ARC (Academic Research Collection) (College Dubin)
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Predicting early hospital readmissions for diabetic patients using machine learning
No full textThis project applies machine learning to predict whether diabetic patients will be readmitted to a hospital within 30 days of discharge. Early readmissions are a costly and critical issue in healthcare, often signalling gaps in post-discharge care and risk management. Diabetic patients face unfair high readmission rates compared to the general population. According to the CDC Diabetes Report Card 37.3 million people in the U.S. or 11.3% of the population had diabetes as of 2019 (CDC, 2021). Our goal here is to develop a binary classification model capable of flagging high risk patient (\u3c 30-day readmission) based on their clinical, demographic, and administrative data. This lets healthcare institutions to take measures
The Actuarial Applications of Machine Learning and Big Data in the Life Assurance industry: Managing customer retention and customer outcomes by the application of data science.
No full textLapses are an issue in the insurance industry in general. They affect a company’s profitability, cash flows and solvency. High levels of lapses can cause reputational damage that could provoke a cycle of even more lapses. It is therefore incumbent on a company to do its utmost to retain the business it has written for the term it was written for.
If a company could predict which of its policies were about to lapse, it could proactively attempt to prevent them by contacting the policyholder and engaging in a discussion to ascertain the likelihood of their choosing to leave. In this paper, various machine learning tools will be employed on a set of life company policy and client data. The tools include sentiment analysis, Random Forests, Artificial Neural Networks (ANN), k-Nearest Neighbour (kNN) and Support Vector Machine Classification. Among the metrics examined will be sentiment over time, confusion matrices and accuracy of the Random Forests, ANN, kNN and SVM.
The data is derived from one company’s life assurance policy and policyholder data as stored on its administration systems and extracted to a SQL database. As there are two different systems, the data from both had to be transformed into a canonical format. Also, as the models used optimally need numerical data, some categorical data had to be transformed into numerical data. Separately, data reflecting policyholder sentiment was also captured.
The modelling found that the Random Forest model was the most accurate, with accuracies of 88.18% for single life and 83.42% for joint life data. The next most accurate was a kNN with accuracies of 87.53% and 81.38%. Then follows ANN with accuracies of the order of 87.83% and 79.69% respectively. (kNN rated higher due to its overall better performance). A Support Vector Machine (trained on the optimal parameters found by a 5-fold cross validation on 12 combinations of parameters) correctly identified 84.99% of cases.
A market basket analysis was carried out (using the apriori algorithm) to see what combinations of benefits were present in the customer base and the results are summarised below in section 4.4 and detailed in the appendix. The results can be used to aid customers in adding benefits to their policies (along with the appropriate checks from a qualified intermediary).
A possible future extension for this work is to split the modelling across multiple PCs, so the training could be run for longer (for example more decision trees in the Random Forest, more training epochs for the neural networks or more parameters in the Grid search on the SVM) or on a distributed system. Other future work could consist of explicit modelling by product type which would produce several smaller but better trained models. The input data could be enhanced if data that is on the admin systems but not currently in the extracts gets added. This could provide more granular policy, life and transaction history information which could refine the models
An investigation into the role of machine learning and deep learning models as a means of leveraging the ever-expanding volume of astronomical data to automate stellar classification.
Get PDFThis research investigates the use of machine learning and neural network models for automated stellar classification in large astronomical surveys, addressing challenges posed by the increasing volume of data. Using the MK scheme as the classification standard, the study focused on spectroscopic data and balanced the dataset using SMOTE techniques to handle class imbalances. Various models, including Random Forest, SVM, MLP, and CNN, were trained and compared for classifying MK main and sub-classes. CNN achieved the highest accuracy (93.86%) for main class classification, while SVM excelled at sub-class classification (63.23%) on balanced datasets. However, when tested on real-world SDSS data, the models showed limited generalisability, highlighting the need for further refinement
Tesla-Addressing eco challenges globally
Get PDFThis dissertation examines Tesla Inc.\u27s contributions to global environmental sustainability through its renewable energy solutions and managerial approach to regulations. Aligning with Tesla\u27s mission as an electric car and energy solutions provider, the study employs secondary qualitative research and thematic analysis to explore the company’s role in sustainability, its market niche, and the regulatory challenges it faces. Key findings highlight differences in legal systems across major markets, the challenges Tesla encounters in meeting diverse environmental standards, and the significance of technology in advancing sustainability initiatives. The research underscores the critical role of multinational enterprises (MNEs) in achieving global sustainability goals and advocates for harmonised regulations, industry-specific sustainability standards, and technological innovation to sustain competitive advantage. This study contributes insights into international business, legal frameworks, and environmentalism
Labor Practices and their Impact on Employee perception at Amazon USA
Get PDFThis study examines Amazon employees\u27 perceptions in the United States and their impact on engagement and organisational culture. Since its founding in 1994, Amazon has grown from an online book retailer to a global platform and one of the Big Four internet companies. Positive employee perceptions foster engagement through a supportive work environment, but challenges such as timely and transparent feedback persist. Recommendations include adopting an open-door policy aligned with the Job Demands-Resources (JD-R) Model to enhance feedback processes and improve engagement, contributing to Amazon’s sustained success
Post-Brexit International Trade: The impact of Brexit import duties on outdoor clothing industry between the United Kingdom and the Republic of Ireland.
Get PDFThis study investigates the effects of changing import duties and customs following Brexit on trade within the outdoor clothing sector in the UK and Ireland. Brexit has caused drastic changes to the terms of trade for the UK following its withdrawal from the EU. These changes particularly affect the outdoor clothing sector and other businesses that rely on already established trade relationships with the Republic of Ireland. This review aims to examine the impacts of Brexit import duties on the outdoor clothing sector
Navigating Cross-Cultural Challenges: Strategies for Effective Management of Diverse Teams within the Organization.
Get PDFThis comprehensive research explores the intricacies of managing cross-cultural challenges within the organizational context of Primark. Leveraging a combination of qualitative and quantitative research methods, the study investigates effective strategies for navigating cultural differences and promoting collaboration in diverse teams.
The literature review delves into two prominent theories: Hofstede\u27s Cultural Dimensions Theory and Cultural Intelligence (CQ) Theory, providing valuable insights into cultural interactions and management practices. Building on these theories, the research examines real-world scenarios within Primark to understand the challenges faced by managers and employees in multicultural environments.
Through in-depth case studies, and surveys, the study uncovers key themes such as recognizing and addressing cultural differences, enhancing cultural intelligence and competency, promoting effective communication, embracing cultural diversity, and fostering continuous learning and development. These themes shed light on the complexities of cross-cultural management and offer actionable recommendations for organizations striving to enhance their cross-cultural effectiveness
Developing a Convolutional Neural Network (CNN) Model for Facial Expression Recognition (FER)
Get PDFThis Capstone Project focused on developing an accurate Facial Expression Recognition (FER) model by leveraging deep learning techniques, specifically Convolutional Neural Networks (CNNs). The objective was to explore, design, and implement custom architectures and evaluate their performance against existing work. The process involved several stages, such as data preprocessing, data augmentation, architecture design, hyperparameter tuning, and performance assessment using metrics like accuracy and F1-score while utilizing the FER-2013 dataset for training. The resulting FER model exhibited competitive accuracy levels and generalization capabilities, opening up opportunities for real-time implementation and application across various domains
Data Analysis of Twitter’s Nasdaq100 Sentiments and Topics as Indicators for News Articles Retrieval: Fine-Tuning RoBERTa and RAG
Get PDFThis study explores the combination of sentiment analysis with vader-lexicon and semantic analysis with latent dirichlet allocation to identify real-life events, particularly in the context of Twitter datasets. While sentiment analysis alone may not provide accurate guidance, the inclusion of semantic analysis enhances the research process by helping to identify relevant news articles and comprehend brand perception on social media. Furthermore, the study fine-tunes the RoBERTa model specifically for question-answering task
Assess the resilience and adaptability of European automotive supply chains in response to unforeseen disruptions. Case study: The COVID-19 disruption of the German and English automotive industries.
Get PDFThe study set out to assess the impact of the COVID-19 pandemic disruption on the established resilience and adaptability measures of the automotive supply chains in Germany and England; identify and analyze the specific measures implemented by automotive supply chain experts in Germany and England to mitigate the effects of the COVID-19 disruption; and note key lessons from the COVID-19 disruption of the German and England automotive supply chains. A multiple case study research approach was applied to gain a comprehensive understanding of the complexities of automotive supply chains