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Beyond Affluence: Unveiling the Motivations behind Luxury Clothing Consumption in Mexico
Get PDFThis research aims to investigate the motivations that drive the consumption of luxury clothing in Mexico. Through the review of the previous literature, five main motivations were identified. The methodology chosen for the research was quantitative and was carried out using a survey method. An online, self-administered 25-question questionnaire with attitudinal statements was used to collect primary data. Information was collected from 139 survey participants. Five research objectives were established, which were linked to the five main motivations for the consumption of luxury products. The results revealed that the motivations of Mexicans for the consumption of luxury clothing are mainly two, both of personal effects. The quality of the luxury clothing was the main motivation but they were also motivated by the pleasure of buying and wearing these luxury clothes. This information
could be applied by luxury companies to improve their sales strategies and understanding of Mexican consumers
Helmet detection using YOLOv5
No full textHelmet detection is an essential task in computer vision and artificial intelligence, aimed at automatically identifying and localizing helmets in images or videos. The goal is to enhance safety in various domains, such as construction sites, sports events, and industrial settings, where the usage of helmets is critical for preventing head injuries. By employing advanced object detection algorithms, helmet detection systems can accurately detect the presence of helmets and draw bounding boxes around them, allowing for effective monitoring and compliance enforcement.
YOLO (You Only Look Once) is a popular family of real-time object detection algorithms known for its speed and accuracy. YOLO processes images in a single pass and directly predicts bounding boxes and class probabilities without using any region proposal methods. YOLOv5 is one of the latest iterations in the YOLO series, boasting improved performance and an efficient architecture, making it an ideal choice for real-time object detection tasks.
In this research project, we chose to employ YOLOv5 for helmet detection due to its impressive real-time processing capabilities and accurate detection results. Additionally, YOLOv5 is relatively lightweight compared to its predecessors, making it more accessible for integration into web applications and systems with limited computational resources.
To begin our work, we trained the YOLOv5 model using the popular Google Colab platform, utilizing its GPU resources for faster training. The training data consisted of annotated images of helmets, and we fine-tuned the model to ensure it learned the intricate features of helmets accurately.
Upon successful training, we proceeded to integrate the trained YOLOv5 model into a user-friendly Flask web application. The application enabled users to upload images and videos, which were then processed by the model to detect helmets. The detected images and videos were displayed back to users with bounding boxes around the identified helmets, providing a visual representation of the detection results.
Throughout the process, we meticulously tuned the model's parameters to strike a balance between detection accuracy and processing speed. This was crucial to ensure that the system performed efficiently while accurately identifying helmets in real-world scenarios.
Our research project successfully developed a Helmet Detection system using YOLOv5, offering a practical solution to enhance safety measures by automating helmet identification and monitoring in various contexts. The combination of YOLOv5's speed and accuracy, along with the user-friendly web application, makes our system a valuable tool for safety compliance and injury prevention in helmet-required environments
Comparative Analysis of Loan Prediction Models with Imbalanced Data and Impact of Loan Eligibility Metrics
No full textLoan prediction models plays a vital role in determining borrowers likelihood of defaulting on loans, but their development is challenging when dealing with imbalanced datasets. This research investigated the impact of including loan eligibility metrics on the performance of balanced loan default prediction models. Two machine learning models, Decision Tree and Random Forest, were compared in handling imbalanced data. To address data imbalance, Synthetic Minority Oversampling Technique (SMOTE), Under Sampling, and Random Over Sampling were used. The study validates the proposed methodology using a dataset from Kaggle. The findings revealed that incorporating loan eligibility metrics significantly improves the accuracy of balanced loan default prediction models. Among the models, Random Forest stands out, achieved the highest accuracy of 93.67%. This research contributes to financial analytics and data science, offering an optimized loan prediction model that empowers banks to enhance their loan decision-making process and effectively manage credit risk
The relationship between job stress And employee well-being in the IT industry in Ireland
No full textThis study explores the relationship, between job stress and employee well-being in the settingof Irelands Information Technology (IT) industry. By using a cross-sectional design, data was collected from 74 IT professionals through a structured online survey consisting of 15 open ended questions. The analysis of themes provided insights into how stress affects physical health job productivity and work life balance. The study revealed that social support plays a role in mitigating the effects of stress. Additionally, it emphasized the importance of leadership in reducing stress levels and promoting well-being highlighting the need for work environments. This research contributes to our understanding of job stress, in Irelands growing IT sector and it emphasizes the significance of employee focused interventions to enhance wellbeing and maintain productivity
Indicators of the Challenges that International Students face in Accessing Financial Services in Dublin
No full textWith the development of international higher education, Ireland has become increasingly popular as an education destination for international students. With that in mind, the aims of this research were to identify the major challenges that these students face in accessing financial services and to propose strategies for improving their financial inclusion. The research adopted an interpretivism philosophy, a qualitative design, an inductive research approach, and a primary data collection method for conducting the research. Five (5) international students from Chile, India, Kenya, and Nigeria were interviewed by the researcher for the purpose of collecting data. From the findings of the study, it was discovered that some of the problems international students faced included a lack of pre-arrival information, documentation and verification issues when opening a bank account, confusing banking processes and products, and ineligibility for loan or credit facilities. Recommendations for financial education, institutional collaborations, and tailored financial products were suggested to enhance the financial inclusion of international students
A Comparative Study between Neural Network Models and Standard Machine Learning Models in Heart Disease Prediction
No full textCardiovascular diseases are the leading cause of death across the world. There has been a gradual increase in cardiovascular diseases. It is also called a ‘Silent Killer’ because most people who have it do not have obvious symptoms. An artificial neural network is a part of machine learning with a potential solution to identify or detect the onset of this disease effectively. Artificial neural network (ANN) is a technological advancement in the field of machine learning that is gaining a lot of traction because of its design which allows it to solve many complex problems. ANNs are playing a vital role in many sectors of the industry, it is used for financial data analysis, speech recognition, emotion detection, disease prediction, image generation, and many other such applications. Artificial neural networks are made to imitate the human brain and function like one. It computes data like the human brain. The node of the artificial neural network resemble the individual neurons of the human brain. Our report analyses and compares the prediction of heart diseases between a few previously implemented standard machine learning models such as the logistic regression model, an ensemble model like random forest and decision trees versus neural networks like Long Short Term Memory Networks (LSTM), which is a type of recurrent neural network (RNN), a simple Convolutional Neural Network (CNN), a simple recurrent neural network, and a feed-forward neural network. To perform this experiment of comparative analysis we use the Cleveland heart disease dataset available at the UCI (University of California, Irvine) machine learning repository donated by Peter Turney, we compare their precision, accuracy, sensitivity, and specificity. The results we obtained were 72.49 % accuracy for the LSTM model, 75.73% accuracy for the CNN model, 74.43% accuracy with the feed forward neural network, 72.17% accuracy for the RNN model
Impact of Artificial Intelligence (AI) on Auditing Intelligence
Get PDFThis research study investigates the multifaceted impact of artificial intelligence (AI) on auditing intelligence. The rapid advancements in AI technology empower auditors with tools for processing and analyzing extensive data, enhancing audit quality. Adapting to AI's prevalence is crucial, considering its potential impact on the auditing profession. However, integrating AI comes with challenges such as data privacy, security, and ethics. AI's potential in enhancing risk assessment and fraud detection is noteworthy. Collaboration between human auditors and AI tools is crucial for responsible integration. The study employs qualitative methods, including in-person interviews, to gather insights from participants. In conclusion, AI's impact on auditing intelligence is profound, enhancing efficiency, but challenges like ethics persist. Collaboration between human auditors and AI tools is essential for navigating the evolving landscape and fostering improved audit quality, efficiency, and ethical practices. Harmonizing technology and ethics ensures a successful future for the auditing profession
The effects of brand equity on millennials’ purchase decision for sports nutrition products in Ireland
Get PDFBased on the theory of planned behaviour, this study investigates whether brand equity influences the purchase intention for sports nutrition products among millennials in Ireland and what factors influence their purchase decision. This research reports the results of a survey of 102 millennials. The results suggest that brand equity influences millennials' purchase intention and that it is also influenced by subjective norms and perceived behavioural control. Furthermore, the analysis shows a correlation between brand equity and its subdimensions (brand awareness/associations, perceived quality, brand loyalty) and purchase intention. This study contributes to the brand management literature in the sports nutrition industry and provided brand managers with various suggestions on how to build brand equity in their business practices
The correlation between learning and development related to career progression.
No full textThe aims and objective of the project is to understand the correlation between learning and development in relation to career progression for those in full-time permanent employment. Through qualitative applied research exploring definitions and aspects of learning, development and career development, the author delves into how career progression is impacted by an individual's investment in learning and development. Primary research consists of a sample size of fifty ranging from twenty to fifty five years old and in full-time permanent employment in Ireland
Special needs assistants: Understanding role conflict and ambiguity, perceived stress and job satisfaction in school climates
No full textThe purpose of this quantitative study was to examine Special Needs Assistants across school types, perceived stress, job satisfaction and role conflict, and role ambiguity. The convenience sample of Special Needs Assistants (n=396), females (N = 384) 97%, males (N= 2.3%), ages range from 18 to 65. Participants completed a self-report questionnaire comprising of The Perceived Stress Scale (Cohen, Kamarck, & Mermelstein, 1983); Job Satisfaction Scale (Spector,1983), and Role Conflict & Role Ambiguity Scale (Rizzo et al,.1970). Analyses Special schools differed significantly in Perceived stress across the four school types, with a positive correlation between role conflict and role ambiguity and job satisfaction, perceived stress is significantly predicated by role conflict and role ambiguity, and perceived stress is significantly predicted by job satisfaction, no significant difference in levels of job satisfaction across school types and significant difference in role conflict and role ambiguity across the school types. This study found that SNAs were dissatisfied with their pay, opportunities for promotion, and salary. This may suggest that employers should consider addressing these issues to improve job satisfaction. Implications of the current study were discussed along with suggestions for future research