Computer Engineering Commons^™

Quantitative Metrics For Mutation Testing, Amani M. Ayad

Dissertations

Program mutation is the process of generating versions of a base program by applying elementary syntactic modifications; this technique has been used in program testing in a variety of applications, most notably to assess the quality of a test data set. A good test set will discover the difference between the original program and mutant except if the mutant is semantically equivalent to the original program, despite being syntactically distinct.

Equivalent mutants are a major nuisance in the practice of mutation testing, because they introduce a significant amount of bias and uncertainty in the analysis of test results; indeed, mutants …

Using Machine Learning Classification Methods To Detect The Presence Of Heart Disease, Nestor Pereira

Dissertations

Cardiovascular disease (CVD) is the most common cause of death in Ireland, and probably, worldwide. According to the Health Service Executive (HSE) cardiovascular disease accounting for 36% of all deaths, and one important fact, 22% of premature deaths (under age 65) are from CVD.

Using data from the Heart Disease UCI Data Set (UCI Machine Learning), we use machine learning techniques to detect the presence or absence of heart disease in the patient according to 14 features provide for this dataset. The different results are compared based on accuracy performance, confusion matrix and area under the Receiver Operating Characteristics (ROC) …

Factor Analysis Of Mixed Data (Famd) And Multiple Linear Regression In R, Nestor Pereira

Dissertations

In the previous projects, it has been worked to statistically analysis of the factors to impact the score of the subjects of Mathematics and Portuguese for several groups of the student from secondary school from Portugal.

In this project will be interested in finding a model, hypothetically multiple linear regression, to predict the final score, dependent variable G3, of the student according to some features divide into two groups. One group, analyses the features or predictors which impact in the final score more related to the performance of the students, means variables like study time or past failures. The second …

Analyzing Evolution Of Rare Events Through Social Media Data, Xiaoyu Lu

Dissertations

Recently, some researchers have attempted to find a relationship between the evolution of rare events and temporal-spatial patterns of social media activities. Their studies verify that the relationship exists in both time and spatial domains. However, few of those studies can accurately deduce a time point when social media activities are most highly affected by a rare event because producing an accurate temporal pattern of social media during the evolution of a rare event is very difficult. This work expands the current studies along three directions. Firstly, we focus on the intensity of information volume and propose an innovative clustering …

Optimal Sampling Paths For Autonomous Vehicles In Uncertain Ocean Flows, Andrew J. De Stefan

Dissertations

Despite an extensive history of oceanic observation, researchers have only begun to build a complete picture of oceanic currents. Sparsity of instrumentation has created the need to maximize the information extracted from every source of data in building this picture. Within the last few decades, autonomous vehicles, or AVs, have been employed as tools to aid in this research initiative. Unmanned and self-propelled, AVs are capable of spending weeks, if not months, exploring and monitoring the oceans. However, the quality of data acquired by these vehicles is highly dependent on the paths along which they collect their observational data. The …

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 …

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 …

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 …

Predicting Customer Retention Of An App-Based Business Using Supervised Machine Learning, Jeswin Jose

Dissertations

Identification of retainable customers is very essential for the functioning and growth of any business. An effective identification of retainable customers can help the business to identify the reasons of retention and plan their marketing strategies accordingly. This research is aimed at developing a machine learning model that can precisely predict the retainable customers from the total customer data of an e-learning business. Building predictive models that can efficiently classify imbalanced data is a major challenge in data mining and machine learning. Most of the machine learning algorithms deliver a suboptimal performance when introduced to an imbalanced dataset. A variety …

Investigation Into The Perceptually Informed Data For Environmental Sound Recognition, Chenglin Kang

Dissertations

Environmental sound is rich source of information that can be used to infer contexts. With the rise in ubiquitous computing, the desire of environmental sound recognition is rapidly growing. Primarily, the research aims to recognize the environmental sound using the perceptually informed data. The initial study is concentrated on understanding the current state-of-the-art techniques in environmental sound recognition. Then those researches are evaluated by a critical review of the literature. This study extracts three sets of features: Mel Frequency Cepstral Coefficients, Mel-spectrogram and sound texture statistics. Two kinds machine learning algorithms are cooperated with appropriate sound features. The models are …

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 …

An Evaluation Of The Information Security Awareness Of University Students, Alan Pike

Dissertations

Between January 2017 and March 2018, it is estimated that more than 1.9 billion personal and sensitive data records were compromised online. The average cost of a data breach in 2018 was reported to be in the region of US$3.62 million. These figures alone highlight the need for computer users to have a high level of information security awareness (ISA). This research was conducted to establish the ISA of students in a university. There were three aspects to this piece of research. The first was to review and analyse the security habits of students in terms of their own personal …

Noise Reduction In Eeg Signals Using Convolutional Autoencoding Techniques, Conor Hanrahan

Dissertations

The presence of noise in electroencephalography (EEG) signals can significantly reduce the accuracy of the analysis of the signal. This study assesses to what extent stacked autoencoders designed using one-dimensional convolutional neural network layers can reduce noise in EEG signals. The EEG signals, obtained from 81 people, were processed by a two-layer one-dimensional convolutional autoencoder (CAE), whom performed 3 independent button pressing tasks. The signal-to-noise ratios (SNRs) of the signals before and after processing were calculated and the distributions of the SNRs were compared. The performance of the model was compared to noise reduction performance of Principal Component Analysis, with …

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 …

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 …

Performance Comparison Of Hybrid Cnn-Svm And Cnn-Xgboost Models In Concrete Crack Detection, Sahana Thiyagarajan

Dissertations

Detection of cracks mainly has been a sort of essential step in visual inspection involved in construction engineering as it is the commonly used building material and cracks in them is an early sign of de-basement. It is hard to find cracks by a visual check for the massive structures. So, the development of crack detecting systems generally has been a critical issue. The utilization of contextual image processing in crack detection is constrained, as image data usually taken under real-world situations vary widely and also includes the complex modelling of cracks and the extraction of handcrafted features. Therefore the …

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 Evaluation Of Learning Employing Natural Language Processing And Cognitive Load Assessment, Mrunal Tipari

Dissertations

One of the key goals of Pedagogy is to assess learning. Various paradigms exist and one of this is Cognitivism. It essentially sees a human learner as an information processor and the mind as a black box with limited capacity that should be understood and studied. With respect to this, an approach is to employ the construct of cognitive load to assess a learner's experience and in turn design instructions better aligned to the human mind. However, cognitive load assessment is not an easy activity, especially in a traditional classroom setting. This research proposes a novel method for evaluating learning …

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.