Computer Sciences Commons^™

Identity Term Sampling For Measuring Gender Bias In Training Data, Nasim Sobhani, Sarah Jane Delany

Conference Papers

Predictions from machine learning models can reflect biases in the data on which they are trained. Gender bias has been identified in natural language processing systems such as those used for recruitment. The development of approaches to mitigate gender bias in training data typically need to be able to isolate the effect of gender on the output to see the impact of gender. While it is possible to isolate and identify gender for some types of training data, e.g. CVs in recruitment, for most textual corpora there is no obvious gender label. This paper proposes a general approach to measure …

Development Of Nucleic Acid Diagnostics For Targeted And Non-Targeted Biosensing, Christopher William Smith

Legacy Theses & Dissertations (2009 - 2024)

The field of nucleic acid technology is rapidly expanding with new impactful discoveriesbeing made each year. Starting from the discovery of the double-helix structure, cloning, gene editing, polymerase chain reaction (PCR), CRISPR technology, and even the late mRNA vaccines; nucleic acid technology is at the forefront of improving medicine. Nucleic acid technology is extremely versatile due to its easy programmability, automated cheap synthesis, and even its catalog for numerous chemical modifications that can be used to alter structure stability. For example, the number of permutations that can be made with DNA just by altering the code for adenine (A), cytosine …

Analysis Of Forensic Artifacts In Database Memory Using Support Vector Machine, Mahfuzul I. Nissan

LSU New Orleans Theses and Dissertations

Memory analysis allows forensic investigators to establish a more complete timeline of system activity using a snapshot of main memory (i.e., RAM). Investigators may rely on such analysis to detect malicious activity and understand the scope of what data was exfiltrated. This is of particular interest in the presence of incomplete or untrusted logs, where a privileged user (or an attacker with such capabilities) can altogether bypass or disable logging. In such instances, a forensic investigator can still rely on the fact that data must ultimately be processed in memory, regardless of the information that is recorded in audit logs. …

Learnfca: A Fuzzy Fca And Probability Based Approach For Learning And Classification, Suraj Ketan Samal

School of Computing: Dissertations, Theses, and Student Research

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering.

This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide …

Glacier Segmentation From Remote Sensing Imagery Using Deep Learning, Bibek Aryal

Open Access Theses & Dissertations

Large-scale study of glaciers improves our understanding of global glacier change and is imperative for monitoring the ecological environment, preventing disasters, and studying the effects of global climate change. In recent years, remote sensing imagery has been preferred over riskier and resource-intensive field visits for tracking landscape level changes like glaciers. However, periodic manual labeling of glaciers over a large area is not feasible due to the considerable amount of time it requires while automatic segmentation of glaciers has its own set of challenges. Our work aims to study the challenges associated with segmentation of glaciers from remote sensing imagery …

Online/Incremental Learning To Mitigate Concept Drift In Network Traffic Classification, Alberto R. De La Rosa

Open Access Theses & Dissertations

Communication networks play a large role in our everyday lives. COVID19 pandemic in 2020 highlighted their importance as most jobs had to be moved to remote work environments. It is possible that the spread of the virus, the death toll, and the economic consequences would have been much worse without communication networks. To remove sole dependence on one equipment vendor, networks are heterogeneous by design. Due to this, as well as their increasing size, network management has become overwhelming for network managers. For this reason, automating network management will have a significant positive impact. Machine learning and software defined networking …

Oil Particle Analysis Using Machine Learning And Holography Imaging, Daniel Cruz

Open Access Theses & Dissertations

Holographic cameras show potential as a sensor to monitor oil spills. Holographic cameras record the light interference from particles in a volume of space, producing an image called a hologram. Processing these holograms is known as hologram reconstruction. It produces a representation of particles located in three-dimensional space. These cameras can record precise shapes and sizes of particles in a volume of water. However, it is very time-consuming and resource-intensive to process the images. Most algorithms that perform particle analysis require the hologram reconstruction step. The well-documented hybrid method is one such algorithm. Machine learning is one possible technique that …

Real–Time Semantic Segmentation For Railway Anomalies Analysis, Paul Stanik Iii

UNLV Theses, Dissertations, Professional Papers, and Capstones

In the past few years, computer vision has made huge jumps due to deep learning which leverages increased computational power and access to data. The computer vision community has also embraced transparency to accelerate research progress by sharing open datasets and open source code. Access to large scale datasets and benchmark challenges propelled and opened the field. The autonomous vehicle community is a prime example. While there has been significant growth in the automotive vision community, not much has been done in the rail domain. Traditional rail inspection methods require special trains that are run during down time, have sensitive …

Design Of Environment Aware Planning Heuristics For Complex Navigation Objectives, Carter D. Bailey

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

A heuristic is the simplified approximations that helps guide a planner in deducing the best way to move forward. Heuristics are valued in many modern AI algorithms and decision-making architectures due to their ability to drastically reduce computation time. Particularly in robotics, path planning heuristics are widely leveraged to aid in navigation and exploration. As the robotic platform explores and navigates, information about the world can and should be used to augment and update the heuristic to guide solutions. Complex heuristics that can account for environmental factors, robot capabilities, and desired actions provide optimal results with little wasted exploration, but …

Predicting Startup Success Using Publicly Available Data, Emily Gavrilenko

Master's Theses

Predicting the success of an early-stage startup has always been a major effort for investors and venture funds. Statistically, there are about 305 million total startups created in a year, but less than 10% of them succeed to become profitable businesses. Accurately identifying the signs of startup growth is the work of countless investors, and in recent years, research has turned to machine learning in hopes of improving the accuracy and speed of startup success prediction.

To learn about a startup, investors have to navigate many different internet sources and often rely on personal intuition to determine the startup’s potential …

Generative Spatio-Temporal And Multimodal Analysis Of Neonatal Pain, Md Sirajus Salekin

USF Tampa Graduate Theses and Dissertations

Neonates can not express their pain like an adult person. Due to the lacking of proper muscle growth and inability to express non-verbally, it is difficult to understand their emotional status. In addition, if the neonates are under any treatment or left monitored after any major surgeries (post-operative), it is more difficult to understand their pain due to the side effect of medications and the caring system (i.e. intubated, masked face, covered body with blanket, etc.). In a clinical environment, usually, bedside nurses routinely observe the neonate and measure the pain status following any standard clinical pain scale. But current …

Autonomous Vehicle Innovation And Implications On Adoption, Liability And Policy, Using Quantum Technologies And Artificial Wisdom, Chia Jie Jun Jeremy

Dissertations and Theses Collection (Open Access)

This paper will explore the use of two new innovations for the issues facing autonomous vehicles (AV), those of quantum technologies and artificial wisdom. The issue of delayed at-scale commercialization and adoption of autonomous vehicles due to the extensive dynamic capability required to derive an optimal process solution for any complex, dynamic and adaptive autonomous vehicle ecosystem is shown to be resolved by the use of these innovations, will be shown to be more widely applicable for other issues for AV and for any scenario where automated decision making is required.

QC might open up the door for the application …

Adaptive Fairness Improvement Based Causality Analysis, Mengdi Zhang, Jun Sun

Research Collection School Of Computing and Information Systems

Given a discriminating neural network, the problem of fairness improvement is to systematically reduce discrimination without significantly scarifies its performance (i.e., accuracy). Multiple categories of fairness improving methods have been proposed for neural networks, including pre-processing, in-processing and postprocessing. Our empirical study however shows that these methods are not always effective (e.g., they may improve fairness by paying the price of huge accuracy drop) or even not helpful (e.g., they may even worsen both fairness and accuracy). In this work, we propose an approach which adaptively chooses the fairness improving method based on causality analysis. That is, we choose the …

Development Of A Smartphone Application As An Asset To Pavement Management Engineers, Smartp3m, Damien Stephens

Electrical Engineering Theses

An application specific multi-platform smartphone application can utilize on-board accelerometer, gyroscope, and GPS sensors, along with software derived signals from the same sensors, to sample vibrational and geolocation datasets to capture pavement distresses such as potholes when mounted in a standardized configuration in a vehicle. Several observations were made with regard to the signals obtained from the accelerometer, gyroscope, and GPS sensors, and it was determined that the raw sensor outputs are capable of sampling statistically significant datasets which can be used to distinguish pavement distress from normal driving conditions. Furthermore, an approximate sensor noise margin is established, and a …

Social Media Time Series Forecasting And User-Level Activity Prediction With Gradient Boosting, Deep Learning, And Data Augmentation, Fred Mubang

USF Tampa Graduate Theses and Dissertations

In the overall history of technological innovations, social media has only existed for a brief time, however its influence is undeniable. Researchers have found that it can be used to influence elections, spread health misinformation, and aid with financial pump-and-dump schemes. Keeping all this in mind, it is clear that more research is needed to predict the spread of information on social media in order to combat its malicious use.

To that end, in this dissertation, we explore the use of Machine Learning algorithms to perform time series forecasting and user-level activity prediction in social media. We address the different …

Machine Learning To Predict Warhead Fragmentation In-Flight Behavior From Static Data, Katharine Larsen

Doctoral Dissertations and Master's Theses

Accurate characterization of fragment fly-out properties from high-speed warhead detonations is essential for estimation of collateral damage and lethality for a given weapon. Real warhead dynamic detonation tests are rare, costly, and often unrealizable with current technology, leaving fragmentation experiments limited to static arena tests and numerical simulations. Stereoscopic imaging techniques can now provide static arena tests with time-dependent tracks of individual fragments, each with characteristics such as fragment IDs and their respective position vector. Simulation methods can account for the dynamic case but can exclude relevant dynamics experienced in real-life warhead detonations. This research leverages machine learning methodologies to …

Overview Of The Clpsych 2022 Shared Task: Capturing Moments Of Change In Longitudinal User Posts, Adam Tsakalidis, Jenny Chim, Iman Munire Bilal, Ayah Zirikly, Dana Atzil-Slonim, Federico Nanni, Philip Resnik, Manas Gaur, Kaushik Roy, Becky Inkster, Jeff Leintz, Maria Liakata

Publications

We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of Moments of Change in longitudinal posts by individuals on social media and its connection with information regarding mental health . This year's task introduced the notion of longitudinal modelling of the text generated by an individual online over time, along with appropriate temporally sensitive evaluation metrics. The Shared Task consisted of two subtasks: (a) the main task of capturing changes in an individual's mood (drastic changes-`Switches'- and gradual changes -`Escalations'- on the basis of textual content shared online; and subsequently (b) the sub-task …

Human Activity Recognition (Har) Using Wearable Sensors And Machine Learning, Chrisogonas Odero Odhiambo

Theses and Dissertations

Humans engage in a wide range of simple and complex activities. Human Activity Recognition (HAR) is typically a classification problem in computer vision and pattern recognition, to recognize various human activities. Recent technological advancements, the miniaturization of electronic devices, and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments, alongside smart wearable sensors, have opened the door to numerous opportunities for adding value and personalized services to citizens. Vision-based and sensory-based HAR find diverse applications in healthcare, surveillance, sports, event analysis, Human-Computer …

Applications Of Machine Learning For Improved Patient Selection And Therapy Recommendations, Brendan Elochukwu Odigwe

Theses and Dissertations

The public health domain continues to battle with illness and the growing need for continuous advancement in our approach to clinical care. Individuals experiencing certain conditions undergo tried and tested therapies and medications, practices that have become the mainstay and standard of care in clinical medicine. As with all therapies and medications, they don't always work the same way and do not work for everyone. Some Treatment regimens, like Hydroxyurea medication, which is commonly administered to Sickle cell anemia patients, come with some adverse side effects due to the chemotherapeutic nature of the drug. This would be particularly disappointing if …

Predicting Mental Health Crisis In Veterans: Early Warning Signs, Precursors And Protective Factors, Priyanka Annapureddy

Dissertations (1934 -)

Mental Health (MH) conditions have recently increased to a large extent due to socio-demographic changes. Posttraumatic Stress Disorder (PTSD) is one of the most common mental health disorders prevalent in US. PTSD is even more troubling at double the rate in combat veterans leaving their service compared to general population. Severity of PTSD is associated with risk taking behaviors such as substance abuse, non-suicidal self-injury, and sexual risk behaviors. Psychological disorders are often preceded by early warning signs and recognizing the early warning signs of PTSD will help in preventing the returning or worsening of PTSD symptoms. Ecological momentary assessment …

Supporting The Discovery, Reuse, And Validation Of Cybersecurity Requirements At The Early Stages Of The Software Development Lifecycle, Jessica Antonia Steinmann

Doctoral Dissertations and Master's Theses

The focus of this research is to develop an approach that enhances the elicitation and specification of reusable cybersecurity requirements. Cybersecurity has become a global concern as cyber-attacks are projected to cost damages totaling more than $10.5 trillion dollars by 2025. Cybersecurity requirements are more challenging to elicit than other requirements because they are nonfunctional requirements that requires cybersecurity expertise and knowledge of the proposed system. The goal of this research is to generate cybersecurity requirements based on knowledge acquired from requirements elicitation and analysis activities, to provide cybersecurity specifications without requiring the specialized knowledge of a cybersecurity expert, and …

A Gpu-Based Machine Learning Approach For Detection Of Botnet Attacks, Michal Motylinski, Áine Macdermott, Farkhund Iqbal, Babar Shah

All Works

Rapid development and adaptation of the Internet of Things (IoT) has created new problems for securing these interconnected devices and networks. There are hundreds of thousands of IoT devices with underlying security vulnerabilities, such as insufficient device authentication/authorisation making them vulnerable to malware infection. IoT botnets are designed to grow and compete with one another over unsecure devices and networks. Once infected, the device will monitor a Command-and-Control (C&C) server indicating the target of an attack via Distributed Denial of Service (DDoS) attack. These security issues, coupled with the continued growth of IoT, presents a much larger attack surface for …

An Analysis Of Android Malware Detection Using Tree Learning Techniques, Kyler D. Dickey

Student Theses and Dissertations

Android malware is a growing threat, coinciding with the increasing adoption of the Android platform. Malware detection methods used to maintain user privacy and system integrity are increasingly becoming the subject of research. Many new methods studied employ learning algorithms to detect malicious programs. This study investigates the use of byte and opcode frequency features as inputs for tree-based machine learning methods. The algorithm is optimized to reduce overfitting given input hyperparameter combinations and is tuned using cross-validation procedures. Lastly, the study deliberates on possible avenues for future research to gather more concrete evidence for the efficacy and cost-effectiveness of …

Development Of The Assessment Of Clinical Prediction Model Transportability (Apt) Checklist, Sean Chonghwan Yu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Clinical Prediction Models (CPM) have long been used for Clinical Decision Support (CDS) initially based on simple clinical scoring systems, and increasingly based on complex machine learning models relying on large-scale Electronic Health Record (EHR) data. External implementation – or the application of CPMs on sites where it was not originally developed – is valuable as it reduces the need for redundant de novo CPM development, enables CPM usage by low resource organizations, facilitates external validation studies, and encourages collaborative development of CPMs. Further, adoption of externally developed CPMs has been facilitated by ongoing interoperability efforts in standards, policy, and …

Integrating Physical Models And Deep Priors For Computational Imaging, Yu Sun

McKelvey School of Engineering Graduate Student Theses & Dissertations

This dissertation addresses integrating physical models and learning priors for computational imaging. The motivation of our work is driven by the recent discussion of learning-based methods that solve the imaging inverse problem by directly learning a measurement-to-image mapping from the existing data: they achieve superior performance over the traditional model-based methods but lack the physical model to impose sufficient interpretation and guarantee of the final image. We adopt the classic statistical inference as the underlying formulation and integrate learning models as implicit image priors, such that our framework is able to simultaneously leverage physical models and learning priors. Additionally, the …

Mathematical Models Yield Insights Into Cnns: Applications In Natural Image Restoration And Population Genetics, Ryan Cecil

Electronic Theses and Dissertations

Due to a rise in computational power, machine learning (ML) methods have become the state-of-the-art in a variety of fields. Known to be black-box approaches, however, these methods are oftentimes not well understood. In this work, we utilize our understanding of model-based approaches to derive insights into Convolutional Neural Networks (CNNs). In the field of Natural Image Restoration, we focus on the image denoising problem. Recent work have demonstrated the potential of mathematically motivated CNN architectures that learn both `geometric' and nonlinear higher order features and corresponding regularizers. We extend this work by showing that not only can geometric features …

Cyberbullying Detection Using Weakly Supervised And Fully Supervised Learning, Abhinav Abhishek

ETD Archive

Machine learning is a very useful tool to solve issues in multiple domains such as sentiment analysis, fake news detection, facial recognition, and cyberbullying. In this work, we have leveraged its ability to understand the nuances of natural language to detect cyberbullying. We have further utilized it to detect the subject of cyberbullying such as age, gender, ethnicity, and religion. Further, we have built another layer to detect the cases of misogyny in cyberbullying. In one of our experiments, we created a three-layered architecture to detect cyberbullying , then to detect if it is gender based and finally if it …

Perturbation Modeling For Molecular Design Of Protein Tyrosine Kinase Inhibitors Using Unsupervised Machine Learning, Keerthi Krishnan

Computational and Data Sciences (MS) Theses

The field of computational drug discovery and development has grown, with the aid of new computational tools for novel molecule discovery. In specific, generative deep learning models have excelled as tools to aid in navigating the large space of known molecules and in the creation of new molecules. These models are fed various representations of molecules as inputs and learn to perform a variety of things, such as the optimization of these molecules towards a targeted property. Ultimately, these generative learning models allow us to build bridges between chemical and continuous spaces to understand the compromise between invoking small incremental …

Development Of Graphical Models And Statistical Physics Motivated Approaches To Genomic Investigations, Yashwanth Lagisetty

Dissertations and Theses (Open Access)

Identifying genes involved in disease pathology has been a goal of genomic research since the early days of the field. However, as technology improves and the body of research grows, we are faced with more questions than answers. Among these is the pressing matter of our incomplete understanding of the genetic underpinnings of complex diseases. Many hypotheses offer explanations as to why direct and independent analyses of variants, as done in genome-wide association studies (GWAS), may not fully elucidate disease genetics. These range from pointing out flaws in statistical testing to invoking the complex dynamics of epigenetic processes. In the …

Hyperspectral Image Analysis Of Food For Nutritional Intake, Shirin Nasr Esfahani

UNLV Theses, Dissertations, Professional Papers, and Capstones

The primary object of this dissertation is to investigate the application of hyperspectral technology to accommodate for the growing demand in the automatic dietary assessment applications. Food intake is one of the main factors that contribute to human health. In other words, it is necessary to get information about the amount of nutrition and vitamins that a human body requires through a daily diet. Manual dietary assessments are time-consuming and are also not precise enough, especially when the information is used for the care and treatment of hospitalized patients. Moreover, the data must be analyzed by nutritional experts. Therefore, researchers …