Physical Sciences and Mathematics Commons^™

Classification And Explanation Of Iron Deficiency Anemia From Complete Blood Count Data Using Machine Learning, Siddartha Pullakhandam

Theses and Dissertations

Anemia is a global health problem, and over 2 billion people are affected. Although, the major cause of anemia is iron deficiency (IDA), global estimates suggest that only about half of anemia could be attributed to ID. The typical test of anemia involves measurement of hemoglobin using Complete Blood Count (CBC) test, which also gives additional information on blood cell numbers and morphology. The diagnosis of iron deficiency anemia (IDA, both anemic and ID co-exist in a subject) requires additional expensive serum ferritin test. However, blood cell count, and morphology can also be utilized for diagnosis of IDA. The goal …

Predicting Energy Expenditure From Physical Activity Videos Using Optical Flows And Deep Learning, Gayatri Kasturi

Theses and Dissertations

This thesis presents a novel approach for predicting energy expenditure of physical activity from videos using optical flows and deep learning. Conventional approaches mainly rely on wearable sensors, which, despite being widely used, are constrained by practicality and accuracy concerns. This proposal introduces a new strategy that utilizes a three-dimensional Convolutional Neural Network (3D-CNN) to evaluate video data and accurately estimate energy costs in metabolic equivalents (METs). Our model utilizes optical flow extraction to analyze video, capturing complex motion patterns and their changes over time. The results are good indicating potential for this method to be deployed in various healthcare …

Use Of Digital Twins To Mitigate Communication Failures In Microgrids, Andrew Eggebeen

Theses and Dissertations

This work investigates digital twin (DT) applications for electric power system (EPS) resilience. A novel DT architecture is proposed consisting of a physical twin, a virtual twin, an intelligent agent, and data communications. Requirements for the virtual twin are identified. Guidelines are provided for generating, capturing, and storing data to train the intelligent agent. The relationship between the DT development process and an existing controller hardware-in-the-loop (CHIL) process is discussed. To demonstrate the proposed DT architecture and development process, a DT for a battery energy storage system (BESS) is created based on the simulation of an industrial nanogrid. The creation …

Big Data Applications And Challenges In Giscience (Case Studies: Natural Disaster And Public Health Crisis Management), Amir Masoud Forati

Theses and Dissertations

This dissertation examines the application and significance of user-generated big data in Geographic Information Science (GIScience), with a focus on managing natural disasters and public health crises. It explores the role of social media data in understanding human-environment interactions and in informing disaster management and public health strategies. A scalable computational framework will be developed to model extensive unstructured geotagged data from social media, facilitating systematic spatiotemporal data analysis.The research investigates how individuals and communities respond to high-impact events like natural disasters and public health emergencies, employing both qualitative and quantitative methods. In particular, it assesses the impact of socio-economic-demographic …

A Design Strategy To Improve Machine Learning Resiliency Of Physically Unclonable Functions Using Modulus Process, Yuqiu Jiang

Theses and Dissertations

Physically unclonable functions (PUFs) are hardware security primitives that utilize non-reproducible manufacturing variations to provide device-specific challenge-response pairs (CRPs). Such primitives are desirable for applications such as communication and intellectual property protection. PUFs have been gaining considerable interest from both the academic and industrial communities because of their simplicity and stability. However, many recent studies have exposed PUFs to machine-learning (ML) modeling attacks. To improve the resilience of a system to general ML attacks instead of a specific ML technique, a common solution is to improve the complexity of the system. Structures, such as XOR-PUFs, can significantly increase the nonlinearity …

Statically Scheduling Circular Remote Attribute Grammars, Seyedamirhossein Hesamian

Theses and Dissertations

Classical attribute grammars invented by Knuth have been the subject of extensive study. Over the years there have been various extensions introduced, each with the goal of making attribute grammar more useful for applications such as program analysis. The first extension described here is circular attribute grammar by Farrow. It is followed by remote attribute grammar, which was introduced separately by Boyland and Hedin. More recently, Hedin introduced circular remote attribute grammars and a proof of concept implementation with demand evaluation. Remote attribute grammars make it possible for semantic rules to access attributes of nodes that are not local, and …

Automated Human Activity Recognition From Controlled Environment Videos, Pranay Mandadapu

Theses and Dissertations

This thesis explores deep learning methods for Human Activity Recognition (HAR) from videos to automate the annotation of human activities in videos. The research is particularly relevant for continuous monitoring in healthcare settings such as nursing homes and hospitals. The innovative part of the approach lies in using YOLO models to first detect humans in video frames and then isolating them from the rest of the image for activity recognition which leads to an improvement in accuracy. The study employs pre-trained deep residual networks, such as ResNet50, ResNet152-V2, and Inception-ResNetV2, which were found to work better than custom CNN-based models. …

Realistic Speed Control Of Agents In Traffic Simulation, Lakshman Karthik Ramkumar

Theses and Dissertations

Agents in multi-agent traffic simulation tend to be more dependent on the rules and existing instructions to move mechanically and unnaturally imitating human behaviors. The agents will not accelerate or decelerate as humans do. Humans have an irregular pattern of acceleration and deceleration when it comes to real-time driving. This includes hitting breaks when not necessary and sometimes even driving above the speed limit to catch up. In prior works, other factors such as drag and simulation-specific parameters were not considered in the models. Additionally, the models were not tested on the traffic simulation frameworks like SUMO. Instead, they utilized …

Parallelization Of Dial-A-Ride Using Tabu Search, Raghuveer Naraharisetti

Theses and Dissertations

Dial-A-Ride is a transport system heavily constrained by following fleet size, vehicle capacity, and a fixed number of requests (pickup and drop-off points) with time windows. It is often modelled as Integer Programming, various solutions are proposed using heuristics. One such heuristic is "Tabu Search". Tabu Search is very CPU intensive with its process of search, therefore many modern computing techniques like using GPUs have been employed to make it efficient.

As with many other greedy algorithms, the local optima is not always the same as the global optima, so it is not possible to go past the local optima …

Methodology For Designing Assistive Robots For Activities Of Daily Living Assistance, Javier Dario Sanjuan De Caro

Theses and Dissertations

The growing prevalence of Upper or Lower Extremities Dysfunctions (ULED), often linked to central nervous disorders such as stroke, Spinal Cord Injury (SCI), and Multiple Sclerosis (MS), underscores the urgent need for innovative support solutions. Over 5.35 million Americans currently live with ULED, a situation that places a significant socioeconomic burden on families and society. Despite invaluable support from caregivers and family members, the need for more scalable, practical solutions persists.

Wheelchair-mounted assistive robots emerge as a promising alternative in this context. These devices, offering continuous and reliable assistance, significantly alleviate caregiver fatigue and enhance the independence and quality of …

Estimating Energy Cost Of Physical Activities From Video Using 3d-Cnn Networks, Pragya Shrestha Chansi

Theses and Dissertations

This research proposes a machine learning model that can estimate the energy cost of physical activities from video input. Currently, wearable sensors are commonly used for this purpose, but they have limitations in terms of practicality and accuracy. A deep learning model using three dimensional convolutional neural network (3D-CNN) architecture was used to process the video data and predict the energy cost in terms of metabolic equivalents (METs). The proposed model was evaluated on a dataset of physical activity videos and achieved an average accuracy of 71% on energy category prediction task and an root mean squared error (RMSE) of …

Concurrency Controls In Event-Driven Programs, Yonglun Li

Theses and Dissertations

Functional reactive programming (FRP) is a programming paradigm that utilizes the concepts of functional programming and time-varying data types to create event-driven applications. In this paradigm, data types in which values can change over time are primitives and can be applied to functions. These values are composable and can be combined with functions to create values that react to changes in values from multiple sources. Events can be modeled as values that change in discrete time steps. Computation can be encoded as values that produce events, with combination operators, it enables us to write concurrent event-driven programs by combining the …

Vision-Based Object Manipulation For Activities Of Daily Living (Adl) Assistance Using Assistive Robot, Md Tanzil Shahria

Theses and Dissertations

Upper and lower extremity (ULE) functional deficiencies, which limit a person's ability to perform everyday tasks, have increased at an alarming rate over the past few decades. It is essential for individuals with impairments to take care of themselves without requiring a significant amount of support from other individuals. Few assistive devices are available in the market to make their life comfortable, yet controlling them sometimes becomes challenging for this group of people. Robotic devices are emerging as assistive devices to assist individuals with limited ULE functionalities in activities of daily living (ADL). As most of these devices only allow …

Extracting Patterns Of Semantic Roles From Accident Narratives, Soundarya Jayakumar

Theses and Dissertations

Accident databases are filled with rich information about accidents. Analyzing these datasets can reveal useful information which can be used to prevent similar accidents in the future. Policy makers, and safety management organizations can design appropriate measures based on the analysis done to prevent accidents. Besides structured data, crash reports include natural language narratives which contain valuable accident-related information which is otherwise not present in the structured data. Using natural language processing (NLP) techniques one can analyze these narratives and mine hidden patterns of accidents from them. The thesis focuses on developing an algorithm to extract common patterns of semantic …

Leveraging Biomedical Ontological Knowledge To Improve Clinical Term Embeddings, Fuad Hatem Abuzahra

Theses and Dissertations

ABSTRACT Leveraging Biomedical Ontological Knowledge to Improve Clinical Term Embeddings by Fuad Abu Zahra The University of Wisconsin-Milwaukee, 2023 Under the Supervision of Dr. Rohit J. Kate This research is on obtaining and using word embeddings for natural language processing tasks in the biomedical domain. Word embeddings are vector representations of words commonly obtained from large text corpora. This research leverages the biomedical ontology of SNOMED CT as an alternate source for obtaining embeddings for clinical terms. The existing graph-based methods can only give embeddings for concepts (i.e., nodes of the graph) of an ontology, hence we developed a novel …

Emotion Classification And Intensity Prediction On Tweets, Sharath Chander Pugazhenthi

Theses and Dissertations

The task of finding an emotion associated with the text from individuals on a social media platform has become very crucial as it influences the current state of mind of a particular individual in real life. It also helps one to understand social behavior at a given point in time. Microblogging platforms like Twitter serves as a powerful tool for expressing one’s thoughts. Several work have been done in classifying the emotion associated with it. The thesis comprises of a system that first classifies the tweet into one of the four emotions - anger, joy, sadness, and fear with good …

Future Of Functional Reactive Programming In Real-Time Systems, Anisha Tasnim

Theses and Dissertations

The evolution of programming paradigms and the development of new programming languages are driven by the needs of problem domains. Functional reactive programming (FRP) combines functional programming (FP) and reactive programming (RP) concepts that leverage asynchronous dataflow from reactive programming and higher-level abstractions building blocks from functional programming to enable developers to define data flows and transformations declaratively. Declarative programming allows developers to concentrate more on the problem to be solved rather than the implementation details, resulting in efficient and concise code. Over the years, various FRP designs have been proposed in real-time application areas. Still, it remains unclear how …

Modeling Wlan Received Signal Strengths Using Gaussian Process Regression On The Sodindoorloc Dataset, Fabian Hermann Josef Fuchs

Theses and Dissertations

While any wireless technology can be used for indoor localization purposes, WLANhas the advantage of having a huge existing infrastructure. A radio map that matches specific locations to received signal strength is needed, to enable most of these indoor localization methods. To create these radio maps, with enough detail to achieve sufficient localization accuracy, is expensive and time consuming. Therefore, methods to interpolate and extrapolate more detailed maps from sparse radio maps are being developed. One recent approach is to use Gaussian process regression. Even though some papers already studied Gaussian process regression, most studied only the basic model with …

An Ethercat Based Real-Time Control System Design For Wheelchair-Mounted 6dof Assistive Robotic Arm, Ivan Alexander Rulik Cote

Theses and Dissertations

Numerous assistive robots for individuals with disabilities have been produced over the past ten years, but researchers have not completely exploited these robotic technologies to enable people with impairments to live independently, especially in respect to activities of daily living. (ADLs). For people with impairments, an assistive system can help them fulfill the requirements of typical ADLs. Assistive robots can help address future healthcare demands due to a growing need for caregivers, a scarcity of them, and an increase in the number of the elderly and people with disabilities. Enhancing functional independence while creating a superior human-machine interaction is one …

Use Of Machine Learning And Natural Language Processing To Enhance Traffic Safety Analysis, Md Abu Sayed

Theses and Dissertations

Despite significant advances in vehicle technologies, safety data collection and analysis, and engineering advancements, tens of thousands of Americans die every year in motor vehicle crashes. Alarmingly, the trend of fatal and serious injury crashes appears to be heading in the wrong direction. In 2021, the actual rate of fatalities exceeded the predicted rate. This worrisome trend prompts and necessitates the development of advanced and holistic approaches to determining the causes of a crash (particularly fatal and major injuries). These approaches range from analyzing problems from multiple perspectives, utilizing available data sources, and employing the most suitable tools and technologies …

A Protocol To Build Trust With Black Box Models, Timothy K. Thielke

Theses and Dissertations

Data scientists are more widely using artificial intelligence and machine learning (ML) algorithms today despite the general mistrust associated with them due to the lack of contextual understanding of the domain occurring within the algorithm. Of the many types of ML algorithms, those that use non-linear activation functions are especially regarded with suspicion because of the lack of transparency and intuitive understanding of what is occurring within the black box of the algorithm. In this thesis, we set out to create a protocol to delve into the black box of an ML algorithm set to predict synoptic severe weather patterns …

Image-Based Cancer Diagnosis Using Novel Deep Neural Networks, Hosein Barzekar

Theses and Dissertations

Cancer is the major cause of death in many nations. This serious illness can only be effectivelytreated if it is diagnosed early. In contrast, biomedical imaging presents challenges to both clinical institutions and researchers. Physiological anomalies are often characterized by modest modifications in individual cells or tissues, making them difficult to detect visually. Physiological anomalies are often characterized by slight abnormalities in individual cells or tissues, making them difficult to detect visually. Traditionally, anomalies are diagnosed by radiologists and pathologists with extensive training. This procedure, however, demands the participation of professionals and incurs a substantial expense, making the classification of …

Pipeline For Calculating Calories For Print Recipes With Minimal User Intervention, Karl W. Holten

Theses and Dissertations

The thesis will provide a pipeline to estimate calorie counts from print recipes. The pipeline takes scanned recipes from cookbooks and uses Optical Character Recognition (OCR) to convert the scanned images of recipes to text. Several OCR tools were tested for their accuracy on fractions using a sample of the data, and the most accurate tool is used on the data. Next, a specially trained named entity recognition model is used to identify ingredients, quantities and units. These ingredients are used to search a database of values from the FDA to compute a calorie count for the recipe. The thesis …

Medical Image Segmentation With Deep Convolutional Neural Networks, Chuanbo Wang

Theses and Dissertations

Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images are time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images has been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and …

Novel Deep Neural Network For Medical Image Classification, Dm Anisuzzaman

Theses and Dissertations

Medical image classification is an essential part of diagnosis, which with automation may benefit both physicians and patients in terms of time and cost. For automation, different Artificial intelligence (AI) methods, including Machine Learning (ML) and Deep Learning (DL), are used widely. Specifically, DL algorithms have become popular in classifying medical images due to their propensity for good performance. This thesis studies medical image classification problems using deep learning models. Four specific medical applications are considered: (1) Osteosarcoma cancer classification in histological images, (2) Burn wound classification, (3) Wound severity classification from clinical images, and (4) Wound type classification using …

Convivial Making: Power In Public Library Creative Places, Shannon Crawford Barniskis

Theses and Dissertations

In 2011, public libraries began to provide access to collaborative creative places, frequently called “makerspaces.” The professional literature portrays these as beneficial for communities and individuals through their support of creativity, innovation, learning, and access to high-tech tools such as 3D printers. As in longstanding “library faith” narratives, which pin the library’s existence to widely held values, makerspace rhetoric describes access to tools and skills as instrumental for a stronger economy or democracy, social justice, and/or individual happiness. The rhetoric generally frames these places as empowering. Yet the concept of power has been neither well-theorized within the library makerspace literature …

Patient Centric Solutions To Mitigate Information Need Of Obstetric Ultrasound Exam Among Pregnant Women: Design-Thinking Approach, Eman Alanazi

Theses and Dissertations

Design thinking approach is an approach used widely to solve problems by providing innovative solutions. In this dissertation I focused on the user experience research filed where I designed new obstetric ultrasound reports by adopting the design thinking approach to reach the main goal of the dissertation which is mitigating pregnant women information needs about obstetric ultrasound exam and improve their understanding and knowledge about the obstetric ultrasound report and the exam. I developed two versions of new designed report called SPOUR (Smart Patient-Oriented Obstetric Ultrasound Report). We have conducted five studies to reach the dissertation goal and designed two …

The Analysis Of User Characteristics On Twitter During Early Stage Of The Covid-19 Pandemic: A Comparison Study Before And After Declaration Of The Covid-19 Pandemic, Mutasim Alfadhel

Theses and Dissertations

In December 2019, the coronavirus disease 2019 (Covid-19) was officially reported as an acute respiratory infection, which was first identified in Wuhan, China. On March 11th, 2020, the World Health Organization (WHO) declared that Covid-19 could be characterized as a pandemic. Governments across the world imposed or recommended various non-medical interventions to reduce transmission of Covid-19, such as washing hands, wearing face masks, social distancing, and quarantining as well as lockdown measures including banning large gatherings, issuing stay-at-home orders, closing certain businesses, and imposing travel restrictions. The increase in social, behavioral, and economic issues that the disease has generated has …

Development Of A Novel Telemanipulation Framework For Human-Robot Collaboration Using Ptc Thingworx And Vuforia Studio, Preet Parag Modi

Theses and Dissertations

Significant advancements in contemporary telehealth care applications are enforcing the demand for effective and intuitive telerehabilitation tools. The current techniques for observing live process parameters of robots frequently require complex and inefficient methods, which fundamentally limits the human administrator's ability to settle on the most educated decisions possible. Telemanipulation can minimize the distance and costs in varieties of robot applications, including industry for object manipulation and robot-aided rehabilitation. This research aims to develop a novel telemanipulation framework to deliver robot-assisted rehabilitation using PTC ThingWorx’s Industrial Internet of Things (IIoT), and Vuforia Studio’s Augmented Reality (AR) platforms. This communication architecture is …

Advanced Analytics In Smart Manufacturing: Anomaly Detection Using Machine Learning Algorithms And Parallel Machine Scheduling Using A Genetic Algorithm, Meiling He

Theses and Dissertations

Industry 4.0 offers great opportunities to utilize advanced data processing tools by generating Big Data from a more connected and efficient data collection system. Making good use of data processing technologies, such as machine learning and optimization algorithms, will significantly contribute to better quality control, automation, and job scheduling in Smart Manufacturing. This research aims to develop a new machine learning algorithm for solving highly imbalanced data processing problems, implement both supervised and unsupervised machine learning auto-selection frameworks for detecting anomalies in smart manufacturing, and develop a genetic algorithm for optimizing job schedules on unrelated parallel machines. This research also …