Physical Sciences and Mathematics Commons^™

Randomness Distillation To Improve Key Quality For Context-Based Authentication Schemes, Jackson West

Master's Theses

Context-based authentication is a method for transparently validating another device’slegitimacy to join a network based on location. Devices can pair with one another by continuously harvesting environmental noise to generate a random key with no user involvement. However, there are gaps in our understanding of the theoretical limitations of environmental noise harvesting, making it difficult for researchers to build efficient algorithms for sampling environmental noise and distilling keys from that noise. This work explores the information-theoretic capacity of context-based authentication mechanisms to generate random bit strings from environmental noise sources with known properties. Using only mild assumptions about the source …

Predicting Drug Misuse Status Using Machine Learning On Electronic Health Records, Robert Arnold Kania

Master's Theses

Substance misuse is a major problem in the world. in 2014, as many as 52,404 deaths in the US were caused by drug overdoses. in 2001, the monetary cost of drug misuse has been estimated to be 414 billion dollars. in this work, we explore the use of different machine learning algorithms in the prediction of cocaine misuse using structured and unstructured data found in electronic health records. These records contain various attributes that can help with this prediction, including but not limited to chart text data, previous diagnoses of certain diseases and information about the area the patient lives …

Wayfinder Application For Autistic Occupational Assistance, Nathaniel Edward Hishon

Master's Theses

Employment among autistic individuals is an area of noted difficulty, with an employment rate well below the general population [1]. Several barriers attributed to autistic unemployment, including difficulties communicating with employers and social interactions with coworkers, obsessive adherence to routine, and trouble organizing and completing workplace tasks, are also attributed to challenges in maintaining employment [2]. Several studies have concluded that long-term employment support is necessary to acquire and maintain autistic employment [3]. The noted benefit of intensive job training, such as access to job coaches, indicates the need for further support to help autistic individuals complete workplace tasks and …

Using Software-Defined Networking And Openflow Switching To Reroute Network Traffic Dynamically Based On Traffic Volume Measurements, Ihab Al Shaikhli

Master's Theses

Traditional switching and routing have been very effective for network packet delivery but does create some constraints. for example, all packets from a given source to a given destination must always take the same path. Within a traditional Ethernet network, a tree topology must be used. Software-Defined Networking (SDN) has the potential to bypass this tree-topology limitation by placing the control of the switches and their forwarding tables under a central device called a controller. SDN also allows for sets of controllers. the controller can identify individual network flows and issue commands to the switches to, in effect, assign individual …

Opioid Misuse Detection In Hospitalized Patients Using Convolutional Neural Networks, Brihat Sharma

Master's Theses

Opioid misuse is a major public health problem in the world. In 2016, 11.3 million people were reported to misuse opioids in the US only. Opioid-related inpatient and emergency department visits have increased by 64 percent and the rate of opioid-related visits has nearly doubled between 2009 and 2014. It is thus critical for healthcare systems to detect opioid misuse cases. Patients hospitalized for consequences of their opioid misuse present an opportunity for intervention but better screening and surveillance methods are needed to guide providers. The current screening methods with self-report questionnaire data are time-consuming and difficult to perform in …

A Study Into The Feasibility Of Using Natural Language Processing And Machine Learning For The Identification Of Alcohol Misuse In Trauma Patients, Andrew Phillips

Master's Theses

Alcohol misuse is a leading cause of premature death in the United States, with nearly a third of trauma patients found to have elevated blood alcohol levels upon admission. However, timely intervention has been shown to reduce this. It is thus important to be able to quickly screen patients to identify alcohol misuse. Many medical centers use standardized questionnaires to identify alcohol misuse, but since these instruments are not usually a part of routine care, there are many cases where it is not done.

In this study, large quantities of notes were processed with natural language processing and machine learning …

Toddler Activity Recognition Using Machine Learning, Pinky Sindhu

Master's Theses

Pinky Sindhu

Loyola University Chicago

TODDLER ACTIVITY RECOGNITION USING MACHINE LEARNING

Toddlers behave differently than adults, to say the least. It is valuable to accurately measure the specific types of physical activity (PA) in toddlers; such information can be analyzed to predict future health prospects in relation to conditions like obesity.

We attached ActiGraph accelerometers to the wrist and waist of toddlers and recorded PAs. Toddlers were videotaped, and their movements were annotated as 20 specific activities. These activities were classified into 3 summary activity intensities including sedentary, light intensity PA (LPA), and moderate to vigorous intensity PA (MVPA).

Automated …

Real-Time Fall Detection And Response On Mobile Phones Using Machine Learning, Ilona Shparii

Master's Theses

Falls are common and often dangerous for groups with impaired mobility, like the elderly or people with lower limb amputations. Finding ways of minimizing the frequency or impact of a fall can improve quality of life dramatically. When someone does fall, real-time detection of the fall and a long-lie can trigger fast medical assistance. Such a system can also collect reliable data on the nature of real-world falls that can be used to better understand the circumstances, to aid in prevention efforts. This work has been to develop a real-time fall tracking system specifically for subjects with lower limb amputations. …

A Mobile App Illustrating Sensory Neural Coding Through An Efficient Coding Of Collected Images And Sounds, Xiaolu Zhao

Master's Theses

Sensory neuroscience in the early auditory and visual systems appears distinct not

only to outside observers, but to many trained neuroscientists as well. However, to a computational neuroscientist, both sensory systems represent an efficient neural coding of information. In fact, on a computational level it appears the brain is using the same processing strategy for both senses - the same algorithm with just a change in inputs. Insights like this can greatly simplify our understanding of the brain, but require a significant computational background to fully appreciate. How can such illuminating results of computational neuroscience be made more accessible to …

Metrics Dashboard Services: A Framework For Analyzing Free/Open Source Team Repositories, Fnu Shilpika

Master's Theses

Software engineering as practiced today (especially in the industry) is no longer about the stereotypical monolithic life cycle processes (e.g. waterfall, spiral, etc.) found in most software engineering textbooks. These heavyweight methods historically have impeded progress for small/medium sized development teams owing to their inherent complexity and rather limited data collection strategies that predominated the 1980s until relatively recently in the mid-2000s. The discipline and practice of software engineering includes software quality, which has an established theoretical foundation for doing software metrics. Software metrics are a critical tool which provide continuous insight to products and processes and help build reliable …

Activity Recognition For Incomplete Spinal Cord Injury Subjects Using Hidden Markov Models, Pichleap Sok

Master's Theses

Successful activity recognition in patients with motor disabilities can improve patient care by providing researchers and clinicians with valuable information on patient movements and quality of life in real-world settings. Understanding the everyday activities of patients is important for rehabilitation. For researchers, having convenient, objective, and continuous data can drastically improve outcome measures to better compare therapies, and ultimately make recommendations. For clinicians, individual assessment of compliance and outcomes outside the clinic can be more objective, permitting much more tailored recommendations to patients. Most importantly, for individual patients, activity recognition can make this improved health care possible by simply having …