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Full-Text Articles in Computer Engineering
Exploring Gender Bias In Semantic Representations For Occupational Classification In Nlp: Techniques And Mitigation Strategies, Joseph Michael O'Carroll
Exploring Gender Bias In Semantic Representations For Occupational Classification In Nlp: Techniques And Mitigation Strategies, Joseph Michael O'Carroll
Dissertations
Gender bias in Natural Language Processing (NLP) models is a non-trivial problem that can perpetuate and amplify existing societal biases. This thesis investigates gender bias in occupation classification and explores the effectiveness of different debiasing methods for language models to reduce the impact of bias in the model’s representations. The study employs a data-driven empirical methodology focusing heavily on experimentation and result investigation. The study uses five distinct semantic representations and models with varying levels of complexity to classify the occupation of individuals based on their biographies.
Probability Expressions In Ai Decision Support: Impacts On Human+Ai Team Performance, Elias Spinn
Probability Expressions In Ai Decision Support: Impacts On Human+Ai Team Performance, Elias Spinn
Dissertations
AI decision support systems aim to assist people in highly complex and consequential domains to make efficient, effective, and high-quality decisions. AI alone cannot be guaranteed to be correct in these complex decision tasks, and a human is often needed to ensure decision accuracy. The ambition is for these human+ AI teams to perform better together than either would individually. To realise this, decision makers must trust their AI partners appropriately, knowing when to rely on their recommendations and when to be sceptical. However, research has shown that decision makers often either mistrust and underutilise these systems, or trust them …
Analyzing Twitter Feeds To Facilitate Crises Informatics And Disaster Response During Mass Emergencies, Arshdeep Kaur
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 …
From Business Understanding To Deployment: An Application Of Machine Learning Algorithms To Forecast Customer Visits Per Hour To A Fast-Casual Restaurant In Dublin, Odunayo David Adedeji
From Business Understanding To Deployment: An Application Of Machine Learning Algorithms To Forecast Customer Visits Per Hour To A Fast-Casual Restaurant In Dublin, Odunayo David Adedeji
Dissertations
This research project identifies the significant factors that affects the number of customer visits to a fast-casual restaurant every hour and proceeds to develop several machine learning models to forecast customer visits. The core value proposition of fast-casual restaurants is quality food delivered at speed which means they have to prepare meals in advance of customers visit but the problem with this approach is in forecasting future demand, under estimating demand could lead to inadequate meal preparation which would leave customers unsatisfied while over estimation of demand could lead to wastage especially with restaurants having to comply with food safety …
Application Of Synthetic Informative Minority Over-Sampling (Simo) Algorithm Leveraging Support Vector Machine (Svm) On Small Datasets With Class Imbalance, Akshatha Fakkeriah Kallappanamatt
Application Of Synthetic Informative Minority Over-Sampling (Simo) Algorithm Leveraging Support Vector Machine (Svm) On Small Datasets With Class Imbalance, Akshatha Fakkeriah Kallappanamatt
Dissertations
Developing predictive models for classification problems considering imbalanced datasets is one of the basic difficulties in data mining and decision-analytics. A classifier’s performance will decline dramatically when applied to an imbalanced dataset. Standard classifiers such as logistic regression, Support Vector Machine (SVM) are appropriate for balanced training sets whereas provides suboptimal classification results when used on unbalanced dataset. Performance metric with prediction accuracy encourages a bias towards the majority class, while the rare instances remain unknown though the model contributes a high overall precision. There are chances where minority instances might be treated as noise and vice versa. (Haixiang et …
An Understanding Of Student Satisfaction, Lorraine Sweeney
An Understanding Of Student Satisfaction, Lorraine Sweeney
Dissertations
Retention is a challenge for all third level institutions and retention rates remain higher than colleges would like them to be, this has intensified in recent years as participants in higher education has increased and diversified. Third level institutions which would not only benefit from increased fees but also through low cost word of mouth promotion and an enhanced reputation. As such, an important concern for colleges is retaining students and understanding the reasons why students may choose to leave a program. While student satisfaction and retention is a well researched topic there remains questions to be answered in terms …
Eliciting Knowledge Bases With Defeasible Reasoning: A Comparative Analysis With Machine Learning, Peter Keogh
Eliciting Knowledge Bases With Defeasible Reasoning: A Comparative Analysis With Machine Learning, Peter Keogh
Dissertations
This thesis compares the ability of an implementation of Defeasible Reasoning (via Argumentation Theory) to model a construct (mental workload) with Machine Learning. In order to perform this comparison a defeasible reasoning system was designed and implemented in software. This software was used to elicit a knowledge base from an expert in an experiment which was then compared with machine learning. The central findings of this thesis were that the knowledge based approach was better at predicting an objective performance measure, time, than machine learning. However, machine learning was better equiped to identify another object measure task membership. The knowledge …
Sports Data Mining Technology Used In Basketball Outcome Prediction, Chenjie Cao
Sports Data Mining Technology Used In Basketball Outcome Prediction, Chenjie Cao
Dissertations
Driven by the increasing comprehensive data in sports datasets and data mining technique successfully used in different area, sports data mining technique emerges and enables us to find hidden knowledge to impact the sport industry. In many instances, predicting the outcomes of sporting events has always been a challenging and attractive work and is therefore drawing a wide concern to conduct research in this field. This project focuses on using machine learning algorithms to build a model for predicting the NBA game outcomes and the algorithms involve Simple Logistics Classifier, Artificial Neural Networks, SVM and Naïve Bayes. In order to …