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Measurement, Analysis, Classification And Detection Of Gunshot And Gunshot-Like Sounds, Rajesh Singh Baliram Jan 2022

Measurement, Analysis, Classification And Detection Of Gunshot And Gunshot-Like Sounds, Rajesh Singh Baliram

Electronic Theses and Dissertations 2020 - Present

The recent uptick in senseless shootings in otherwise quiet and relatively safe environments is powerful evidence of the need, now more than ever, to reduce these occurrences. Artificial intelligence (AI) can play a significant role in deterring individuals from attempting these acts of violence. The installation of audio sensors can assist in the proper surveillance of surroundings linked to public safety, which is the first step toward AI-driven surveillance. With the increasing popularity of machine learning (ML) processes, systems are being developed and optimized to assist personnel in highly dangerous situations. In addition to saving innocent lives, supporting the capture …

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Applying Data Science And Machine Learning Techniques For Hydrological Applications : Case Study In Cambodia, Biplov Bhandari Jan 2022

Applying Data Science And Machine Learning Techniques For Hydrological Applications : Case Study In Cambodia, Biplov Bhandari

Theses

Cambodia suffers from flooding and flash-flooding events every year, and thus better flood management and forecasting are crucial in taking early action. The Streamflow Prediction Tool (SPT) provides forecast discharge 15 days in advance globally. This thesis applies data-driven and Machine Learning (ML) techniques to improve Cambodia’s hydrological applications. Two case studies are presented – the first predicts the river discharge from the water level using time-series information; the second uses the predicted discharge from the first to bias-correct the SPT’s historical reanalysis data using several ML techniques. The historical Bias Correction (BC) is the first step toward the forecasted …

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Let The Machines Do The Dirty Work: Social Media, Machine Learning Technology And The Iteration Of Racialized Surveillance, Subhah Wadhawan Jan 2022

Let The Machines Do The Dirty Work: Social Media, Machine Learning Technology And The Iteration Of Racialized Surveillance, Subhah Wadhawan

Canadian Journal of Law and Technology

Post 9/11, where the current social and cultural temperature has constructed Islam as interchangeable with terrorism, digital intermediaries have responded with increased censorship of speech related to, emerging from, or advocating Islamic ideology. At the heart of this paper is the argument that digital intermediaries have relied on the opaqueness of machine learning technology (‘‘MLT”) to realize racialized surveillance, whereby speech concerning Islamic content has been disproportionally censored. This paper maps out how inherent biases concerning the ideology of Islam have been interwoven into the coding and machine learning used by the major tech giants. As a result of the …

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Deeply Learning Deep Inelastic Scattering Kinematics, Markus Diefenthaler, Abdullah Farhat, Andrii Verbytskyi, Yuesheng Xu Jan 2022

Deeply Learning Deep Inelastic Scattering Kinematics, Markus Diefenthaler, Abdullah Farhat, Andrii Verbytskyi, Yuesheng Xu

Mathematics & Statistics Faculty Publications

We study the use of deep learning techniques to reconstruct the kinematics of the neutral current deep inelastic scattering (DIS) process in electron–proton collisions. In particular, we use simulated data from the ZEUS experiment at the HERA accelerator facility, and train deep neural networks to reconstruct the kinematic variables Q2 and x. Our approach is based on the information used in the classical construction methods, the measurements of the scattered lepton, and the hadronic final state in the detector, but is enhanced through correlations and patterns revealed with the simulated data sets. We show that, with the appropriate selection …

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Estimating Fuel-Efficient Air Plane Trajectories Using Machine Learning, Jaiteg Singh, Gaurav Goyal, Farman Ali, Babar Shah, Sangheon Pack Jan 2022

Estimating Fuel-Efficient Air Plane Trajectories Using Machine Learning, Jaiteg Singh, Gaurav Goyal, Farman Ali, Babar Shah, Sangheon Pack

All Works

Airline industry has witnessed a tremendous growth in the recent past. Percentage of people choosing air travel as first choice to commute is continuously increasing. Highly demanding and congested air routes are resulting in inadvertent delays, additional fuel consumption and high emission of greenhouse gases. Trajectory planning involves creation identification of cost-effective flight plans for optimal utilization of fuel and time. This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required. In this paper, an algorithm for dynamic planning of optimized flight trajectories has been proposed. The proposed algorithm …

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A Convolutional Neural Network (Cnn) For Defect Detection Of Additively Manufactured Parts, Musarrat Farzana Rahman Jan 2022

A Convolutional Neural Network (Cnn) For Defect Detection Of Additively Manufactured Parts, Musarrat Farzana Rahman

Masters Theses

“Additive manufacturing (AM) is a layer-by-layer deposition process to fabricate parts with complex geometries. The formation of defects within AM components is a major concern for critical structural and cyclic loading applications. Understanding the mechanisms of defect formation and identifying the defects play an important role in improving the product lifecycle. The convolutional neural network (CNN) has been demonstrated to be an effective deep learning tool for automated detection of defects for both conventional and AM processes. A network with optimized parameters including proper data processing and sampling can improve the performance of the architecture. In this study, for the …

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Camouflaged Poisoning Attack On Graph Neural Networks, Chao Jiang, Yi He, Richard Chapman, Hongyi Wu Jan 2022

Camouflaged Poisoning Attack On Graph Neural Networks, Chao Jiang, Yi He, Richard Chapman, Hongyi Wu

Computer Science Faculty Publications

Graph neural networks (GNNs) have enabled the automation of many web applications that entail node classification on graphs, such as scam detection in social media and event prediction in service networks. Nevertheless, recent studies revealed that the GNNs are vulnerable to adversarial attacks, where feeding GNNs with poisoned data at training time can lead them to yield catastrophically devastative test accuracy. This finding heats up the frontier of attacks and defenses against GNNs. However, the prior studies mainly posit that the adversaries can enjoy free access to manipulate the original graph, while obtaining such access could be too costly in …

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Predictive Models In Software Engineering: Challenges And Opportunities, Yanming Yang, Xin Xia, David Lo, Tingting Bi, John C. Grundy, Xiaohu Yang Jan 2022

Predictive Models In Software Engineering: Challenges And Opportunities, Yanming Yang, Xin Xia, David Lo, Tingting Bi, John C. Grundy, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Predictive models are one of the most important techniques that are widely applied in many areas of software engineering. There have been a large number of primary studies that apply predictive models and that present well-performed studies in various research domains, including software requirements, software design and development, testing and debugging, and software maintenance. This article is a first attempt to systematically organize knowledge in this area by surveying a body of 421 papers on predictive models published between 2009 and 2020. We describe the key models and approaches used, classify the different models, summarize the range of key application …

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A Synthetic Prediction Market For Estimating Confidence In Published Work, Sarah Rajtmajer, Christopher Griffin, Jian Wu, Robert Fraleigh, Laxmann Balaji, Anna Squicciarini, Anthony Kwasnica, David Pennock, Michael Mclaughlin, Timothy Fritton, Nishanth Nakshatri, Arjun Menon, Sai Ajay Modukuri, Rajal Nivargi, Xin Wei, Lee Giles Jan 2022

A Synthetic Prediction Market For Estimating Confidence In Published Work, Sarah Rajtmajer, Christopher Griffin, Jian Wu, Robert Fraleigh, Laxmann Balaji, Anna Squicciarini, Anthony Kwasnica, David Pennock, Michael Mclaughlin, Timothy Fritton, Nishanth Nakshatri, Arjun Menon, Sai Ajay Modukuri, Rajal Nivargi, Xin Wei, Lee Giles

Computer Science Faculty Publications

[First paragraph] Concerns about the replicability, robustness and reproducibility of findings in scientific literature have gained widespread attention over the last decade in the social sciences and beyond. This attention has been catalyzed by and has likewise motivated a number of large-scale replication projects which have reported successful replication rates between 36% and 78%. Given the challenges and resources required to run high-powered replication studies, researchers have sought other approaches to assess confidence in published claims. Initial evidence has supported the promise of prediction markets in this context. However, they require the coordinated, sustained effort of collections of human experts …

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Developing And Validating A Machine Learning-Based Student Attentiveness Tracking System, Andrew L. Sanders Jan 2022

Developing And Validating A Machine Learning-Based Student Attentiveness Tracking System, Andrew L. Sanders

College of Graduate Studies: Theses & Dissertations

Academic instructors and institutions desire the ability to accurately and autonomously measure the attentiveness of students in the classroom. Generally, college departments use unreliable direct communication from students (i.e. emails, phone calls), distracting and Hawthorne effect-inducing observational sit-ins, and end-of-semester surveys to collect feedback regarding their courses. Each of these methods of collecting feedback is useful but does not provide automatic feedback regarding the pace and direction of lectures. Young et al. discuss that attention levels during passive classroom lectures generally drop after about ten to thirty minutes and can be restored to normal levels with regular breaks, novel activities, …

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Artificial Intelligence And Machine Learning In Optical Information Processing: Introduction To The Feature Issue, Khan Iftekharuddin, Chrysanthe Preza, Abdul Ahad S. Awwal, Michael E. Zelinski Jan 2022

Artificial Intelligence And Machine Learning In Optical Information Processing: Introduction To The Feature Issue, Khan Iftekharuddin, Chrysanthe Preza, Abdul Ahad S. Awwal, Michael E. Zelinski

Electrical & Computer Engineering Faculty Publications

This special feature issue covers the intersection of topical areas in artificial intelligence (AI)/machine learning (ML) and optics. The papers broadly span the current state-of-the-art advances in areas including image recognition, signal and image processing, machine inspection/vision and automotive as well as areas of traditional optical sensing, interferometry and imaging.

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A Dnn-Ensemble Method For Error Reduction And Training Data Selection In Dnn Based Modeling, Ling Zhang, Da Li, Jiayi He, Bhyrav Mutnury, Bo Pu, Xiao Ding Cai, Chulsoon Hwang, Jun Fan, James L. Drewniak, Er Ping Li Jan 2022

A Dnn-Ensemble Method For Error Reduction And Training Data Selection In Dnn Based Modeling, Ling Zhang, Da Li, Jiayi He, Bhyrav Mutnury, Bo Pu, Xiao Ding Cai, Chulsoon Hwang, Jun Fan, James L. Drewniak, Er Ping Li

Electrical and Computer Engineering Faculty Research & Creative Works

Deep neural networks (DNNs) have been widely adopted in modeling electromagnetic compatibility (EMC) problems, but the training data acquisition is usually time-consuming through various simulators. This paper presents a powerful approach using an ensemble of DNN s to effectively reduce the training data size in DNN-based modeling problems. A batch of training data with the largest uncertainties is selected using active learning through the variance among the ensemble of DNNs. Subsequently, a greedy sampling algorithm is applied to select a data subset using diversity. Thus, the proposed method can achieve both uncertainty and diversity in data selection. By averaging the …

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Srf Cavity Fault Classification And Prediction At Jefferson Lab, Chris Tennant, Adam Carpenter, Lasitha Vidyaratne, Md. Monibor Rahman, Khan Iftekharuddin Jan 2022

Srf Cavity Fault Classification And Prediction At Jefferson Lab, Chris Tennant, Adam Carpenter, Lasitha Vidyaratne, Md. Monibor Rahman, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Over the last few years several machine learning projects at Jefferson Lab have had a common focus to optimize operation of superconducting RF (SRF) cavities in the Continuous Electron Beam Accelerator Facility (CEBAF). In this talk we highlight work to identify and classify types of faults from C100-type cavities and then to extend those capabilities to provide real-time fault prediction. Early prediction may enable mitigation strategies to prevent some types of faults. In our approach we apply a two-step fault prediction pipeline. In the first step, a model distinguishes between faulty and normal signals. In the second step, signals flagged …

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An Intelligent Approach For Predicting The Strength Of Geosynthetic-Reinforced Subgrade Soil, Muhammad Nouman Amjad Raja, Sanjay K. Shukla, Muhammad Umer Arif Khan Jan 2022

An Intelligent Approach For Predicting The Strength Of Geosynthetic-Reinforced Subgrade Soil, Muhammad Nouman Amjad Raja, Sanjay K. Shukla, Muhammad Umer Arif Khan

Research outputs 2014 to 2021

In the recent times, the use of geosynthetic-reinforced soil (GRS) technology has become popular for constructing safe and sustainable pavement structures. The strength of the subgrade soil is routinely assessed in terms of its California bearing ratio (CBR). However, in the past, no effort was made to develop a method for evaluating the CBR of the reinforced subgrade soil. The main aim of this paper is to explore and appraise the competency of the several intelligent models such as artificial neural network (ANN), least median of squares regression, Gaussian processes regression, elastic net regularisation regression, lazy K-star, M-5 model …

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A Non-Deterministic Deep Learning Based Surrogate For Ice Sheet Modeling, Hannah Jordan Jan 2022

A Non-Deterministic Deep Learning Based Surrogate For Ice Sheet Modeling, Hannah Jordan

Graduate Student Theses, Dissertations, & Professional Papers

Surrogate modeling is a new and expanding field in the world of deep learning, providing a computationally inexpensive way to approximate results from computationally demanding high-fidelity simulations. Ice sheet modeling is one of these computationally expensive models, the model used in this study currently requires between 10 and 20 minutes to complete one simulation. While this process is adequate for certain applications, the ability to use sampling approaches to perform statistical inference becomes infeasible. This issue can be overcome by using a surrogate model to approximate the ice sheet model, bringing the time to produce output down to a tenth …

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A Survey On Deep Learning For Software Engineering, Yanming Yang, Xin Xia, David Lo Jan 2022

A Survey On Deep Learning For Software Engineering, Yanming Yang, Xin Xia, David Lo

Research Collection School Of Computing and Information Systems

In 2006, Geoffrey Hinton proposed the concept of training "Deep Neural Networks (DNNs)" and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstrated the powerful learning ability of deep learning and its enormous potential. Deep learning has been increasingly used to develop state-of-the-art software engineering (SE) research tools due to its ability to boost performance for various SE tasks. There are many factors, e.g., deep learning model selection, internal structure differences, and model optimization techniques, that may have an impact on the performance of DNNs applied in …

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Machine Learning-Driven Surrogate Models For Electrolytes, Tong Gao Jan 2022

Machine Learning-Driven Surrogate Models For Electrolytes, Tong Gao

Dissertations, Master's Theses and Master's Reports

We have developed a lattice Monte Carlo (MC) simulation based on the diffusion-limited aggregation model that accounts for the effect of the physical properties of ionic liquids (ILs) on lithium dendrite growth. Our simulations show that the size asymmetry between the cation and anion, the dielectric constant, and the volume fraction of ILs are critical factors to significantly suppress the dendrite growth, primarily due to substantial changes in electric-field screening. Specifically, the volume fraction of ILs has the optimal value for dendrite suppression. The present simulation method indicates potential challenges for the model extension to macroscopic systems. Therefore, we also …

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Self-Vernier Effect-Assisted Optical Fiber Sensor Based On Microwave Photonics And Its Machine Learning Analysis, Chen Zhu, Jie Huang Jan 2022

Self-Vernier Effect-Assisted Optical Fiber Sensor Based On Microwave Photonics And Its Machine Learning Analysis, Chen Zhu, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Optical Vernier Effect Has Been Recently Demonstrated as a Tool to Enhance the Sensitivity of Optical Fiber Interferometric Sensors and Has Become a Hot Topic in the Last Few Years. the Generation of the Vernier Effect Relies on the Superposition of Interferograms of Two Interferometers (A Sensing One and a Reference One) with Marginally Different Optical Path Differences (OPDs), Where an Amplitude Modulation-Like Signal is Sustained in the Output Spectrum. the Vernier Modulation Envelope Exhibits Significantly Magnified Sensitivity in Response to External Perturbations, compared to the Individual Sensing Interferometer, Providing a New Route to New Generations of Ultra-Sensitive Optical Fiber …

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Applications Of A Combined Approach Of Kinetic Monte Carlo Simulations And Machine Learning To Model Atomic Layer Deposition (Ald) Of Metal Oxides, Emily Justus Jan 2022

Applications Of A Combined Approach Of Kinetic Monte Carlo Simulations And Machine Learning To Model Atomic Layer Deposition (Ald) Of Metal Oxides, Emily Justus

Graduate Theses/Dissertations

Metal-oxides such as ZnO or Al2O3 synthesized through Atomic Layer Deposition (ALD) have been of great research interest as the candidate materials for ultra-thin tunnel barriers. In this study, I have applied a 3D on-lattice Kinetic Monte Carlo (kMC) code developed by Timo Weckman’s group to simulate the growth mechanisms of the tunnel barrier layer and to evaluate the role of various experimentally relevant factors in the ALD processes. I have systematically studied the effect of parameters such as the chamber pressure temperature, pulse, and purge times. The database generated from the kMC simulations was subsequently used …

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Detecting User Emotions From Audio Conversations With The Smart Assistants, Sunanda Guha Jan 2022

Detecting User Emotions From Audio Conversations With The Smart Assistants, Sunanda Guha

Graduate Theses/Dissertations

With the proliferation of smart home devices like Google Home or Amazon Alexa, significant research endeavors are being carried out to improve the user experience while interacting with these smart assistants. One such dimension in this endeavor is ongoing research on successful emotion detection from short voice commands used in smart home environment. Besides facial expression and body language, etc., speech plays a pivotal role in the classification of emotions when it comes to smart home application. Upon successful implementation of accurate emotion recognition, the smart devices will be able to intelligently and empathetically suggest appropriate actions based on the …

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Advances In Non-Invasive Biosensing Measures To Monitor Wound Healing Progression, Walker D Short, Oluyinka O Olutoye, Benjamin W Padon, Umang M Parikh, Daniel Colchado, Hima Vangapandu, Shayan Shams, Taiyun Chi, Jangwook P Jung, Swathi Balaji Jan 2022

Advances In Non-Invasive Biosensing Measures To Monitor Wound Healing Progression, Walker D Short, Oluyinka O Olutoye, Benjamin W Padon, Umang M Parikh, Daniel Colchado, Hima Vangapandu, Shayan Shams, Taiyun Chi, Jangwook P Jung, Swathi Balaji

Faculty, Staff and Student Publications

Impaired wound healing is a significant financial and medical burden. The synthesis and deposition of extracellular matrix (ECM) in a new wound is a dynamic process that is constantly changing and adapting to the biochemical and biomechanical signaling from the extracellular microenvironments of the wound. This drives either a regenerative or fibrotic and scar-forming healing outcome. Disruptions in ECM deposition, structure, and composition lead to impaired healing in diseased states, such as in diabetes. Valid measures of the principal determinants of successful ECM deposition and wound healing include lack of bacterial contamination, good tissue perfusion, and reduced mechanical injury and …

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Harnessing The Power Of Interdisciplinary Research With Psychology-Informed Cyberbullying Detection Models, Deborah Hall, Yasin N. Silva, Brittany Wheeler, Lu Cheng, Katie Baumel Jan 2022

Harnessing The Power Of Interdisciplinary Research With Psychology-Informed Cyberbullying Detection Models, Deborah Hall, Yasin N. Silva, Brittany Wheeler, Lu Cheng, Katie Baumel

Computer Science: Faculty Publications and Other Works

Cyberbullying has become increasingly prevalent, particularly on social media. There has also been a steady rise in cyberbullying research across a range of disciplines. Much of the empirical work from computer science has focused on developing machine learning models for cyberbullying detection. Whereas machine learning cyberbullying detection models can be improved by drawing on psychological theories and perspectives, there is also tremendous potential for machine learning models to contribute to a better understanding of psychological aspects of cyberbullying. In this paper, we discuss how machine learning models can yield novel insights about the nature and defining characteristics of cyberbullying and …

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Finding The Best Predictors For Foot Traffic In Us Seafood Restaurants, Isabel Paige Beaulieu Jan 2022

Finding The Best Predictors For Foot Traffic In Us Seafood Restaurants, Isabel Paige Beaulieu

Honors Theses and Capstones

COVID-19 caused state and nation-wide lockdowns, which altered human foot traffic, especially in restaurants. The seafood sector in particular suffered greatly as there was an increase in illegal fishing, it is made up of perishable goods, it is seasonal in some places, and imports and exports were slowed. Foot traffic data is useful for business owners to have to know how much to order, how many employees to schedule, etc. One issue is that the data is very expensive, hard to get, and not available until months after it is recorded. Our goal is to not only find covariates that …

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Cracking The Sparse Code: Lateral Competition Forms Robust V1-Like Representations In Convolutional Neural Networks, Michael Teti Jan 2022

Cracking The Sparse Code: Lateral Competition Forms Robust V1-Like Representations In Convolutional Neural Networks, Michael Teti

Electronic Theses and Dissertations 2020 - Present

Although state-of-the-art Convolutional Neural Networks (CNNs) are often viewed as a model of biological object recognition, they lack many computational and architectural motifs that are postulated to contribute to robust perception in biological neural systems. For example, modern CNNs lack lateral connections, which greatly outnumber feed-forward excitatory connections in primary sensory cortical areas and mediate feature-specific competition between neighboring neurons to form robust, sparse representations of sensory stimuli for downstream tasks. In this thesis, I hypothesize that CNN layers equipped with lateral competition better approximate the response characteristics and dynamics of neurons in the mammalian primary visual cortex, leading to …

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Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner, Torrey J. Wagner, Paul Auclair, Brent T. Langhals Jan 2022

Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner, Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

Land-cover and land-use classification generates categories of terrestrial features, such as water or trees, which can be used to track how land is used. This work applies classical, ensemble and neural network machine learning algorithms to a multispectral remote sensing dataset containing 405,000 28x28 pixel image patches in 4 electromagnetic frequency bands. For each algorithm, model metrics and prediction execution time were evaluated, resulting in two families of models; fast and precise. The prediction time for an 81,000-patch group of predictions wasmodels, and >5s for the precise models, and there was not a significant change in prediction time when a …

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Rangeland Forage Growth Prediction, Logistics, Energy, And Economics Analysis And Tool Development Using Open-Source Software, Subhashree Navaneetha Srinivasagan Jan 2022

Rangeland Forage Growth Prediction, Logistics, Energy, And Economics Analysis And Tool Development Using Open-Source Software, Subhashree Navaneetha Srinivasagan

Agribusiness and Biosystems Engineering

Forage availability was crucial for livestock production across the United States. Rangelands occupied vast areas 31 % of land and were the primary source of forage for livestock. However, extreme climatic conditions such as drought affect rangeland forage production and pose a serious threat to the rangeland enterprise. This increases the need to monitor forage in vast rangelands and adapt to other measures such as cultivating or buying forage to balance demand and supply. Despite this need, resources (studies and tools) on rangeland forage monitoring and existing forage production, handling, and economics were scattered and scarce. Therefore, a comprehensive systematic …

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Fake Malware Generation Using Hmm And Gan, Harshit Trehan, Fabio Di Troia Jan 2022

Fake Malware Generation Using Hmm And Gan, Harshit Trehan, Fabio Di Troia

Faculty Research, Scholarly, and Creative Activity

In the past decade, the number of malware attacks have grown considerably and, more importantly, evolved. Many researchers have successfully integrated state-of-the-art machine learning techniques to combat this ever present and rising threat to information security. However, the lack of enough data to appropriately train these machine learning models is one big challenge that is still present. Generative modelling has proven to be very efficient at generating image-like synthesized data that can match the actual data distribution. In this paper, we aim to generate malware samples as opcode sequences and attempt to differentiate them from the real ones with the …

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Screen Media Use Among Children And Adolescents – Applications Of Supervised And Unsupervised Machine Learning And Sentiment Analysis, Yifan Zhang Jan 2022

Screen Media Use Among Children And Adolescents – Applications Of Supervised And Unsupervised Machine Learning And Sentiment Analysis, Yifan Zhang

Graduate Theses, Dissertations, and Problem Reports (ETD)

Screen media has become increasingly pervasive in our everyday lives and has profoundly changed the way people communicate and interact with each other. However, we are still unclear about the long-term influence of screen media use on our physical health, mental health, and social wellbeing. Children and adolescents are in an important stage of brain development and are susceptible to the environmental influence that screen media possess. This dissertation pursued three aims to address research gaps related to screen media use among children and adolescents: 1) identify topics and knowledge gaps in screen media use research among children and adolescents …

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Exploring Cyberterrorism, Topic Models And Social Networks Of Jihadists Dark Web Forums: A Computational Social Science Approach, Vivian Fiona Guetler Jan 2022

Exploring Cyberterrorism, Topic Models And Social Networks Of Jihadists Dark Web Forums: A Computational Social Science Approach, Vivian Fiona Guetler

Graduate Theses, Dissertations, and Problem Reports (ETD)

This three-article dissertation focuses on cyber-related topics on terrorist groups, specifically Jihadists’ use of technology, the application of natural language processing, and social networks in analyzing text data derived from terrorists' Dark Web forums. The first article explores cybercrime and cyberterrorism. As technology progresses, it facilitates new forms of behavior, including tech-related crimes known as cybercrime and cyberterrorism. In this article, I provide an analysis of the problems of cybercrime and cyberterrorism within the field of criminology by reviewing existing literature focusing on (a) the issues in defining terrorism, cybercrime, and cyberterrorism, (b) ways that cybercriminals commit a crime in …

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Classifying Blood Glucose Levels Through Noninvasive Features, Rishi Reddy Jan 2022

Classifying Blood Glucose Levels Through Noninvasive Features, Rishi Reddy

Graduate Theses, Dissertations, and Problem Reports (ETD)

Blood glucose monitoring is a key process in the prevention and management of certain chronic diseases, such as diabetes. Currently, glucose monitoring for those interested in their blood glucose levels are confronted with options that are primarily invasive and relatively costly. A growing topic of note is the development of non-invasive monitoring methods for blood glucose. This development holds a significant promise for improvement to the quality of life of a significant portion of the population and is overall met with great enthusiasm from the scientific community as well as commercial interest. This work aims to develop a potential pipeline …

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