes the task difficult. This study develops a U-Net based neural network for automated liver and tumor segmentation for purposes of radiotherapy treatment planning. Non-contrast simulation based abdominal CT axial scans of 52 patients with primary liver tumors were utilized. …
Equations Of State For Warm Dense Carbon From Quantum Espresso, Derek J. Schauss
Equations Of State For Warm Dense Carbon From Quantum Espresso, Derek J. Schauss
Theses and Dissertations
Warm dense plasma is the matter that exists, roughly, in the range of 10,000 to 10,000,000 Kelvin and has solid-like densities, typically between 0.1 and 10 grams per centimeter. Warm dense fluids like hydrogen, helium, and carbon are believed to make up the interiors of many planets, white dwarfs, and other stars in our universe. The existence of warm dense matter (WDM) on Earth, however, is very rare, as it can only be created with high-energy sources like a nuclear explosion. In such an event, theoretical and computational models that accurately predict the response of certain materials are thus very …
Information Architecture For A Chemical Modeling Knowledge Graph, Adam R. Luxon
Information Architecture For A Chemical Modeling Knowledge Graph, Adam R. Luxon
Theses and Dissertations
Machine learning models for chemical property predictions are high dimension design challenges spanning multiple disciplines. Free and open-source software libraries have streamlined the model implementation process, but the design complexity remains. In order better navigate and understand the machine learning design space, model information needs to be organized and contextualized. In this work, instances of chemical property models and their associated parameters were stored in a Neo4j property graph database. Machine learning model instances were created with permutations of dataset, learning algorithm, molecular featurization, data scaling, data splitting, hyperparameters, and hyperparameter optimization techniques. The resulting graph contains over 83,000 nodes …
Learning From Multi-Class Imbalanced Big Data With Apache Spark, William C. Sleeman Iv
Learning From Multi-Class Imbalanced Big Data With Apache Spark, William C. Sleeman Iv
Theses and Dissertations
With data becoming a new form of currency, its analysis has become a top priority in both academia and industry, furthering advancements in high-performance computing and machine learning. However, these large, real-world datasets come with additional complications such as noise and class overlap. Problems are magnified when with multi-class data is presented, especially since many of the popular algorithms were originally designed for binary data. Another challenge arises when the number of examples are not evenly distributed across all classes in a dataset. This often causes classifiers to favor the majority class over the minority classes, leading to undesirable results …
Applied Machine Learning In Extrusion-Based Bioprinting, Shuyu Tian
Applied Machine Learning In Extrusion-Based Bioprinting, Shuyu Tian
Theses and Dissertations
Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time and resource-intensive and not easily translatable across different laboratories. A machine learning (ML) approach to EBB parameter optimization can accelerate this process for laboratories across the field through training using data collected from published literature. In this work, regression-based and classification-based ML models were investigated for their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite hydrogels. Regression-based models were investigated for their ability to predict suitable extrusion pressure given desired cell viability when keeping …
K-Nearest Neighbors Density-Based Clustering, Avory C. Bryant
K-Nearest Neighbors Density-Based Clustering, Avory C. Bryant
Theses and Dissertations
Traditional density-based clustering approaches rely on a distance-based parameter to define data connectivity and density. However, an appropriate value of this parameter can be difficult to determine as it is highly dependent on the underlying distribution of the data. In particular, distribution parameters affect the scale of inter-group distances (e.g., variance); this dependence leads to a well-known inability to simultaneously detect clusters at varying levels of density. In this work, connectivity and density are defined according to the rank-order induced by the distance metric (i.e., invariant to the expected scale of the distances). Connectivity by k-nearest neighbors and density by …
Reliable And Interpretable Machine Learning For Modeling Physical And Cyber Systems, Daniel L. Marino Lizarazo
Reliable And Interpretable Machine Learning For Modeling Physical And Cyber Systems, Daniel L. Marino Lizarazo
Theses and Dissertations
Over the past decade, Machine Learning (ML) research has predominantly focused on building extremely complex models in order to improve predictive performance. The idea was that performance can be improved by adding complexity to the models. This approach proved to be successful in creating models that can approximate highly complex relationships while taking advantage of large datasets. However, this approach led to extremely complex black-box models that lack reliability and are difficult to interpret. By lack of reliability, we specifically refer to the lack of consistent (unpredictable) behavior in situations outside the training data. Lack of interpretability refers to the …
Improving Space Efficiency Of Deep Neural Networks, Aliakbar Panahi
Improving Space Efficiency Of Deep Neural Networks, Aliakbar Panahi
Theses and Dissertations
Language models employ a very large number of trainable parameters. Despite being highly overparameterized, these networks often achieve good out-of-sample test performance on the original task and easily fine-tune to related tasks. Recent observations involving, for example, intrinsic dimension of the objective landscape and the lottery ticket hypothesis, indicate that often training actively involves only a small fraction of the parameter space. Thus, a question remains how large a parameter space needs to be in the first place — the evidence from recent work on model compression, parameter sharing, factorized representations, and knowledge distillation increasingly shows that models can be …
Using Network Modeling To Understand The Relationship Between Sars-Cov-1 And Sars-Cov-2, Elizabeth Brooke Haywood, Nicole A. Bruce
Using Network Modeling To Understand The Relationship Between Sars-Cov-1 And Sars-Cov-2, Elizabeth Brooke Haywood, Nicole A. Bruce
Biology and Medicine Through Mathematics Conference
No abstract provided.
Sparsity And Weak Supervision In Quantum Machine Learning, Seyran Saeedi
Sparsity And Weak Supervision In Quantum Machine Learning, Seyran Saeedi
Theses and Dissertations
Quantum computing is an interdisciplinary field at the intersection of computer science, mathematics, and physics that studies information processing tasks on a quantum computer. A quantum computer is a device whose operations are governed by the laws of quantum mechanics. As building quantum computers is nearing the era of commercialization and quantum supremacy, it is essential to think of potential applications that we might benefit from. Among many applications of quantum computation, one of the emerging fields is quantum machine learning. We focus on predictive models for binary classification and variants of Support Vector Machines that we expect to be …