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- Chit funds (2)
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Articles 1 - 24 of 24
Full-Text Articles in Physical Sciences and Mathematics
Image Classification Using Bag-Of-Visual-Words Model, Kaiqiang Huang
Image Classification Using Bag-Of-Visual-Words Model, Kaiqiang Huang
Dissertations
Recently, with the explosive growth of digital technologies, there has been a rapid proliferation of the size of image collection. The technique of supervised image clas sification has been widely applied in many domains in order to organize, search, and retrieve images. However, the traditional feature extraction approaches yield the poor classification accuracy. Therefore, the Bag-of-visual-words model, inspired by Bag-of Words model in document classification, was used to present images with the local descriptors for image classification, and also it performs well in some fields. This research provides the empirical evidence to prove that the BoVW model outperforms the traditional …
A Javascript Framework Comparison Based On Benchmarking Software Metrics And Environment Configuration, Jefferson Ferreira
A Javascript Framework Comparison Based On Benchmarking Software Metrics And Environment Configuration, Jefferson Ferreira
Dissertations
JavaScript is a client-side programming language that can be used in multi-platform applications. It controls HTML and CSS to manipulate page behaviours and is widely used in most websites over the internet. JavaScript frameworks are structures made to help web developers build web applications faster by offering features that enhance the user interaction with the web page. An increasing number of JavaScript frameworks have been released in recent years in the market to help front-end developers build applications in a shorter space of time. Decision makers in software companies have been struggling to determine which frameworks are best suited for …
Classification Using Association Rules, Colin Kane
Classification Using Association Rules, Colin Kane
Dissertations
This research investigates the use of an unsupervised learning technique, association rules, to make class predictions. The use of association rules to make class predictions is a growing area of focus within data mining research. The research to date has focused predominately on balanced datasets or synthetized imbalanced datasets. There have been concerns raised that the algorithms using association rules to make classifications do not perform well on imbalanced datasets. This research comprehensively evaluates the accuracy of a number of association rule classifiers in predicting home loan sales in an Irish retail banking context. The experiments designed test three associative …
Using Machine Learning Techniques To Predict A Risk Score For New Members Of A Chit Fund Group, Sinead Aherne
Using Machine Learning Techniques To Predict A Risk Score For New Members Of A Chit Fund Group, Sinead Aherne
Dissertations
Predicting the risk score of new and potential customers is used across the financial industry. By implementing the prediction of risk scores for their customers a chit fund company can improve the knowledge and customer understanding without relying on human knowledge. Data is collected on each customer before they have taken out credit and during the time they contribute to a chit fund. Having collected the necessary data, the company can then decide whether modelling customer risk would benefit them. As the data is available historically, one aspect of risk score prediction will be the focus of this thesis, supervised …
Comparing The Effectiveness Of Support Vector Machines And Convolutional Neural Networks For Determining User Intent In Conversational Agents, Kieran O Sullivan
Comparing The Effectiveness Of Support Vector Machines And Convolutional Neural Networks For Determining User Intent In Conversational Agents, Kieran O Sullivan
Dissertations
Over the last fifty years, conversational agent systems have evolved in their ability to understand natural language input. In recent years Natural Language Processing (NLP) and Machine Learning (ML) have allowed computer systems to make great strides in the area of natural language understanding. However, little research has been carried out in these areas within the context of conversational systems. This paper identifies Convolutional Neural Network (CNN) and Support Vector Machine (SVM) as the two ML algorithms with the best record of performance in ex isting NLP literature, with CNN indicated as generating the better results of the two. A …
Investigation Into The Predictive Power Of Artificial Neural Networks And Logistic Regression For Predicting Default In Chit Funds, Ciara Kerrigan
Investigation Into The Predictive Power Of Artificial Neural Networks And Logistic Regression For Predicting Default In Chit Funds, Ciara Kerrigan
Dissertations
This study evaluated the performance of an artificial neural network (ANN) multi-layer perceptron model and a logistic regression logitboost (LR) model to predict default in chit funds. The two types of default investigated were late payment of 30 days and late payment of 90 days. The dataset was broken up into training and validation datasets using random sampling and K folds cross validation was used on the training dataset to assess performance of the tuning parameters. The validation dataset was used to compare performance of both algorithms. Principle component analysis (PCA) was used to reduce the feature set while still …
Predicting Happiness - Comparison Of Supervised Machine Learning Techniques Performance On A Multiclass Classification Problem, Dorota Nieciecka
Predicting Happiness - Comparison Of Supervised Machine Learning Techniques Performance On A Multiclass Classification Problem, Dorota Nieciecka
Dissertations
In the modern world, especially in contemporary economies and politics, a population's subjective well-being is a frequent subject of the public debate. As comparisons of happiness levels in different countries are published, different circumstances and their effect on the value of the subjective well-being reported by people are also analysed. However, a significant amount of the research related to subjective well-being and its determinants is still based upon survey answers and employing conventional statistical methods providing details regarding correlations and causality between different factors and subjective well-being. Application of Supervised Machine Learning techniques for prediction of subjective well-being may provide …
Clicking Into Mortgage Arrears: A Study Into Arrears Prediction With Clickstream Data, Gavin O'Brien
Clicking Into Mortgage Arrears: A Study Into Arrears Prediction With Clickstream Data, Gavin O'Brien
Dissertations
This research project investigates the predictive capability of clickstream data when used for the purpose of mortgage arrears prediction. With an ever growing number of people switching to digital channels to handle their daily banking requirements, there is a wealth of ever increasing online usage data, otherwise known as clickstream data. If leveraged correctly, this clickstream data can be a powerful data source for organisations as it provides detailed information about how their customers are interacting with their digital channels. Much of the current literature associated with clickstream data relates to organisations employing it within their customer relationship management mechanisms …
Through The Net: Investigating How User Characteristics Influence Susceptibility To Phishing, Charlie Marriott
Through The Net: Investigating How User Characteristics Influence Susceptibility To Phishing, Charlie Marriott
Dissertations
In the past 25 years, the internet has grown and evolved from a niche networking technology, used almost exclusively by researchers and enthusiasts, into the driving force of modern economies. Fraud has evolved too, with rates of cybercrime on the increase as criminals become increasingly sophisticated in using technology to deceive their victims. The world is an online place, and data is the new oil. Phishing is a form of social engineering that is not that different from traditional fraud. Phishing attackers try to trick their victims into revealing valuable private information, usually for financial gain, by posing as a …
Automation Of Authorisation Vulnerability Detection In Authenticated Web Applications, Niall Caffrey
Automation Of Authorisation Vulnerability Detection In Authenticated Web Applications, Niall Caffrey
Dissertations
In the beginning the World Wide Web, also known as the Internet, consisted mainly of websites. These were essentially information depositories containing static pages, with the flow of information mostly one directional, from the server to the user’s browser. Most of these websites didn’t authenticate users, instead, each user was treated the same, and presented with the same information. A malicious party that gained access to the web server hosting these websites would usually not gain access to confidential information as most of the information on the web server would already be accessible to the public. Instead, the malicious party …
A Demographic Analysis To Determine User Vulnerability Among Several Categories Of Phishing Attacks., Robert Griffin
A Demographic Analysis To Determine User Vulnerability Among Several Categories Of Phishing Attacks., Robert Griffin
Dissertations
Phishing attacks have been on a meteoric rise in the last number of years, with 2016 seeing a 65% increase. The attacks range from targeting individuals with personalised messages to spam attacks from bot accounts. With the chances of being targeted by a phishing attack increasing, it is important to identify who is most at risk in order to help alleviate this threat. The aim of this study is to examine members from several demographics and their vulnerability to three types of phishing using data collected from a survey (n = 198). The survey tested the participant’s ability to recognise …
Assesing Completeness Of Solvency And Financial Condition Reports Through The Use Of Machine Learning And Text Classification, Ruairí Nugent
Assesing Completeness Of Solvency And Financial Condition Reports Through The Use Of Machine Learning And Text Classification, Ruairí Nugent
Dissertations
Text mining is a method for extracting useful information from unstructured data through the identification and exploration of large amounts of text. It is a valuable support tool for organisations. It enables a greater understanding and identification of relevant business insights from text. Critically it identifies connections between information within texts that would otherwise go unnoticed. Its application is prevalent in areas such as marketing and political science however, until recently it has been largely overlooked within economics. Central banks are beginning to investigate the benefits of machine learning, sentiment analysis and natural language processing in light of the large …
Augmented Reality As A Potential Tool For Filmmaking, Paul Blachfield
Augmented Reality As A Potential Tool For Filmmaking, Paul Blachfield
Dissertations
Augmented Reality (AR) has been used for a wide variety of industries. The purpose of this study was to determine the suitability of this technology for use in filmmaking. One of the problems on a film set is the time taken to block a scene. Blocking involves the placement of subjects and props within a scene. Different ideas have been used for blocking including previzualisation and Virtual Reality (VR). This study proposesed the use of AR as a tool to solve this problem. Marker-based and Markerless AR were assessed in turn to determine their suitability for addressing the problem. The …
An Analysis Of Software Testing Practices On Migrations From On Premise To Cloud Hosted Environments, Ronan Mullen
An Analysis Of Software Testing Practices On Migrations From On Premise To Cloud Hosted Environments, Ronan Mullen
Dissertations
This research project examines the differences between software testing practices that are carried out on software that is installed locally (i.e. on premise) versus software that has migrated to a cloud hosted environment. In conjunction with this, focus was placed on determining what methodologies and frameworks are in existence for assisting with software migrations to the cloud. The reason for carrying out this research project was that the transition to cloud computing is becoming more and more mainstream, as a result organisations are required to focus their efforts on how best to move their software to the cloud while ensuring …
An Exploration Of Parliamentary Speeches In The Irish Parliament Using Topic Modeling, Fiona Leheny
An Exploration Of Parliamentary Speeches In The Irish Parliament Using Topic Modeling, Fiona Leheny
Dissertations
The only resource available in the public domain which highlights parliamentary ac tivity is parliamentary questions. Up until the last ten years, manual content analysis was carried out to classify these. More recently, machine learning techniques have been used to automatically classify and analyse these data sets. This study analyses the verbal parliamentary speeches in the Irish Parliament (known as the D´ail) over a ten year period using unsupervised machine learning. It does so by applying a less utilised topic modeling technique, known as Non-negative Matrix Factorisation (NMF), to de tect the latent themes in these speeches. A two-layer dynamic …
A Performance Comparison Of Neural Network And Svm Classifiers Using Eeg Spectral Features To Predict Epileptic Seizures, Ian Thomas Tennant Watson
A Performance Comparison Of Neural Network And Svm Classifiers Using Eeg Spectral Features To Predict Epileptic Seizures, Ian Thomas Tennant Watson
Dissertations
Epilepsy is one of the most common neurological disorders, and afflicts approximately 70 million people globally. 30-40% of patients have refractory epilepsy, where seizures cannot be controlled by anti-epileptic medication, and surgery is neither appropriate, nor available. The unpredictable nature of epileptic seizures is the primary cause of mortality among patients, and leads to significant psychosocial disability. If seizures could be predicted in advance, automatic seizure warning systems could transform the lives of millions of people. This study presents a performance comparison of artificial neural network and sup port vector machine classifiers, using EEG spectral features to predict the onset …
Visualization Of Co-Authorshipin Dit Arrow, Dan Xu
Visualization Of Co-Authorshipin Dit Arrow, Dan Xu
Dissertations
With the popularization of information technology and the unprecedented development of online reading, the management and service of the library are facing severe challenges; the traditional library operation mode has been challenging to optimize the service. At the same time, there is also a fatal impact on library collection and systematic management, however, with the development of visualization techniques in management and service, the library can alleviate the effect of the current network information basically, which achieves the intellectual development of library field. This study empirically provides the evidence to indicate that the force directed layout has the statistically significant …
An Investigation Into Factors Which Explain The Scores And Voting Patterns Of The Eurovision Song Contest., Oisín Leonard
An Investigation Into Factors Which Explain The Scores And Voting Patterns Of The Eurovision Song Contest., Oisín Leonard
Dissertations
The Eurovision Song Contest (ESC) is an annual international television song competition. Participating countries send a group or individual artist to perform an original song at the competition. The winner is decided by all participating countries using a voting system that incorporates both a public televote and an expert jury vote. Countries are excluded from voting for their entry and the country with the highest score wins. A high scoring performance and the voting patterns of the ESC can be explained by a complex set of factors. These factors can be divided into three groups; performance factors, competition factors and …
Intergrating The Fruin Los Into The Multi-Objective Ant Colony System, Tirdad Kiafar
Intergrating The Fruin Los Into The Multi-Objective Ant Colony System, Tirdad Kiafar
Dissertations
Building evacuation simulation provides the planners and designers an opportunity to analyse the designs and plan a precise, scenario specific instruction for disaster times. Nevertheless, when disaster strikes, the unexpected may happen and many egress paths may get blocked or the conditions of evacuees may not let the execution of emergency plans go smoothly. During disaster times, effective route-finding methods can help efficient evacuation process, in which the directors are able to react to the sudden changes in the environment. This research tries to integrate the highly accepted human dynamics methods proposed by Fruin into the Ant-Colony optimisation route-finding method. …
Exploring The Features To Classify The Musical Period Of Western Classical Music, Arturo Martínez Gallardo
Exploring The Features To Classify The Musical Period Of Western Classical Music, Arturo Martínez Gallardo
Dissertations
Music Information Retrieval (MIR) focuses on extracting meaningful information from music content. MIR is a growing field of research with many applications such as music recommendation systems, fingerprinting, query-by-humming or music genre classification. This study aims to classify the styles of Western classical music, as this has not been explored to a great extent by MIR. In particular, this research will evaluate the impact of different music characteristics on identifying the musical period of Baroque, Classical, Romantic and Modern. In order to easily extract features related to music theory, symbolic representation or music scores were used, instead of audio format. …
That Seems Made Up: Deep Learning Classifiers For Fiction & Non Fiction Book Reviews, Clement Manger
That Seems Made Up: Deep Learning Classifiers For Fiction & Non Fiction Book Reviews, Clement Manger
Dissertations
The thesis aims to take the first step towards automated extraction of the information found in book reviews, by using machine learning tools to assign a label of fiction or non fiction to the text. The thesis makes use of neural networks and performs experiments around architecture, hyper-parameters and text processing from which an optimized model is produced. The thesis enjoys certain successes; it was possible to match the state of the art achieved by (Kim, 2014) and computation was sped up considerably from the default to the optimized model by 13.8 seconds per 50 steps. Further it is confirmed …
Identifying And Scoping Context-Specific Use Cases For Blockchain-Enabled Systems In The Wild., Fiona Delaney
Identifying And Scoping Context-Specific Use Cases For Blockchain-Enabled Systems In The Wild., Fiona Delaney
Dissertations
Advances in technology often provide a catalyst for digital innovation. Arising from the global banking crisis at the end of the first decade of the 21st Century, decentralised and distributed systems have seen a surge in growth and interest. Blockchain technology, the foundation of the decentralised virtual currency Bitcoin, is one such catalyst. The main component of a blockchain, is its public record of verified, timestamped transactions maintained in an append-only, chain-like, data structure. This record is replicated across n-nodes in a network of co-operating participants. This distribution offers a public proof of transactions verified in the past. Beyond tokens …
Automatic Table Extension With Open Data, Benedikt Kleppmann
Automatic Table Extension With Open Data, Benedikt Kleppmann
Dissertations
With thousands of data sources available on the web as well as within organisations, data scientists increasingly spend more time searching for data than analysing it. To ease the task of find and integrating relevant data for data mining projects, this dissertation presents two new methods for automatic table extension. Automatic table extension systems take over the task of tata discovery and data integration by adding new columns with new information (new attributes) to any table. The data values in the new columns are extracted from a given corpus of tables.
Handwritten Digit Recognition And Classification Using Machine Learning, Ke Zhao
Handwritten Digit Recognition And Classification Using Machine Learning, Ke Zhao
Dissertations
In this paper, multiple learning techniques based on Optical character recognition (OCR) for the handwritten digit recognition are examined, and a new accuracy level for recognition of the MNIST dataset is reported. The proposed framework involves three primary parts, image pre-processing, feature extraction and classification. This study strives to improve the recognition accuracy by more than 99% in handwritten digit recognition. As will be seen, pre-processing and feature extraction play crucial roles in this experiment to reach the highest accuracy.