ARC (Academic Research Collection) (College Dubin)
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359 research outputs found
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The impact of sustainability initiatives on corporate culture: Bord Bia
Get PDFThis study focuses on the relationship between sustainability practices and culture in Bord Bia, the Irish Food Board, with a clear objective of studying the effects of these practices on environmental, economic and social sustainability. Using literature review and expert interviews, the study determine what organizational cultural values and practices enable sustainable practices within the organization. In the case of Bord Bia, the paper suggests that most of the sustainability goals focused on the organization’s people and practices, especially those under the Origin Green programme, have been the catalyst towards reestablishing that corporates value long-term environmental and economic wellbeing. However, there are also issues for improvement such as a lack of common integration of sustainability aspirations by the various stakeholders and the need for a cross-cutting approach towards complex ecological and social problems. Achieving a major change in corporate culture and improving sustainability outcomes requires a comprehensive and long-term strategy. This strategy must address issues across all parts of the organization. It will also contribute to a better understanding of how integrating sustainability into corporate culture can enhance overall performance in sustainable business practices
Bord Bia: Global marketing challenges
Get PDFThis research examines global marketing challenges faced by Bord Bia, an Irish agency in the food, drink, and horticulture sector, as it seeks to increase market share in the European Union (EU). The study combines primary and secondary research, utilising a case study approach to analyse Bord Bia’s branding, marketing strategies, and digital transformation efforts. Key areas of focus include leveraging sustainable production as a unique selling point, enhancing the Country of Origin (COO) effect, and addressing cultural diversity in marketing activities. Findings highlight the value of a blended marketing strategy, integrating traditional and digital methods, and recommend adopting AI-driven analytics to support business expansion. This research provides actionable insights for Bord Bia and similar organisations navigating competitive foreign markets
Data Analysis of Twitter’s Nasdaq100 Sentiments and Topics as Indicators for News Articles Retrieval: Fine-Tuning RoBERTa and RAG.
Get PDFThis study investigates the combination of sentiment analysis using the VADER lexicon and semantic analysis through Latent Dirichlet Allocation (LDA) to identify real-life events, focusing on Twitter datasets. The research shows that while sentiment analysis alone may be insufficient, combining it with semantic analysis improves the process, particularly for identifying relevant news articles and understanding brand perception on social media. The study also fine-tunes the RoBERTa model for question-answering tasks, yielding significant improvements in the SQuAD evaluation metric. The exact match (EM) score rose dramatically from 2.06% to 62%, and the F1 score improved from 9.41% to 65%. A retrieval and generator system was developed to extract and generate question-and-answer responses from news articles using both the original and fine-tuned model iterations. Initial results showed an EM score of 4.5% and an F1 score of 24.26% for the original model, while the fine-tuned model improved the EM score to 17%, though the F1 score decreased to 21.85%. These findings suggest the potential for catastrophic forgetting, indicating a need for further refinement to balance improved subjective question-answering capabilities with overall knowledge retention
Development and Optimisation of Convolutional Neural Networks (CNNs) to predict the nutrition and sustainability scores of foods from crowd sourced images.
Get PDFThis research explores the use of Convolutional Neural Networks (CNNs) for the automated classification and profiling of food products based on publicly sourced data. With the vast array of food products available worldwide and the complexity of labelling regulations, food business operators face challenges in ensuring compliance, while regulators struggle to verify adherence. This study addresses the need for efficient and accurate methods for food classification and eco/nutritional profiling. It begins with a comprehensive literature review on the application of CNNs in food product classification, followed by the collection of a large-scale dataset from Open Food Facts. A CNN architecture tailored for food product classification was developed, focusing on optimising the model\u27s architecture and hyperparameters. Additionally, a user-friendly tool was created using Streamlit, in line with trends in the literature. The research investigates the effectiveness of this integrated approach compared to traditional manual methods. The study highlights the importance of high-quality, extensive datasets and the challenges of recognising visually complex food images. The results indicate that while the CNN models performed well during training, validation accuracy was lower, suggesting potential overfitting. Hyperparameter tuning, focusing on learning rate, optimizer type, and dropout rate, was used to mitigate this. The findings underscore the need for a balance between model complexity and efficiency, with various techniques explored to improve performance. A front-end user interface was developed and is publicly available
Statistical and Machine Learning Techniques for Predicting Solar Power Generation in a Microgrid.
Get PDFThis study investigates statistical and machine learning models for forecasting solar power generation in microgrids, focusing on the solar installation at Powell-Focht Bioengineering Hall, UC San Diego. Accurate predictions are critical due to the variability of solar energy, aiming to optimise microgrid operations and solar power efficiency. The research compares the performance of SARIMAX, LSTM, Random Forest, and ANN models using meteorological and solar power time series data. It finds that current meteorological inputs, especially solar radiation, enhance short-term forecasting accuracy over reliance on historical patterns.
The Random Forest Auto Regressor (RFAR) outperformed other models in 10-day-ahead solar power forecasting, followed closely by SARIMAX. The study highlights the practical benefits of combining clear-sky radiation with ground-level solar radiation to mitigate meteorological effects, offering valuable insights for microgrid energy management. Future directions include integrating these models into real-time control systems and leveraging advanced weather prediction technologies to further improve accuracy
Evaluating the performance of different Long Short-Term Memory networks (LSTM’s) on financial timeseries data using mean squared error in order to identify the optimum LSTM variant for regression performance on financial timeseries data.
Get PDFThis study explores the use of Long Short Term Memory (LSTM) networks, a variant of Recurrent Neural Networks (RNNs), in the context of financial forecasting, specifically oil price prediction. The research follows the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology and tests six different LSTM variants. The models are evaluated based on Mean Squared Error (MSE), aiming to determine the optimal parameter settings for each LSTM type. Among the variants tested, the Gated Recurrent Unit (GRU) emerged as the highest performer, achieving an MSE of 0.100. This was surprising, as simpler variants outperformed more complex ones, suggesting that simpler LSTM models may be better suited for financial time series forecasting, especially with simpler datasets.
In addition to the model experiments, primary research, including interviews with industry professionals, was conducted to validate the results and gather suggestions for improving the methodology. It was concluded that future studies could improve the reproducibility and robustness of the findings by using a random seed for model training and implementing multiple code versions to gather a distribution of results, which would enhance the general reliability of the outcomes
Global Marketing and Cross-Cultural Challenges in the Home Interiors Industry: Neptune Case Studies.
Get PDFThis thesis examines the challenges of global marketing for Neptune, a British furniture company, with a focus on cultural differences, branding, and overcoming language and regulatory obstacles. Using a case study approach, the research explores how Neptune adapts its marketing strategies in various countries through partnerships. Through in-depth interviews with key leaders, field research, and document analysis, the study underscores the significance of cultural exchange and social dialogue in maintaining brand identity while supporting local cultures. It also highlights the crucial role of collaborating with local partners to overcome marketing challenges
Applying Neural Networks to Predict Factors Affecting Harmful Algal Blooms for Timely Alerting and Implementing Preventive Measures in Ireland\u27s Marine Ecosystem.
Get PDFThis study applies neural networks to predict harmful algal blooms (HABs) along the Irish coast, addressing ecological, health, and economic risks. Using primary interviews and secondary data on HAB species like Alexandrium and Karenia mikimotoi, the research incorporated Exploratory Data Analysis and tested three neural models: LSTM, Ensemble Stacking LSTM, and CNN-LSTM. Key factors influencing HABs, such as sea surface temperature and euphotic zone depth, were identified.
Results demonstrate the potential of neural networks to improve HAB prediction and monitoring, despite limitations. Future work aims to enhance model accuracy and integrate them into HAB warning systems
Comparison of Classification Methodologies using Convolutional Neural Networks in a Dataset of Plant Leaf Diseases.
Get PDFThis project investigates the impact of classification methodology selection on the performance of four Convolutional Neural Network (CNN) models applied to a multi-label image dataset. The dataset consists of plant leaf images with one or more diseases. Two classification methodologies—multi-label and multi-class—are compared based on their model performance metrics. It was hypothesised that multi-label classification would perform better, but the results show that although multi-label models performed better for Loss and Accuracy metrics, they underperformed in terms of the F1 score, which is considered a more appropriate metric for this task. This surprising result refutes the initial hypothesis. Transfer learning was used to train the models, with hyperparameter tuning applied to the best-performing model. The research contributes to understanding multi-label classification and its potential in plant disease detection using machine learning
Analyzing the impact of negative publicity about employee turnover on customer loyalty -a case study of Amazon.
Get PDFThis study examines the effect of negative publicity regarding employee turnover on customer loyalty, focusing on Amazon. Using in-depth interviews and surveys, the research highlights how social media platforms, particularly Facebook, and digital communication have facilitated the spread of negative information about Amazon\u27s high employee turnover. While findings suggest that persistent negative media coverage could erode customer confidence over time, no statistical correlation between high turnover, negative publicity, and customer loyalty was established due to an insufficient sample size. The study recommends robust public relations strategies to protect corporate reputation and contributes to the understanding of how internal organisational issues can influence external consumer behaviour