Computer Engineering Commons^™

Can Deep Learning Techniques Improve The Risk Adjusted Returns From Enhanced Indexing Investment Strategies, Anthony Grace

Dissertations

Deep learning techniques have been widely applied in the field of stock market prediction particularly with respect to the implementation of active trading strategies. However, the area of portfolio management and passive portfolio management in particular has been much less well served by research to date. This research project conducts an investigation into the science underlying the implementation of portfolio management strategies in practice focusing on enhanced indexing strategies. Enhanced indexing is a passive management approach which introduces an element of active management with the aim of achieving a level of active return through small adjustments to the portfolio weights. …

Investigation Into The Application Of Personality Insights And Language Tone Analysis In Spam Classification, Colm Mcgetrick

Dissertations

Due to its persistence spam remains as one of the biggest problems facing users and suppliers of email communication services. Machine learning techniques have been very successful at preventing many spam mails from arriving in user mailboxes, however they still account for over 50% of all emails sent. Despite this relative success the economic cost of spam has been estimated as high as $50 billion in 2005 and more recently at $20 billion so spam can still be considered a considerable problem. In essence a spam email is a commercial communication trying to entice the receiver to take some positive …

An Investigation Of The Correlation Between Mental Workload And Web User’S Interaction, Joaquim Filipe Romero

Dissertations

Mental Workload, a psychological concept, was identified as being linked with task’s and system’s performance. In the context of Human-Computer Interaction, recent research has identified Mental Workload as an important measure in the designing and evaluation of web interfaces, and as an additional and supplemental insight to typical usability evaluation methods. Simultaneously, web logs containing data related to web users’ interaction (e.g. scrolling; mouse clicks) have been proved useful in evaluating the usability of web sites by analysing the data tracked for hundreds of users. In order to study if the potential of logs of user interaction can be applied …

An Investigation Of The Correlation Between Mental Workload And Web User’S Interaction, Joaquim Filipe Romero

Dissertations

Mental Workload, a Psychology concept, was identified as being linked with task’s and system’s performance. In the context of Human-Computer Interaction, recent research has identified Mental Workload as an important measure in the designing and evaluation of web interfaces, and as an additional and supplemental insight to typical Usability evaluation methods. Simultaneously, web logs containing data related to web users’ interaction (e.g. scrolling; mouse clicks) have been proved useful in evaluating the Usability of web sites by levering the data tracked for hundreds of users. In order to study if the potential of logs of user interaction can be applied …

Online Algorithms For Content Caching: An Economic Perspective, Ammar Gharaibeh

Dissertations

Content Caching at intermediate nodes, such that future requests can be served without going back to the origin of the content, is an effective way to optimize the operations of computer networks. Therefore, content caching reduces the delivery delay and improves the users’ Quality of Experience (QoE). The current literature either proposes offline algorithms that have complete knowledge of the request profile a priori, or proposes heuristics without provable performance. In this dissertation, online algorithms are presented for content caching in three different network settings: the current Internet Network, collaborative multi-cell coordinated network, and future Content Centric Networks (CCN). Due …

The Evaluation Of Ensemble Sentiment Classification Approach On Airline Services Using Twitter, Zechen Wang

Dissertations

In the field of sentiment classification, much research has been done on reviews of topics such as movies, software and books. Little research has been done in the airline service domain. In the airline industry, the use of social media as a customer service tool has become a growing phenomenon. The research conducted by Wan and Gao (2015) has proposed an ensemble classification approach for airline service sentiment classification using Twitter data. In accordance, the objective of improving the performance of ensemble classification approach is the primary consideration. This research proposed new hybrid classification approach that uses the state-of-art approach …

A Model For Anomalies Detection In Internet Of Things (Iot) Using Inverse Weight Clustering And Decision Tree, Ahod Alghuried

Dissertations

Internet of Things (IoT) is one of the fast growing technologies today. It is a technology by which billions of smart objects or devices known as “Things” can use several types of sensors to collect various types of data about themselves and/or the surrounding environment. They can then share this with authorized parties to serve several purposes such as controlling and monitoring industrial facilities or improving business service or functions. There are currently 3 billion devices connected to the Internet. The number will increase to 20 billion by 2020. While these devices make our life easier, safer and healthier, they …

Application Of Supervised Machine Learning To Predict The Mortality Risk In Elderly Using Biomarkers, Priyanka Sonkar

Dissertations

The idea of long-term survival amongst older individuals has been a major medical and social concern. A wide range of biomarkers have been identified to prospectively predict disability, morbidity, and mortality outcomes in older adult populations. The machine learning techniques applied with clinically relevant biomarkers provide new ways of understanding diseases and solutions to tackle challenges to the health of the aging population. This paper describes two supervised machine learning techniques, Logistic Regression (LR) and Support Vector Machine (SVM) which are used in the prediction of the mortality in elderly people. LR is one of the traditionally used predictive modeling …

Comparison Study Of The Most Common Virtual Machine Load Balancing Algorithms In Large-Scale Cloud Environment Using Cloud Simulator, Rowaa Filimban

Dissertations

This is era of internet. There is barely any field where internet do not play important role. Nowadays, CloudComputing links to the internet that has rebelled the whole universe. CloudComputing is a rapid enlarging domain in computing industry and research. Three main services offered by the cloud are SaaS, PaaS and IaaS. With the technology advancement of the CloudComputing, there are many new chances pioneer on how applications can be developed and how several services can be provided to the end user throughout Virtualization, on the internet. What's more, there are cloud service providers who offer and provide large-scaled computing …

Modeling Mortgage Assessment With Computational Argumentation Theory And Defeasible Reasoning, Henrik Szucs

Dissertations

In the mortgage lending business of a bank, a key focus area is risk analysis, which supports the mortgage awarding process and the prediction of the risk of defaulting (repayment issues). The standard risk assessment method at most banks is a scorecard calculation. A new way of predicting the defaulting is proposed using Defeasible Reasoning (DR) and computational Argumentation Theory (AT), areas of interdisciplinary research, in the discipline of Articial Intelligence (AI). Argumentation is formalised by reasoning models which are inspired by human reasoning. For a more realistic representation AT employs DR which is a non-monotonic reasoning process, meaning that …

Benchmarking Javascript Frameworks, Carl Lawrence Mariano

Dissertations

JavaScript programming language has been in existence for many years already and is one of the most widely known, if not, the most used front-end programming language in web development. However, JavaScript is still evolving and with the emergence of JavaScript Frameworks (JSF), there has been a major change in how developers develop software nowadays. Developers these days often use more than one framework in order to fulfil their job which has given rise to the problem for developers when it comes to choosing the right JavaScript framework to develop software which is partly due to the availability of countless …

Critical Comparison Of The Classification Ability Of Deep Convolutional Neural Network Frameworks With Support Vector Machine Techniques In The Image Classification Process, Robert Kelly

Dissertations

Recently, a number of new image classification models have been developed to diversify the number of options available to prospective machine learning classifiers, such as Deep Learning. This is particularly important in the field of medical image classification as a misdiagnosis could have a severe impact on the patient. However, an assessment on the level to which a deep learning based Convolutional Neural Network can outperform a Support Vector Machine has not been discussed. In this project, the use of CNN and SVM classifiers is used on a dataset of approx. 55,000 images. This dataset was used to assess the …

An Exploration Study Of Using The Universities Performance And Enrolments Features For Predicting The International Quality, Aeshah Althagafi

Dissertations

Quality ranking systems are crucial in the assessment of the academic performance of an institution because these assessment systems give details about how different learning institutions deliver their services. Education quality is also of paramount importance to the students because it is through quality education that these students develop skills that are needed in the job market. Besides, education enhances a student's academic and reasoning capacities. When universities are subjected to ranking systems, they are likely to improve their quality to be ranked high in the system. When the university administrators are exposed to ranking, competition gears up. Through competition, …

Exploring The Factors That Affect Secondary Student’S Mathematics And Portuguese Performance In Portugal, Lulu Cheng

Dissertations

Secondary education provides not only knowledge and skills, but also inculcates values, training of instincts, fostering right attitude and habits to enable adolescents to move into tertiary education or to ensure a workplace for students who decided to terminate their secondary schooling. Without secondary education to guide the development of young people through their adolescence, they will be ill prepared for tertiary education or for workplace, moreover, the possibility of juvenile delinquency and teenage pregnancy becomes higher. These negative effects will increase the pressures and expenditures on society and socio-economic. In 2006, Portugal was reported having the higher school-leaving rate …

The Influence Of Sensor-Based Intelligent Traffic Light Control On Traffic Flow In Dublin, Katja Rademacher

Dissertations

With growing cities and the increased use of vehicles for transportation purposes, there is a demand to make the traffic management in cities smarter. An intelligent traffic light control that dynamically adapts to the existing traffic conditions can help reduce traffic congestion and CO2 emissions. This thesis reviews the popular traffic light control approaches - static, actuated and adaptive – based on their influences on recorded traffic conditions in Dublin. The Irish capital relies heavily on busses for public transport adding to the number of already moving vehicles in the city centre. Using vehicle count data from inductive loop detectors …

Can Phishing Education Enable Users To Recognize Phishing Attacks?, Hanaa Alghamdi

Dissertations

Phishing attacks have increased rapidly and caused many drastic damages and losses for internet users‟ .The purpose of this research is to investigate on effectiveness of phishing education and training to help users identify different forms of phishing threats. The study has been conducted through developing a phishing quiz mobile application which includes four kinds of phishing threats. It tested the ability of users to recognize spoofed emails, SMS phishing (SMshing), scam phone calls (Vishing), and phishing through social media networks. A comprehensive literature review was discussed to investigate on the research area, understand the research problem, support the proposed …

A Comparison Of Supervised Machine Learning Classification Techniques And Theory-Driven Approaches For The Prediction Of Subjective Mental Workload, Dmitrii Gmyzin

Dissertations

In the modern world of technological progress, systems and interfaces are becoming more and more complex. As a consequence, it is a crucial to design the human-computer interaction in the most optimal way to improve the user experience. The construct of Mental Workload is a valid metric that can be used for such a goal. Among the different ways of measuring Mental Workload, self-reporting procedures are the most adopted for their ease of use and application. This research is focused on the application of Machine Learning as an alternative to theory-driven approaches for Mental Workload measurement. In particular, the study …

Evaluating The Effectiveness Of The Gestalt Principles Of Perceptual Observation For Virtual Reality User Interface Design, William Macnamara

Dissertations

There is a lot of interest and excitement surrounding the areas of Virtual Reality and Head-Mounted Displays with the recent releases of devices such as the Oculus Rift, Sony PSVR and the HTC Vive. While much of the focus for these devices has been related to sectors of the entertainment industries, namely the cinema and video game industries, there are many more practical applications for these technologies, with potential benefits in educational, training/simulation, therapeutic and modelling/design software. Developing a set of reliable guidelines for Virtual Reality User Interface Design could play a crucial role in whether the medium successfully integrates …

Physical Human Activity Recognition Using Machine Learning Algorithms, Haritha Vellampalli

Dissertations

With the rise in ubiquitous computing, the desire to make everyday lives smarter and easier with technology is on the increase. Human activity recognition (HAR) is the outcome of a similar motive. HAR enables a wide range of pervasive computing applications by recognizing the activity performed by a user. In order to contribute to the multi facet applications that HAR is capable to offer, predicting the right activity is of utmost importance. Simplest of the issues as the use of incorrect data manipulation or utilizing a wrong algorithm to perform prediction can hinder the performance of a HAR system. This …

Towards Improving Visqol (Virtual Speech Quality Objective Listener) Using Machine Learning Techniques, Joseph Mcnally

Dissertations

Vast amounts of sound data are transmitted every second over digital networks. VoIP services and cellular networks transmit speech data in increasingly greater volumes. Objective sound quality models provide an essential function to measure the quality of this data in real-time. However, these models can suffer from a lack of accuracy with various degradations over networks. This research uses machine learning techniques to create one support vector regression and three neural network mapping models for use with ViSQOLAudio. Each of the mapping models (including ViSQOL and ViSQOLAudio) are tested against two separate speech datasets in order to comparatively study accuracy …

Support Vector Machines And Artificial Neural Networks: Assessing The Validity Of Using Technical Features For Security Forecasting, James Dipadua

Dissertations

Stock forecasting is an enticing and well-studied problem in both finance and machine learning literature with linear-based models such as ARIMA and ARCH to non-linear Artificial Neural Networks (ANN) and Support Vector Machines (SVM). However, these forecasting techniques also use very different input features, some of which are seen by economists as irrational and theoretically unjustified. In this comparative study using ANNs and SVMs for 12 publicly traded companies, derivative price “technicals” are evaluated against macro- and microeconomic fundamentals to evaluate the efficacy of model performance. Despite the efficient market hypothesis positing the ill-suitability of technicals as model inputs, this …

Support Vector Machines And Artificial Neural Networks: Assessing The Validity Of Using Technical Features For Security Forecasting, James Di Padua

Dissertations

Stock forecasting is an enticing and well studied problem in both finance and machine learning literature with linear based models such as ARIMA and ARCH to nonlinear Artificial Neural Networks (ANN) and Support Vector Machines (SVM). However, these forecasting techniques also use very different input features, some of which are seen by economists as irrational and theoretically unjustified. In this comparative study using ANNs and SVMs for 12 publicly traded companies, derivative price “technicals” are evaluated against macro and microeconomic fundamentals to evaluate the efficacy of model performance. Despite the efficient market hypothesis positing the ill suitability of technicals as …

Using Spatialisation To Support Exploratory Search Behaviour, Clement Roux

Dissertations

Information-seekers traditionally interact with digital content through keyword-based search interfaces displaying results in list views. Well-defined lookup search tasks are performed brilliantly with these interfaces, enabling users to find relevant information and develop a relative understanding of the underlying information space. However, it is feasible to suggest that ill-defined and abstract search tasks could be better supported with a different interface that could allow the user to explore a library’s content and develop an appropriate mental model of the information space. One such approach is based on the use of visualisation, an approach to data analysis that aims to reduce …

A Regression Study Of Salary Determinants In Indian Job Markets For Entry Level Engineering Graduates, Rajveer Singh

Dissertations

The economic liberalisation of Indian markets in early 90s boosted the economic growth of the nation in various sectors over the next two decades. One such sector that has seen a massive growth in this time is Information Technology (IT). The IT industry has played a very crucial role in transforming India from a slow moving economy to one of the largest exporters of IT services. This growth created a huge demand in the labour markets for skilled labour, which in turn made engineering one of the top choices of study after high school over the years. In addition, the …

Investigating The Impact Of Unsupervised Feature-Extraction From Multi-Wavelength Image Data For Photometric Classification Of Stars, Galaxies And Qsos, Annika Lindh

Dissertations

This thesis reviews the current state of photometric classification in Astronomy and identifies two main gaps: a dependence on handcrafted rules, and a lack of interpretability in the more successful classifiers. To address this, Deep Learning and Computer Vision were used to create a more interpretable model, using unsupervised training to reduce human bias.

The main contribution is the investigation into the impact of using unsupervised feature-extraction from multi-wavelength image data for the classification task. The feature-extraction is achieved by implementing an unsupervised Deep Belief Network to extract lower-dimensionality features from the multi-wavelength image data captured by the Sloan Digital …

An Exploration Of The Impact Of Animal Agriculture On Human Sustainable Development Index And The Child Health Indicator, Hithesan Pandian

Dissertations

Several studies have shown that animal agriculture is one of the major contributors to climate change due to greenhouse gas emissions, deforestation, land degradation, freshwater shortages, general environmental pollution and world hunger. Apart from this, meat consumption is strongly associated with certain fatal health conditions such as cancer, cardiovascular disease and diabetes (Wu, 2014). Although meat production and export could be economically beneficial in the short run, it could lead to over-exploitation of natural resources and in turn the destruction of the environment in the long run. Hence, it is essential for the leaders of a nation to make smart …

Multi-Sensory Emotion Recognition With Speech And Facial Expression, Qingmei Yao

Dissertations

Emotion plays an important role in human beings’ daily lives. Understanding emotions and recognizing how to react to others’ feelings are fundamental to engaging in successful social interactions. Currently, emotion recognition is not only significant in human beings’ daily lives, but also a hot topic in academic research, as new techniques such as emotion recognition from speech context inspires us as to how emotions are related to the content we are uttering.

The demand and importance of emotion recognition have highly increased in many applications in recent years, such as video games, human computer interaction, cognitive computing, and affective computing. …

Subspace Methods For Portfolio Design, Onur Yilmaz

Dissertations

Financial signal processing (FSP) is one of the emerging areas in the field of signal processing. It is comprised of mathematical finance and signal processing. Signal processing engineers consider speech, image, video, and price of a stock as signals of interest for the given application. The information that they will infer from raw data is different for each application. Financial engineers develop new solutions for financial problems using their knowledge base in signal processing. The goal of financial engineers is to process the harvested financial signal to get meaningful information for the purpose.

Designing investment portfolios have always been at …

An Investigation Into The Effectiveness Of Hedonic Features In Regression Models For Domestic Rental Prices, Peter Prunty

Dissertations

Housing is a fundamental human right. Increasing rents and rising unemployment contribute to increased rates of homelessness. Traditionally housing prices are determined by supply and demand. This project will investigate the relationship between hedonic features and domestic rental prices in California and New York, using multivariate regression models. The literature outlines a number of approaches taken to model real estate pricing using hedonic regression.

Two models were created to analyse the difference between California and New York. Features were selected using correlation analysis. Some features were derived using logarithmic and dummy feature transformations. The models themselves were evaluated by assessing …

Investigation Into The Predictive Capability Of Macro-Economic Features In Modelling Credit Risk For Small Medium Enterprises, Kevin Mctiernan

Dissertations

This research project investigates the predictive capability of macro-economic features in modelling credit risk for small medium enterprises (SME/SMEs). There have been indications that there is strong correlation between economic growth and the size of the SME sector in an economy. However, since the financial crisis and consequent policies and regulations, SMEs have been hampered in attempts to access credit. It has also been noted that while there is a substantial amount of credit risk literature, there is little research on how macro-economic factors affect credit risk. Being able to improve credit scoring by even a small amount can have …