3830 research outputs found
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Ethical Cataloguing: exploring a potential role for the academic librarian in the promotion of social justice
No full textThe aim of this study is to evaluate the manner in which information is organised by academic libraries in Dublin. The researcher undertook this qualitative research by conducting in-depth, semi-structured interviews with academic librarians from private, third level colleges in Dublin. Invitations to participate in the research, interview questions, a Participant Information Sheet, and Interview questions were emailed to each of the librarians before the interview. Interviews with each librarian were conducted online and saved to the researcher’s personal computer. The interviews were manually transcribed. The researcher adopted an inductive approach and the researcher’s objectives were based on a review of the literature on the topic. The results indicated to the researcher the manner in which academic librarians in Dublin implement cataloguing ethics. The researcher concluded that there is an overall awareness amongst academic librarians of ethical principles and that academic libraries could benefit from developing an ethical framework
A comparison of four machine learning algorithms to predict product sales in a retail store
No full textControlling the retail market is the secret to sustainability in today's business world. Many business entities depend heavily on historical data and product demand projection of sales patterns. The accuracy of these projections has a significant effect on business. Data mining techniques are effective tools for retrieving concealed information from large datasets to increase predictions' precision and reliability. The systematic research and review of comprehensible machine learning classification models to boost product sales predictions are carried out in this work. The traditional statistical forecasting/predicting methods are challenging to cope with big data and accuracy in predicting product sales. However, these problems can be addressed through the use of different data mining and machine learning techniques. In this work, we briefly analyzed sales data and the prediction of product sales. Various techniques used in machine learning and data mining are discussed in the latter part of the research. For the performance evaluation, the best-suited classification model is proposed for the product sales type prediction. The findings are presented in terms of the reliability and accuracy of the different prediction algorithms used. The study shows that the best fit model is Random Forest, which produced the prediction's highest accuracy
Check-It-Chatbot
No full textRecently, the use of chatbots has progressed exponentially in diverse areas, including marketing, help networks, schooling, cultural heritage, entertainment and many more. One of the major and substantial aspects of this paper in which the chatbot eases the lives of people is healthcare.Chatbot and health have a history of working well together. The Check-it-chatbot can assist individuals with COVID-19 and SARS as well as many common disease-related queries. As well as help individuals select a language according to their choice (English, Hindi, French, Japanese, Chinese). Query received from users is analyzed and checked in the database for appropriate result and then result then displayed back to the user. There are three levels of Databases inside the Check-it-chat .JSON files as the primary database .TXT files as secondary and Wolframalpha as the third level database. The purpose of the research is to establish an atmosphere where reliable and suitable information and data can be shared between users and the system. It creates a good human-like conversational environment for interaction between the user and the system
Considerations for running and interpreting a binary logistic regression analysis – a research note
No full textThis research note discusses key considerations for analysis of categorical data using a Pearson’s chi-square and binary logistic regression. It draws on experience from analysis of a country-level household survey (Northern Ireland Health Survey 2014/15), using SPSSv25, that examined the relationship between household food insecurity status and identified demographic predictors, using Pearson’s Chi-Square test to check associations and binary logistic regressions to derive the predictive models. This note presents an overview of the assumptions for both tests which must be satisfied to ensure the tests are appropriate, discusses the usefulness of using Pearson’s Chi-Square test as a preliminary test before using binary logistic regression, and presents an overview of how to interpret the output from a binary logistic regression model
Librarian as editor: amplifying the voices of the marginalised
No full textThis article outlines the establishment of a cross-institutional, peer-reviewed, academic journal, Studies in Arts and Humanities, and describes the librarian-edited Special Issue on Minorities and Indigenous People which was published to commemorate the granting of official ethnic minority status to Irish Travellers by the Irish Government in March 2017
Consumer behavior in Ireland: an analysis of the barriers to purchase sustainable clothing.
No full textThis study aimed to understand the contradiction among consumers in Ireland, who stated they are willing to buy sustainable clothing, but few of them convert this intention into behavior, generating an intention-behavior gap. In parallel, it purposed to identify the barriers consumers have to change their behavior toward greener clothing consumption. It is a positivist research philosophy and to gather data from a convenience sample, it applied an online quantitative questionnaire survey, which worked on consumer purchase behavior, as the dependent variable, and environmental knowledge, consumer values, perceived products attributes, ethics and sustainability, the high price of sustainable clothing, subjective norms, and implementation intention, as the independent variables. The findings emphasized that consumers are price-sensitive, as the high prices of sustainable garments in comparison to fast fashion undermine their ethical intention, which has not been converted into purchase behavior so often
Machine Learning to aid mental health among youth during COVID-19
No full textThis research presents a comparative study of specialized deep learning and state of the art machine learning approaches for multiclass classification of six emotions: joy, sadness, surprise, fear, love, anger. Specialized deep learning algorithm Bi-LSTM; state of the art H2O ANN; traditional SVM and state of the art H2O Gradient Boosting Machines are applied on vectorised text. Cross validated performance using different vectorization techniques: text to sequences, word to vectors and TF-IDF are presented. The specialized Bi-LSTM on text to sequence vectorised data outperforms SVM and both outperform H2O ANN. A Gradient Boosted Machine age classifier is used to stratify test data. The traditional TF-IDF applied SVM outperforms the Bi-STM model on both Youth and Adult test data. The research is further extended to present a chatbot deployment of the emotion classifier
Online film distribution (Movierium)
No full textThis dissertation focuses on improving the connection between a filmmaker and the
audience. It provides more accessibility and availability to streaming movies for audiences,
not in the country or state of a cinema release. A filmmaker should reach a wider audience
than those just in the released environs, by developing and designing a pay-per-view videoon-
demand system and working in collaboration with filmmakers. Movierium is an online
film distribution (streaming) website, developed using the following technologies: HTML5,
CSS3, Python (Django Framework), Bootstrap, MySQL, Heroku, PostgreSQL, and SendGrid
and Stripe APIs. The system is secure, accessible, reliable, an exemplary user interface (UI),
and user experience (UX). Filmmakers shall provide the movies available on the platform,
and they shall be accessible to users for a limited time after payment
#Influenced: The Impact of Influencer Marketing on the Travel and Tourism Industry of Ireland. A qualitative study.
Get PDFThis research project aimed to investigate how influencer marketing impacts the sale of services in the travel and tourism industry in Ireland. This research was focused on this specific industry for multiple reasons. Some of those reasons being; a hard hit industry during the covid-19 pandemic and an industry that experiences more international than domestic contributions. The research involved a semi-structured design whereby the researcher conducted interviews through the online platform, Zoom. The purpose of the interviews was to gain rich insights from marketing managers to understand how effective influencer marketing can be on the sale of services in this industry. With these insights, marketers could correctly apply an appropriate strategy which will result in a higher contribution to the businesses and industry alike. The main objective of this research is to study the influencer
marketing phenomenon and the impact it has on this particular industry by looking at the traits and characteristics influencers obtain and how these affect the success of a brands influencer marketing campaign. The results found that influencer marketing can be a highly effective communication tool for advertising services within this industry, however, the strategy implemented still needs to be carefully considered and applied to the overall marketing approach