ARC (Academic Research Collection) (College Dubin)
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359 research outputs found
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Data-Driven Public Transport Planning for Dublin: A Clustering and Forecasting Approach
No full textDublin has been experiencing severe traffic congestion due to rapid economic and population growth, with residents losing an average of 158 hours per year in traffic during rush hour (Europe Data, 2025). A 2022 European Commission study found that 76% of Irish people use a car as their primary mode of transport on a typical day—an 8% increase from 2019, compared to the EU average of 47% (MacCarthaigh, 2022).
This project proposes a data-driven approach to identifying current transport accessibility gaps and forecasting future population growth across Dublin to support sustainable infrastructure development. Using Ireland’s Census data, an unsupervised method was applied to cluster EDs based on similarities in population dynamics. Forecasts were generated in 5-year intervals, revealing key growth across Dublin using a clustered VAR model.
These findings can offer actionable insights to policymakers, urban planners, transport authorities and private sector stakeholders. The combined models provide a scalable framework for demand forecasting and transport prioritisation relevant today and at transport project completion
Data Driven Public Transport Planning in Dublin : A Clustering and Forecasting Approach
No full textDublin faces increasing traffic congestions with over 76% of Irish residents relying on private cars for daily transport, well above the EU average (MacCarthaigh, 2022). This contributes to increased greenhouse gas emissions, challenging Ireland’s goals to reduce emissions by 55% by 2030. This project proposes a data-driven approach to identifying current transport accessibility gaps and forecasting future population growth across Dublin to support sustainable infrastructure development. Using Ireland’s Census data, an unsupervised method was applied to cluster EDs based on similarities in population dynamics. Forecasts were generated in 5-year intervals, revealing key growth corridors across Dublin using a clustered VAR model. These findings can offer actionable insights to policymakers, urban planners, transport authorities and private sector stakeholders. The combined models provide a scalable framework for demand forecasting and transport prioritisation
Dogs Emotion System- Poster
No full textThis project is all about a deep learning-based “Dog Emotion System” that can figure out how dogs are feeling just by looking at their faces. We used a balanced set of 4,000 dog images with four different emotion categories and followed the CRISP-DM process to build it. The model was trained from scratch using a Convolutional Neural Network (CNN) without any pre-existing models. It is deployed using Steamlit, where people can upload pictures of their dogs and get their emotional state predicted in real time. The goal of this tech is to make it easier for pet owners to understand their dog\u27s emotions using AI, which could really improve how we take care of them
Predicting Early Customer Inactivity in the Banking Sector Using Machine Learning: A Churn Prevention
No full textCustomer churn, when customers stop using a company’s services, is a challenge for the banking sector (Singh et al., 2023). High churn rates often signal poor customer experiences, resulting in revenue losses and increased costs to obtain new clients. Goyal and Srivastava (2015) stress that fostering loyalty through exceptional service and understanding customer needs is important for long-term retention.
This project aims to predict early customer inactivity, an indicator of churn, by using machine learning. Early identification of at-risk customers will allow banks to apply targeted interventions, reduce acquisition costs, and improve customer satisfaction (Singh et al., 2023). By analysing a dataset containing customer demographics, financial activity, and behavioural patterns, this project provides insights that align with banks strategic goals of improving profitability and customer loyalty.
The CRISP-DM methodology (Cross-Industry Standard Process for Data Mining) will guide this project. This structured, iterative framework ensures a systematic progression through key phases: business understanding, data understanding, data preparation, modelling, evaluation, and deployment. The methodology ensures alignment with business objectives and allows for iterative refinement, ensuring robust and actionable outcomes (Hotz, 2024)
European Air pollution and the proposed timelines of implementing the World Health Organization 2021 Air Quality Guidelines CA3.
No full textThis research examines Ireland’s air pollution trends and evaluates whether current reductions in PM2.5, PM10, and NO₂ are sufficient to meet the WHO 2021 Air Quality Guidelines by 2040. Using four years of EPA-validated pollutant data (2020–2023), alongside Building Energy Rating (BER) and national transport datasets, the study applies the CRISP-DM methodology to guide analysis, preprocessing, modelling, and evaluation. Extensive data cleaning and alignment were required due to inconsistent station coverage, varying formats, and missing values. Forecasting models—including Random Forest, Gradient Boosting, SVR, and linear regression—were assessed using MSE, R², and trend significance to project pollutant levels across different Irish settlement types. Preliminary findings indicate persistent exceedances of WHO daily and annual limits for PM2.5 and NO₂, driven primarily by solid fuel burning and vehicle emissions, while PM10 generally remains within guideline levels. Results suggest that without accelerated policy and behavioural changes, Ireland is unlikely to meet WHO 2040 targets, particularly in urban regions such as Dublin and large towns. Additional analyses of building energy efficiency and transport behaviour highlight the need for rapid reductions in home-heating emissions and increased public transport uptake. This study underscores the health risks posed by ongoing air pollution—estimated to contribute to 1,600 premature deaths annually in Ireland—and provides modelling insights to support targeted environmental and public-health interventions
CCT College Teaching, Learning and Assessment Strategy 2024-2027
Full text linkCCT\u27s Institutional Strategic Plan in respect of Teaching, Learning and Assessment effective from 2024-2027
Improving Chatbot Interactions Through AI-Driven Hate Speech Detection: Evolving to a Safer Digital Environment-Poster
No full textThis project aims to explore how Machine Learning can contribute to a better digital interaction, mainly focusing on environments such as online chats, social media, and customer support as they are now an imperative part of daily communication. With this, concerns around hate speech in digital conversations is critical (Council of Europe, 2024). This study focus on the development of a Hate Speech Language Detection Chatbot using machine learning techniques. The key purpose of the chatbot is to monitor and detect harmful content in real time, reducing the need for manual intervention. The creation and implementation of such a tool is meant to support businesses to mitigate legal risks and encourage more respectful communication. The automation of content moderation is a central part to enhance user experience and save operational tim
Strategic Analysis of Employment Permit Statistics and Predictive Analytics for Workforce planning in Ireland
No full textBy analysing historical employment permit data from Enterprise.gov.ie (Enterprise.gov.ie, 2024), this project has the aim to use Data Analytics and Machine Learning to make predictions of employment permits trends across sectors and companies, providing insights to optimize workforce planning for Recruitment Agencies and guide international job seekers requiring work visas. The insights gained are intended to enhance strategic recruitment practices and empower job seekers to make informed career decisions in Ireland’s competitive labour market
Detecting Fake News Using AI
No full textThis project investigates how Artificial Intelligence (AI), specifically supervised machine learning techniques, can be applied to detect and classify fake news with high accuracy. The motivation stems from the widespread dissemination of misinformation on social media, where false narratives often spread faster than verified content. To address this issue, two distinct classification models were implemented and evaluated: one combining TF-IDF vectorization with Random Forest classifier, and another using TF-IDF with Logistic Regression. The TF-IDF technique was used to convert raw textual data into meaningful numerical features, capturing word frequency and relevance within the corpus. Both models were trained and tested on a balanced dataset of true and fake news articles. Their performance was compared based on key metrics such as accuracy, precision, recall, and F1-score. The findings revealed that the Random Forest model consistently outperformed Logistic Regression, particularly when trained on uncleaned (raw) text. This suggests that Random Forest is more robust to linguistic noise, making it a more suitable choice for deployment in real-time fake news detection systems, especially on user-generated content platforms like social media