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Impact of Visual Acuity, Background Brightness, Gazes, Distractors, and Design on Exit Signs Identification
No full textThis study experimentally investigated differences in ‘Accuracy’ (ACC) and ‘Response Times’ (RTs) in exit signs identification based on the distance of the exit sign from the fixation point, size of the exit sign, background brightness, number, motion state, and congruency of gaze cues, and number and design of the distractor signs. (N = 40) participants over the age of 18 from Ireland, including Dublin Business School students, were assessed with the Open Sesame Web platform through every experimental condition in random order. The data was exported into SPSS and the main results found that switching the background brightness from bright to dark and increasing the number of distractors with similar target design significantly increased average RTs (p < .001), but did not significantly decrease average ACC. The current findings advance and support human safety research in this field, as well as universal design principles for buildings signage and policies
Problematic smartphone use in young adults: examining gender differences, self-control, self-efficacy, loneliness and trait mindfulness
No full textThe present quantitative mixed-design study aimed to investigate between-subjects cross-sectional gender differences in problematic smartphone use, study the predictive role of self-control and self-efficacy on problematic smartphone use, examine the predictive role of problematic smartphone use on loneliness and analyse the causal relationship between trait mindfulness and problematic smartphone use in a population of young adults. 83 participants aged 18 to 25 who own a smartphone completed an anonymous online questionnaire, containing demographic questions and self-report measures, which was shared on social media platforms. An independent samples t-test revealed that there were no significant gender differences among males and females based on problematic smartphone use levels. For the within-subjects correlations, 3 regressions where run, resulting in the following findings: 1) Self-control but not self-efficacy significantly, negatively predicted problematic smartphone use levels. 2) There was no significant correlation between problematic smartphone use and loneliness. 3) There was a significant, inverse causal relationship between trait mindfulness and problematic smartphone use. These findings elucidate the importance of self-control and trait-mindfulness in the prevention and reduction of smartphone-related problematic behaviours, and they provide a basis for the investigation and development of effective interventions to assist young adults
Credit Risk Modelling
No full textThrough a series of loan evaluations, a credit risk modeling method evaluated the dependability of the borrower. High-risk candidates were initially weeded out using basic borrower data and credit scores. For a more accurate risk assessment, detailed financial data including income, assets, and liabilities were subsequently examined. The algorithm continuously included new variables, improving accuracy and offering a sophisticated default probability evaluation. This strategy offered a solid framework for anticipating credit problems by combining personal behavior and financial information. These models proved essential for lenders, enabling informed lending decisions and reducing possible losses due to defaults
Open Banking System in Ireland: University Students' Perceptions on Data Security and Privacy
No full textUnderstanding user data security and privacy perceptions is crucial as open banking systems change and reshape the financial landscape. This study examines Irish university students' views on open banking. Students' security and privacy are examined through various relevant indicators confidentiality, integrity, availability, verification and. privacy This study analyses the complex relationship between three dimensions i.e. confidentiality, integrity, verification and user perceptions to illuminate the factors that affect open banking adoption and trust.A diverse sample of Irish university students was surveyed quantitatively using a quantitative research method through deductive approach. The aforementioned three dimensions' associations with students' security and privacy perceptions are significant. Students' increased concern for personal and financial privacy is evident in their confidentiality concerns. Data integrity also builds trust, as financial data accuracy and consistency are crucial. User perceptions are also improved by robust open banking verification mechanisms. User confidence in system security increases with efficient and effective verification processes. The study emphasizes user empowerment, transparent communication, and education in shaping open banking data security and privacy perceptions. The implications of these findings are vast. These insights will help financial institutions, technology providers, and policymakers create user-centric open banking systems that meet Irish university students' needs. By prioritizing confidentiality, data integrity, and verification, stakeholders can build trust, adoption, and open banking in Ireland. Therefore, this study fills a crucial gap in open banking user perceptions. The study allows academia and industry to improve data security, prioritize user trust, and shape open banking systems in a rapidly changing financial landscape
Gender differences in performance when WFH with child and non-child related audio distractions
No full textThe aim of the study is to investigate the effects of different distracter types (child-related and non-child-related audio), parental status, and task complexity on participants' reaction time (RT) and accuracy (Acc) in a visual search task. The study also aims to examine whether gender and WFH status moderate these effects. A quantitative within-subject’s design was used to assess performance when WFH. Participants included 23 males and 25 females. Participants’ scores were analysed using demographic questions together with their Acc and RT during the experiment. The study found that there was no significant difference in RT or accuracy between two distracter groups, regardless of gender. Parental status and difficulty level also did not have any significant impact on RT or Acc. Further research is required to incorporate a control group without exposure to children to control for habituation effects
Emotion-driven music recommendations: Integrating CNN and KNN for personalized playlists
Get PDFThis thesis delves into the captivating world of personalized music playlists, where emotions, technology, and user preferences converge. Through a unique blend of sentiment analysis, Convolutional Neural Networks (CNN), and K-Nearest Neighbors (KNN), we embark on a journey to create playlists that resonate with your feelings. The CNN deciphers emotions from facial expressions, shaping the emotional landscape, while KNN fine-tunes song recommendations for a harmonious experience. To ensure accuracy, a gradient boosting model steps in to validate emotional connections. We also explore the power of user feedback loops and the potential of multi-modal emotion recognition. With a strong ethical compass and an interdisciplinary approach, we uncover the profound connection between emotions and music recommendation. The results magnify the importance of emotions in shaping musical experiences, leading to a symphony of personalized playlist
A Study of the Impact of Social Media Sentiment on Stock Prices: an Analysis of Twitter Data and Stock Market Performance
No full textBy examining Twitter data and illuminating sector-specific differences, geographic inequalities, and engagement indicators, this study intends to examine the effect of social media sentiment on stock prices. The analysis finds a nuanced relationship, with sentiment showing a weak negative correlation with engagement metrics while a robust positive connection exists between likes and retweets. Geographical analysis reveals varied sentiment-stock price correlations, with North America and Oceania showing the strongest positive associations. Sector-wise, the Financial and Technology sectors exhibit moderate to positive correlations while Healthcare has a weakly negative correlation with social media sentiment. The predictive model used in this research highlights challenges in using social media sentiment alone for stock price forecasting. In conclusion, this research enhances understanding of the intricate interplay between social media sentiment and stock market dynamics, providing insights for informed decision-making amidst the complexities of digital discourse and market behavior
The Impact of HR practices on employee’s engagement and satisfaction level in the workplace
No full textStarting from the consideration of HR department as a strategic element in modern management, psychologists and economists everywhere agree that in the coming years competition will no longer focus on products, but on labour. The objective of this research is to study what relationships exist between employees' perceptions of five different HR practices, their engagement level, and their satisfaction level. It is also investigated what relationship exists between employee's engagement and their satisfaction level having controlled for age and tenure. To collect data a 47-item questionnaire was used on a sample of 130 employees. No significant relationships were identified between employees' perception of HR practices and their engagement and satisfaction level. It was found that the employee's engagement level correlates positively with their level of satisfaction. The findings of the study emphasize on the importance of HR practices on employee’s engagement and job satisfaction levels
Experiencing services for autism spectrum disorder in Ireland: The viewpoint of parents
No full textThe aim of this qualitative study was to explore the experiences of parents of children diagnosed with Autism Spectrum Disorder (ASD) with accessing diagnostic and support services for their children and themselves. 9 participants took part in individual semi-structured interviews online. A data-driven approach to thematic analysis rendered 5 major themes (1) Service-Related Struggles, (2) Diagnosis and Treatment Barriers, (3) Other Struggles Faced by Families, (4) Supports Outside of the ‘Therapy Room” and (5) Desired Improvements. Findings of this study point towards flaws within the current ASD-related service provision framework in Ireland. Long waiting lists, poor communication, lack of support from service providers and delays in accessing publicly funded services were identified as a source of frustration and distress. Support groups and other parents were cited as supportive. The need for increased parent support, education the general population and for parents and improvements in the service-access framework were highlighted
Detection of Hindi spam emails using NLP
No full textIn modern times, the business and education sectors embrace email for collaboration and interaction. Email is a fast and easy means of communicating for both quick and prolonged periods of time. Emails is growing into an effective way of exchanging information, which results in unsolicited bulk or spam. Such emails harvest sensitive information from individuals or business-related facts, as well as cultivate pornographic material or marketing services. Since the Hindi and English languages are so dissimilar, detecting spam emails in Hindi is challenging. These tactics are broadly characterized as contextbased or non-context-based. We analyzed and assessed many research materials in this paper. Previous research papers’ findings assist in the development of spam detection algorithms for a variety of platforms, including social media, email, and text messaging. This project aims to increase the precision and efficacy of spam identification in order to improve user experiences, defend users from potential threats or malicious activities, and keep online communication channels safe. Researchers have widely employed Natural Language Processing (NLP) techniques to detect spam emails in the English language during the previous five years. These methods attempt to analyze the textual content of emails in order to identify components that can discriminate between legitimate and spam messages. The aim of this research is to develop an efficient system for identifying and filtering spam emails in Hindi using similar techniques. It is necessary to have a reliable Hindi spam detection system. At least research available in the Hindi language was a major challenge in this field. We proposed a system that reliably detects Hindi spam emails using NLP. We analyzed and studied multiple machine learning techniques such as Logistic Regression, Random Forest, Decision Tree, Naive Bayes, and Support Vector Classifier. Ultimately choosing logistic regression to construct the system. The system provides an average accuracy of 97.72% by implementing the K-fold Cross Validation techniqu