Digital Commons Network^™

Terramechanics And Machine Learning For The Characterization Of Terrain, Bryan W. Southwell

Electronic Thesis and Dissertation Repository

An instrumented rover wheel can collect vast amounts of data about a planetary surface. Planetary surfaces are changed by complex geological processes which can be better understood with an abundance of surface data and the use of terramechanics. Identifying terrain parameters such as cohesion and angle of friction hold importance for both the rover driver and the planetary scientist. Knowledge of terrain characteristics can warn of unsafe terrain and flag potential interesting scientific sites. The instrumented wheel in this research utilizes a pressure pad to sense load and sinkage, a string potentiometer to measure slip, and records motor current draw. …

Optimized Machine Learning Models Towards Intelligent Systems, Mohammadnoor Ahmad Mohammad Injadat

Electronic Thesis and Dissertation Repository

The rapid growth of the Internet and related technologies has led to the collection of large amounts of data by individuals, organizations, and society in general [1]. However, this often leads to information overload which occurs when the amount of input (e.g. data) a human is trying to process exceeds their cognitive capacities [2]. Machine learning (ML) has been proposed as one potential methodology capable of extracting useful information from large sets of data [1]. This thesis focuses on two applications. The first is education, namely e-Learning environments. Within this field, this thesis proposes different optimized ML ensemble models to …

Automated Anomaly Detection And Localization System For A Microservices Based Cloud System, Priyanka Prakash Naikade

Electronic Thesis and Dissertation Repository

Context: With an increasing number of applications running on a microservices-based cloud system (such as AWS, GCP, IBM Cloud), it is challenging for the cloud providers to offer uninterrupted services with guaranteed Quality of Service (QoS) factors. Problem Statement: Existing monitoring frameworks often do not detect critical defects among a large volume of issues generated, thus affecting recovery response times and usage of maintenance human resource. Also, manually tracing the root causes of the issues requires a significant amount of time. Objective: The objective of this work is to: (i) detect performance anomalies, in real-time, through monitoring KPIs (Key Performance …

Visual Analytics Of Electronic Health Records With A Focus On Acute Kidney Injury, Sheikh S. Abdullah

Electronic Thesis and Dissertation Repository

The increasing use of electronic platforms in healthcare has resulted in the generation of unprecedented amounts of data in recent years. The amount of data available to clinical researchers, physicians, and healthcare administrators continues to grow, which creates an untapped resource with the ability to improve the healthcare system drastically. Despite the enthusiasm for adopting electronic health records (EHRs), some recent studies have shown that EHR-based systems hardly improve the ability of healthcare providers to make better decisions. One reason for this inefficacy is that these systems do not allow for human-data interaction in a manner that fits and supports …

A Hybrid Approach To Procedural Dungeon Generation, Mathias Paul Babin

Electronic Thesis and Dissertation Repository

This thesis presents a novel approach to the Procedural Content Generation (PCG) of both maze and dungeon environments. The solution we propose in this thesis borrows techniques from both Procedural Content Generation via Machine Learning as well as Constructive PCG methods. The approach we take involves decomposing the problem of level generation into a series of stages which begins with the production of macro-level functional structures and ends with micro-level aesthetic details; specifically, we train a Deep Convolutional Neural Network to produce high-quality mazes, which in turn, are transformed into the rooms of larger dungeon levels using a constructive algorithm. …

Machine Learning With Digital Signal Processing For Rapid And Accurate Alignment-Free Genome Analysis: From Methodological Design To A Covid-19 Case Study, Gurjit Singh Randhawa

Electronic Thesis and Dissertation Repository

In the field of bioinformatics, taxonomic classification is the scientific practice of identifying, naming, and grouping of organisms based on their similarities and differences. The problem of taxonomic classification is of immense importance considering that nearly 86% of existing species on Earth and 91% of marine species remain unclassified. Due to the magnitude of the datasets, the need exists for an approach and software tool that is scalable enough to handle large datasets and can be used for rapid sequence comparison and analysis. We propose ML-DSP, a stand-alone alignment-free software tool that uses Machine Learning and Digital Signal Processing to …

Intelligent And Efficient Ultra-Dense Heterogeneous Networks For 5g And Beyond, Yanan Liu

Electronic Thesis and Dissertation Repository

Ultra-dense heterogeneous network (HetNet), in which densified small cells overlaying the conventional macro-cells, is a promising technique for the fifth-generation (5G) mobile network. The dense and multi-tier network architecture is able to support the extensive data traffic and diverse quality of service (QoS) but meanwhile arises several challenges especially on the interference coordination and resource management. In this thesis, three novel network schemes are proposed to achieve intelligent and efficient operation based on the deep learning-enabled network awareness. Both optimization and deep learning methods are developed to achieve intelligent and efficient resource allocation in these proposed network schemes.

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