Engineering Commons^™

An Investigation Into The Predictive Capability Of Customer Spending In Modelling Mortgage Default, Donal Finn [Thesis]

Dissertations

The mortgage arrears crisis in Ireland was and is among the most severe experienced on record and although there has been a decreasing trend in the number of mortgages in default in the past four years, it still continues to cause distress to borrowers and vulnerabilities to lenders. There are indications that one of the main factors associated with mortgage default is loan affordability, of which the level of disposable income is a driver. Additionally, guidelines set out by the European Central Bank instructed financial institutions to adopt measures to further reduce and prevent loans defaulting, including the implementation and …

Modeling And Evaluating Cost-Effectiveness Of Host-Microbiome Investigations, Renuka Panchagavi

Dissertations

Cost-effectiveness modeling accounts for how expenditures impact outcomes and is an appropriate step towards efficacy of the different methods used for modeling the dynamics of microbial communities. This will help to identify challenging aspects of microbiome studies and the associated costs, including the major differences in research designs (cross-sectional or time series-based) used for conducting such studies. The two major stages of our investigation were to first collect and model cost variable data for microbiome investigations, and then to evaluate how trade-offs related to sample size and expenditures impact investigational outcomes. We screened different potential sources of data for microbiome …

Mechanical Behavior Of Cement Paste At Nanoscale - Reactive Molecular Dynamics Modeling And Experimental Corroborations, Ingrid M. Padilla Espinosa

Dissertations

Concrete is the most used material for construction, and it is the most produced man-made product in the world. Concrete makes the contemporary architecture of the world conceivable and brings significant development to communities. Nevertheless, there are many downsides to the use of concrete such as the large amount of energy and water used in its production, large carbon footprint, shrinkage and expansion that can cause cracking and other failure mechanisms, and the required use of large amounts of material to ensure stability of concrete structures. These drawbacks create the need for engineering and tailoring the properties of concrete. An …

A Formal Systems Engineering Methodology For Cyber-Physical Systems: The Verifiable Design Process, Nadew S. Kibret

Dissertations

Cyber-physical systems (CPS) are systems that exhibit tight integration between their physical and computational components. They are hybrid systems containing continuous states and discrete states emanating from the physical and computational components, respectively. Systems engineering of these systems is challenging due to the tight integration of their computational, physical and communication technologies. As their level of acceptance increases in mission critical applications such as health care, smart grid, autonomous vehicles and smart cities, the need to ensure their safe operation is paramount importance as well. Therefore, design methodologies followed in their development are required to result in system behavior that …

Managing Uncertainty In Sensor Data: An Evidence Theory Based Multisensor Data Fusion Approach, Gabriel Idowu Awogbami

Dissertations

Sensors play a critical role in the development of intelligent systems. Intelligent agents are equipped with an array of sensors to acquire information about themselves and the environment to make a reasonable decision. Information extracted from the sensor data is often characterized by uncertainty. Modeling and reasoning under such uncertainty poses a great challenge. Multisensor data fusion is a viable approach to address this problem. The Dempster Shafer (DS) theory of belief functions, also known as the evidence theory, is a well-known data fusion formalism due to its close relationship with other mathematical theories of uncertainty and its elegant way …

Surface And Subsurface Damages In Rotary Ultrasonic Machining Of Ceramics: A Molecular Dynamics Method With Experimental Study, Yasser Hamouda Ahmed

Dissertations

Rotary ultrasonic machining (RUM) is one of the advanced machining processes for ceramics. Although many research papers about RUM have been published, there are very limited studies focusing on the effects of ultrasonic vibration on surface and subsurface damages. In this research, the surface and subsurface damages are compared for machining of dental ceramics (Al2O3) with and without ultrasonic vibration. The surface chippings are evaluated under microscope and the subsurface cracks are observed and quantified under scanning electron microscope (SEM). The contribution of this research is directed to use Molecular Dynamics (MD) method, which plays an important role in modeling …

Predicting Violent Crime Reports From Geospatial And Temporal Attributes Of Us 911 Emergency Call Data, Vincent Corcoran

Dissertations

The aim of this study is to create a model to predict which 911 calls will result in crime reports of a violent nature. Such a prediction model could be used by the police to prioritise calls which are most likely to lead to violent crime reports. The model will use geospatial and temporal attributes of the call to predict whether a crime report will be generated. To create this model, a dataset of characteristics relating to the neighbourhood where the 911 call originated will be created and combined with characteristics related to the time of the 911 call. Geospatial …

An Automated Negotiation System For Ecommerce Store Owners To Enable Flexible Product Pricing, Jake O'Halloran

Dissertations

If a store owner wishes to sell a product online, they traditionally have two options for deciding on a price. They can sell the product at a fixesd price like the products sold on sites like Amazon, or they can put the product in an auction and let demand from customers drive the final sales price like the products sold on sites like eBay. Both options have their pros and cons. An alternative option for deciding on a final sales price for the product is to enable negotiation on the product. With this, there is a dynamic nature to the …

Analyzing Twitter Feeds To Facilitate Crises Informatics And Disaster Response During Mass Emergencies, Arshdeep Kaur

Dissertations

It is a common practice these days for general public to use various micro-blogging platforms, predominantly Twitter, to share ideas, opinions and information about things and life. Twitter is also being increasingly used as a popular source of information sharing during natural disasters and mass emergencies to update and communicate the extent of the geographic phenomena, report the affected population and casualties, request or provide volunteering services and to share the status of disaster recovery process initiated by humanitarian-aid and disaster-management organizations. Recent research in this area has affirmed the potential use of such social media data for various disaster …

Augmenting American Fuzzy Lop To Increase The Speed Of Bug Detection, Raviraj Mahajan

Dissertations

Whitebox fuzz testing is a vital part of the software testing process in the software development life cycle (SDLC). It is used for bug detection and security vulnerability checking as well. But current tools lack the ability to detect all the bugs and cover the entire code under test in a reasonable time. This study will explore some of the various whitebox fuzzing techniques and tools (AFL, SAGE, Driller, etc.) currently in use followed by a discussion of their strategies and the challenges facing them. One of the most popular state-of-the-art fuzzers, American Fuzzy Lop (AFL) will be discussed in …

An Investigation Of Three Subjective Rating Scales Of Mental Workload In Third Level Education, Nha Vu Thanh Nguyen

Dissertations

Mental Workload assessment in educational settings is still recognized as an open research problem. Although its application is useful for instructional design, it is still unclear how it can be formally shaped and which factors compose it. This paper is aimed at investigating a set of features believed to shape the construct of mental workload and aggregating them together in models trained with supervised machine learning techniques. In detail, multiple linear regression and decision trees have been chosen for training models with features extracted respectively from the NASA Task Load Index and the Workload Profile, well-known self-reporting instruments for assessing …

Comparing Defeasible Argumentation And Non-Monotonic Fuzzy Reasoning Methods For A Computational Trust Problem With Wikipedia, Ryan Kirwan

Dissertations

Computational trust is an ever-more present issue with the surge in autonomous agent development. Represented as a defeasible phenomenon, problems associated with computational trust may be solved by the appropriate reasoning methods. This paper compares two types of such methods, Defeasible Argumentation and Non-Monotonic Fuzzy Logic to assess which is more effective at solving a computational trust problem centred around Wikipedia editors. Through the application of these methods with real-data and a set of knowledge-bases, it was found that the Fuzzy Logic approach was statistically significantly better than the Argumentation approach in its inferential capacity.

Multi-Sensory Deep Learning Architectures For Slam Dunk Scene Classification, Paul Minogue

Dissertations

Basketball teams at all levels of the game invest a considerable amount of time and effort into collecting, segmenting, and analysing footage from their upcoming opponents previous games. This analysis helps teams identify and exploit the potential weaknesses of their opponents and is commonly cited as one of the key elements required to achieve success in the modern game. The growing importance of this type of analysis has prompted research into the application of computer vision and audio classification techniques to help teams classify scoring sequences and key events using game footage. However, this research tends to focus on classifying …

Is There A Correlation Between Wikidata Revisions And Trending Hashtags On Twitter?, Paula Dooley [Thesis]

Dissertations

Twitter is a microblogging application used by its members to interact and stay socially connected by sharing instant messages called tweets that are up to 280 characters long. Within these tweets, users can add hashtags to relate the message to a topic that is shared among users. Wikidata is a central knowledge base of information relying on its members and machines bots to keeping its content up to date. The data is stored in a highly structured format with the added SPARQL protocol and RDF Query Language (SPARQL) endpoint to allow users to query its knowledge base.

Forecasting Anomalous Events And Performance Correlation Analysis In Event Data, Sonya Leech [Thesis]

Dissertations

Classical and Deep Learning methods are quite common approaches for anomaly detection. Extensive research has been conducted on single point anomalies. Collective anomalies that occur over a set of two or more durations are less likely to happen by chance than that of a single point anomaly. Being able to observe and predict these anomalous events may reduce the risk of a server’s performance. This paper presents a comparative analysis into time-series forecasting of collective anomalous events using two procedures. One is a classical SARIMA model and the other is a deep learning Long-Short Term Memory (LSTM) model. It then …

Enhancing Partially Labelled Data: Self Learning And Word Vectors In Natural Language Processing, Eamon Mcentee

Dissertations

There has been an explosion in unstructured text data in recent years with services like Twitter, Facebook and WhatsApp helping drive this growth. Many of these companies are facing pressure to monitor the content on their platforms and as such Natural Language Processing (NLP) techniques are more important than ever. There are many applications of NLP ranging from spam filtering, sentiment analysis of social media, automatic text summarisation and document classification.

Detection Of Offensive Youtube Comments, A Performance Comparison Of Deep Learning Approaches, Priyam Bansal

Dissertations

Social media data is open, free and available in massive quantities. However, there is a significant limitation in making sense of this data because of its high volume, variety, uncertain veracity, velocity, value and variability. This work provides a comprehensive framework of text processing and analysis performed on YouTube comments having offensive and non-offensive contents.

YouTube is a platform where every age group of people logs in and finds the type of content that most appeals to them. Apart from this, a massive increase in the use of offensive language has been apparent. As there are massive volume of new …