Physical Sciences and Mathematics Commons^™

Perovskite Materials In X-Ray Detection And Imaging: Recent Progress, Challenges, And Future Prospects, Md Helal Miah, Mayeen U. Khandaker, Mohammad A. Islam, Mohammad Nur-E-Alam, Hamid Osman, Md Habib Ullah

Research outputs 2022 to 2026

Perovskite materials have attracted significant attention as innovative and efficient X-ray detectors owing to their unique properties compared to traditional X-ray detectors. Herein, chronologically, we present an in-depth analysis of X-ray detection technologies employing organic-inorganic hybrids (OIHs), all-inorganic and lead-free perovskite material-based single crystals (SCs), thin/thick films and wafers. Particularly, this review systematically scrutinizes the advancement of the diverse synthesis methods, structural modifications, and device architectures exploited to enhance the radiation sensing performance. In addition, a critical analysis of the crucial factors affecting the performance of the devices is also provided. Our findings revealed that the improvement from single crystallization …

Examination Of Traditional Botnet Detection On Iot-Based Bots, Ashley Woodiss-Field, Michael N. Johnstone, Paul Haskell-Dowland

Research outputs 2022 to 2026

A botnet is a collection of Internet-connected computers that have been suborned and are controlled externally for malicious purposes. Concomitant with the growth of the Internet of Things (IoT), botnets have been expanding to use IoT devices as their attack vectors. IoT devices utilise specific protocols and network topologies distinct from conventional computers that may render detection techniques ineffective on compromised IoT devices. This paper describes experiments involving the acquisition of several traditional botnet detection techniques, BotMiner, BotProbe, and BotHunter, to evaluate their capabilities when applied to IoT-based botnets. Multiple simulation environments, using internally developed network traffic generation software, were …

A Machine Learning Approach For Dyslexia Detection Using Turkish Audio Records, Tuğberk Taş, Muhammed Abdullah Bülbül, Abas Haşi̇moğlu, Yavuz Meral, Yasi̇n Çalişkan, Gunay Budagova, Mücahi̇d Kutlu

Turkish Journal of Electrical Engineering and Computer Sciences

Dyslexia is a learning disorder, characterized by impairment in the ability to read, spell, and decode letters. It is vital to detect dyslexia in earlier stages to reduce its effects. However, diagnosing dyslexia is a time-consuming and costly process. In this paper, we propose a machine-learning model that predicts whether a Turkish-speaking child has dyslexia using his/her audio records. Therefore, our model can be easily used by smart phones and work as a warning system such that children who are likely to be dyslexic according to our model can seek an examination by experts. In order to train and evaluate, …

A Study On Feature Selection Using Multi-Domain Feature Extraction For Automated K-Complex Detection, Yabing Li, Xinglong Dong, Kun Song, Xiangyun Bai, Hongye Li, Fakhreddine Karray

Machine Learning Faculty Publications

Background: K-complex detection plays a significant role in the field of sleep research. However, manual annotation for electroencephalography (EEG) recordings by visual inspection from experts is time-consuming and subjective. Therefore, there is a necessity to implement automatic detection methods based on classical machine learning algorithms. However, due to the complexity of EEG signal, current feature extraction methods always produce low relevance to k-complex detection, which leads to a great performance loss for the detection. Hence, finding compact yet effective integrated feature vectors becomes a crucially core task in k-complex detection. Method: In this paper, we first extract multi-domain features based …

Stream-Evolving Bot Detection Framework Using Graph-Based And Feature-Based Approaches For Identifying Social Bots On Twitter, Eiman Alothali

Dissertations

This dissertation focuses on the problem of evolving social bots in online social networks, particularly Twitter. Such accounts spread misinformation and inflate social network content to mislead the masses. The main objective of this dissertation is to propose a stream-based evolving bot detection framework (SEBD), which was constructed using both graph- and feature-based models. It was built using Python, a real-time streaming engine (Apache Kafka version 3.2), and our pretrained model (bot multi-view graph attention network (Bot-MGAT)). The feature-based model was used to identify predictive features for bot detection and evaluate the SEBD predictions. The graph-based model was used to …

Fabrication, Measurements, And Modeling Of Semiconductor Radiation Detectors For Imaging And Detector Response Functions, Corey David Ahl

Doctoral Dissertations

In the first part of this dissertation, we cover the development of a diamond semiconductor alpha-tagging sensor for associated particle imaging to solve challenges with currently employed scintillators. The alpha-tagging sensor is a double-sided strip detector made from polycrystalline CVD diamond. The performance goals of the alpha-tagging sensor are 700-picosecond timing resolution and 0.5 mm spatial resolution. A literature review summarizes the methodology, goals, and challenges in associated particle imaging. The history and current state of alpha-tagging sensors, followed by the properties of diamond semiconductors are discussed to close the literature review. The materials and methods used to calibrate the …

Fraud Pattern Detection For Nft Markets, Andrew Leppla, Jorge Olmos, Jaideep Lamba

SMU Data Science Review

Non-Fungible Tokens (NFTs) enable ownership and transfer of digital assets using blockchain technology. As a relatively new financial asset class, NFTs lack robust oversight and regulations. These conditions create an environment that is susceptible to fraudulent activity and market manipulation schemes. This study examines the buyer-seller network transactional data from some of the most popular NFT marketplaces (e.g., AtomicHub, OpenSea) to identify and predict fraudulent activity. To accomplish this goal multiple features such as price, volume, and network metrics were extracted from NFT transactional data. These were fed into a Multiple-Scale Convolutional Neural Network that predicts suspected fraudulent activity based …

Burmese Pythons In Florida: A Synthesis Of Biology, Impacts, And Management Tools, Jacquelyn C. Guzy, Bryan G. Falk, Brian J. Smith, Johnd David Willson, Robert N. Reed, Nicholas G. Aumen, Michael L. Avery, Ian A. Bartoszek, Earl Campbell, Michael S. Cherkiss, Natalie M. Claunch, Andrea F. Currylow, Tylan Dean, Jeremy Dixon, Richard Engeman, Sarah Funck, Rebekah Gibble, Kodiak C. Hengstebeck, John S. Humphrey, Margaret E. Hunter, Jillian M. Josimovich, Jennifer Ketterlin, Michael Kirkland, Frank J. Mazzotti, Robert Mccleery, Melissa A. Miller, Matthew Mccollister, M. Rockwell Parker, Shannon E. Pittman, Michael Rochford, Christina Romagosa, Art Roybal, Ray W. Snow, Mckayla M. Spencer, J. Hardin Waddle, Any A. Yackel Adams, Kristen M. Hart

USDA Wildlife Services: Staff Publications

Burmese pythons (Python molurus bivittatus) are native to southeastern Asia, however, there is an established invasive population inhabiting much of southern Florida throughout the Greater Everglades Ecosystem. Pythons have severely impacted native species and ecosystems in Florida and represent one of the most intractable invasive-species management issues across the globe. The difficulty stems from a unique combination of inaccessible habitat and the cryptic and resilient nature of pythons that thrive in the subtropical environment of southern Florida, rendering them extremely challenging to detect. Here we provide a comprehensive review and synthesis of the science relevant to managing invasive …

A Machine Learning Approach To Deepfake Detection, Delaney Conrad

All Undergraduate Theses and Capstone Projects

The ability to manipulate videos has been around for decades but a process that once would take time, money, and professionals, can now be created by anyone due to the rapid advancement of deepfake technology. Deepfakes use deep learning artificial intelligence to make fake digital content, typically in the form of swapping a person’s face in a video or image. This technology could easily threaten and manipulate individuals, corporations, and political organizations, so it is essential to find methods for detecting deepfakes. As the technology for creating deepfakes continues to improve, these manipulated videos are becoming increasingly undetectable. It is …

An Update On The Influence Of Natural Climate Variability And Anthropogenic Climate Change On Tropical Cyclones, Suzana J. Camargo, Hiroyuki Murakami, Nadia Bloemendaal, Savin S. Chand, Medha S. Deshpande, Christian Dominguez-Sarmiento, Juan Jesús González-Alemán, Thomas R. Knutson, I. I. Lin, Il- Ju Moon, Christina M. Patricola, Kevin A. Reed, Malcolm J. Roberts, Enrico Scoccimarro, Chi Yung Tam, Elizabeth J. Wallace, Liguang Wu, Yohei Yamada, Wei Zhang, Haikun Zhao

OES Faculty Publications

A substantial number of studies have been published since the Ninth International Workshop on Tropical Cyclones (IWTC-9) in 2018, improving our understanding of the effect of climate change on tropical cyclones (TCs) and associated hazards and risks. These studies have reinforced the robustness of increases in TC intensity and associated TC hazards and risks due to anthropogenic climate change. New modeling and observational studies suggested the potential influence of anthropogenic climate forcings, including greenhouse gases and aerosols, on global and regional TC activity at the decadal and century time scales. However, there are still substantial uncertainties owing to model uncertainty …

Range-Wide Sources Of Variation In Reproductive Rates Of Northern Spotted Owls, Jeremy T. Rockweit, Julianna M. Jenkins, James E. Hines, James D. Nichols, Katie M. Dugger, Alan B. Franklin, Peter C. Carlson, William L. Kendall, Damon B. Lesmeister, Christopher Mccafferty, Steven H. Ackers, L. Steven Andrews, Larissa L. Bailey, Jesse Burgher, Kenneth P. Burnham, Tara Chestnut, Mary M. Conner, Raymond J. Davis, Krista E. Dilione, Eric D. Forsman, Elizabeth M. Glenn, Scott A. Gremel, Keith A. Hamm, Dale R. Herter, J. Mark Higley, Rob B. Horn, David W. Lamphear, Trent L. Mcdonald, Janice A. Reid, Carl J. Schwarz, David C. Simon, Stan G. Sovern, James K. Swingle, J. David Wiens, Heather Wise, Charles B. Yackulic

USDA Wildlife Services: Staff Publications

We conducted a range-wide investigation of the dynamics of site-level reproductive rate of northern spotted owls using survey data from 11 study areas across the subspecies geographic range collected during 1993–2018. Our analytical approach accounted for imperfect detection of owl pairs and misclassification of successful reproduction (i.e., at least one young fledged) and contributed further insights into northern spotted owl population ecology and dynamics. Both nondetection and state misclassification were important, especially because factors affecting these sources of error also affected focal ecological parameters. Annual probabilities of site occupancy were greatest at sites with successful reproduction in the previous year …

Artificial Intelligence And Precision Health Through Lenses Of Ethics And Social Determinants Of Health: Protocol For A State-Of-The-Art Literature Review, Sarah Wamala-Andersson, Matt X. Richardson, Sara Landerdahl Stridsberg, Jillian Ryan, Felix Sukums, Yong-Shian Goh

Research outputs 2022 to 2026

Background: Precision health is a rapidly developing field, largely driven by the development of artificial intelligence (AI)–related solutions. AI facilitates complex analysis of numerous health data risk assessment, early detection of disease, and initiation of timely preventative health interventions that can be highly tailored to the individual. Despite such promise, ethical concerns arising from the rapid development and use of AI-related technologies have led to development of national and international frameworks to address responsible use of AI. Objective: We aimed to address research gaps and provide new knowledge regarding (1) examples of existing AI applications and what role they play …

Malware Detection And Analysis, Namratha Suraneni

Culminating Experience Projects

Malicious software poses a serious threat to the cybersecurity of network infrastructures and is a global pandemic in the form of computer viruses, Trojan horses, and Internet worms. Studies imply that the effects of malware are deteriorating. The main defense against malware is malware detectors. The methods that such a detector employ define its level of quality. Therefore, it is crucial that we research malware detection methods and comprehend their advantages and disadvantages. Attackers are creating malware that is polymorphic and metamorphic and has the capacity to modify their source code as they spread. Furthermore, existing defenses, which often utilize …

Cnn-Based Dendrite Core Detection From Microscopic Images Of Directionally Solidified Ni-Base Alloys, Xiaoguang Li

Theses and Dissertations

Dendrite core is the center point of the dendrite. The information of dendrite core is very helpful for material scientists to analyze the properties of materials. Therefore, detecting the dendrite core is a very important task in the material science field. Meanwhile, because of some special properties of the dendrites, this task is also very challenging. Different from the typical detection problems in the computer vision field, detecting the dendrite core aims to detect a single point location instead of the bounding-box. As a result, the existing regressing bounding-box based detection methods can not work well on this task because …

Pstr: End-To-End One-Step Person Search With Transformers, Jiale Cao, Pang Yanwei, Rao Anwer, Hisham Cholakkal, Jin Xie, Mubarak Shah, Fahad Shahbaz Khan

Computer Vision Faculty Publications

We propose a novel one-step transformer-based person search framework, PSTR, that jointly performs person detection and re-identification (re-id) in a single architecture. PSTR comprises a person search-specialized (PSS) module that contains a detection encoder-decoder for person detection along with a discriminative re-id decoder for person re-id. The discriminative re-id decoder utilizes a multi-level supervision scheme with a shared decoder for discriminative re-id feature learning and also comprises a part attention block to encode relationship between different parts of a person. We further introduce a simple multi-scale scheme to support re-id across person instances at different scales. PSTR jointly achieves the …

The Efficacy Of Video Cameras To Account For Northern Bobwhites Flushed, But Undetected During Aerial Surveys, Andrea Montalvo, Leonard A. Brennan, Michael L. Morrison, Eric D. Grahmann, Andrew N. Tri

National Quail Symposium Proceedings

Over the past 20 years, conventional distance sampling from a helicopter platform has been used to estimate northern bobwhite (Colinus virginianus; hereafter, bobwhite) density over large areas of rangeland vegetation. However, it has been speculated that aerial surveys can complicate the ability to meet the distance sampling assumption of detecting 100% of the target objects on the transect line due to the restricted observer view from the helicopter. We attempted to use video cameras to determine whether missed detections occurred and whether digital methods could improve the precision of bobwhite density estimates. Our objectives were to 1) determine …

Analysis Of Predator Avoidance Behavior In California Valley Quail, Curt Vandenberg, Jeffrey G. Whitt, Kelly S. Reyna

National Quail Symposium Proceedings

Quail populations have been in decline across the United States, primarily due to habitat loss and climate. For remedy, landowners and game managers have attempted to restore populations by releasing captive-reared quail. These releases were largely unsuccessful, presumably due to high predation losses. Recently, there has been an increased interest in quail translocations, which tend to have lower mortality rates than captive-reared bird releases. Translocations are expensive and unpredictable, and require many person-hours; releasing captive-reared quail would be more efficient if the practice were successful. We compared predator avoidance behavior between captive-reared and wild-translocated California quail (Callipepla californica) …

Highly Variable Autumn Calling Rates Of Northern Bobwhite Following Translocation, John Palarski, Shelby Simons, Bradley W. Kubečka, Theron M. Terhune Ii, Greg Hagan

National Quail Symposium Proceedings

Fall covey counts are a popular index for monitoring population trends of northern bobwhite (Colinus virginianus; hereafter, bobwhite), but their utility is tenuous under different scenarios. Detecting an individual covey is the product of the probability that the covey’s activity center is located within the sampling frame, the probability the covey is located within the sampling frame during the sampling periods, the probability of the covey vocalizing, and the probability an observer will detect a calling covey. Researchers attempt to maximize detection or account for these potential sources of error using standardized protocol of limiting counts to certain …

Range-Wide Sources Of Variation In Reproductive Rates Of Northern Spotted Owls, Jeremy T. Rockweit, Julianna M. Jenkins, James E. Hines, James D. Nichols, Katie M. Dugger, Alan B. Franklin, Peter C. Carlson, William L. Kendall, Damon B. Lesmeister, Christopher Mccafferty, Steven H. Ackers, L. Steven Andrews, Larissa L. Bailey, Jesse Burgher, Kenneth P. Burnham, Tara Chestnut, Mary M. Conner, Raymond J. Davis, Krista E. Dilione, Eric D. Forsman, Elizabeth M. Glenn, Scott A. Gremel, Keith A. Hamm, Dale R. Herter, J. Mark Higley, Rob B. Horn, David W. Lamphear, Trent L. Mcdonald, Janice A. Reid, Carl J. Schwarz, David C. Simon, Stan G. Sovern, James K. Swingle, J. David Wiens, Heather Wise, Charles B. Yackulic

Wildland Resources Faculty Publications

We conducted a range-wide investigation of the dynamics of site-level reproductive rate of northern spotted owls using survey data from 11 study areas across the subspecies geographic range collected during 1993–2018. Our analytical approach accounted for imperfect detection of owl pairs and misclassification of successful reproduction (i.e., at least one young fledged) and contributed further insights into northern spotted owl population ecology and dynamics. Both nondetection and state misclassification were important, especially because factors affecting these sources of error also affected focal ecological parameters. Annual probabilities of site occupancy were greatest at sites with successful reproduction in the previous year …

Towards Color-Based Two-Hand 3d Global Pose Estimation, Fanqing Lin

Theses and Dissertations

Pose estimation and tracking is essential for applications involving human controls. Specifically, as the primary operating tool for human activities, hand pose estimation plays a significant role in applications such as hand tracking, gesture recognition, human-computer interaction and VR/AR. As the field develops, there has been a trend to utilize deep learning to estimate the 2D/3D hand poses using color-based information without depth data. Within the depth-based as well as color-based approaches, the research community has primarily focused on single-hand scenarios in a localized/normalized coordinate system. Due to the fact that both hands are utilized in most applications, we propose …

Fine-Tuning A 𝑘-Nearest Neighbors Machine Learning Model For The Detection Of Insurance Fraud, Alliyah Stout

Honors Theses

Billions of dollars are lost within insurance companies due to fraud. Large money losses force insurance companies to increase premium costs and/or restrict policies. This negatively affects a company’s loyal customers. Although this is a prevalent problem, companies are not urgently working toward bettering their machine learning algorithms. Underskilled workers paired with inefficient computer algorithms make it difficult to accurately and reliably detect fraud.

The goal of this study is to understand the idea of -Nearest Neighbors ( -NN) and to use this classification technique to accurately detect fraudulent auto insurance claims. Using -NN requires choosing a value and a …

A Simple Data Mixing Prior For Improving Self-Supervised Learning, Sucheng Ren, Huiyu Wang, Zhengqi Gao, Shengfeng He, Alan Yuille, Yuyin Zhou, Cihang Xie

Research Collection School Of Computing and Information Systems

Data mixing (e.g., Mixup, Cutmix, ResizeMix) is an essential component for advancing recognition models. In this paper, we focus on studying its effectiveness in the self-supervised setting. By noticing the mixed images that share the same source images are intrinsically related to each other, we hereby propose SDMP, short for Simple Data Mixing Prior, to capture this straightforward yet essential prior, and position such mixed images as additional positive pairs to facilitate self-supervised representation learning. Our experiments verify that the proposed SDMP enables data mixing to help a set of self-supervised learning frameworks (e.g., MoCo) achieve better accuracy and out-of-distribution …

Group Contextualization For Video Recognition, Yanbin Hao, Hao Zhang, Chong-Wah Ngo, Xiangnan He

Research Collection School Of Computing and Information Systems

Learning discriminative representation from the complex spatio-temporal dynamic space is essential for video recognition. On top of those stylized spatio-temporal computational units, further refining the learnt feature with axial contexts is demonstrated to be promising in achieving this goal. However, previous works generally focus on utilizing a single kind of contexts to calibrate entire feature channels and could hardly apply to deal with diverse video activities. The problem can be tackled by using pair-wise spatio-temporal attentions to recompute feature response with cross-axis contexts at the expense of heavy computations. In this paper, we propose an efficient feature refinement method that …

Development And Assessment Of An Environmental Dna (Edna) Assay For A Cryptic Siren (Amphibia: Sirenidae), Krista M. Ruppert, Drew R. Davis, Md Saydur Rahman, Richard J. Kline

School of Earth, Environmental, and Marine Sciences Faculty Publications and Presentations

Environmental DNA (eDNA) assays have become a major aspect of surveys for aquatic organisms in the past decade. These methods are highly sensitive, making them well-suited for monitoring rare and cryptic species. Current efforts to study the Rio Grande Siren in southern Texas have been hampered due to the cryptic nature of these aquatic salamanders. Arid conditions further add to the difficulty in studying this species, as many water bodies they inhabit are ephemeral, sometimes constraining sampling efforts to a short window after heavy rain. Additionally, sirens are known to cease activity and reside underground when ponds begin to dry …

Afnd: Arabic Fake News Dataset For The Detection And Classification Of Articles Credibility, Ashwaq Khalil, Moath Jarrah, Monther Aldwairi, Manar Jaradat

All Works

The news credibility detection task has started to gain more attention recently due to the rapid increase of news on different social media platforms. This article provides a large, labeled, and diverse Arabic Fake News Dataset (AFND) that is collected from public Arabic news websites. This dataset enables the research community to use supervised and unsupervised machine learning algorithms to classify the credibility of Arabic news articles. AFND consists of 606912 public news articles that were scraped from 134 public news websites of 19 different Arab countries over a 6-month period using Python scripts. The Arabic fact-check platform, Misbar, is …

Lipophilic Probes For Cellular Ethylene Detection, Morgan R. Brown

Electronic Theses and Dissertations

The structure of ethylene is simple, yet its biological effects are significant. When considering its role in biology it is almost exclusively regarded as a plant hormone. Research on ethylene from plants was progressed by several advancements in analytical instrumentation, from its discovery to elucidation of its signaling pathway. There is currently limited understanding of ethylene’s role in mammals, but evidence suggests that it may be a biomarker for oxidative stress! Additional tools and technology are crucial to study this surprising and important signaling role in mammals. Our group has developed molecular ethylene probes as a strategy to detect ethylene …

System Measurements For X-Ray Phase And Diffraction Imaging, Erik Wolfgang Tripi

Legacy Theses & Dissertations (2009 - 2024)

In medical imaging, X rays are used to look inside the body to find fractures in bones, abnormal masses, cavities in teeth, and so on. What makes X rays so good at looking at these types of structures is the X ray’s penetration power. When imaging soft tissue to search for tumors, X-ray images tend to have difficulty performing well. The reason for this is that the background structures, such as fat or fibro glandular tissue have similar absorption coefficients as the tumor. Mammography tends to have a high false positive rate and can miss tumors entirely as well. There …

Broadband Dielectric Spectroscopic Detection Of Aliphatic Alcohol Vapors With Surface-Mounted Hkust-1 Mofs As Sensing Media, Papa K. Amoah, Zeinab Mohammed Hassan, Rhonda R. Franklin, Helmut Baumgart, Engelbert Redel, Yaw S. Obeng

Electrical & Computer Engineering Faculty Publications

We leveraged chemical-induced changes to microwave signal propagation characteristics (i.e., S-parameters) to characterize the detection of aliphatic alcohol (methanol, ethanol, and 2-propanol) vapors using TCNQ-doped HKUST-1 metal-organic-framework films as the sensing material, at temperatures under 100 °C. We show that the sensitivity of aliphatic alcohol detection depends on the oxidation potential of the analyte, and the impedance of the detection setup depends on the analyte-loading of the sensing medium. The microwaves-based detection technique can also afford new mechanistic insights into VOC detection, with surface-anchored metal-organic frameworks (SURMOFs), which is inaccessible with the traditional coulometric (i.e., resistance-based) measurements.

Biometric Security: A Novel Ear Recognition Approach Using A 3d Morphable Ear Model, Md Mursalin, Mohiuddin Ahmed, Paul Haskell-Dowland

Research outputs 2022 to 2026

Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and ears. This work proposes a novel approach for human identification using 3D ear images. Usually, in conventional methods, the probe image is registered with each gallery image using computational heavy registration algorithms, making it practically infeasible due to the time-consuming recognition process. Therefore, this work proposes a recognition pipeline that reduces the one-to-one registration between probe and gallery. First, a …

Deep Learning Detection In The Visible And Radio Spectrums, Greg Clancy Murray

Graduate Theses, Dissertations, and Problem Reports

Deep learning models with convolutional neural networks are being used to solve some of the most difficult problems in computing today. Complicating factors to the use and development of deep learning models include lack of availability of large volumes of data, lack of problem specific samples, and the lack variations in the specific samples available. The costs to collect this data and to compute the models for the task of detection remains a inhibitory condition for all but the most well funded organizations. This thesis seeks to approach deep learning from a cost reduction and hybrid perspective — incorporating techniques …