Digital Commons Network^™

Countering Internet Packet Classifiers To Improve User Online Privacy, Sina Fathi-Kazerooni

Dissertations

Internet traffic classification or packet classification is the act of classifying packets using the extracted statistical data from the transmitted packets on a computer network. Internet traffic classification is an essential tool for Internet service providers to manage network traffic, provide users with the intended quality of service (QoS), and perform surveillance. QoS measures prioritize a network's traffic type over other traffic based on preset criteria; for instance, it gives higher priority or bandwidth to video traffic over website browsing traffic. Internet packet classification methods are also used for automated intrusion detection. They analyze incoming traffic patterns and identify malicious …

Sensitivity Analysis Of An Agent-Based Simulation Model Using Reconstructability Analysis, Andey M. Nunes, Martin Zwick, Wayne Wakeland

Systems Science Faculty Publications and Presentations

Reconstructability analysis, a methodology based on information theory and graph theory, was used to perform a sensitivity analysis of an agent-based model. The NetLogo BehaviorSpace tool was employed to do a full 2^k factorial parameter sweep on Uri Wilensky’s Wealth Distribution NetLogo model, to which a Gini-coefficient convergence condition was added. The analysis identified the most influential predictors (parameters and their interactions) of the Gini coefficient wealth inequality outcome. Implications of this type of analysis for building and testing agent-based simulation models are discussed.

Validation Of A Machine Learning Brain Electrical Activity-Based Index To Aid In Diagnosing Concussion Among Athletes, Jeffery J. Bazarian, Robert J. Elbin, Douglas J. Casa, Gillian A. Hotz, Christopher Neville, Rebecca M. Lopez, David M. Schnyer, Susan Yeargin Ph.D., Atc

Faculty Publications

Importance An objective, reliable indicator of the presence and severity of concussive brain injury and of the readiness for the return to activity has the potential to reduce concussion-related disability.

Objective To validate the classification accuracy of a previously derived, machine learning, multimodal, brain electrical activity–based Concussion Index in an independent cohort of athletes with concussion.

Design, Setting, and Participants This prospective diagnostic cohort study was conducted at 10 clinical sites (ie, US universities and high schools) between February 4, 2017, and March 20, 2019. A cohort comprising a consecutive sample of 207 athletes aged 13 to 25 years with …

A Conformation Variant Of P53 Combined With Machine Learning Identifies Alzheimer Disease In Preclinical And Prodromal Stages, Giulia Abate, Marika Vezzoli, Letizia Polito, Antonio Guaita, Diego Albani, Moira Marizzoni, Emirena Garrafa, Alessandra Marengoni, Gianluigi Forloni, Giovanni B. Frisoni, Jeffrey L. Cummings, Maurizio Memo, Daniela Uberti

School of Medicine Faculty Publications

© 2020 by the authors. Li-censee MDPI, Basel, Switzerland. Early diagnosis of Alzheimer’s disease (AD) is a crucial starting point in disease man-agement. Blood-based biomarkers could represent a considerable advantage in providing AD-risk information in primary care settings. Here, we report new data for a relatively unknown blood-based biomarker that holds promise for AD diagnosis. We evaluate a p53-misfolding conformation rec-ognized by the antibody 2D3A8, also named Unfolded p53 (U-p532D3A8+), in 375 plasma samples derived from InveCe.Ab and PharmaCog/E-ADNI longitudinal studies. A machine learning approach is used to combine U-p532D3A8+ plasma levels with Mini-Mental State Examination (MMSE) and apolipoprotein E …

Development Of The Algorithm For Supporting Decision-Making In Self-Government Bodies Using Machine Learning, X J. Raximboyev

Scientific-technical journal

The article deals with the problem of constructing a model and algorithm for decision support in self-government bodies using machine learning. The method of multiple linear regression for processing the training sample was chosen as a machine learning method. In the training sample, independent data consists of parametric estimates in numerical form of self-government bodies in three areas of activity, such as education, social environment and crime. And the dependent parameter consists of generalized expert assessments of self-government bodies, also in numerical form. The model and algorithm of the decision support process using the method of multiple linear regression are …

A Study On An Accurate Underwater Location Of Hybrid Underwater Gliders Using Machine Learning, Sang-Ki Jeong, Hyeung-Sik Choi, Dea-Hyung Ji, Mai The Vu, Joon-Young Kim, Sung Min Hong, Hyun Joon Cho

Journal of Marine Science and Technology

A hybrid underwater glider (HUG) is marine observation equipment that consumes a small amount of energy and offers greater range and navigation times. To achieve reduced energy consumption, however, the HUG uses imprecise navigation sensors, such as mems-type GPS and AHRS, resulting in inaccurate coordination. This study makes a new attempt on the application of machine learning algorithms in a way that complements sensor data errors to improve navigation performance. The proposed algorithm was used to a simulation of the HUG’s navigation and control system, after which the updated heading angle was decided by using the previous position data and …

Leveraging The Inductive Bias Of Large Language Models For Abstract Textual Reasoning, Christopher Michael Rytting

Theses and Dissertations

Large natural language models (such as GPT-2 or T5) demonstrate impressive abilities across a range of general NLP tasks. Here, we show that the knowledge embedded in such models provides a useful inductive bias, not just on traditional NLP tasks, but also in the nontraditional task of training a symbolic reasoning engine. We observe that these engines learn quickly and generalize in a natural way that reflects human intuition. For example, training such a system to model block-stacking might naturally generalize to stacking other types of objects because of structure in the real world that has been partially captured by …

Improving A Wireless Localization System Via Machine Learning Techniques And Security Protocols, Zachary Yorio

Masters Theses, 2020-current

The recent advancements made in Internet of Things (IoT) devices have brought forth new opportunities for technologies and systems to be integrated into our everyday life. In this work, we investigate how edge nodes can effectively utilize 802.11 wireless beacon frames being broadcast from pre-existing access points in a building to achieve room-level localization. We explain the needed hardware and software for this system and demonstrate a proof of concept with experimental data analysis. Improvements to localization accuracy are shown via machine learning by implementing the random forest algorithm. Using this algorithm, historical data can train the model and make …

A Comparative Study On Statistical And Machine Learning Forecasting Methods For An Fmcg Company, Zenah Yaser Alzubaidi

Theses

Demand forecasting has been an area of study among scholars and businessmen ever since the start of the industrial revolution and has only gained focus in recent years with the advancements in AI. Accurate forecasts are no longer a luxury, but a necessity to have for effective decisions made in planning production and marketing. Many aspects of the business depend on demand, and this is particularly true for the Fast-Moving Consumer Goods industry where the high volume and demand volatility poses a challenge for planners to generate accurate forecasts as consumer demand complexity rises. Inaccurate demand forecasts lead to multiple …

The Machines Aren’T Taking Over (Yet): An Empirical Comparison Of Traditional, Profiling, And Machine Learning Approaches To Criterion-Related Validation, Kristin S. Allen, Mathijs Affourtit, Craig M. Reddock

Personnel Assessment and Decisions

Criterion-related validation (CRV) studies are used to demonstrate the effectiveness of selection procedures. However, traditional CRV studies require significant investment of time and resources, as well as large sample sizes, which often create practical challenges. New techniques, which use machine learning to develop classification models from limited amounts of data, have emerged as a more efficient alternative. This study empirically investigates the effectiveness of traditional CRV with a variety of profiling approaches and machine learning techniques using repeated cross-validation. Results show that the traditional approach generally performs best both in terms of predicting performance and larger group differences between candidates …

Reasoning About User Feedback Under Identity Uncertainty In Knowledge Base Construction, Ariel Kobren

Doctoral Dissertations

Intelligent, automated systems that are intertwined with everyday life---such as Google Search and virtual assistants like Amazon’s Alexa or Apple’s Siri---are often powered in part by knowledge bases (KBs), i.e., structured data repositories of entities, their attributes, and the relationships among them. Despite a wealth of research focused on automated KB construction methods, KBs are inevitably imperfect, with errors stemming from various points in the construction pipeline. Making matters more challenging, new data is created daily and must be integrated with existing KBs so that they remain up-to-date. As the primary consumers of KBs, human users have tremendous potential to …

Intelligent Networks For High Performance Computing, William Whitney Schonbein

Computer Science ETDs

There exists a resurgence of interest in `smart' network interfaces that can operate on data as it flows through a network. However, while smart capabilities have been expanding, what they can do for high-performance computing (HPC) is not well-understood. In this work, we advance our understanding of the capabilities and contributions of smart network interfaces to HPC. First, we show current offloaded message demultiplexing can mitigate (but not eliminate) overheads incurred by multithreaded communication. Second, we demonstrate current offloaded capabilities can be leveraged to provide Turing complete program execution on the interface. We elaborate with a framework for offloading arbitrary …

Forecasting Bitcoin Prices Using N-Beats Deep Learning Architecture, Alikhan Bulatov

Student Theses

The use of computationally intensive systems that employ machine learning algorithms is increasingly common in the field of finance. New state of the art deep learning architectures for time series forecasting are being developed each year making them more accurate than ever. This study evaluates the predictive power of the N-BEATS deep learning architecture trained on Bitcoin daily, hourly, and up-to-the-minute data in comparison with other popular time series forecasting methods such as LSTM and ARIMA. Prediction errors are measured with Mean Average Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The results suggest that the developed N-BEATS model …

Current Available Computer-Aided Detection Catches Cancer But Requires A Human Operator, Florentino Saenz Rios, Giri Movva, Hari Movva, Quan D. Nguyen

School of Medicine Publications and Presentations

Introduction: This study intends to show that the current widely used computer-aided detection (CAD) may be helpful, but it is not an adequate replacement for the human input required to interpret mammograms accurately. However, this is not to discredit CAD’s ability but to further encourage the adoption of artificial intelligence-based algorithms into the toolset of radiologists.

Methods: This study will use Hologic (Marlborough, MA, USA) and General Electric (Boston, MA, USA) CAD read images provided by patients found to be Breast Imaging Reporting and Data System (BI-RADS) 6 from 2019 to 2020. In addition, patient information will be pulled …

Pathway‐Extended Gene Expression Signatures Integrate Novel Biomarkers That Improve Predictions Of Patient Responses To Kinase Inhibitors, Ashis Bagchee‐Clark, Eliseos J. Mucaki, Tyson Whitehead, Peter Rogan

Biochemistry Publications

Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for the same drug. This study utilizes machine learning to derive multi‐gene expression signatures that predict individual patient responses to specific tyrosine kinase inhibitors, including erlotinib, gefitinib, sorafenib, sunitinib, lapatinib and imatinib. Support vector machine (SVM) learning was used to train mathematical models that distinguished sensitivity from resistance to these drugs using a novel systems biology‐based approach. This began with expression of genes previously implicated in specific drug responses, then expanded to evaluate genes whose products were related through biochemical pathways and interactions. Optimal pathway‐extended SVMs predicted responses in …

Technology Criticism And Data Literacy: The Case For An Augmented Understanding Of Media Literacy, Thomas Knaus

Journal of Media Literacy Education

Reviewing the history of media literacy education might help us to identify how creating media as an approach can contribute to fostering knowledge, understanding technical issues, and to establishing a critical attitude towards technology and data. In a society where digital devices and services are omnipresent and decisions are increasingly based on data, critical analysis must penetrate beyond the “outer shell” of machines – their interfaces – through the technology itself, and the data, and algorithms, which make these devices and services function. Because technology and data constitute the basis of all communication and collaboration, media literate individuals …

Metarec: Meta-Learning Meets Recommendation Systems, James Le

Theses

Artificial neural networks (ANNs) have recently received increasing attention as powerful modeling tools to improve the performance of recommendation systems. Meta-learning, on the other hand, is a paradigm that has re-surged in popularity within the broader machine learning community over the past several years. In this thesis, we will explore the intersection of these two domains and work on developing methods for integrating meta-learning to design more accurate and flexible recommendation systems.

In the present work, we propose a meta-learning framework for the design of collaborative filtering methods in recommendation systems, drawing from ideas, models, and solutions from modern approaches …

Clustered Hyperspectral Target Detection, Sean Onufer Stalley

Dissertations and Theses

Aerial target detection is often used to search for relatively small things over large areas of land. Depending on the size and signature of the target, detection can be a very easy or very difficult task. By capturing images with several hundred color channels, hyperspectral sensors provide a new way of looking at this task, both literally and figuratively. Hyperspectral sensors can be used in many aerial target detection tasks such as identifying unhealthy trees in a forest, searching for minerals at a mining site, or finding the sources of chemical leaks at a factory. The high spectral resolution of …

Machine Learning Based Applications For Data Visualization, Modeling, Control, And Optimization For Chemical And Biological Systems, Yan Ma

LSU Doctoral Dissertations

This dissertation report covers Yan Ma’s Ph.D. research with applicational studies of machine learning in manufacturing and biological systems. The research work mainly focuses on reaction modeling, optimization, and control using a deep learning-based approaches, and the work mainly concentrates on deep reinforcement learning (DRL). Yan Ma’s research also involves with data mining with bioinformatics. Large-scale data obtained in RNA-seq is analyzed using non-linear dimensionality reduction with Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP), followed by clustering analysis using k-Means and Hierarchical Density-Based Spatial Clustering with Noise (HDBSCAN). This report focuses …

The Role Of Ai & Big Data In Habit Formation, Jingyu Cao

Theses

Forming habits are not easy for everyone. It requires professional methods and strong perseverance, which people usually feel hard to do by themself. However, people are eager to form good habits to have a better life.

This study aims to determine how AI & big data could help people to form habits. There are many applications on the market that already use this method to study user behavior in order to provide better service. My research has focused on how to conduct the personal plan and its effects on the action.

In this context, Marvelous is defined as the AI …

An Assessment Of The Hydrological Trends Using Synergistic Approaches Of Remote Sensing And Model Evaluations Over Global Arid And Semi-Arid Regions, Wenzhao Li, Hesham El-Askary, Rejoice Thomas, Surya Prakash Tiwari, Karuppasamy Manikandan, Thomas Piechota, Daniele Struppa

Mathematics, Physics, and Computer Science Faculty Articles and Research

Drylands cover about 40% of the world’s land area and support two billion people, most of them living in developing countries that are at risk due to land degradation. Over the last few decades, there has been warming, with an escalation of drought and rapid population growth. This will further intensify the risk of desertification, which will seriously affect the local ecological environment, food security and people’s lives. The goal of this research is to analyze the hydrological and land cover characteristics and variability over global arid and semi-arid regions over the last decade (2010–2019) using an integrative approach of …

An Investigation Of Grammar Gender-Bias Correction For Google Translate When Translating From English To French, Ahmed Samy Merah

Student Theses

This work investigated how to address the Google Translate's gender-bias when translating from English to French. The developed solution is called GT gender-bias corrector that was built based on combining natural language processing and machine learning methods. The natural language processing was used to analyze the original sentences and their translations grammatically identifying parts of speech. The parts of speech analysis facilitated the identification of three patterns that are associated with the gender bias of Google Translate when translating from English to French. The three patterns were labeled simple, intermediate and complex to reflect the structure complexity. Samples of texts …

Using Machine Learning To Regulate Intensity Of Immersion Therapy Treatment Of Phobias Through Vital Feedback, Mark Beauchamp

Student Theses

The treatment of acrophobia has been trying to keep up with newer technology with the incorporation of virtual reality for exposure therapy, but that approach still lacks automation and still leaves a good portion for human error. The proposed method introduced in this paper is that a machine learning model could replace the need for continuous human intervention. With a few different models of bridges and buildings and the ability for a machine learning model to dynamically alter the height of these building we could theoretically put the patient in the exact situation that will maximize the efficiency of their …

Reducing Body Contact Using Smart Mobile App And Machine Learning Soltutions, Rashed Saeed Abdulrahman Shaliya

Theses

The physical contact or the daily body interaction with people by shaking hands, using electronic and payment cards or touching objects such as devices, pens, access cards and gates, all these habits increase the proportion of spreading microbes, viruses and spread diseases among the people all over the world. This project illustrates how body contact can lead to a global disaster by spreading dangerous diseases and deadly viruses among people because of their daily dealings and routine. Analytical techniques were used to explore the relevant data and visualize how body contact increases the infection of a disease to become a …

Automated Intelligent Cueing Device To Improve Ambient Gait Behaviors For Patients With Parkinson's Disease, Nader Naghavi

Doctoral Dissertations

Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson’s disease (PD). FoG impairs walking and is associated with increased fall risk. Although pharmacological treatments have shown promise during ON-medication periods, FoG remains difficult to treat during medication OFF state and in advanced stages of the disease. External cueing therapy in the forms of visual, auditory, and vibrotactile, has been effective in treating gait deviations. Intelligent (or on-demand) cueing devices are novel systems that analyze gait patterns in real-time and activate cues only at moments when specific gait alterations are detected. In this study we developed methods …

Machine Learning Identifies Clinical And Genetic Factors Associated With Anthracycline Cardiotoxicity In Pediatric Cancer Survivors, Marie A. Chaix, Neha Parmar, Caroline Kinnear, Myriam Lafreniere-Roula, Oyediran Akinrinade, Roderick Yao, Anastasia Miron, Emily Lam, Guoliang Meng, Anne Christie, Ashok Kumar Manickaraj, Stacey Marjerrison, Rejane Dillenburg, Mylène Bassal, Jane Lougheed, Shayna Zelcer, Herschel Rosenberg, David Hodgson, Leonard Sender, Paul Kantor, Cedric Manlhiot, James Ellis, Luc Mertens, Paul C. Nathan, Seema Mital

Paediatrics Publications

Background: Despite known clinical risk factors, predicting anthracycline cardiotoxicity remains challenging. Objectives: This study sought to develop a clinical and genetic risk prediction model for anthracycline cardiotoxicity in childhood cancer survivors. Methods: We performed exome sequencing in 289 childhood cancer survivors at least 3 years from anthracycline exposure. In a nested case-control design, 183 case patients with reduced left ventricular ejection fraction despite low-dose doxorubicin (≤250 mg/m2), and 106 control patients with preserved left ventricular ejection fraction despite doxorubicin >250 mg/m2 were selected as extreme phenotypes. Rare/low-frequency variants were collapsed to identify genes differentially enriched for variants between case patients …

Diagnostic Utility Of Genome-Wide Dna Methylation Analysis In Mendelian Neurodevelopmental Disorders, Sadegheh Haghshenas, Pratibha Bhai, Erfan Aref-Eshghi, Bekim Sadikovic

Paediatrics Publications

Mendelian neurodevelopmental disorders customarily present with complex and overlapping symptoms, complicating the clinical diagnosis. Individuals with a growing number of the so-called rare disorders exhibit unique, disorder-specific DNA methylation patterns, consequent to the underlying gene defects. Besides providing insights to the pathophysiology and molecular biology of these disorders, we can use these epigenetic patterns as functional biomarkers for the screening and diagnosis of these conditions. This review summarizes our current understanding of DNA methylation episignatures in rare disorders and describes the underlying technology and analytical approaches. We discuss the computational parameters, including statistical and machine learning methods, used for the …

Machine Learning Identifies Clinical And Genetic Factors Associated With Anthracycline Cardiotoxicity In Pediatric Cancer Survivors, Marie-A Chaix, Neha Parmar, Caroline Kinnear, Myriam Lafreniere-Roula,, Oyediran Akinrinade, Roderick Yao, Anastasia Miron, Emily Lam, Guoliang Meng, Anne Christie, Ashok Kumar Manickaraj, Stacey Marjerrison, Rejane Dillenburg, Mylène Bassal, Jane Lougheed, Shayna Zelcer, Herschel Rosenberg, David Hodgson, Leonard Sender, Paul Kantor, Cedric Manlhiot, James Ellis, Luc Mertens, Paul C. Nathan, Seema Mital

Paediatrics Publications

BACKGROUND Despite known clinical risk factors, predicting anthracycline cardiotoxicity remains challenging. OBJECTIVES This study sought to develop a clinical and genetic risk prediction model for anthracycline cardiotoxicity in childhood cancer survivors. METHODS We performed exome sequencing in 289 childhood cancer survivors at least 3 years from anthracycline exposure. In a nested case-control design, 183 case patients with reduced left ventricular ejection fraction despite low-dose doxorubicin (<= 250 mg/m(2)), and 106 control patients with preserved left ventricular ejection fraction despite doxorubicin >250 mg/m(2) were selected as extreme phenotypes. Rare/low-frequency variants were collapsed to identify genes differentially enriched for variants between case patients and control patients. The expression levels of 5 top-ranked genes were evaluated in …

Fire Code Violation Detection, Salim Elewa

Student Theses

his paper explores the creation of an object detection system for mobile using YOLO(You Only Look Once) algorithm., a real-time object detection model that is developed to run on a portable device such as a cellphone that does not have a Graphics Processing Unit (GPU). This algorithm is utilized to detect fire code violations, specifically the obstructed door in a fire separation: the areas surround- ing the door opening shall be kept clear of anything that would be likely to ob- struct. The machine learning algorithm utilized has been fine-tuned to fit the model based on accuracy levels. The author …

Fall Detection Using Neural Networks, Warren Zajac

Student Theses

Falls inside of the home is a major concern facing the aging population. Monitoring the home environment to detect a fall can prevent profound consequences due to delayed emergency response. One option to monitor a home environment is to use a camera-based fall detection system. Conceptual designs vary from 3D positional monitoring (multi-camera monitoring) to body position and limb speed classification. Research shows varying degree of success with such concepts when designed with multi-camera setup. However, camera-based systems are inherently intrusive and costly to implement. In this research, we use a sound-based system to detect fall events. Acoustic sensors are …