Computer Engineering Commons^™

Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego

Electrical & Computer Engineering Theses & Dissertations

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …

Metabolomic Differentiation Of Tumor Core And Edge In Glioma., Mary E. Baxter

Electronic Theses and Dissertations

Glioma is one of the most aggressive forms of brain cancer. It has been shown that the microenvironments differ significantly between the core and edge regions of glioma tumors. This study obtained metabolomic profiles of glioma core and edge regions using paired glioma core and edge tissue samples from 27 human patients. Data was acquired by performing liquid-liquid metabolite extraction and 2DLC-MS/MS on the tissue samples. In addition, a boosted generalized linear machine learning model was employed to predict the metabolomic profiles associated with O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation.

A panel of 66 metabolites was found to be statistically significant …

A Framework For Stable Robot-Environment Interaction Based On The Generalized Scattering Transformation, Kanstantsin Pachkouski

Electronic Thesis and Dissertation Repository

This thesis deals with development and experimental evaluation of control algorithms for stabilization of robot-environment interaction based on the conic systems formalism and scattering transformation techniques. A framework for stable robot-environment interaction is presented and evaluated on a real physical system. The proposed algorithm fundamentally generalizes the conventional passivity-based approaches to the coupled stability problem. In particular, it allows for stabilization of not necessarily passive robot-environment interaction. The framework is based on the recently developed non-planar conic systems formalism and generalized scattering-based stabilization methods. A comprehensive theoretical background on the scattering transformation techniques, planar and non-planar conic systems is presented. …

Breast Cancer Detection From Histopathology Images Using Machine Learning Techniques: A Bibliometric Analysis, Shubhangi A. Joshi, Anupkumar M. Bongale Dr., Arunkumar M. Bongale Dr.

Library Philosophy and Practice (e-journal)

Computer aided diagnosis has become upcoming area of research over past few years. With the advent of machine learning and especially deep learning techniques, the scenario of work flow management in healthcare sector is changing drastically. Artificial intelligence has shown potential in the field of breast cancer care. With datasets for machine learning frameworks getting eventually richer with time, we can definitely get newer insights in the field of breast cancer care. This will help in narrowing down the treatment range for patients and increasing patient survivability. The purpose of this study was to perform bibliometric analysis of the literature …

Machine Learning Approaches For Lung Cancer Diagnosis., Ahmed Mahmoud Ahmed Shaffie

Electronic Theses and Dissertations

The enormity of changes and development in the field of medical imaging technology is hard to fathom, as it does not just represent the technique and process of constructing visual representations of the body from inside for medical analysis and to reveal the internal structure of different organs under the skin, but also it provides a noninvasive way for diagnosis of various disease and suggest an efficient ways to treat them. While data surrounding all of our lives are stored and collected to be ready for analysis by data scientists, medical images are considered a rich source that could provide …

Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han

Public Health Faculty Publications

The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures …

Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead

Engineering Faculty Articles and Research

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …

Design Of A Distributed Real-Time E-Health Cyber Ecosystem With Collective Actions: Diagnosis, Dynamic Queueing, And Decision Making, Yanlin Zhou

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

In this thesis, we develop a framework for E-health Cyber Ecosystems, and look into different involved actors. The three interested parties in the ecosystem including patients, doctors, and healthcare providers are discussed in 3 different phases. In Phase 1, machine-learning based modeling and simulation analysis is performed to remotely predict a patient's risk level of having heart diseases in real time. In Phase 2, an online dynamic queueing model is devised to pair doctors with patients having high risk levels (diagnosed in Phase 1) to confirm the risk, and provide help. In Phase 3, a decision making paradigm is proposed …

Psychophysiological Analysis Of A Pedagogical Agent And Robotic Peer For Individuals With Autism Spectrum Disorders., Mohammad Nasser Saadatzi

Electronic Theses and Dissertations

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by ongoing problems in social interaction and communication, and engagement in repetitive behaviors. According to Centers for Disease Control and Prevention, an estimated 1 in 68 children in the United States has ASD. Mounting evidence shows that many of these individuals display an interest in social interaction with computers and robots and, in general, feel comfortable spending time in such environments. It is known that the subtlety and unpredictability of people’s social behavior are intimidating and confusing for many individuals with ASD. Computerized learning environments and robots, however, prepare a predictable, …

Analog Spiking Neuromorphic Circuits And Systems For Brain- And Nanotechnology-Inspired Cognitive Computing, Xinyu Wu

Boise State University Theses and Dissertations

Human society is now facing grand challenges to satisfy the growing demand for computing power, at the same time, sustain energy consumption. By the end of CMOS technology scaling, innovations are required to tackle the challenges in a radically different way. Inspired by the emerging understanding of the computing occurring in a brain and nanotechnology-enabled biological plausible synaptic plasticity, neuromorphic computing architectures are being investigated. Such a neuromorphic chip that combines CMOS analog spiking neurons and nanoscale resistive random-access memory (RRAM) using as electronics synapses can provide massive neural network parallelism, high density and online learning capability, and hence, paves …