Digital Commons Network^™

Towards Wearable Blood Pressure Measurement Systems From Biosignals: A Review, Ümi̇t Şentürk, Kemal Polat, İbrahi̇m Yücedağ

Turkish Journal of Electrical Engineering and Computer Sciences

Blood pressure is the pressure by the blood to the vein wall. High blood pressure, which is called silent death, is the cause of nearly 13 % of mortality all over the world. Blood pressure is not only measured in the medical environment, but the blood pressure measurement is also a need for people in their daily life. Blood pressure estimation systems with low error rates have been developed besides the new technologies and algorithms. Blood pressure measurements are differentiated as invasive blood pressure (IBP) measurement and noninvasive blood pressure (NIBP) measurement methods. Although IBP measurement provides the most accurate …

Improving Anomaly Detection In Bgp Time-Series Data By New Guide Features And Moderated Feature Selection Algorithm, Mahmoud Hashem, Ahmed Bashandy, Samir Shaheen

Turkish Journal of Electrical Engineering and Computer Sciences

The Internet infrastructure relies on the Border Gateway Protocol (BGP) to provide essential routing information where abnormal routing behavior impairs global Internet connectivity and stability. Hence, employing anomaly detection algorithms is important for improving the performance of BGP routing protocol. In this paper, we propose two algorithms; the first is the guide feature generator (GFG), which generates guide features from traditional features in BGP time-series data using moving regression in combination with smoothed moving average. The second is a modified random forest feature selection algorithm which is employed to automatically select the most dominant features (ASMDF). Our mechanism shows that …

A Statistical Analysis And Machine Learning Of Genomic Data, Jongyun Jung

All Graduate Theses, Dissertations, and Other Capstone Projects

Machine learning enables a computer to learn a relationship between two assumingly related types of information. One type of information could thus be used to predict any lack of informaion in the other using the learned relationship. During the last decades, it has become cheaper to collect biological information, which has resulted in increasingly large amounts of data. Biological information such as DNA is currently analyzed by a variety of tools. Although machine learning has already been used in various projects, a flexible tool for analyzing generic biological challenges has not yet been made. The recent advancements in the DNA …

The Efficacy Of Weakly Supervised Learning For Biomedical Imaging, Solomon Lee Hatcher

All ETDs from UAB

This paper aims to investigate the use of unsupervised saliency proposals in extracting suitable prior information from computed tomography scans of the head using image-level labels. The weak supervision learning method obtains useful information about an object’s location and fuses this result with an unsupervised saliency proposal to propagate labels to all related pixels. However, it is shown that unsupervised saliency guidance fails to supply the synthetic annotations with enough coherence to produce suitable segmentation.

The D&D Sorting Hat: Predicting Dungeons And Dragons Characters From Textual Backstories, Joseph C. Macinnes

Senior Independent Study Theses

Dungeons and Dragons is a tabletop roleplaying game which focuses heavily on character interaction and creating narratives. The current state of the game's character creation process often bogs down new players in decisions related to game mechanics, not a character's identity and personality. This independent study investigates the use of machine learning and natural language processing to make these decisions for a player based on their character's backstory - the textual biography or description of a character. The study presents a collection of existing characters and uses these examples to create a family of models capable of predicting a character's …

Predicting Cascading Failures In Power Grids Using Machine Learning Algorithms, Rezoan Ahmed Shuvro, Pankaz Das, Majeed M. Hayat, Mitun Talukder

Electrical and Computer Engineering Faculty Research and Publications

Although there has been notable progress in modeling cascading failures in power grids, few works included using machine learning algorithms. In this paper, cascading failures that lead to massive blackouts in power grids are predicted and classified into no, small, and large cascades using machine learning algorithms. Cascading-failure data is generated using a cascading failure simulator framework developed earlier. The data set includes the power grid operating parameters such as loading level, level of load shedding, the capacity of the failed lines, and the topological parameters such as edge betweenness centrality and the average shortest distance for numerous combinations of …

Investigating Risk Factors And Predicting Complications In Deep Brain Stimulation Surgery With Machine Learning Algorithms, Farrokh Farrokhi, Quinlan D. Buchlak, Matt Sikora, Nazanin Esmaili, Maria Marsans, Pamela Mcleod, Jamie Mark, Emily Cox, Christine Bennett, Jonathan Carlson

Medical Papers and Journal Articles

Background: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurological symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to (1) investigate preoperative clinical risk factors, and (2) build machine learning models to predict adverse outcomes.

Methods: This multicenter registry collected clinical and demographic characteristics of patients undergoing DBS surgery (n=501) and tabulated occurrence of complications. Logistic regression was used to evaluate risk factors. Supervised learning algorithms were trained and validated on 70% …

Identifying Key Topics Bearing Negative Sentiment On Twitter: Insights Concerning The 2015-2016 Zika Epidemic, Ravali Mamidi, Michele Miller, Tanvi Banerjee, William Romine, Amit Sheth

Publications

Background To understand the public sentiment regarding the Zika virus, social media can be leveraged to understand how positive, negative, and neutral sentiments are expressed in society. Specifically, understanding the characteristics of negative sentiment could help inform federal disease control agencies’ efforts to disseminate relevant information to the public about Zika-related issues.

Objective The purpose of this study was to analyze the public sentiment concerning Zika using posts on Twitter and determine the qualitative characteristics of positive, negative, and neutral sentiments expressed.

Methods Machine learning techniques and algorithms were used to analyze the sentiment of tweets concerning Zika. A supervised …

Qoe Enhancement In Next Generation Wireless Ecosystems: A Machine Learning Approach, Eva Ibarrola, Mark Davis, Camille Voisin, Ciara Close, Leire Cristobo

Articles

Next-generation wireless ecosystems are expected to comprise heterogeneous technologies and diverse deployment scenarios. Ensuring quality of service (QoS) will be one of the major challenges on account of a variety of factors that are beyond the control of network and service providers in these environments. In this context, ITU-T is working on defining new Recommendations related to QoS and users' quality of experience (QoE) for the 5G era. Considering the new ITU-T QoS framework, we propose a methodology to develop a global QoS management model for next generation wireless ecosystems taking advantage of big data and machine learning (ML). The …

Deep Learning-Based Visual Crack Detection Using Google Street View Images, Mohsen Maniat

Electronic Theses and Dissertations Archive

The need for developing an economical and efficient quality assessment system for pave-ment motivates this study to take advantage of available new technologies and provide a novel approach to address this need. In this study, the utility of using Google Street View (GSV) for evaluating the quality of pavement is investigated. GSV is a technology featured in Google Maps and Google Earth that provides interactive panoramas along many streets throughout the world. This technology provides a large data set of pavement images that can be used for pavement evaluation. Advanced deep learning algorithms are utilized to automate the pavement assessment …

Analyzing Twitter Feeds To Facilitate Crises Informatics And Disaster Response During Mass Emergencies, Arshdeep Kaur

Dissertations

It is a common practice these days for general public to use various micro-blogging platforms, predominantly Twitter, to share ideas, opinions and information about things and life. Twitter is also being increasingly used as a popular source of information sharing during natural disasters and mass emergencies to update and communicate the extent of the geographic phenomena, report the affected population and casualties, request or provide volunteering services and to share the status of disaster recovery process initiated by humanitarian-aid and disaster-management organizations. Recent research in this area has affirmed the potential use of such social media data for various disaster …

Eeg-Based Processing And Classification Methodologies For Autism Spectrum Disorder: A Review, Gunavaran Brihadiswaran, Dilantha Haputhanthri, Sahan Gunathilaka, Dulani Meedeniya, Sampath Jayarathna

Computer Science Faculty Publications

Autism Spectrum Disorder is a lifelong neurodevelopmental condition which affects social interaction, communication and behaviour of an individual. The symptoms are diverse with different levels of severity. Recent studies have revealed that early intervention is highly effective for improving the condition. However, current ASD diagnostic criteria are subjective which makes early diagnosis challenging, due to the unavailability of well-defined medical tests to diagnose ASD. Over the years, several objective measures utilizing abnormalities found in EEG signals and statistical analysis have been proposed. Machine learning based approaches provide more flexibility and have produced better results in ASD classification. This paper presents …

Data Patterns Discovery Using Unsupervised Learning, Rachel A. Lewis

College of Graduate Studies: Theses & Dissertations

Self-care activities classification poses significant challenges in identifying children’s unique functional abilities and needs within the exceptional children healthcare system. The accuracy of diagnosing a child's self-care problem, such as toileting or dressing, is highly influenced by an occupational therapists’ experience and time constraints. Thus, there is a need for objective means to detect and predict in advance the self-care problems of children with physical and motor disabilities. We use clustering to discover interesting information from self-care problems, perform automatic classification of binary data, and discover outliers. The advantages are twofold: the advancement of knowledge on identifying self-care problems in …

Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods

Maseeh Summer Undergraduate Research Experience

In machine learning research, adversarial examples are normal inputs to a classifier that have been specifically perturbed to cause the model to misclassify the input. These perturbations rarely affect the human readability of an input, even though the model’s output is drastically different. Recent work has demonstrated that image-classifying deep neural networks (DNNs) can be reliably fooled with the modification of a single pixel in the input image, without knowledge of a DNN’s internal parameters. This “one-pixel attack” utilizes an iterative evolutionary optimizer known as differential evolution (DE) to find the most effective pixel to perturb, via the evaluation of …

Computational Modeling Of Trust Factors Using Reinforcement Learning, C. M. Kuzio, A. Dinh, C. Stone, L. Vidyaratne, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

As machine-learning algorithms continue to expand their scope and approach more ambiguous goals, they may be required to make decisions based on data that is often incomplete, imprecise, and uncertain. The capabilities of these models must, in turn, evolve to meet the increasingly complex challenges associated with the deployment and integration of intelligent systems into modern society. Historical variability in the performance of traditional machine-learning models in dynamic environments leads to ambiguity of trust in decisions made by such algorithms. Consequently, the objective of this work is to develop a novel computational model that effectively quantifies the reliability of autonomous …

Differentiation Of Patients With Balance Insufficiency (Vestibular Hypofunction) Versus Normal Subjects Using A Low-Cost Small Wireless Wearable Gait Sensor, Tam Q. Nguyen, Jonathan H. Young, Amanda Rodriquez, Steven Zupancic, Donald Y. C. Lie

Department of Special Education and Communication Disorders: Faculty Publications

Balance disorders present a significant healthcare burden due to the potential for hospitalization or complications for the patient, especially among the elderly population when considering intangible losses such as quality of life, morbidities, and mortalities. This work is a continuation of our earlier works where we now examine feature extraction methodology on Dynamic Gait Index (DGI) tests and machine learning classifiers to differentiate patients with balance problems versus normal subjects on an expanded cohort of 60 patients. All data was obtained using our custom designed low-cost wireless gait analysis sensor (WGAS) containing a basic inertial measurement unit (IMU) worn by …

Applied Deep Learning In Orthopaedics, William Stewart Burton Ii

Electronic Theses and Dissertations

The reemergence of deep learning in recent years has led to its successful application in a wide variety of fields. As a subfield of machine learning, deep learning offers an array of powerful algorithms for data-driven applications. Orthopaedics stands to benefit from the potential of deep learning for advancements in the field. This thesis investigated applications of deep learning for the field of orthopaedics through the development of three distinct projects.

First, algorithms were developed for the automatic segmentation of the structures in the knee from MRI. The resulting algorithms can be used to accurately segment full MRI scans in …

Transfer Learning Approach To Multiclass Classification Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

The classification of facial expression has been extensively studied using adult facial images which are not appropriate ground truths for classifying facial expressions in children. The state-of-the-art deep learning approaches have been successful in the classification of facial expressions in adults. A deep learning model may be better able to learn the subtle but important features underlying child facial expressions and improve upon the performance of traditional machine learning and feature extraction methods. However, unlike adult data, only a limited number of ground truth images exist for training and validating models for child facial expression classification and there is a …

Transfer Learning For Detecting Unknown Network Attacks, Juan Zhao, Sachin Shetty, Jan Wei Pan, Charles Kamhoua, Kevin Kwiat

VMASC Publications

Network attacks are serious concerns in today’s increasingly interconnected society. Recent studies have applied conventional machine learning to network attack detection by learning the patterns of the network behaviors and training a classification model. These models usually require large labeled datasets; however, the rapid pace and unpredictability of cyber attacks make this labeling impossible in real time. To address these problems, we proposed utilizing transfer learning for detecting new and unseen attacks by transferring the knowledge of the known attacks. In our previous work, we have proposed a transfer learning-enabled framework and approach, called HeTL, which can find the common …

When Ais Outperform Doctors: Confronting The Challenges Of A Tort-Induced Over-Reliance On Machine Learning, A. Michael Froomkin, Ian Kerr, Joelle Pineau

Articles

Someday, perhaps soon, diagnostics generated by machine learning (ML) will have demonstrably better success rates than those generated by human doctors. What will the dominance of ML diagnostics mean for medical malpractice law, for the future of medical service provision, for the demand for certain kinds of doctors, and in the long run for the quality of medical diagnostics itself?

This Article argues that once ML diagnosticians, such as those based on neural networks, are shown to be superior, existing medical malpractice law will require superior ML-generated medical diagnostics as the standard of care in clinical settings. Further, unless implemented …

Reliability Analysis For Systems With Outsourced Components, Zhengwei Hu

Doctoral Dissertations

"The current business model for many industrial firms is to function as system integrators, depending on numerous outsourced components from outside component suppliers. This practice has resulted in tremendous cost savings; it makes system reliability analysis, however, more challenging due to the limited component information available to system designers. The component information is often proprietary to component suppliers. Motivated by the need of system reliability prediction with outsourced components, this work aims to explore feasible ways to accurately predict the system reliability during the system design stage. Four methods are proposed. The first method reconstructs component reliability functions using limited …

Applications Of Machine Learning In Nuclear Imaging And Radiation Detection, Shaikat Mahmood Galib

Doctoral Dissertations

"The main focus of this work is to use machine learning and data mining techniques to address some challenging problems that arise from nuclear data. Specifically, two problem areas are discussed: nuclear imaging and radiation detection. The techniques to approach these problems are primarily based on a variant of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN), which is one of the most popular forms of 'deep learning' technique.

The first problem is about interpreting and analyzing 3D medical radiation images automatically. A method is developed to identify and quantify deformable image registration (DIR) errors from lung CT scans …

Machine Learning At The Patent Office: Lessons For Patents And Administrative Law, Arti K. Rai

Faculty Scholarship

The empirical data indicate that a relatively small increment of additional U.S. Patent and Trademark Office (“Patent Office” or “USPTO”) investment in prior art search at the initial examination stage could be a cost-effective mechanism for improving accuracy in the patent system. This contribution argues that machine learning provides a promising arena for such investment. Notably, the use of machine learning in patent examination does not raise the same potent concerns about individual rights and discrimination that it raises in other areas of administrative and judicial process. To be sure, even an apparently easy case like prior art search at …

Machine Learning To Classify Animal Species In Camera Trap Images: Applications In Ecology, Michael A. Tabak, Mohammad S. Norouzzadeh, David W. Wolfson, Steven J. Sweeney, Kurt C. Vercauteren, Nathan P. Snow, Joseph M. Halseth, Paul A. Di Salvo, Jesse S. Lewis, Michael D. White, Ben Teton, James C. Beasley, Peter E. Schlichting, Raoul K. Boughton, Bethany Wight, Eric S. Newkirk, Jacob S. Ivan, Eric A. Odell, Ryan K. Brook, Paul M. Lukacs, Anna K. Moeller, Elizabeth G. Mandeville, Jeff Clune, Ryan S. Miller

United States Department of Agriculture Wildlife Services: Staff Publications

1. Motion-activated cameras (“camera traps”) are increasingly used in ecological and management studies for remotely observing wildlife and are amongst the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analysed, typically by visually observing each image, in order to extract data that can be used in ecological analyses.

2. We trained machine learning models using convolutional neural networks with the ResNet-18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an …

Big Data Analytics And Engineering For Medicare Fraud Detection, Matthew Andrew Herland

Electronic Theses and Dissertations 2010 - 2019

The United States (U.S.) healthcare system produces an enormous volume of data with a vast number of financial transactions generated by physicians administering healthcare services. This makes healthcare fraud difficult to detect, especially when there are considerably less fraudulent transactions than non-fraudulent. Fraud is an extremely important issue for healthcare, as fraudulent activities within the U.S. healthcare system contribute to significant financial losses. In the U.S., the elderly population continues to rise, increasing the need for programs, such as Medicare, to help with associated medical expenses. Unfortunately, due to healthcare fraud, these programs are being adversely affected, draining resources and …

Data Collection Framework And Machine Learning Algorithms For The Analysis Of Cyber Security Attacks, Chad Calvert

Electronic Theses and Dissertations 2010 - 2019

The integrity of network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation strategies. Also, many detection methods for popular network attacks have been developed using outdated or non-representative attack data. To effectively develop modern detection methodologies, there exists a need to acquire data that can fully encompass the behaviors of persistent and emerging threats. When collecting modern day network traffic for intrusion detection, substantial amounts of traffic can be collected, much of which consists of relatively few attack instances as …

Generative Adversarial Network Data Generation For The Use Of Real Time Image Detection In Side-Scan Sonar Imagery, James Patrick Mcginley

Electronic Theses and Dissertations 2010 - 2019

Automatic target recognition of unexploded ordnances in side scan sonar imagery has been a struggling task, due to the lack of publicly available side-scan sonar data. Real time image detection and classification algorithms have been implemented to combat this task, however, machine learning algorithms require a substantial amount of training data to properly detect specific targets. Transfer learning methods are used to replace the need of large datasets, by using a pre trained network on the side-scan sonar images. In the present study the implementation of a generative adversarial network is used to generate meaningful sonar imagery from a small …

Investigating Machine Learning Algorithms With Imbalanced Big Data, Tawfiq Hasanin

Electronic Theses and Dissertations 2010 - 2019

Recent technological developments have engendered an expeditious production of big data and also enabled machine learning algorithms to produce high-performance models from such data. Nonetheless, class imbalance (in binary classifications) between the majority and minority classes in big data can skew the predictive performance of the classification algorithms toward the majority (negative) class whereas the minority (positive) class usually holds greater value for the decision makers. Such bias may lead to adverse consequences, some of them even life-threatening, when the existence of false negatives is generally costlier than false positives. The size of the minority class can vary from fair …

Multi-Model Deep Learning For Grouper Sound Classification And Seizure Prediction, Ali K. Ibrahim

Electronic Theses and Dissertations 2010 - 2019

Deep learning models have been successfully applied to a variety of machine learning tasks, including image identification, image segmentation, object detection, speaker recognition, natural language processing, bioinformatics and drug discovery, among other things. This dissertation introduces Multi-Model Deep Learning (MMDL), a new ensemble deep learning approach for signal classification and event forecasting. The ultimate goal of the MMDL method is to improve classification and forecasting performances of individual classifiers by fusing results of participating deep learning models. The performance of such an ensemble model, however, depends heavily on the following two design features. Firstly, the diversity of the participating (or …

The Right To Explanation, Explained, Margot E. Kaminski

Publications

Many have called for algorithmic accountability: laws governing decision-making by complex algorithms, or AI. The EU’s General Data Protection Regulation (GDPR) now establishes exactly this. The recent debate over the right to explanation (a right to information about individual decisions made by algorithms) has obscured the significant algorithmic accountability regime established by the GDPR. The GDPR’s provisions on algorithmic accountability, which include a right to explanation, have the potential to be broader, stronger, and deeper than the preceding requirements of the Data Protection Directive. This Essay clarifies, largely for a U.S. audience, what the GDPR actually requires, incorporating recently released …