Digital Commons Network^™

Machine Learning Methods For The Analysis Of Clinical Conversation, Md Mehedi Hasan

Wayne State University Dissertations

Motivational Interviewing (MI) is an evidence-based communication technique to increase intrinsic motivation and self-efficacy for behavior change. This goal is achieved through the exploration of the patient's own desires, ability, reasons, need for and commitment to the targeted behavior change. However, communication science approaches to understanding the efficacy of MI are inherently limited by traditional qualitative coding methods which is a time-consuming and resource-intensive process. Thus, an efficient method is required to automate the coding process which will accelerate the pace of communication research in behavioral science. The specific provider behaviors responsible for the elicitation of change talk, are also …

Utilizing Knowledge Bases In Information Retrieval For Clinical Decision Support And Precision Medicine, Saeid Balaneshinkordan

Wayne State University Dissertations

Accurately answering queries that describe a clinical case and aim at finding articles in a collection of medical literature requires utilizing knowledge bases in capturing many explicit and latent aspects of such queries. Proper representation of these aspects needs knowledge-based query understanding methods that identify the most important query concepts as well as knowledge-based query reformulation methods that add new concepts to a query. In the tasks of Clinical Decision Support (CDS) and Precision Medicine (PM), the query and collection documents may have a complex structure with different components, such as disease and genetic variants that should be transformed to …

Parameter Assignment And Schedulability Analysis For Real-Time Multiframe Task Systems, Bo Peng

Wayne State University Dissertations

Schedulability analysis has been considered as one of the most important subjects in real-time systems. Schedulability analysis decides whether all tasks work correctly and safely in a system. For example, the schedulability analysis of an Air Traffic Control (ATC) system should ensure that all airplanes do not have conflicts on departure lanes and are scheduled on time. In a modern car system, it has been shown that there are more than one hundred engine control units (ECUs), and more than twenty million lines of code in a typical modern car [19]. The scheduling of such complex systems is required to …

Bundle: Taming The Cache And Improving Schedulability Of Multi-Threaded Hard Real-Time Systems, Corey Tessler

Wayne State University Dissertations

For hard real-time systems, schedulability of a task set is paramount. If a task set is not deemed schedulable under all conditions, the system may fail during operation and cannot be deployed in a high risk environment. Schedulability testing has typically been separated from worst-case execution time (WCET) analysis. Each task’s WCET value is calculated independently and provided as input to a schedulability test. However, a task’s WCET value is influenced by scheduling decisions and the impact of cache memory. Thus, schedulability tests have been augmented to include cache-related preemption delay (CRPD). From this classical perspective, the effect of cache …

Deep Learning Beyond Traditional Supervision, Shixing Chen

Wayne State University Dissertations

With the rapid development of innovative models and huge success on various applications, the field of deep learning has attracted enormous attention in computer vision, machine learning, and artificial intelligence. Countless researches have validated the superior performance and unprecedented extensiveness of deep learning models, especially with the advantages of high performance computing by GPUs and parallel computation. Nonetheless, drawbacks including strong dependency on supervision (sufficient labeled data) and monotonous usage of categorized labels are negatively interfering the advancement of deep learning.

In this dissertation, we plan to expose and exploit some possibilities of deep learning without using data and labels …

Data-Driven Intelligent Scheduling For Long Running Workloads In Large-Scale Datacenters, Guoyao Xu

Wayne State University Dissertations

Cloud computing is becoming a fundamental facility of society today. Large-scale public or private cloud datacenters spreading millions of servers, as a warehouse-scale computer, are supporting most business of Fortune-500 companies and serving billions of users around the world. Unfortunately, modern industry-wide average datacenter utilization is as low as 6% to 12%. Low utilization not only negatively impacts operational and capital components of cost efficiency, but also becomes the scaling bottleneck due to the limits of electricity delivered by nearby utility. It is critical and challenge to improve multi-resource efficiency for global datacenters.

Additionally, with the great commercial success of …

Integrating Heuristics To Support Impact Analysis In Software Evolution, Yibin Wang

Wayne State University Dissertations

Iterative impact analysis (IIA) is a process that allows developers to estimate the impacted units of a software change. Starting from a single impacted unit, the developers inspect its interacting units via program dependencies to identify the ones that are also impacted, and this process continues iteratively. Experience has shown that developers often miss impacted units and inspect many irrelevant units.

In order to enhance IIA, first we put forward a new program representation that provides more precise dependencies for software change propagation. Our study showed that the precision of IIA was indeed improved using such a program representation while …

Improving Energy Consumption Of Java Programs, Mohit Kumar

Wayne State University Dissertations

Information and Communications Technologies (ICT) amounts for 10% of the world energy which will keep on growing in the future and 3% of the overall carbon footprint which is now more than the level of CO2 emission as that of the aviation industry. For many past years, the focus was on hardware to optimize the energy consumption of ICT systems. This includes dynamic adaptation of hardware techniques such as fine-grain clock gating, power gating, and dynamic voltage/frequency scaling. However, recent demands of exascale computation, as well as the increasing carbon footprint, require new breakthroughs to make ICT systems more energy-efficient. …

Data Driven Approach To Characterize And Forecast The Impact Of Freeway Work Zones On Mobility Using Probe Vehicle Data, Mohsen Kamyab

Wayne State University Dissertations

The presence of work zones on freeways causes traffic congestion and creates hazardous conditions for commuters and construction workers. Traffic congestion resulting from work zones causes negative impacts on traffic mobility (delay), the environment (vehicle emissions), and safety when stopped or slowed vehicles become vulnerable to rear-end collisions. Addressing these concerns, a data-driven approach was utilized to develop methodologies to measure, predict, and characterize the impact work zones have on Michigan interstates. This study used probe vehicle data, collected from GPS devices in vehicles, as the primary source for mobility data. This data was used to fulfill three objectives: develop …

Toward Energy Efficient Systems Design For Data Centers, Bing Luo

Wayne State University Dissertations

Surge growth of numerous cloud services, Internet of Things, and edge computing promotes continuous increasing demand for data centers worldwide. Significant electricity consumption of data centers has tremendous implications on both operating and capital expense. The power infrastructure, along with the cooling system cost a multi-million or even billion dollar project to add new data center capacities. Given the high cost of large-scale data centers, it is important to fully utilize the capacity of data centers to reduce the Total Cost of Ownership. The data center is designed with a space budget and power budget. With the adoption of high-density …

Effective And Efficient Preemption Placement For Cache Overhead Minimization In Hard Real-Time Systems, John Cavicchio

Wayne State University Dissertations

Schedulability analysis for real-time systems has been the subject of prominent research over the past several decades. One of the key foundations of schedulability analysis is an accurate worst case execution time (WCET) for each task. In preemption based real-time systems, the CRPD can represent a significant component (up to 44% as documented in research literature) of variability to overall task WCET. Several methods have been employed to calculate CRPD with significant levels of pessimism that may result in a task set erroneously declared as non-schedulable. Furthermore, they do not take into account that CRPD cost is inherently a function …

The Use Of Cultural Algorithms To Learn The Impact Of Climate On Local Fishing Behavior In Cerro Azul, Peru, Khalid Kattan

Wayne State University Dissertations

Recently it has been found that the earth’s oceans are warming at a pace that is 40% faster than predicted by a United Nations panel a few years ago. As a result, 2019 has become the warmest year on record for the earth’s oceans. That is because the oceans have acted as a buffer by absorbing 93% of the heat produced by the greenhouse gases [40].

The impact of the oceanic warming has already been felt in terms of the periodic warming of the Pacific Ocean as an effect of the ENSO process. The ENSO process is a cycle of …

Learning From Heterogeneous Data, Lu Wang

Wayne State University Dissertations

Data with both heterogeneity and homogeneity is now ubiquitous due to the development of multitudinous data collection techniques. To encode the data heterogeneity and homogeneity, we focus on unsupervised and supervised learning approaches. In unsupervised learning, to consider both data heterogeneity and homogeneity, we develop three clustering frameworks to maximize the heterogeneity among data sub-groups and homogeneity within each data sub-group for over-dispersed data in three different data types, i.e., alphabetic, network and mixed feature types data. In supervised learning, the traditional approaches, however, either build a global model for a whole group including all sub-groups, which fail to consider …

3d Surface Registration Using Geometric Spectrum Of Shapes, Hajar Hamidian

Wayne State University Dissertations

Morphometric analysis of 3D surface objects are very important in many biomedical applications and clinical diagnoses. Its critical step lies in shape comparison and registration. Considering that the deformations of most organs such as heart or brain structures are non-isometric, it is very difficult to find the correspondence between the shapes before and after deformation, and therefore, very challenging for diagnosis purposes.

To solve these challenges, we propose two spectral based methods. The first method employs the variation of the eigenvalues of the Laplace-Beltrami operator of the shape and optimize a quadratic equation in order to minimize the distance between …

Capso: A Multi-Objective Cultural Algorithm System To Predict Locations Of Ancient Sites, Samuel Dustin Stanley

Wayne State University Dissertations

ABSTRACT

CAPSO: A MULTI-OBJECTIVE CULTURAL ALGORITHM SYSTEM TO PREDICT LOCATIONS OF ANCIENT SITES

by

SAMUEL DUSTIN STANLEY

August 2019

Advisor: Dr. Robert Reynolds

Major: Computer Science

Degree: Doctor of Philosophy

The recent archaeological discovery by Dr. John O’Shea at University of Michigan of prehistoric caribou remains and Paleo-Indian structures underneath the Great Lakes has opened up an opportunity for Computer Scientists to develop dynamic systems modelling these ancient caribou routes and hunter-gatherer settlement systems as well as the prehistoric environments that they existed in. The Wayne State University Cultural Algorithm team has been interested assisting Dr. O’Shea’s archaeological team by …

Attention-Based Models For Deep Reinforcement Learning, Elaheh Barati

Wayne State University Dissertations

Attention mechanism has shown promising results in many fields of machine learning such as image captioning and machine translation. In this work, we focus on attention-based models for deep reinforcement learning. We concentrate on developing deep neural networks

that are fed with a sequence of high-dimensional raw pixels. Particularly, we design attention-based models for challenging tasks including navigation, autonomous driving, and video captioning. In these tasks, deep reinforcement learning algorithms facilitate training of their sophisticated models, and the attention mechanism serves different purposes. In the navigation and autonomous driving tasks, through the attention mechanism, our model attends over different views …