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Effects of Sentiment Analysis on Airline industry
No full textThis study examines the complex dynamics of how passengers rate and review airline services, aiming to uncover the diverse factors influencing travelers' evaluations of their
travel experiences. The research draws insights from platforms like "Yelp," "TripAdvisor," and "Google Reviews," as well as airline-specific channels, to analyze how passengers assess various aspects of airline services. These aspects range from check-in and boarding procedures to in-flight amenities, staff interactions, baggage handling, and overall customer service. Cleanliness, seat comfort, punctuality, seamless connections, and issue resolution are among the elements emphasized in passenger evaluations. Both positive and negative feedback provide nuance, with glowing reviews of outstanding services contrasting with criticisms of delays, poor customer support, and inadequate amenities. These reviews significantly impact airline’s reputations and influence potential travelers. Airlines value customer feedback for improving services and satisfaction. However, it's crucial to avoid generalizing individual experiences to represent overall airline performance. The study explores existing literature on the passenger-airline relationship, highlighting motivations behind rating behaviors. By analyzing models, the research identifies ways to enhance the rating system. It emphasizes interpreting feedback in a balanced manner, considering positive and negative perspectives along with broader consensus. Ultimately, the study underscores the value of the rating and review system as an informative tool for decision making rather than the sole determinant of airline quality
Comparative study of image processing algorithms to detect defects in cast components
Get PDFIn the manufacturing industry, the non-destructive evaluation (NDE) of components is crucial. These cast components are susceptible to blowholes and other anomalies. If such flaws are included in the components, the fatigue life will be harmed, which would almost certainly result in catastrophic accidents. Humans currently evaluate cast components by various methods. We propose an automatic approach for detecting faults in casts with the goal of producing a category that will eliminate the need for manual testing. The technique looks for defects in cast components, In the previous years, image processing technology has advanced significantly. The method proposed utilizes Convolutional Neural Networks (CNN) and Support Vector Classifiers (SVC’s). This process classifies if the component has a defect or not. According to the hypothesis, human examiners may benefit from the approach because it reduces their workload
A Comparison of Traditional Models and Machine Learning Techniques for Volatility Forecasting
No full textThe calculation of volatility plays an important role in many applications such as derivatives pricing, portfolio optimization, risk-based value modelling and risk management. This study aims investigate and compare the efficacy of different forecasting methods for financial market realized volatility, specifically focusing on the SPX and DAX Index for identified range of time. The methodological design incorporated both machine learning techniques, such as Support Vector
Machines (SVM), Neural Networks, and Long Short-Term Memory (LSTM) networks, as well as traditional econometric models like GARCH. Procedures followed a structured evaluation process, which involved training the models on a designated dataset, followed by validation and testing phases to measure their forecasting accuracy. Based on the findings, any machine learning techniques offer a more efficacious approach for forecasting volatility in the financial markets, compared to the traditional volatility models such as ARCH&GARCH models
Differences of Attitude towards Eco-friendly Products in Ireland between Generations from Gen Z to Baby Boomer
Get PDFIn Ireland, opinions regarding eco-friendly products are examined and compared among several generational groups, including Generation Z, Millennials, Generation X, and Baby Boomers. The study intends to comprehend differences in attitudes, preferences, and intentions regarding eco-friendly items among various generations by a quantitative cross-sectional survey. The main research topics look at attitudes, the perceived value of eco-friendliness, what influences green purchases, and how people perceive their own environmental responsibilities. To guarantee thorough representation, the sample of 400 participants, who represented various age groups, was purposefully chosen and stratified. The findings suggest that consumers of all ages have a favorable attitude toward environmentally friendly items, with younger cohorts demonstrating a greater understanding of environmental issues. When it comes to influencing consumer behavior,
social factors and perceived behavioural control are key. Results show a shift in consumer behavior that is in favour of sustainability and highlight potential environmental effects. This study helps us gain a thorough understanding of how different generations feel about environmentally friendly items, which will help us develop sustainable consumption practices
The role of social support and coping strategies in the acculturation of Brazilians in Ireland
No full textThe present study investigated how coping self-efficacy skills, perceived social support and loneliness influence acculturative stress and life satisfaction among Brazilian migrants living in Ireland. An online survey was used and collected a sample of 96 participants. Acculturative stress and life satisfaction were examined as the main dependent variables in a quantitative correlational study. Time living in Ireland were significantly correlated to acculturation stress and life satisfaction. Different forms of social support and integration had significant relationship to acculturative stress, life satisfaction and coping self-efficacy. While perceived loneliness did not differ between migrants who moved to Ireland alone or accompanied, and was found to have weak and no significant relationship to coping self-efficacy skills
Comparison of the effects of SKY meditation and mindfulness meditation on stress in Irish workers
No full textThe aim of the study is to address the rise in work-related stress and its negative effects by comparing the effectiveness of SKY meditation and Mindfulness Meditation on stress management. The 48 participants recruited using convenience sampling and online snowballing were placed into SKY meditation or Mindfulness Meditation group. A cross-sectional and between–group design was employed, and Depression, Anxiety, Stress Scale-21, Perceived Stress Scale-10, and Positive and Negative Affect scale were used to measure stress, anxiety, depression, perceived stress, positive affect, and negative affect scores. A Mann-Whitney U test was used to test the hypotheses. The findings suggested no significant differences between the two groups on all the variables. However, the mean scores indicated that SKY meditation was slightly more effective than mindfulness meditation on stress management. Further research is required as there are practical implications of using an effective meditation intervention to offset the negative effects of stress
Support workers insights into people with intellectual disabilities experiences of gaining employment
No full textThe aim of this study is to identify the challenges associated with gaining employment for people with an ID. Along with the benefits for people with an intellectual disability and the employer in creating an inclusive work environment. The design of the study is qualitative. Eight participants, 6 female and 2 male, took part in semi-structured online Zoom interviews that were audio recorded. The interviews consisted of four key questions. Participants were selected using purposive sampling. They were recruited using convenience and snowball sampling. The data was analysed using Braun & Clarke’s (2006) method of thematic analysis. Six overarching themes emerged, 1.Resources, 2.Recruitment, 3.Education, 4.Relationships, 5.Value, and 6.Funding. Key findings highlighted a need for a revision of government policies on inclusive employment, sustainable funding, adaptions to recruitment processes, increased training for employers and people with an intellectual disability. Recommendations for future research and dissemination of this study are discussed
Sentiment Polarity Classification of Retail Product Reviews Using Machine Learning and Deep Learning
No full textA comparative study has been undertaken to compare the performance of conventional machine learning classifiers and deep learning models to classify the sentiment polarity of the Amazon musical instrument reviews dataset. Text preprocessing and the Tf-idf feature vectorisation techniques were applied to the machine learning models. The 10-fold cross validation technique and Grid-search hyperparameter tuning were implemented to compare the performance of the Multinomial Naïve Bayes classifier, Logistic regression classifier and the Linear Support Vector classifier. The Google pre-trained Word2vec word embeddings and a custom word embedding trained using the Word2vec algorithm on the Amazon reviews dataset were used to train the artificial neural network (ANN) and recurrent neural network long short-term neural network (RNN-LSTM) models. The RNN-LSTM trained using the custom-trained word embeddings achieved the best accuracy and F1-score of 92.31% followed by the RNN-LSTM trained using the Google word2vec embeddings with an accuracy and F1-score of 91.92%. The SVM classifier achieved the greatest accuracy of 91.82% among the machine learning classifiers
Stock Price Prediction methods: A case Study of Apple
No full textPredicting stock prices is very important for finance practitioners to best allocate their assets and to build better and more accurate asset pricing models. Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock market. With the introduction of Apple Inc and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. In this work Long short-term memory and Linear regression techniques have been utilized for predicting the next day closing price for Apple Inc belonging sectors of operation. The financial data: Open, High, Low and Close prices of stock are used for creating new variables which are used as inputs to the model. The models are evaluated using standard strategic indicator: MSE. The low value of this indicator shows that the models are efficient in predicting stock closing price
Breast Cancer Survival Prediction Using Machine Learning and Deep Learning
No full textBreast cancer continues to be a highly widespread and formidable kind of malignancy on a global
scale. The precise estimation of survival is of utmost importance in order to customize treatment
plans and enhance patient outcomes. The objective of this study was to improve the accuracy of
breast cancer survival prediction by combining two well-known datasets: METABRIC, which
provides extensive molecular profiling data, and CBIS-DDSM, a valuable collection of
mammographic images. The study utilized three sophisticated machine learning models, namely
Inception Net, Adam Net, and DenseNet, to make the most of how molecular and imaging
information can work together effectively. The DenseNet model stood out, achieving an 81%
accuracy in predicting breast cancer patient survival, surpassing Inception Net (66%) and Adam
Net (71%). We can create a helpful tool using DenseNet121 to study how well breast cancer
patients might survive by looking at their mammograms